diff --git a/CLAUDE.md b/CLAUDE.md index 1318f10e..4931128f 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,70 +1,70 @@ # Cake Development Guide -## Cluster Machines - -| Machine | Role | GPU | VRAM | OS | Work Dir | SSH | -|---------|------|-----|------|----|----------|-----| -| **blade.local** | Master (local) | RTX 3080 Laptop | 16 GB | Linux | `/home/evilsocket/Lab/cake` | N/A | -| **bahamut.local** | Worker | 2× TITAN X Pascal | 2×12 GB | Linux | `~/Lab/cake` | `ssh bahamut.local` | -| **stevie.local** | Worker | Apple M3 Pro | 36 GB unified | macOS | `~/Lab/cake` | `ssh stevie.local` | - ## Build Commands ```bash -# blade.local (local, CUDA) +# Linux (CUDA) cargo build --release --features cuda -# bahamut.local (CUDA — MUST use cuda-12.4, driver only supports up to 12.4) +# Linux (CUDA, specific version) CUDA_HOME=/usr/local/cuda-12.4 LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64 cargo build --release --features cuda -# stevie.local (Metal) +# macOS (Metal) cargo build --release --features metal + +# CPU only +cargo build --release ``` -## Run Commands (Qwen3.5-0.8B cluster) +## Run Commands (example: Qwen3.5-0.8B cluster) ```bash -# Workers first (on each machine): -# bahamut: -LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64 ./target/release/cake worker \ - --model Qwen/Qwen3.5-0.8B --name bahamut \ - --topology topology-0.8B.yml --address 0.0.0.0:10128 - -# stevie: +# Worker (on each worker machine): ./target/release/cake worker \ - --model Qwen/Qwen3.5-0.8B --name stevie \ - --topology topology-0.8B.yml --address 0.0.0.0:10128 + --model Qwen/Qwen3.5-0.8B --name worker1 \ + --topology topology.yml --address 0.0.0.0:10128 -# Master (blade, local): +# Master: ./target/release/cake master \ --model Qwen/Qwen3.5-0.8B \ - --topology topology-0.8B.yml \ + --topology topology.yml \ --prompt "Explain quantum computing in simple terms" ``` +## Testing + +```bash +# Run all tests (no model files required) +cargo test --features cuda + +# Integration tests require model files — set env vars: +# CAKE_TEST_MODEL=./path/to/Llama-3.2-1B-Instruct/ +# CAKE_TEST_QWEN2_MODEL=./path/to/Qwen2-0.5B/ +# Tests skip gracefully when model paths are not available. + +# Run protocol benchmarks +cargo test --test protocol -- --ignored --nocapture +``` + ## Model: Qwen/Qwen3.5-0.8B - **Architecture**: Qwen3_5ForConditionalGeneration -- **Layers**: 24 (48 GatedDeltaNet linear attn + 16 full attn... wait, 0.8B has 24 total) +- **Layers**: 24 - **Hidden size**: 1024 - **Layer prefix**: `model.language_model.layers.{N}` -- **Location**: HuggingFace cache on all 3 machines (`~/.cache/huggingface/hub/models--Qwen--Qwen3.5-0.8B/`) +- **Location**: HuggingFace cache (`~/.cache/huggingface/hub/models--Qwen--Qwen3.5-0.8B/`) - **Size**: ~1.6 GB in F16 -## Topology: `topology-0.8B.yml` - -24 layers split evenly: bahamut 0-7, stevie 8-15, blade master keeps 16-23. - ## Self Improving Loop -This is an iterative optimization process for maximizing inference speed: +Iterative optimization process for maximizing inference speed: ### Process 1. **Instrument**: Add timing/profiling logs to hot code paths (forward pass, attention, MLP, network, serialization) 2. **Commit & push**: Commit changes, push to origin -3. **Deploy**: Pull on all 3 machines via SSH, rebuild with appropriate features (cuda/metal) -4. **Run experiment**: Start workers on bahamut and stevie, then master on blade with a test prompt +3. **Deploy**: Pull on all machines, rebuild with appropriate features (cuda/metal) +4. **Run experiment**: Start workers, then master with a test prompt 5. **Collect metrics**: Capture tok/s, per-layer timing, network latency from logs 6. **Analyze**: Identify the current bottleneck (slowest component) 7. **Optimize**: Make targeted code changes to address the bottleneck @@ -78,25 +78,3 @@ This is an iterative optimization process for maximizing inference speed: - **network round-trip time** (ms) — identifies network bottlenecks - **embedding + lm_head time** (ms) — head/tail overhead - **total forward pass time** (ms) — end-to-end per token - -### Deploy script pattern -```bash -# Push from blade -git push - -# Pull & build on bahamut -ssh bahamut.local "cd ~/Lab/cake && git pull && CUDA_HOME=/usr/local/cuda-12.4 LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64 cargo build --release --features cuda" - -# Pull & build on stevie -ssh stevie.local "cd ~/Lab/cake && git pull && cargo build --release --features metal" -``` - -### Run experiment pattern -```bash -# Start workers (background SSH sessions) -ssh bahamut.local "cd ~/Lab/cake && LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64 ./target/release/cake worker --model Qwen/Qwen3.5-0.8B --name bahamut --topology topology-0.8B.yml --address 0.0.0.0:10128" -ssh stevie.local "cd ~/Lab/cake && ./target/release/cake worker --model Qwen/Qwen3.5-0.8B --name stevie --topology topology-0.8B.yml --address 0.0.0.0:10128" - -# Run master (blade, local) -./target/release/cake master --model Qwen/Qwen3.5-0.8B --topology topology-0.8B.yml --prompt "Explain quantum computing in simple terms" -``` diff --git a/Cargo.lock b/Cargo.lock index 4079a32f..388d0d87 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -331,6 +331,15 @@ dependencies = [ "backtrace", ] +[[package]] +name = "approx" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "cab112f0a86d568ea0e627cc1d6be74a1e9cd55214684db5561995f6dad897c6" +dependencies = [ + "num-traits", +] + [[package]] name = "arbitrary" version = "1.3.2" @@ -734,6 +743,8 @@ dependencies = [ "serde_yaml", "sha2", "speedy", + "statrs", + "thiserror 2.0.18", "tokenizers", "tokio", "tracing-chrome", @@ -787,7 +798,7 @@ dependencies = [ "objc2-foundation", "objc2-metal", "rand 0.9.2", - "rand_distr", + "rand_distr 0.5.1", "rayon", "safetensors 0.7.0", "thiserror 2.0.18", @@ -1628,7 +1639,7 @@ dependencies = [ "half", "num-traits", "rand 0.9.2", - "rand_distr", + "rand_distr 0.5.1", ] [[package]] @@ -2168,7 +2179,7 @@ dependencies = [ "crunchy", "num-traits", "rand 0.9.2", - "rand_distr", + "rand_distr 0.5.1", "zerocopy 0.8.27", ] @@ -2864,6 +2875,16 @@ dependencies = [ "libc", ] +[[package]] +name = "matrixmultiply" +version = "0.3.10" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a06de3016e9fae57a36fd14dba131fccf49f74b40b7fbdb472f96e361ec71a08" +dependencies = [ + "autocfg", + "rawpointer", +] + [[package]] name = "maybe-rayon" version = "0.1.1" @@ -2997,6 +3018,23 @@ dependencies = [ "syn", ] +[[package]] +name = "nalgebra" +version = "0.33.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "26aecdf64b707efd1310e3544d709c5c0ac61c13756046aaaba41be5c4f66a3b" +dependencies = [ + "approx", + "matrixmultiply", + "num-complex", + "num-rational", + "num-traits", + "rand 0.8.5", + "rand_distr 0.4.3", + "simba", + "typenum", +] + [[package]] name = "native-tls" version = "0.2.12" @@ -3584,6 +3622,16 @@ dependencies = [ "getrandom 0.3.4", ] +[[package]] +name = "rand_distr" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "32cb0b9bc82b0a0876c2dd994a7e7a2683d3e7390ca40e6886785ef0c7e3ee31" +dependencies = [ + "num-traits", + "rand 0.8.5", +] + [[package]] name = "rand_distr" version = "0.5.1" @@ -3673,6 +3721,12 @@ dependencies = [ "bitflags 2.6.0", ] +[[package]] +name = "rawpointer" +version = "0.2.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60a357793950651c4ed0f3f52338f53b2f809f32d83a07f72909fa13e4c6c1e3" + [[package]] name = "rayon" version = "1.10.0" @@ -3924,6 +3978,15 @@ version = "1.0.18" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f3cb5ba0dc43242ce17de99c180e96db90b235b8a9fdc9543c96d2209116bd9f" +[[package]] +name = "safe_arch" +version = "0.7.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "96b02de82ddbe1b636e6170c21be622223aea188ef2e139be0a5b219ec215323" +dependencies = [ + "bytemuck", +] + [[package]] name = "safetensors" version = "0.4.5" @@ -4179,6 +4242,19 @@ dependencies = [ "libc", ] +[[package]] +name = "simba" +version = "0.9.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c99284beb21666094ba2b75bbceda012e610f5479dfcc2d6e2426f53197ffd95" +dependencies = [ + "approx", + "num-complex", + "num-traits", + "paste", + "wide", +] + [[package]] name = "simd-adler32" version = "0.3.7" @@ -4306,6 +4382,18 @@ version = "1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "a2eb9349b6444b326872e140eb1cf5e7c522154d69e7a0ffb0fb81c06b37543f" +[[package]] +name = "statrs" +version = "0.18.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2a3fe7c28c6512e766b0874335db33c94ad7b8f9054228ae1c2abd47ce7d335e" +dependencies = [ + "approx", + "nalgebra", + "num-traits", + "rand 0.8.5", +] + [[package]] name = "strsim" version = "0.11.1" @@ -5360,6 +5448,16 @@ version = "0.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "53a85b86a771b1c87058196170769dd264f66c0782acf1ae6cc51bfd64b39082" +[[package]] +name = "wide" +version = "0.7.33" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0ce5da8ecb62bcd8ec8b7ea19f69a51275e91299be594ea5cc6ef7819e16cd03" +dependencies = [ + "bytemuck", + "safe_arch", +] + [[package]] name = "winapi" version = "0.3.9" diff --git a/Makefile b/Makefile index d362482a..7e901e4e 100644 --- a/Makefile +++ b/Makefile @@ -14,17 +14,6 @@ build_release: cargo build --release -sync_bahamut: - @echo "@ bahamut sync && build ..." - @rsync -rvzc --exclude=cake-data --exclude=.git --exclude=target . bahamut.local:/home/evilsocket/cake - @rsync -rvzc cake-data/8b-test/bahamut-node bahamut.local:/home/evilsocket/cake-data - -sync_blade: - @echo "@ blade sync && build ..." - @rsync -rvzc --exclude=cake-data --exclude=.git --exclude=target . blade.local:/home/evilsocket/cake - @rsync -rvzc cake-data/8b-test/blade-node blade.local:/home/evilsocket/cake-data - -sync: sync_bahamut sync_blade publish: cargo publish -p cake-core diff --git a/cake-cli/src/main.rs b/cake-cli/src/main.rs index 2101f298..bdb0761e 100644 --- a/cake-cli/src/main.rs +++ b/cake-cli/src/main.rs @@ -196,6 +196,56 @@ async fn main() -> Result<()> { #[cfg(feature = "master")] async fn run_master(ctx: Context) -> Result<()> { use cake_core::cake::Master; + use cake_core::cake::master::VideoMaster; + + // Video models use VideoMaster (VideoGenerator trait) instead of Master (ImageGenerator) + if ctx.args.model_type == ModelType::ImageModel { + match ctx.args.image_model_arch { + ImageModelArch::LtxVideo => { + #[cfg(feature = "llama")] + { + let master = VideoMaster::::new(ctx).await?; + return master.run().await; + } + #[cfg(not(feature = "llama"))] + anyhow::bail!("ltx-video master requires the llama feature as a type placeholder"); + } + ImageModelArch::Ltx2 => { + #[cfg(feature = "llama")] + { + let master = VideoMaster::::new(ctx).await?; + return master.run().await; + } + #[cfg(not(feature = "llama"))] + anyhow::bail!("ltx-2 master requires the llama feature as a type placeholder"); + } + _ => {} // Non-video image models handled below + } + } + + macro_rules! run_with_image_model { + ($text_model:ty, $ctx:expr) => { + match $ctx.args.image_model_arch { + #[cfg(feature = "flux")] + ImageModelArch::Flux => { + Master::<$text_model, cake_core::models::flux::FluxGen>::new($ctx) + .await? + .run() + .await + } + ImageModelArch::LtxVideo | ImageModelArch::Ltx2 => { + // Handled above via VideoMaster + unreachable!() + } + ImageModelArch::SD | ImageModelArch::Auto => { + Master::<$text_model, cake_core::models::sd::SD>::new($ctx) + .await? + .run() + .await + } + } + }; + } // Image model dispatch — early return to avoid duplicating text arch arms if ctx.args.model_type == ModelType::ImageModel { @@ -205,17 +255,11 @@ async fn run_master(ctx: Context) -> Result<()> { match ctx.text_model_arch { #[cfg(feature = "qwen2")] TextModelArch::Qwen2 => { - Master::::new(ctx) - .await? - .run() - .await + run_with_image_model!(cake_core::models::qwen2::Qwen2, ctx) } #[cfg(feature = "qwen3_5")] TextModelArch::Qwen3_5 => { - Master::::new(ctx) - .await? - .run() - .await + run_with_image_model!(cake_core::models::qwen3_5::Qwen3_5, ctx) } #[cfg(feature = "qwen3")] TextModelArch::Qwen3 => { @@ -282,10 +326,7 @@ async fn run_master(ctx: Context) -> Result<()> { } #[cfg(feature = "llama")] TextModelArch::Llama | TextModelArch::Auto => { - Master::::new(ctx) - .await? - .run() - .await + run_with_image_model!(cake_core::models::llama3::LLama, ctx) } #[allow(unreachable_patterns)] _ => anyhow::bail!( @@ -301,7 +342,7 @@ async fn run_master_image(ctx: Context) -> Result<()> { // Use LLama as dummy TG — it's never loaded for ImageModel. match ctx.args.image_model_arch { - ImageModelArch::SD => { + ImageModelArch::SD | ImageModelArch::Auto => { Master::::new(ctx) .await? .run() @@ -314,6 +355,10 @@ async fn run_master_image(ctx: Context) -> Result<()> { .run() .await } + ImageModelArch::LtxVideo | ImageModelArch::Ltx2 => { + // Handled by run_master_video, should not reach here + unreachable!("video models should be dispatched via VideoMaster") + } #[allow(unreachable_patterns)] _ => anyhow::bail!( "no image model feature enabled for architecture {:?}", @@ -421,7 +466,7 @@ async fn run_worker(ctx: &mut Context) -> Result<()> { ), }, ModelType::ImageModel => match ctx.args.image_model_arch { - ImageModelArch::SD => { + ImageModelArch::SD | ImageModelArch::Auto => { Worker::::new(ctx) .await? .run() @@ -434,12 +479,24 @@ async fn run_worker(ctx: &mut Context) -> Result<()> { .run() .await } + ImageModelArch::LtxVideo => { + Worker::::new(ctx) + .await? + .run() + .await + } + ImageModelArch::Ltx2 => { + Worker::::new(ctx) + .await? + .run() + .await + } #[allow(unreachable_patterns)] _ => anyhow::bail!( "no image model feature enabled for architecture {:?}", ctx.args.image_model_arch ), - } + }, } } diff --git a/cake-core/Cargo.toml b/cake-core/Cargo.toml index ab4f48a7..603c1597 100644 --- a/cake-core/Cargo.toml +++ b/cake-core/Cargo.toml @@ -40,6 +40,8 @@ candle-nn = { version = "0.9" } candle-transformers = { version = "0.9" } candle-flash-attn = { version = "0.9", optional = true } image = "0.25.2" +statrs = "0.18" +thiserror = "2" hf-hub = "0.5" libc = "0.2" sha2 = "0.10" diff --git a/cake-core/src/cake/api/image.rs b/cake-core/src/cake/api/image.rs index 6f0c789c..55a9a222 100644 --- a/cake-core/src/cake/api/image.rs +++ b/cake-core/src/cake/api/image.rs @@ -31,7 +31,10 @@ where TG: TextGenerator + Send + Sync + 'static, IG: ImageGenerator + Send + Sync + 'static, { - let client = req.peer_addr().unwrap(); + let client = req + .peer_addr() + .map(|a| a.to_string()) + .unwrap_or_else(|| "unknown".to_string()); log::info!("starting generating image for {} ...", &client); @@ -40,7 +43,7 @@ where let result_images = Arc::new(Mutex::new(Vec::new())); let result_images_cloned = Arc::clone(&result_images); - master + if let Err(e) = master .generate_image(image_request.image_args.clone(), move |images| { let mut base64_images: Vec = images .iter() @@ -60,7 +63,11 @@ where locked_result_images.append(&mut base64_images); }) .await - .expect("Error generating images"); + { + log::error!("image generation failed: {}", e); + return HttpResponse::InternalServerError() + .json(serde_json::json!({"error": e.to_string()})); + } let locked_result_images = result_images.lock().expect("Error acquiring lock"); let response = ImageResponse { diff --git a/cake-core/src/cake/api/mod.rs b/cake-core/src/cake/api/mod.rs index f3ea10ef..5e7b4492 100644 --- a/cake-core/src/cake/api/mod.rs +++ b/cake-core/src/cake/api/mod.rs @@ -1,7 +1,9 @@ mod image; pub mod text; mod ui; +pub mod video; +use std::sync::atomic::{AtomicUsize, Ordering}; use std::sync::Arc; use actix_web::web; @@ -9,15 +11,70 @@ use actix_web::App; use actix_web::HttpResponse; use actix_web::HttpServer; use serde::Serialize; -use tokio::sync::RwLock; +use tokio::sync::{RwLock, Semaphore}; -use crate::models::{ImageGenerator, TextGenerator}; +use crate::models::{ImageGenerator, TextGenerator, VideoGenerator}; use image::*; use text::*; +use super::master::VideoMaster; use super::Master; +/// Bounded request queue for backpressure. +/// Limits concurrent waiting requests and tracks queue depth. +pub struct RequestQueue { + /// Semaphore limiting how many requests can wait concurrently. + semaphore: Arc, + /// Current number of requests in the queue (waiting + processing). + pending: Arc, + /// Maximum allowed pending requests. + max_pending: usize, +} + +impl RequestQueue { + pub fn new(max_pending: usize) -> Self { + Self { + semaphore: Arc::new(Semaphore::new(max_pending)), + pending: Arc::new(AtomicUsize::new(0)), + max_pending, + } + } + + /// Try to acquire a slot. Returns None if queue is full. + /// The returned guard is `'static` and can be moved into spawned tasks. + pub fn try_acquire(&self) -> Option { + let permit = self.semaphore.clone().try_acquire_owned().ok()?; + self.pending.fetch_add(1, Ordering::Relaxed); + Some(QueueGuard { + _permit: permit, + pending: self.pending.clone(), + }) + } + + pub fn pending(&self) -> usize { + self.pending.load(Ordering::Relaxed) + } + + pub fn max_pending(&self) -> usize { + self.max_pending + } +} + +/// RAII guard that decrements the pending counter on drop. +/// Owns its references so it can be moved into spawned tasks. +pub struct QueueGuard { + #[allow(dead_code)] + _permit: tokio::sync::OwnedSemaphorePermit, + pending: Arc, +} + +impl Drop for QueueGuard { + fn drop(&mut self) { + self.pending.fetch_sub(1, Ordering::Relaxed); + } +} + #[derive(Serialize)] struct ModelObject { id: String, @@ -49,10 +106,42 @@ where HttpResponse::Ok().json(response) } +pub async fn list_models_video( + _state: web::Data>>>, +) -> HttpResponse +where + TG: TextGenerator + Send + Sync + 'static, + VG: VideoGenerator + Send + Sync + 'static, +{ + let response = ModelsResponse { + object: "list".to_string(), + data: vec![ModelObject { + id: VG::MODEL_NAME.to_string(), + object: "model".to_string(), + owned_by: "cake".to_string(), + }], + }; + HttpResponse::Ok().json(response) +} + async fn not_found() -> actix_web::Result { Ok(HttpResponse::NotFound().body("nope")) } +/// GET /v1/status — queue depth and server health. +async fn status(queue: web::Data>) -> HttpResponse { + HttpResponse::Ok().json(serde_json::json!({ + "status": "ok", + "queue": { + "pending": queue.pending(), + "max_pending": queue.max_pending(), + } + })) +} + +/// Maximum concurrent pending requests before returning 503. +const MAX_PENDING_REQUESTS: usize = 8; + pub(crate) async fn start(master: Master) -> anyhow::Result<()> where TG: TextGenerator + Send + Sync + 'static, @@ -60,14 +149,16 @@ where { let address = master.ctx.args.api.as_ref().unwrap().to_string(); - log::info!("starting api on http://{} ...", &address); + log::info!("starting api on http://{} (max_pending={}) ...", &address, MAX_PENDING_REQUESTS); let state = Arc::new(RwLock::new(master)); + let queue = Arc::new(RequestQueue::new(MAX_PENDING_REQUESTS)); HttpServer::new( move || { App::new() .app_data(web::Data::new(state.clone())) + .app_data(web::Data::new(queue.clone())) .route( "/v1/chat/completions", web::post().to(generate_text::), @@ -77,6 +168,7 @@ where web::post().to(generate_text::), ) .route("/v1/models", web::get().to(list_models::)) + .route("/v1/status", web::get().to(status)) .route("/api/v1/image", web::post().to(generate_image::)) .route("/api/v1/topology", web::get().to(ui::topology::)) .route("/", web::get().to(ui::index::)) @@ -89,3 +181,31 @@ where .await .map_err(|e| anyhow!(e)) } + +pub(crate) async fn start_video(master: VideoMaster) -> anyhow::Result<()> +where + TG: TextGenerator + Send + Sync + 'static, + VG: VideoGenerator + Send + Sync + 'static, +{ + let address = master.ctx.args.api.as_ref().unwrap().to_string(); + + log::info!("starting video api on http://{} ...", &address); + + let state = Arc::new(RwLock::new(master)); + + HttpServer::new(move || { + App::new() + .app_data(web::Data::new(state.clone())) + .route("/v1/models", web::get().to(list_models_video::)) + .route( + "/api/v1/video", + web::post().to(video::generate_video::), + ) + .default_service(web::route().to(not_found)) + }) + .bind(&address) + .map_err(|e| anyhow!(e))? + .run() + .await + .map_err(|e| anyhow!(e)) +} diff --git a/cake-core/src/cake/api/text.rs b/cake-core/src/cake/api/text.rs index 93b81f59..d3f5bd87 100644 --- a/cake-core/src/cake/api/text.rs +++ b/cake-core/src/cake/api/text.rs @@ -8,6 +8,8 @@ use std::sync::Arc; use std::time::{SystemTime, UNIX_EPOCH}; use tokio::sync::RwLock; +use super::RequestQueue; + #[derive(Deserialize)] pub struct ChatRequest { pub messages: Vec, @@ -101,6 +103,7 @@ struct StreamResponse { pub async fn generate_text( state: web::Data>>>, + queue: web::Data>, req: HttpRequest, body: web::Json, ) -> impl Responder @@ -114,11 +117,32 @@ where .unwrap_or_else(|| "unknown".to_string()); let stream = body.0.stream.unwrap_or(false); - log::info!("starting chat for {} (stream={}) ...", &client, stream); + // Acquire queue slot or reject with 503 + let _guard = match queue.try_acquire() { + Some(guard) => guard, + None => { + log::warn!("rejecting request from {} — queue full ({}/{})", + &client, queue.pending(), queue.max_pending()); + return HttpResponse::ServiceUnavailable() + .json(serde_json::json!({ + "error": { + "message": "Server is busy, please retry later", + "type": "server_error", + "code": "queue_full" + } + })); + } + }; + + log::info!("starting chat for {} (stream={}, queue={}/{}) ...", + &client, stream, queue.pending(), queue.max_pending()); if stream { - generate_text_stream(state, body.0).await + // For streaming, the guard is moved into the spawned task so the slot + // stays occupied for the full generation duration. + generate_text_stream(state, body.0, _guard).await } else { + // Blocking: _guard lives until this function returns (after generation completes) generate_text_blocking(state, body.0).await } } @@ -188,6 +212,7 @@ where async fn generate_text_stream( state: web::Data>>>, request: ChatRequest, + queue_guard: super::QueueGuard, ) -> HttpResponse where TG: TextGenerator + Send + Sync + 'static, @@ -204,6 +229,8 @@ where let state_clone = state.clone(); tokio::spawn(async move { + // Hold queue guard for the full duration of generation + let _guard = queue_guard; let mut master = state_clone.write().await; if let Err(e) = master.reset() { diff --git a/cake-core/src/cake/api/video.rs b/cake-core/src/cake/api/video.rs new file mode 100644 index 00000000..e7370d10 --- /dev/null +++ b/cake-core/src/cake/api/video.rs @@ -0,0 +1,136 @@ +use crate::cake::master::VideoMaster; +use crate::models::TextGenerator; +use crate::models::VideoGenerator; +use crate::ImageGenerationArgs; +use actix_web::{web, HttpRequest, HttpResponse, Responder}; +use base64::engine::general_purpose; +use base64::Engine; +use serde::{Deserialize, Serialize}; +use std::sync::Arc; +use tokio::sync::RwLock; + +#[derive(Deserialize)] +pub struct VideoRequest { + pub image_args: ImageGenerationArgs, + /// Output format: "avi" (binary) or "base64" (JSON with base64-encoded AVI). + /// Default: "avi" + #[serde(default = "default_format")] + pub format: String, + /// If true, also return individual frames as base64 PNGs alongside the video. + #[serde(default)] + pub include_frames: bool, +} + +fn default_format() -> String { + "avi".to_string() +} + +#[derive(Serialize)] +struct VideoJsonResponse { + /// Base64-encoded AVI data. + pub video: String, + /// Video format identifier. + pub format: String, + /// Number of frames. + pub num_frames: usize, + /// Frames per second. + pub fps: usize, + /// Frame width. + pub width: u32, + /// Frame height. + pub height: u32, + /// Duration in seconds. + pub duration_secs: f64, + /// Optional individual frames as base64 PNGs. + #[serde(skip_serializing_if = "Option::is_none")] + pub frames: Option>, +} + +pub async fn generate_video( + state: web::Data>>>, + req: HttpRequest, + video_request: web::Json, +) -> impl Responder +where + TG: TextGenerator + Send + Sync + 'static, + VG: VideoGenerator + Send + Sync + 'static, +{ + let client = req + .peer_addr() + .map(|a| a.to_string()) + .unwrap_or_else(|| "unknown".to_string()); + log::info!("starting video generation for {} ...", &client); + + let mut master = state.write().await; + + let video_output = match master.generate_video(video_request.image_args.clone()).await { + Ok(v) => v, + Err(e) => { + log::error!("video generation failed: {}", e); + return HttpResponse::InternalServerError() + .json(serde_json::json!({"error": e.to_string()})); + } + }; + + let avi_bytes = match video_output.to_avi() { + Ok(b) => b, + Err(e) => { + log::error!("AVI encoding failed: {}", e); + return HttpResponse::InternalServerError() + .json(serde_json::json!({"error": e.to_string()})); + } + }; + + match video_request.format.as_str() { + "avi" | "binary" => { + // Return raw AVI bytes + HttpResponse::Ok() + .content_type("video/x-msvideo") + .append_header(( + "Content-Disposition", + "attachment; filename=\"output.avi\"", + )) + .body(avi_bytes) + } + _ => { + // Return JSON with base64-encoded video + let frames = if video_request.include_frames { + Some(encode_frames_as_png(&video_output)) + } else { + None + }; + + let response = VideoJsonResponse { + video: general_purpose::STANDARD.encode(&avi_bytes), + format: "avi".to_string(), + num_frames: video_output.num_frames(), + fps: video_output.fps, + width: video_output.width, + height: video_output.height, + duration_secs: video_output.duration_secs(), + frames, + }; + + HttpResponse::Ok().json(response) + } + } +} + +fn encode_frames_as_png(video: &crate::video::VideoOutput) -> Vec { + use image::{DynamicImage, ImageFormat}; + use std::io::Cursor; + + video + .frames + .iter() + .map(|frame| { + let dynamic_image = DynamicImage::ImageRgb8(frame.clone()); + let mut png_bytes = Vec::new(); + let mut cursor = Cursor::new(&mut png_bytes); + dynamic_image + .write_to(&mut cursor, ImageFormat::Png) + .unwrap(); + general_purpose::STANDARD.encode(png_bytes) + }) + .collect() +} diff --git a/cake-core/src/cake/client.rs b/cake-core/src/cake/client.rs index 331cb650..ffffa6dc 100644 --- a/cake-core/src/cake/client.rs +++ b/cake-core/src/cake/client.rs @@ -1,3 +1,6 @@ +use std::collections::HashMap; +use std::time::Duration; + use anyhow::Result; use async_trait::async_trait; use candle_core::{Device, Tensor}; @@ -5,6 +8,90 @@ use tokio::net::TcpStream; use super::{Context, Message, WorkerInfo}; +/// TCP connect timeout. +const CONNECT_TIMEOUT: Duration = Duration::from_secs(10); +/// Maximum number of connection attempts before giving up. +const MAX_CONNECT_RETRIES: u32 = 3; +/// Base delay between retries (doubles each attempt). +const RETRY_BASE_DELAY: Duration = Duration::from_secs(1); + +/// Lightweight stub for non-primary remote layer slots. +/// +/// When multiple layers map to the same worker, only the first gets a real +/// `Client` (TCP connection). The rest get a `RemoteRef` that returns the +/// same `ident()` so the batching logic groups them correctly, but holds +/// no connection. Its `forward_*` methods are never called directly. +#[derive(Debug)] +pub struct RemoteRef { + address: String, + layer_name: String, +} + +impl RemoteRef { + pub fn new(address: &str, layer_name: &str) -> Self { + Self { + address: address.to_string(), + layer_name: layer_name.to_string(), + } + } +} + +impl std::fmt::Display for RemoteRef { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{}@{} [ref]", &self.layer_name, &self.address) + } +} + +#[async_trait] +impl super::Forwarder for RemoteRef { + fn load(_: String, _: &Context) -> Result> { + Err(anyhow!("load should never be called on RemoteRef")) + } + + async fn forward(&self, _: &Tensor, _: usize, _: usize, _: &mut Context) -> Result { + Err(anyhow!("forward should never be called on RemoteRef (batching uses the primary Client)")) + } + + async fn forward_mut(&mut self, _: &Tensor, _: usize, _: usize, _: &mut Context) -> Result { + Err(anyhow!("forward_mut should never be called on RemoteRef (batching uses the primary Client)")) + } + + fn layer_name(&self) -> &str { + &self.layer_name + } + + fn ident(&self) -> &str { + &self.address + } +} + +/// Connect to remote workers, deduplicating by host address. +/// +/// Returns a map of layer_index → Box. The first layer for +/// each worker gets a real `Client`; subsequent layers get a `RemoteRef`. +pub async fn connect_remote_layers( + remote_layers: &[(usize, String, String)], // (index, layer_name, host) + device: &Device, + cluster_key: Option<&str>, +) -> Result>> { + let mut result: HashMap> = HashMap::new(); + let mut connected_hosts: HashMap = HashMap::new(); // host → first layer index + + for (idx, layer_name, host) in remote_layers { + if connected_hosts.contains_key(host) { + log::info!(" {} → {} [shared connection]", layer_name, host); + result.insert(*idx, Box::new(RemoteRef::new(host, layer_name))); + } else { + log::info!("connecting {} to {} ...", layer_name, host); + let client = Client::new(device.clone(), host, layer_name, cluster_key).await?; + connected_hosts.insert(host.clone(), *idx); + result.insert(*idx, Box::new(client)); + } + } + + Ok(result) +} + /// A client object used by the master to connect and orchestrate the workers. /// From the Cake perspective, each worker is a server and the master uses /// multiple Client instances to connect to them. @@ -32,9 +119,47 @@ impl Client { ) -> Result { let address = address.to_string(); let layer_name = layer_name.to_string(); - let stream = TcpStream::connect(&address) - .await - .map_err(|e| anyhow!("can't connect to {address}: {e}"))?; + + let mut last_err = None; + let mut stream_opt = None; + for attempt in 0..MAX_CONNECT_RETRIES { + match tokio::time::timeout(CONNECT_TIMEOUT, TcpStream::connect(&address)).await { + Ok(Ok(s)) => { + stream_opt = Some(s); + break; + } + Ok(Err(e)) => { + last_err = Some(format!("{e}")); + if attempt + 1 < MAX_CONNECT_RETRIES { + let delay = RETRY_BASE_DELAY * 2u32.pow(attempt); + log::warn!( + "connection to {} failed (attempt {}/{}): {} — retrying in {:?}", + &address, attempt + 1, MAX_CONNECT_RETRIES, e, delay + ); + tokio::time::sleep(delay).await; + } + } + Err(_) => { + last_err = Some("connection timed out".to_string()); + if attempt + 1 < MAX_CONNECT_RETRIES { + let delay = RETRY_BASE_DELAY * 2u32.pow(attempt); + log::warn!( + "connection to {} timed out (attempt {}/{}) — retrying in {:?}", + &address, attempt + 1, MAX_CONNECT_RETRIES, delay + ); + tokio::time::sleep(delay).await; + } + } + } + } + let stream = stream_opt.ok_or_else(|| { + anyhow!( + "can't connect to {} after {} attempts: {}", + &address, + MAX_CONNECT_RETRIES, + last_err.unwrap_or_default() + ) + })?; stream.set_nodelay(true)?; let worker_info = WorkerInfo::default(); diff --git a/cake-core/src/cake/master.rs b/cake-core/src/cake/master.rs index 63d8fcb8..ae0cbca6 100644 --- a/cake-core/src/cake/master.rs +++ b/cake-core/src/cake/master.rs @@ -1,6 +1,7 @@ use std::io::Write; -use crate::models::{chat::Message, ImageGenerator, TextGenerator}; +use crate::models::{chat::Message, ImageGenerator, TextGenerator, VideoGenerator}; +use crate::video::VideoOutput; use super::{api, Context}; @@ -117,7 +118,12 @@ impl= sample_len { + break; + } + if index == 1 { // record start time again since the first token is the warmup start_gen = std::time::Instant::now() @@ -139,6 +145,8 @@ impl { + pub ctx: Context, + pub llm_model: Option>, + pub video_model: Option>, +} + +impl + VideoMaster +{ + pub async fn new(mut ctx: Context) -> Result { + match ctx.args.model_type { + ModelType::ImageModel => { + let video_model = VG::load(&mut ctx).await?; + Ok(Self { + ctx, + video_model, + llm_model: None, + }) + } + ModelType::TextModel => { + anyhow::bail!("VideoMaster cannot be used for text models"); + } + } + } + + pub async fn run(mut self) -> Result<()> { + if self.ctx.args.api.is_some() { + api::start_video(self).await?; + } else { + std::fs::create_dir_all("videos")?; + let video = self.generate_video(self.ctx.args.sd_img_gen_args.clone()).await?; + + // Save as AVI + let avi_path = std::path::PathBuf::from("videos/output.avi"); + video.save_avi(&avi_path)?; + log::info!( + "Saved video: {} frames, {:.1}s @ {} fps -> {}", + video.num_frames(), + video.duration_secs(), + video.fps, + avi_path.display() + ); + + // Also save individual frames for convenience + video.save_frames(std::path::Path::new("videos/frames"), "frame")?; + log::info!("Saved {} individual frames to videos/frames/", video.num_frames()); + } + + Ok(()) + } + + pub async fn generate_video(&mut self, args: ImageGenerationArgs) -> Result { + let video_model = self.video_model.as_mut().expect("Video model not found"); + video_model.generate_video(&args).await + } +} diff --git a/cake-core/src/cake/mod.rs b/cake-core/src/cake/mod.rs index dab1e0c7..f6cd2204 100644 --- a/cake-core/src/cake/mod.rs +++ b/cake-core/src/cake/mod.rs @@ -17,10 +17,10 @@ use candle_nn::VarBuilder; #[cfg(feature = "master")] pub mod api; #[cfg(feature = "master")] -mod master; +pub mod master; pub mod auth; -mod client; +pub mod client; pub mod discovery; mod proto; pub mod setup; @@ -99,7 +99,15 @@ impl Context { data_path } else if !data_path.exists() { if utils::hf::looks_like_hf_repo(&args.model) { - utils::hf::ensure_model_downloaded(&args.model)? + // Image models (LTX-2, Flux, etc.) use diffusers format without a root + // config.json — their forwarders handle HF resolution internally. + // Only download via the generic path for text models. + if args.model_type == ModelType::TextModel { + utils::hf::ensure_model_downloaded(&args.model)? + } else { + // Pass the repo ID through; forwarders resolve it themselves + data_path + } } else { bail!("model path does not exist: {}", data_path.display()); } @@ -212,66 +220,79 @@ impl Context { } }; - let model_tensors_index: PathBuf = data_path.join("model.safetensors.index.json"); - fp8 = utils::fp8::is_fp8_quantized(&config_filename); - if fp8 { - log::info!("model uses FP8 quantization — weights will be dequantized at load time"); - } - let gptq_group_size = if utils::gptq::is_gptq_quantized(&config_filename) { - let gs = utils::gptq::gptq_group_size(&config_filename); - log::info!("model uses GPTQ quantization (group_size={gs}) — weights will be dequantized at load time"); - Some(gs) - } else { - None - }; - let is_master = matches!(args.mode, Mode::Master); - let my_layers: Vec = if !is_master { - topology.all_worker_layers().into_iter().collect() + // Check for GGUF file first, then fall back to safetensors + let gguf_file = utils::gguf::detect_gguf_file(&data_path); + + if let Some(ref gguf_path) = gguf_file { + log::info!("detected GGUF model: {}", gguf_path.display()); + var_builder = Some(utils::gguf::load_var_builder_from_gguf( + gguf_path, + dtype, + device.clone(), + &config_internal.model_prefix, + )?); } else { - vec![] - }; + let model_tensors_index: PathBuf = data_path.join("model.safetensors.index.json"); + fp8 = utils::fp8::is_fp8_quantized(&config_filename); + if fp8 { + log::info!("model uses FP8 quantization — weights will be dequantized at load time"); + } + let gptq_group_size = if utils::gptq::is_gptq_quantized(&config_filename) { + let gs = utils::gptq::gptq_group_size(&config_filename); + log::info!("model uses GPTQ quantization (group_size={gs}) — weights will be dequantized at load time"); + Some(gs) + } else { + None + }; + let is_master = matches!(args.mode, Mode::Master); + let my_layers: Vec = if !is_master { + topology.all_worker_layers().into_iter().collect() + } else { + vec![] + }; - var_builder = Some(if is_master { - // Master: exclude shards that only contain remote-worker tensors - let worker_layers = topology.all_worker_layers(); - if worker_layers.is_empty() { - utils::load_var_builder_from_index( + var_builder = Some(if is_master { + // Master: exclude shards that only contain remote-worker tensors + let worker_layers = topology.all_worker_layers(); + if worker_layers.is_empty() { + utils::load_var_builder_from_index( + model_tensors_index, + dtype, + device.clone(), + fp8, + gptq_group_size, + )? + } else { + utils::load_var_builder_for_local_layers( + model_tensors_index, + dtype, + device.clone(), + &worker_layers, + fp8, + gptq_group_size, + )? + } + } else if !my_layers.is_empty() { + // Worker with known layers: only load shards containing our layers + utils::load_var_builder_for_specific_layers( model_tensors_index, dtype, device.clone(), + &my_layers, fp8, gptq_group_size, )? } else { - utils::load_var_builder_for_local_layers( + // Worker without known layers: load everything + utils::load_var_builder_from_index( model_tensors_index, dtype, device.clone(), - &worker_layers, fp8, gptq_group_size, )? - } - } else if !my_layers.is_empty() { - // Worker with known layers: only load shards containing our layers - utils::load_var_builder_for_specific_layers( - model_tensors_index, - dtype, - device.clone(), - &my_layers, - fp8, - gptq_group_size, - )? - } else { - // Worker without known layers: load everything - utils::load_var_builder_from_index( - model_tensors_index, - dtype, - device.clone(), - fp8, - gptq_group_size, - )? - }); + }); + } cache = Some(Cache::new(true, dtype, &config_internal, &device)?); config = Some(config_internal); } diff --git a/cake-core/src/cake/proto/message.rs b/cake-core/src/cake/proto/message.rs index dca55ceb..8268d898 100644 --- a/cake-core/src/cake/proto/message.rs +++ b/cake-core/src/cake/proto/message.rs @@ -174,11 +174,20 @@ impl Message { // Yes, I could use GRPC, but this is simpler and faster. // Check speedy benchmarks ;) - /// Serializes the message to raw bytes. + /// Serializes the message to raw bytes (used by tests and `from_bytes`). + #[cfg(test)] fn to_bytes(&self) -> Result> { Ok(self.write_to_vec_with_ctx(BigEndian::default())?) } + /// Serialize this message directly into `buf`, appending after any existing content. + /// Uses speedy's `Write` impl on `Vec` to avoid an intermediate allocation. + fn serialize_into(&self, buf: &mut Vec) -> Result<()> { + use speedy::Writable; + self.write_to_stream_with_ctx(BigEndian::default(), buf)?; + Ok(()) + } + /// Deserializes a Message from raw bytes. fn from_bytes(raw: &[u8]) -> Result { Ok(Self::read_from_buffer_with_ctx(BigEndian::default(), raw)?) @@ -226,26 +235,32 @@ impl Message { } /// Write a Message, reusing `buf` to avoid per-message heap allocation. + /// + /// Serializes directly into `buf` (via speedy's `Write` impl on `Vec`) + /// to avoid an intermediate `to_bytes()` allocation — eliminates one full + /// copy of tensor data on the hot path. pub async fn to_writer_buf(&self, writer: &mut W, buf: &mut Vec) -> Result where W: AsyncWriteExt + Unpin, { - let payload = self.to_bytes()?; - let payload_size = payload.len() as u32; + buf.clear(); + // Reserve 8 bytes for the header (magic + size), filled in after serialization. + buf.extend_from_slice(&[0u8; 8]); + // Serialize message directly into buf (appends after the header placeholder). + self.serialize_into(buf)?; + + let payload_size = (buf.len() - 8) as u32; if payload_size > super::MESSAGE_MAX_SIZE { return Err(anyhow!("request size {payload_size} > MESSAGE_MAX_SIZE")); } - // Coalesce header + payload into a single write to avoid Nagle delays. - let frame_len = 8 + payload.len(); - buf.clear(); - buf.reserve(frame_len); - buf.extend_from_slice(&super::PROTO_MAGIC.to_be_bytes()); - buf.extend_from_slice(&payload_size.to_be_bytes()); - buf.extend_from_slice(&payload); + // Fill in the header now that we know the payload size. + buf[0..4].copy_from_slice(&super::PROTO_MAGIC.to_be_bytes()); + buf[4..8].copy_from_slice(&payload_size.to_be_bytes()); + writer.write_all(buf).await?; - Ok(frame_len) + Ok(buf.len()) } } diff --git a/cake-core/src/cake/proto/mod.rs b/cake-core/src/cake/proto/mod.rs index 44b3d577..eb7e837f 100644 --- a/cake-core/src/cake/proto/mod.rs +++ b/cake-core/src/cake/proto/mod.rs @@ -3,8 +3,10 @@ /// Cake protocol header magic value. pub(crate) const PROTO_MAGIC: u32 = 0x104F4C7; -/// Cake protocol message max size. -pub(crate) const MESSAGE_MAX_SIZE: u32 = 512 * 1024 * 1024; +/// Cake protocol message max size (1 GB). +/// Increased from 512 MB to support high-resolution video tensor transport +/// (e.g., 768×1024 @ 97 frames produces ~873 MB F32 VAE output). +pub(crate) const MESSAGE_MAX_SIZE: u32 = 1024 * 1024 * 1024; mod message; diff --git a/cake-core/src/cake/topology.rs b/cake-core/src/cake/topology.rs index 0128bc3d..47c90b49 100644 --- a/cake-core/src/cake/topology.rs +++ b/cake-core/src/cake/topology.rs @@ -132,3 +132,156 @@ impl std::ops::DerefMut for Topology { &mut self.0 } } + +#[cfg(test)] +mod tests { + use super::*; + + fn make_node(host: &str, layers: &[&str]) -> Node { + Node { + host: host.to_string(), + description: None, + layers: layers.iter().map(|s| s.to_string()).collect(), + vram_bytes: 0, + tflops: 0.0, + backend: String::new(), + hostname: String::new(), + os: String::new(), + } + } + + #[test] + fn test_empty_topology() { + let topo = Topology::new(); + assert!(topo.is_empty()); + assert!(topo.all_worker_layers().is_empty()); + assert!(topo.get_node_for_layer("model.layers.0").is_none()); + } + + #[test] + fn test_node_layer_ownership() { + let node = make_node("worker1:10128", &["model.layers.0", "model.layers.1"]); + + // Full layer name with sub-path matches + assert!(node.is_text_model_layer_owner("model.layers.0.self_attn")); + assert!(node.is_text_model_layer_owner("model.layers.1.mlp")); + + // Layer not assigned + assert!(!node.is_text_model_layer_owner("model.layers.2.self_attn")); + + // Exact name without trailing dot doesn't match + assert!(!node.is_text_model_layer_owner("model.layers.0")); + } + + #[test] + fn test_get_node_for_layer() { + let mut topo = Topology::new(); + topo.insert("gpu1".into(), make_node("10.0.0.1:10128", &["model.layers.0", "model.layers.1"])); + topo.insert("gpu2".into(), make_node("10.0.0.2:10128", &["model.layers.2"])); + + let (name, node) = topo.get_node_for_layer("model.layers.0").unwrap(); + assert_eq!(name, "gpu1"); + assert_eq!(node.host, "10.0.0.1:10128"); + + let (name, node) = topo.get_node_for_layer("model.layers.2").unwrap(); + assert_eq!(name, "gpu2"); + assert_eq!(node.host, "10.0.0.2:10128"); + + assert!(topo.get_node_for_layer("model.layers.99").is_none()); + } + + #[test] + fn test_all_worker_layers() { + let mut topo = Topology::new(); + topo.insert("w1".into(), make_node("a:1", &["model.layers.0", "model.layers.1"])); + topo.insert("w2".into(), make_node("b:1", &["model.layers.2"])); + + let layers = topo.all_worker_layers(); + assert_eq!(layers.len(), 3); + assert!(layers.contains("model.layers.0")); + assert!(layers.contains("model.layers.1")); + assert!(layers.contains("model.layers.2")); + } + + #[test] + fn test_topology_yaml_parsing() { + let yaml = r#" +gpu1: + host: "10.0.0.1:10128" + layers: + - "model.layers.0-2" +gpu2: + host: "10.0.0.2:10128" + layers: + - "model.layers.3" + - "model.layers.4" +"#; + let mut topo: Topology = serde_yaml::from_str(yaml).unwrap(); + + // Before range expansion, gpu1 has the raw range string + assert_eq!(topo["gpu1"].layers.len(), 1); + assert_eq!(topo["gpu1"].layers[0], "model.layers.0-2"); + + // Simulate range expansion (from_path does this for TextModel) + let re = regex::Regex::new(r"(?m)^(.+[^\d])(\d+)-(\d+)$").unwrap(); + for (_name, node) in topo.iter_mut() { + let mut expanded = vec![]; + for layer in &node.layers { + if let Some(caps) = re.captures(layer) { + let base = caps.get(1).unwrap().as_str(); + let start: usize = caps.get(2).unwrap().as_str().parse().unwrap(); + let stop: usize = caps.get(3).unwrap().as_str().parse().unwrap(); + for n in start..=stop { + expanded.push(format!("{}{}", base, n)); + } + } else { + expanded.push(layer.clone()); + } + } + node.layers = expanded; + } + + // After expansion: gpu1 should have 3 layers + assert_eq!(topo["gpu1"].layers, vec![ + "model.layers.0", "model.layers.1", "model.layers.2" + ]); + // gpu2 unchanged + assert_eq!(topo["gpu2"].layers, vec!["model.layers.3", "model.layers.4"]); + } + + #[test] + fn test_topology_component_layers() { + // Non-text-model topology (Flux/LTX/HunyuanVideo style) + let yaml = r#" +worker1: + host: "10.0.0.1:10128" + layers: + - "flux-t5" + - "flux-clip" +worker2: + host: "10.0.0.2:10128" + layers: + - "flux-transformer" +"#; + let topo: Topology = serde_yaml::from_str(yaml).unwrap(); + + assert!(topo.get_node_for_layer("flux-t5").is_some()); + assert!(topo.get_node_for_layer("flux-transformer").is_some()); + assert!(topo.get_node_for_layer("flux-vae").is_none()); + } + + #[test] + fn test_node_optional_fields_default() { + let yaml = r#" +worker: + host: "10.0.0.1:10128" + layers: ["layer0"] +"#; + let topo: Topology = serde_yaml::from_str(yaml).unwrap(); + let node = &topo["worker"]; + assert_eq!(node.vram_bytes, 0); + assert_eq!(node.tflops, 0.0); + assert_eq!(node.backend, ""); + assert!(node.description.is_none()); + } +} diff --git a/cake-core/src/lib.rs b/cake-core/src/lib.rs index 23de7127..c0b8cd29 100644 --- a/cake-core/src/lib.rs +++ b/cake-core/src/lib.rs @@ -10,6 +10,7 @@ use serde::Deserialize; pub mod cake; pub mod models; pub mod utils; +pub mod video; #[derive(Copy, Clone, Parser, Default, Debug, Eq, PartialEq, PartialOrd, Ord, ValueEnum)] pub enum ModelType { @@ -21,11 +22,17 @@ pub enum ModelType { /// Supported image model architectures. #[derive(Copy, Clone, Parser, Default, Debug, Eq, PartialEq, PartialOrd, Ord, ValueEnum)] pub enum ImageModelArch { - /// Stable Diffusion (v1.5, v2.1, XL, Turbo) + /// Auto-detect (defaults to Stable Diffusion) #[default] + Auto, + /// Stable Diffusion (v1.5, v2.1, XL, Turbo) SD, /// FLUX.2-klein (flow-matching transformer) Flux, + /// Lightricks LTX-Video (0.9.x series) + LtxVideo, + /// Lightricks LTX-2 (19B audio+video, Gemma-3 text encoder) + Ltx2, } /// Supported text model architectures. @@ -137,6 +144,16 @@ pub struct Args { #[arg(skip)] pub topology_override: Option, + /// Draft model for speculative decoding (path or HuggingFace repo). + /// Must share the same tokenizer as the main model. + /// Example: --draft-model Qwen/Qwen2.5-0.5B-Instruct + #[arg(long)] + pub draft_model: Option, + + /// Number of speculative tokens to draft before verification (default: 4). + #[arg(long, default_value_t = 4)] + pub spec_tokens: usize, + /// Run on CPU rather than on GPU. #[arg(long, default_value_t = false)] pub cpu: bool, @@ -148,18 +165,21 @@ pub struct Args { #[arg(long, default_value = "auto")] pub text_model_arch: TextModelArch, - /// Image model architecture (only used with --model-type image-model). - #[arg(long, default_value = "sd")] + /// Image model architecture (defaults to auto/stable-diffusion). + #[arg(long, default_value = "auto")] pub image_model_arch: ImageModelArch, #[clap(flatten)] pub sd_args: SDArgs, + #[clap(flatten)] + pub sd_img_gen_args: ImageGenerationArgs, + #[clap(flatten)] pub flux_args: FluxArgs, #[clap(flatten)] - pub sd_img_gen_args: ImageGenerationArgs, + pub ltx_args: LtxVideoArgs, } #[derive(Clone, clap::Args, Default, Debug)] @@ -366,3 +386,113 @@ impl StableDiffusionVersion { } } } + + +#[derive(Clone, Parser, Default, Debug)] +pub struct LtxVideoArgs { + /// LTX-Video model version (e.g., "0.9.8-13b-distilled"). + #[arg(long = "ltx-version", default_value = "0.9.8-13b-distilled")] + pub ltx_version: String, + + /// Override HuggingFace repo for LTX-Video weights. + #[arg(long = "ltx-model")] + pub ltx_model: Option, + + /// Override path to LTX transformer weights (safetensors). + #[arg(long = "ltx-transformer")] + pub ltx_transformer: Option, + + /// Override path to T5-XXL encoder weights (safetensors, comma-separated for sharded). + #[arg(long = "ltx-t5")] + pub ltx_t5: Option, + + /// Override path to T5 config.json. + #[arg(long = "ltx-t5-config")] + pub ltx_t5_config: Option, + + /// Override path to T5 tokenizer (tokenizer.json). + #[arg(long = "ltx-t5-tokenizer")] + pub ltx_t5_tokenizer: Option, + + /// Override path to LTX VAE weights (safetensors). + #[arg(long = "ltx-vae")] + pub ltx_vae: Option, + + /// Number of video frames to generate. + #[arg(long = "ltx-num-frames", default_value_t = 41)] + pub ltx_num_frames: usize, + + /// Video frame rate. + #[arg(long = "ltx-fps", default_value_t = 24)] + pub ltx_fps: usize, + + /// Output video height. + #[arg(long = "ltx-height", default_value_t = 512)] + pub ltx_height: usize, + + /// Output video width. + #[arg(long = "ltx-width", default_value_t = 704)] + pub ltx_width: usize, + + /// Number of sampling steps (default from model config). + #[arg(long = "ltx-num-steps")] + pub ltx_num_steps: Option, + + /// STG (Spatio-Temporal Guidance) scale. 0 to disable. Default: 1.0. + #[arg(long = "ltx-stg-scale")] + pub ltx_stg_scale: Option, + + /// STG block index to perturb. Default: 28 (LTX-2.3). + #[arg(long = "ltx-stg-block")] + pub ltx_stg_block: Option, + + /// Guidance rescale factor. Prevents oversaturation. Default: 0.7. + #[arg(long = "ltx-rescale")] + pub ltx_rescale: Option, + + /// Path to a GGUF file for quantized Gemma-3 (runs on GPU instead of CPU). + /// Example: --ltx-gemma-gguf /path/to/gemma-3-12b-pt-Q4_K_M.gguf + #[arg(long = "ltx-gemma-gguf")] + pub ltx_gemma_gguf: Option, +} + +impl LtxVideoArgs { + /// Get the HuggingFace repo ID for the LTX-Video model. + pub fn ltx_repo(&self) -> String { + if let Some(ref repo) = self.ltx_model { + return repo.clone(); + } + match self.ltx_version.as_str() { + // LTX-2.3 (22B, improved training + gated attention) + "2.3" | "2.3-dev" | "2.3-22b-dev" => "Lightricks/LTX-2.3".to_string(), + "2.3-distilled" | "2.3-22b-distilled" => "Lightricks/LTX-2.3".to_string(), + + // LTX-2 (19B, audio+video, Gemma-3 text encoder) + "2-19b-dev" | "2.0" | "2" => "Lightricks/LTX-2".to_string(), + "2-19b-distilled" => "Lightricks/LTX-2".to_string(), + + // LTX-Video 0.9.8 + "0.9.8-13b-distilled" | "0.9.8-13b" => { + "Lightricks/LTX-Video-0.9.8-13b-distilled".to_string() + } + "0.9.8-13b-dev" => "Lightricks/LTX-Video-0.9.8-13b-dev".to_string(), + "0.9.8-2b-distilled" | "0.9.8-distilled" => { + "Lightricks/LTX-Video-0.9.8-distilled".to_string() + } + + // LTX-Video 0.9.6 + "0.9.6-distilled" | "0.9.6-2b-distilled" => { + "Lightricks/LTX-Video-0.9.6-distilled".to_string() + } + "0.9.6-dev" | "0.9.6-2b-dev" => "Lightricks/LTX-Video-0.9.6-dev".to_string(), + + _ => "Lightricks/LTX-Video".to_string(), + } + } + + /// Whether this is an LTX-2.3 model (gated attention, 8 connector blocks). + pub fn is_ltx23(&self) -> bool { + let repo = self.ltx_repo(); + repo.contains("LTX-2.3") || self.ltx_version.starts_with("2.3") + } +} diff --git a/cake-core/src/models/chat.rs b/cake-core/src/models/chat.rs index c8b44ee7..c12b1e33 100644 --- a/cake-core/src/models/chat.rs +++ b/cake-core/src/models/chat.rs @@ -60,3 +60,47 @@ impl Message { } } } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_message_constructors() { + let sys = Message::system("sys".into()); + assert!(matches!(sys.role, MessageRole::System)); + assert_eq!(sys.content, "sys"); + + let usr = Message::user("usr".into()); + assert!(matches!(usr.role, MessageRole::User)); + + let asst = Message::assistant("asst".into()); + assert!(matches!(asst.role, MessageRole::Assistant)); + } + + #[test] + fn test_role_display() { + assert_eq!(format!("{}", MessageRole::System), "system"); + assert_eq!(format!("{}", MessageRole::User), "user"); + assert_eq!(format!("{}", MessageRole::Assistant), "assistant"); + } + + #[test] + fn test_message_json_roundtrip() { + let msg = Message::user("Hello world".into()); + let json = serde_json::to_string(&msg).unwrap(); + assert!(json.contains("\"role\":\"user\"")); + assert!(json.contains("\"content\":\"Hello world\"")); + + let decoded: Message = serde_json::from_str(&json).unwrap(); + assert!(matches!(decoded.role, MessageRole::User)); + assert_eq!(decoded.content, "Hello world"); + } + + #[test] + fn test_role_deserialize_lowercase() { + let json = r#"{"role":"system","content":"test"}"#; + let msg: Message = serde_json::from_str(json).unwrap(); + assert!(matches!(msg.role, MessageRole::System)); + } +} diff --git a/cake-core/src/models/common/attention.rs b/cake-core/src/models/common/attention.rs index 9234bbbb..cba0b791 100644 --- a/cake-core/src/models/common/attention.rs +++ b/cake-core/src/models/common/attention.rs @@ -226,6 +226,7 @@ impl CausalSelfAttention { let in_dtype = q.dtype(); + #[allow(unused_labels)] let y = 'attn: { // Flash Attention on CUDA — fused kernel, native GQA (no repeat_kv needed) @@ -245,12 +246,17 @@ impl CausalSelfAttention { // The actual kv seq_len (may differ from query seq_len with sliding window) let kv_seq_len = k.dims()[2]; + // Fused SDPA on Metal — single kernel, native GQA (no repeat_kv needed) #[cfg(feature = "metal")] if matches!(q.device(), candle_core::Device::Metal(_)) { + let q = q.to_dtype(DType::F32)?; + let k = k.to_dtype(DType::F32)?; + let v = v.to_dtype(DType::F32)?; let scale = 1.0 / (self.head_dim as f32).sqrt(); - break 'attn candle_nn::ops::sdpa(&q, &k, &v, None, seq_len > 1, scale, 1.0) + let y = candle_nn::ops::sdpa(&q, &k, &v, None, seq_len > 1, scale, 1.0) .map_err(|e| anyhow!("sdpa: {e}"))?; + break 'attn y.to_dtype(in_dtype)?; } // Manual attention with GQA head expansion (CPU fallback) @@ -291,7 +297,7 @@ impl CausalSelfAttention { .map_err(|e| anyhow!("masked_fill -> {e}"))? }; let att = candle_nn::ops::softmax_last_dim(&att)?; - att.matmul(&v.contiguous()?)? + att.matmul(&v)?.to_dtype(in_dtype)? }; let y = y.to_dtype(in_dtype)?; diff --git a/cake-core/src/models/common/cache.rs b/cake-core/src/models/common/cache.rs index c619aea6..3c57e42e 100644 --- a/cake-core/src/models/common/cache.rs +++ b/cake-core/src/models/common/cache.rs @@ -1,6 +1,7 @@ use std::collections::HashMap; use candle_core::{DType, Device, Result, Tensor}; +use candle_nn::kv_cache::KvCache; use super::Config; @@ -12,8 +13,7 @@ pub struct Cache { masks: HashMap, use_kv_cache: bool, - kvs: Vec>, - max_seq_len: usize, + kvs: Vec, /// Recurrent state matrices for linear attention layers (Gated DeltaNet). /// Shape per entry: (batch=1, num_heads, key_dim, value_dim). @@ -104,8 +104,7 @@ impl Cache { Ok(Self { masks: HashMap::new(), use_kv_cache, - kvs: vec![None; num_layers], - max_seq_len, + kvs: (0..num_layers).map(|_| KvCache::new(2, max_seq_len)).collect(), recurrent_states: vec![None; num_layers], conv_states: vec![None; num_layers], device: device.clone(), @@ -144,14 +143,21 @@ impl Cache { self.masks.get(&seq_len).unwrap().clone().to_device(device) } - /// Process the input k and v by either generating their cache entry or applying a previously cached one. + /// Process the input k and v using pre-allocated KV cache. + /// + /// Uses candle-nn's KvCache with `slice_set` for O(1) per-token append + /// instead of O(N) concatenation, making total generation O(N) instead of O(N²). pub fn process_kv( &mut self, block_idx: usize, k: Tensor, v: Tensor, ) -> Result<(Tensor, Tensor)> { - self.process_kv_inner(block_idx, k, v, self.max_seq_len) + if self.use_kv_cache { + self.kvs[block_idx].append(&k, &v) + } else { + Ok((k, v)) + } } /// Like `process_kv` but caps the KV cache to `window` tokens (sliding window attention). @@ -161,36 +167,20 @@ impl Cache { k: Tensor, v: Tensor, window: usize, - ) -> Result<(Tensor, Tensor)> { - let limit = window.min(self.max_seq_len); - self.process_kv_inner(block_idx, k, v, limit) - } - - fn process_kv_inner( - &mut self, - block_idx: usize, - mut k: Tensor, - mut v: Tensor, - limit: usize, ) -> Result<(Tensor, Tensor)> { if self.use_kv_cache { - if let Some((cache_k, cache_v)) = &self.kvs[block_idx] { - // tensor shape is (batch, num_heads, seq_len, head_dim) - k = Tensor::cat(&[cache_k, &k], 2)?.contiguous()?; - v = Tensor::cat(&[cache_v, &v], 2)?.contiguous()?; - - let k_seq_len = k.dims()[2]; - if k_seq_len > limit { - k = k.narrow(2, k_seq_len - limit, limit)?.contiguous()?; - } - let v_seq_len = v.dims()[2]; - if v_seq_len > limit { - v = v.narrow(2, v_seq_len - limit, limit)?.contiguous()?; - } + let (k_out, v_out) = self.kvs[block_idx].append(&k, &v)?; + let k_seq_len = k_out.dims()[2]; + if k_seq_len > window { + let k_trimmed = k_out.narrow(2, k_seq_len - window, window)?.contiguous()?; + let v_trimmed = v_out.narrow(2, k_seq_len - window, window)?.contiguous()?; + Ok((k_trimmed, v_trimmed)) + } else { + Ok((k_out, v_out)) } - self.kvs[block_idx] = Some((k.clone(), v.clone())) + } else { + Ok((k, v)) } - Ok((k, v)) } /// Get the recurrent state for a linear attention layer. @@ -223,7 +213,9 @@ impl Cache { /// Clear the cache. pub fn clear(&mut self) { self.masks.clear(); - self.kvs = vec![None; self.kvs.len()]; + for kv in &mut self.kvs { + kv.reset(); + } self.recurrent_states = vec![None; self.recurrent_states.len()]; self.conv_states = vec![None; self.conv_states.len()]; } diff --git a/cake-core/src/models/common/text_model.rs b/cake-core/src/models/common/text_model.rs index ef87a32c..d7c8be07 100644 --- a/cake-core/src/models/common/text_model.rs +++ b/cake-core/src/models/common/text_model.rs @@ -12,6 +12,7 @@ use super::EosTokenId; use crate::{ cake::{Context, Forwarder}, models::Token, + models::speculative::{SpeculativeState, speculate_and_verify}, }; /// Load the tokenizer and resolve EOS token ID(s). @@ -133,6 +134,11 @@ pub struct TextModelBase { pub logits_processor: LogitsProcessor, pub tokens: Vec, + + /// Optional draft model for speculative decoding. + pub draft: Option>, + /// Speculative decoding state (present when draft model is loaded). + pub spec_state: Option, } impl TextModelBase { @@ -199,20 +205,25 @@ impl TextModelBase { } } - // Pass 2: connect to remote layers - for i in 0..config.num_hidden_layers { - let block_layer_name = format!("{prefix}.layers.{i}"); - if let Some((_node_name, node)) = ctx.topology.get_node_for_layer(&block_layer_name) { - log::info!("connecting {} to {} ...", &block_layer_name, &node.host); - blocks[i] = Some(Box::new( - crate::cake::Client::new( - ctx.device.clone(), - &node.host, - &block_layer_name, - ctx.args.cluster_key.as_deref(), - ) - .await?, - )); + // Pass 2: connect to remote layers (one TCP connection per worker) + let remote_layers: Vec<(usize, String, String)> = (0..config.num_hidden_layers) + .filter_map(|i| { + let name = format!("{prefix}.layers.{i}"); + ctx.topology + .get_node_for_layer(&name) + .map(|(_, node)| (i, name, node.host.clone())) + }) + .collect(); + + if !remote_layers.is_empty() { + let connected = crate::cake::client::connect_remote_layers( + &remote_layers, + &ctx.device, + ctx.args.cluster_key.as_deref(), + ) + .await?; + for (idx, forwarder) in connected { + blocks[idx] = Some(forwarder); } } @@ -248,9 +259,50 @@ impl TextModelBase { ln_f, lm_head, logits_processor, + draft: None, + spec_state: None, }) } + /// Load a draft model for speculative decoding. + /// Creates a separate Context for the draft model (all layers local, no topology). + /// `default_eos_token` should match the main model's EOS token string. + pub async fn load_draft( + &mut self, + draft_model_path: &str, + default_eos_token: &str, + ) -> Result<()> { + use crate::cake::Mode; + + log::info!("loading draft model from {} for speculative decoding ...", draft_model_path); + + // Create draft args: same device/dtype, no topology, all local + let mut draft_args = self.ctx.args.clone(); + draft_args.model = draft_model_path.to_string(); + draft_args.topology = None; + draft_args.topology_override = None; + draft_args.mode = Mode::Master; + // Draft model should not recursively load another draft + draft_args.draft_model = None; + + let spec_tokens = draft_args.spec_tokens; + + let mut draft_ctx = Context::from_args(draft_args)?; + let mut draft_base = TextModelBase::load::(&mut draft_ctx, default_eos_token).await?; + + // Override draft EOS to match main model (they must agree on when to stop) + if self.eos_token_id.is_some() { + draft_base.eos_token_id = self.eos_token_id.clone(); + } + + self.draft = Some(Box::new(draft_base)); + self.spec_state = Some(SpeculativeState::new(spec_tokens)); + + log::info!("draft model loaded, speculative decoding enabled (K={})", spec_tokens); + + Ok(()) + } + /// Forward pass through all blocks. pub async fn forward(&mut self, x: &Tensor, idx: usize) -> Result { let forward_start = std::time::Instant::now(); @@ -270,8 +322,7 @@ impl TextModelBase { let mut local_count: usize = 0; while block_idx < num_blocks { - let curr_block_id = self.blocks[block_idx].ident().to_owned(); - if curr_block_id == "local" { + if self.blocks[block_idx].ident() == "local" { let local_start = std::time::Instant::now(); x = self.blocks[block_idx] .forward_mut(&x, idx, block_idx, &mut self.ctx) @@ -287,6 +338,7 @@ impl TextModelBase { // collect all contiguous layers running on the same worker let mut batch = vec![]; let first = block_idx; + let curr_block_id = self.blocks[block_idx].ident().to_owned(); while block_idx < num_blocks && self.blocks[block_idx].ident() == curr_block_id { batch.push(( self.blocks[block_idx].layer_name().to_string(), @@ -373,6 +425,12 @@ impl TextModelBase { // Track prompt length for repeat penalty scoping self.prompt_len = self.tokens.len(); + // Sync draft model with the same prompt tokens + if let Some(ref mut draft) = self.draft { + draft.tokens = self.tokens.clone(); + draft.prompt_len = self.prompt_len; + } + Ok(()) } @@ -380,6 +438,11 @@ impl TextModelBase { pub async fn next_token(&mut self, index: usize) -> Result { log::trace!("model.next_token({index})"); + // Speculative decoding path: drain from buffer, refill via speculate_and_verify + if self.draft.is_some() && self.spec_state.is_some() { + return self.next_token_speculative(index).await; + } + let num_tokens = self.tokens.len(); let (context_size, context_index) = if self .ctx @@ -477,6 +540,47 @@ impl TextModelBase { }) } + /// Speculative decoding path for next_token. + async fn next_token_speculative(&mut self, _index: usize) -> Result { + // Check if we have buffered tokens from a previous speculation round + if let Some(ref mut state) = self.spec_state { + if let Some((token_id, text, is_eos)) = state.accepted_buffer.pop_front() { + return Ok(Token { + id: token_id, + text, + is_end_of_stream: is_eos, + }); + } + } + + // Buffer is empty — run a new speculation round + // We need to temporarily take ownership of draft and state + let mut draft = self.draft.take().unwrap(); + let mut state = self.spec_state.take().unwrap(); + + let results = speculate_and_verify(self, &mut draft, &mut state).await?; + + // Put them back + self.draft = Some(draft); + self.spec_state = Some(state); + + // Put all results into buffer, then pop the first one to return + let state = self.spec_state.as_mut().unwrap(); + for (token_id, text, is_eos) in results { + state.accepted_buffer.push_back((token_id, text, is_eos)); + } + + if let Some((token_id, text, is_eos)) = state.accepted_buffer.pop_front() { + Ok(Token { + id: token_id, + text, + is_end_of_stream: is_eos, + }) + } else { + bail!("speculative decoding produced no tokens") + } + } + /// Reset all generation state. pub fn reset(&mut self) { self.tokens.clear(); @@ -485,6 +589,15 @@ impl TextModelBase { self.generated = 0; self.prompt_len = 0; + if let Some(ref mut draft) = self.draft { + draft.reset(); + } + if let Some(ref mut state) = self.spec_state { + state.accepted_buffer.clear(); + state.total_accepted = 0; + state.total_drafted = 0; + } + // Clear any stale CUDA error state left by tensor cleanup (CudaSlice drops). // cudarc's error_state is an atomic that gets poisoned by internal operations // (e.g. SyncOnDrop event recording, async memory frees) and causes the NEXT @@ -497,15 +610,18 @@ impl TextModelBase { } /// Notify all remote blocks of session end (clears their KV caches). + /// Only sends goodbye once per unique worker (skips RemoteRef stubs). pub async fn goodbye(&mut self) -> Result<()> { - let num_blocks = self.blocks.len(); - let mut block_idx = 0; - while block_idx < num_blocks { + let mut seen = HashSet::new(); + for block_idx in 0..self.blocks.len() { + let ident = self.blocks[block_idx].ident().to_owned(); + if ident != "local" && !seen.insert(ident) { + continue; // already sent goodbye to this worker + } self.blocks[block_idx] .goodbye() .await .map_err(|e| anyhow!("error in goodbye operation for block {block_idx}: {e}"))?; - block_idx += 1; } Ok(()) } diff --git a/cake-core/src/models/llama3/history.rs b/cake-core/src/models/llama3/history.rs index 2a6aa13d..4eeacd92 100644 --- a/cake-core/src/models/llama3/history.rs +++ b/cake-core/src/models/llama3/history.rs @@ -45,3 +45,64 @@ impl std::ops::DerefMut for History { &mut self.0 } } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_empty_history_has_assistant_header() { + let history = History::new(); + let prompt = history.encode_dialog_to_prompt(); + assert!(prompt.starts_with("<|begin_of_text|>")); + assert!(prompt.contains("<|start_header_id|>assistant<|end_header_id|>")); + } + + #[test] + fn test_single_turn_encoding() { + let mut history = History::new(); + history.push(Message::system("You are helpful.".into())); + history.push(Message::user("Hello".into())); + + let prompt = history.encode_dialog_to_prompt(); + + assert!(prompt.starts_with("<|begin_of_text|>")); + assert!(prompt.contains("<|start_header_id|>system<|end_header_id|>\n\nYou are helpful.<|eot_id|>")); + assert!(prompt.contains("<|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|>")); + assert!(prompt.ends_with("<|start_header_id|>assistant<|end_header_id|>\n\n")); + } + + #[test] + fn test_multi_turn_encoding() { + let mut history = History::new(); + history.push(Message::system("Sys".into())); + history.push(Message::user("Q1".into())); + history.push(Message::assistant("A1".into())); + history.push(Message::user("Q2".into())); + + let prompt = history.encode_dialog_to_prompt(); + + // All messages present in order + let sys_pos = prompt.find("Sys").unwrap(); + let q1_pos = prompt.find("Q1").unwrap(); + let a1_pos = prompt.find("A1").unwrap(); + let q2_pos = prompt.find("Q2").unwrap(); + assert!(sys_pos < q1_pos); + assert!(q1_pos < a1_pos); + assert!(a1_pos < q2_pos); + + // Ends with assistant header for completion + assert!(prompt.ends_with("<|start_header_id|>assistant<|end_header_id|>\n\n")); + } + + #[test] + fn test_whitespace_trimmed() { + let mut history = History::new(); + history.push(Message::user(" hello ".into())); + + let prompt = history.encode_dialog_to_prompt(); + assert!(prompt.contains("hello<|eot_id|>")); + // Leading/trailing whitespace in content should be trimmed + assert!(!prompt.contains(" hello")); + } +} diff --git a/cake-core/src/models/llama3/llama.rs b/cake-core/src/models/llama3/llama.rs index fb24d5ca..d8a8464d 100644 --- a/cake-core/src/models/llama3/llama.rs +++ b/cake-core/src/models/llama3/llama.rs @@ -54,7 +54,12 @@ impl Generator for LLama { /// Load this model from the context. async fn load(ctx: &mut Context) -> Result>> { - let base = TextModelBase::load::(ctx, DEFAULT_EOS_TOKEN).await?; + let mut base = TextModelBase::load::(ctx, DEFAULT_EOS_TOKEN).await?; + + // Load draft model for speculative decoding if requested + if let Some(ref draft_model) = ctx.args.draft_model.clone() { + base.load_draft::(draft_model, DEFAULT_EOS_TOKEN).await?; + } // Auto-detect chat template: LLaMA-3 uses <|begin_of_text|>; // other LlamaForCausalLM models (e.g. SmolLM2) may use ChatML (<|im_start|>). diff --git a/cake-core/src/models/ltx2/gemma.rs b/cake-core/src/models/ltx2/gemma.rs new file mode 100644 index 00000000..581959ef --- /dev/null +++ b/cake-core/src/models/ltx2/gemma.rs @@ -0,0 +1,223 @@ +use anyhow::Result; +use async_trait::async_trait; +use candle_core::{DType, Tensor}; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use log::info; +use std::path::PathBuf; + +use crate::cake::{Context, Forwarder}; +use crate::models::sd::{pack_tensors, unpack_tensors}; + +use super::vendored::config::Ltx2ConnectorConfig; +use super::vendored::connector::Ltx2TextConnectors; + +/// LTX-2 text connector Forwarder. +/// +/// Layer name: `"ltx2-gemma"` +/// +/// This component runs ONLY the LTX2TextConnectors (self-attention transformer +/// with registers). The Gemma-3 text encoder runs on the master GPU and sends +/// pre-computed packed embeddings here. +/// +/// Input (packed tensors): +/// - `[0]` = packed Gemma embeddings `[B, L, 188160]` +/// - `[1]` = attention mask `[B, L]` +/// +/// Output: `[B, seq_len, cross_attention_dim]` — context for transformer +pub struct Ltx2Gemma { + name: String, + connector: Option, +} + +impl std::fmt::Debug for Ltx2Gemma { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + f.debug_struct("Ltx2Gemma") + .field("name", &self.name) + .field("connector", &self.connector) + .finish() + } +} + +impl std::fmt::Display for Ltx2Gemma { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{} (local)", &self.name) + } +} + +/// Resolve a file from an HF repo, trying direct path first, then HF cache. +fn resolve_hf_file(repo: &str, filename: &str, model_base: &str) -> Result { + // Try direct path first: model_base/filename + let direct = PathBuf::from(model_base).join(filename); + if direct.exists() { + return Ok(direct); + } + + // Fall back to HF cache + let model_base_path = PathBuf::from(model_base); + let cache_path = model_base_path.join("hub"); + let api = if model_base_path.is_dir() && cache_path.is_dir() { + ApiBuilder::from_cache(Cache::new(cache_path)).build()? + } else { + let mut builder = ApiBuilder::new(); + if let Ok(token) = std::env::var("HF_TOKEN") { + builder = builder.with_token(Some(token)); + } + builder.build()? + }; + let model_api = api.model(repo.to_string()); + Ok(model_api.get(filename)?) +} + +impl Ltx2Gemma { + pub fn load_model(ctx: &Context) -> Result> { + let ltx_args = &ctx.args.ltx_args; + let ltx_repo = ltx_args.ltx_repo(); + let is_ltx23 = ltx_args.is_ltx23(); + + // Load connector weights only — Gemma encoder lives on the master + let connector_path = resolve_hf_file( + <x_repo, + "connectors/diffusion_pytorch_model.safetensors", + &ctx.args.model, + )?; + + info!("Loading LTX-2{} text connectors from {:?}...", + if is_ltx23 { ".3" } else { "" }, connector_path); + + // LTX-2 connector weights are BF16 — load as BF16 to avoid NaN + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &[connector_path], + DType::BF16, + &ctx.device, + )? + }; + + let config = if is_ltx23 { + // Try loading config from connectors/config.json (created by conversion script) + let config_path = resolve_hf_file( + <x_repo, + "connectors/config.json", + &ctx.args.model, + ); + match config_path { + Ok(path) => { + let config_str = std::fs::read_to_string(&path)?; + serde_json::from_str(&config_str).unwrap_or_else(|_| Ltx2ConnectorConfig::for_ltx23()) + } + Err(_) => Ltx2ConnectorConfig::for_ltx23(), + } + } else { + Ltx2ConnectorConfig::default() + }; + let connector = Ltx2TextConnectors::new(&config, false, vb)?; + + info!("LTX-2 text connectors loaded!"); + + Ok(Box::new(Self { + name: "ltx2-gemma".to_string(), + connector: Some(connector), + })) + } + + /// Encode text through the connector pipeline. + /// + /// `text_embeds` should be pre-computed packed Gemma embeddings `[B, L, 188160]`. + pub async fn encode( + forwarder: &mut Box, + text_embeds: Tensor, + text_mask: Option, + ctx: &mut Context, + ) -> Result { + let mut tensors = vec![text_embeds]; + if let Some(mask) = text_mask { + tensors.push(mask); + } + let packed = pack_tensors(tensors, &ctx.device)?; + forwarder.forward_mut(&packed, 0, 0, ctx).await + } +} + +#[async_trait] +impl Forwarder for Ltx2Gemma { + fn load(name: String, ctx: &Context) -> Result> { + let ltx_args = &ctx.args.ltx_args; + let ltx_repo = ltx_args.ltx_repo(); + let is_ltx23 = ltx_args.is_ltx23(); + + let connector_path = resolve_hf_file( + <x_repo, + "connectors/diffusion_pytorch_model.safetensors", + &ctx.args.model, + )?; + + // LTX-2 connector weights are BF16 — load as BF16 to avoid NaN + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &[connector_path], + DType::BF16, + &ctx.device, + )? + }; + + let config = if is_ltx23 { + Ltx2ConnectorConfig::for_ltx23() + } else { + Ltx2ConnectorConfig::default() + }; + let connector = Ltx2TextConnectors::new(&config, false, vb)?; + + Ok(Box::new(Self { + name, + connector: Some(connector), + })) + } + + async fn forward( + &self, + x: &Tensor, + _index_pos: usize, + _block_idx: usize, + _ctx: &mut Context, + ) -> Result { + let connector = self + .connector + .as_ref() + .ok_or_else(|| anyhow::anyhow!("LTX-2 text connector not loaded"))?; + + let unpacked = unpack_tensors(x)?; + // Connector weights are BF16 — convert inputs to match + let text_embeds = unpacked[0].to_dtype(DType::BF16)?; + let text_mask = if unpacked.len() > 1 { + Some(unpacked[1].to_dtype(DType::F32)?) + } else { + None + }; + + if text_embeds.rank() == 2 { + anyhow::bail!( + "Expected packed Gemma embeddings [B, L, 188160], got rank-2 tensor. \ + Gemma encoder should run on the master and send packed embeddings." + ); + } + + info!("LTX-2 text connector forwarding..."); + let (result, _mask) = connector.forward_video(&text_embeds, text_mask.as_ref())?; + Ok(result) + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> Result { + self.forward(x, index_pos, block_idx, ctx).await + } + + fn layer_name(&self) -> &str { + &self.name + } +} diff --git a/cake-core/src/models/ltx2/gemma_encoder.rs b/cake-core/src/models/ltx2/gemma_encoder.rs new file mode 100644 index 00000000..0fb77781 --- /dev/null +++ b/cake-core/src/models/ltx2/gemma_encoder.rs @@ -0,0 +1,994 @@ +//! Gemma-3 text encoder for LTX-2. +//! +//! This wraps the candle-transformers Gemma-3 model to extract hidden states +//! from ALL layers (embedding + 48 transformer layers = 49 total), normalize +//! them, and pack into the format expected by the LTX-2 text connector: +//! `[B, seq_len, hidden_dim * num_layers]` = `[B, 1024, 188160]`. + +use anyhow::Result; +use candle_core::{DType, Device, Module, Tensor, D}; +use candle_nn::VarBuilder; +use candle_transformers::models::gemma3; +use log::info; +use tokenizers::Tokenizer; + +use super::quantized_gemma::Gemma3QuantizedAllHidden; + +/// Gemma-3 config for the 12B model used by LTX-2. +pub fn gemma3_12b_config() -> gemma3::Config { + gemma3::Config { + attention_bias: false, + head_dim: 256, + hidden_activation: candle_nn::Activation::GeluPytorchTanh, + hidden_size: 3840, + intermediate_size: 15360, + num_attention_heads: 16, + num_hidden_layers: 48, + num_key_value_heads: 8, + rms_norm_eps: 1e-6, + rope_theta: 1_000_000.0, + rope_local_base_freq: 10_000.0, + vocab_size: 262_208, + final_logit_softcapping: None, + attn_logit_softcapping: None, + query_pre_attn_scalar: 256, + sliding_window: 1024, + sliding_window_pattern: 6, // 5 local : 1 global + max_position_embeddings: 131_072, + } +} + +/// Maximum sequence length for text encoding. +/// Matches the default `max_sequence_length=1024` in the Python LTX-2 pipeline. +/// The connector's register tiling depends on this (seq_len / 128 = 8 tiles). +/// Using 256 produces muddy output because the connector operates differently +/// with only 2 register tiles vs 8. +pub const MAX_SEQ_LEN: usize = 1024; + +/// Scale factor for normalization (matches Python pipeline). +#[allow(dead_code)] +pub const PACK_SCALE_FACTOR: f32 = 8.0; + +/// Backend for Gemma-3 model weights — either full-precision safetensors +/// or quantized GGUF. +enum GemmaBackend { + /// Full-precision model loaded from safetensors (typically F32 on CPU). + Full(Gemma3AllHidden), + /// Quantized model loaded from GGUF (typically Q4_K_M on GPU). + Quantized(Gemma3QuantizedAllHidden), +} + +/// Gemma-3 text encoder that extracts all hidden states. +/// +/// Unlike the standard `gemma3::Model` which only returns logits, +/// this version collects hidden states from all 49 layers +/// (1 embedding + 48 transformer layers) for the LTX-2 connector. +/// +/// Supports two backends: +/// - **Safetensors** (F32 on CPU, ~24 GB) — via `load()` +/// - **GGUF quantized** (Q4_K_M on GPU, ~7.4 GB) — via `load_gguf()` +pub struct Gemma3TextEncoder { + model: GemmaBackend, + #[allow(dead_code)] + tokenizer: Tokenizer, + device: Device, + dtype: DType, +} + +impl Gemma3TextEncoder { + /// Load Gemma-3 model and tokenizer from safetensors files. + pub fn load( + model_paths: &[std::path::PathBuf], + tokenizer_path: &std::path::Path, + config: &gemma3::Config, + dtype: DType, + device: &Device, + ) -> Result { + info!("Loading Gemma-3 tokenizer from {:?}...", tokenizer_path); + let tokenizer = Tokenizer::from_file(tokenizer_path) + .map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {}", e))?; + + info!( + "Loading Gemma-3 model ({} layers, {}d) from {} file(s)...", + config.num_hidden_layers, + config.hidden_size, + model_paths.len() + ); + + let vb = unsafe { + VarBuilder::from_mmaped_safetensors(model_paths, dtype, device)? + }; + + let model = Gemma3AllHidden::new(false, config, vb)?; + + info!("Gemma-3 model loaded (safetensors)!"); + + Ok(Self { + model: GemmaBackend::Full(model), + tokenizer, + device: device.clone(), + dtype, + }) + } + + /// Load Gemma-3 model from a GGUF file (quantized, runs on GPU). + /// + /// Q4_K_M of Gemma-3-12B is ~7.4 GB, fitting easily on a 24 GB GPU + /// alongside the LTX-2 connector (~5 GB) and VAE (~1.4 GB). + pub fn load_gguf( + gguf_path: &std::path::Path, + tokenizer_path: &std::path::Path, + device: &Device, + ) -> Result { + info!("Loading Gemma-3 tokenizer from {:?}...", tokenizer_path); + let tokenizer = Tokenizer::from_file(tokenizer_path) + .map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {}", e))?; + + info!("Loading quantized Gemma-3 from GGUF: {:?}", gguf_path); + let mut file = std::fs::File::open(gguf_path)?; + let ct = candle_core::quantized::gguf_file::Content::read(&mut file) + .map_err(|e| anyhow::anyhow!("Failed to read GGUF: {}", e))?; + let model = Gemma3QuantizedAllHidden::from_gguf(ct, &mut file, device) + .map_err(|e| anyhow::anyhow!("Failed to load quantized Gemma-3: {}", e))?; + + info!("Gemma-3 model loaded (GGUF quantized on {:?})!", device); + + Ok(Self { + model: GemmaBackend::Quantized(model), + tokenizer, + device: device.clone(), + // Quantized models work in F32 intermediate dtype + dtype: DType::F32, + }) + } + + /// Encode a text prompt into packed hidden states for LTX-2 connector. + /// + /// Returns `(packed_embeds, attention_mask)`: + /// - `packed_embeds`: `[B, seq_len, hidden_dim * num_layers]` = `[1, L, 188160]` + /// - `attention_mask`: `[B, seq_len]` binary mask (1=valid, 0=padding) + #[allow(dead_code)] + pub fn encode(&mut self, prompt: &str) -> Result<(Tensor, Tensor)> { + let encoding = self + .tokenizer + .encode(prompt, true) + .map_err(|e| anyhow::anyhow!("Tokenization failed: {}", e))?; + + let tokens = encoding.get_ids(); + let seq_len = tokens.len().min(MAX_SEQ_LEN); + + // Left-pad to MAX_SEQ_LEN (Gemma uses left padding) + let pad_len = MAX_SEQ_LEN.saturating_sub(seq_len); + let mut padded_ids = vec![0u32; pad_len]; + padded_ids.extend_from_slice(&tokens[..seq_len]); + + // Attention mask: 0 for padding, 1 for valid + let mut mask_vals = vec![0.0f32; pad_len]; + mask_vals.extend(vec![1.0f32; seq_len]); + + let input_ids = Tensor::new(padded_ids.as_slice(), &self.device)? + .unsqueeze(0)?; // [1, MAX_SEQ_LEN] + let attention_mask = Tensor::new(mask_vals.as_slice(), &self.device)? + .unsqueeze(0)?; // [1, MAX_SEQ_LEN] + + // Run Gemma-3 forward pass, collecting all hidden states + self.clear_kv_cache(); + let all_hidden = self.forward_all_hidden(&input_ids, 0, Some(&attention_mask))?; + // all_hidden: Vec of 49 tensors, each [1, MAX_SEQ_LEN, 3840] + + // Stack to [B, seq_len, hidden_dim, num_layers] + let stacked = Tensor::stack(&all_hidden, D::Minus1)?; + + // Compute sequence lengths for normalization + let sequence_lengths = Tensor::new(&[seq_len as f32], &self.device)?; + + let packed = pack_text_embeds( + &stacked, + &sequence_lengths, + "left", + PACK_SCALE_FACTOR, + )? + .to_dtype(self.dtype)?; + + Ok((packed, attention_mask.to_dtype(DType::F32)?)) + } + + /// Encode from pre-tokenized input tensors (for worker-side encoding). + /// + /// `input_ids`: `[B, L]` u32 token IDs (left-padded to MAX_SEQ_LEN) + /// `attention_mask`: `[B, L]` float mask (1=valid, 0=padding) + /// + /// Returns `(packed_embeds, attention_mask)` same as `encode()`. + #[allow(dead_code)] + pub fn encode_from_tokens( + &mut self, + input_ids: &Tensor, + attention_mask: &Tensor, + ) -> Result<(Tensor, Tensor)> { + let _seq_len = input_ids.dim(1)?; + + // Move tensors to encoder's device if needed + let input_ids = input_ids.to_device(&self.device)?; + let attention_mask_f = attention_mask.to_dtype(DType::F32)?.to_device(&self.device)?; + + // Run Gemma-3 forward pass + self.clear_kv_cache(); + let all_hidden = self.forward_all_hidden(&input_ids, 0, Some(&attention_mask_f))?; + + // Stack to [B, seq_len, hidden_dim, num_layers] + let stacked = Tensor::stack(&all_hidden, D::Minus1)?; + + // Compute sequence lengths from mask (sum of valid tokens per batch) + let sequence_lengths = attention_mask_f.sum(1)?; // [B] + + let packed = pack_text_embeds( + &stacked, + &sequence_lengths, + "left", + PACK_SCALE_FACTOR, + )? + .to_dtype(self.dtype)?; + + Ok((packed, attention_mask_f)) + } + + fn forward_all_hidden( + &mut self, + input_ids: &Tensor, + seqlen_offset: usize, + padding_mask: Option<&Tensor>, + ) -> candle_core::Result> { + match &mut self.model { + GemmaBackend::Full(m) => m.forward_all_hidden(input_ids, seqlen_offset, padding_mask), + GemmaBackend::Quantized(m) => m.forward_all_hidden(input_ids, seqlen_offset, padding_mask), + } + } + + fn clear_kv_cache(&mut self) { + match &mut self.model { + GemmaBackend::Full(m) => m.clear_kv_cache(), + GemmaBackend::Quantized(m) => m.clear_kv_cache(), + } + } +} + +/// Pack and normalize text encoder hidden states. +/// +/// Matches the Python `_pack_text_embeds` function in the LTX-2 pipeline. +/// +/// Input: `[B, seq_len, hidden_dim, num_layers]` +/// Output: `[B, seq_len, hidden_dim * num_layers]` +/// +/// Normalization per batch, per layer: +/// 1. Compute masked mean over non-padding positions +/// 2. Compute masked min/max over non-padding positions +/// 3. Normalize: `(x - mean) / (max - min + eps) * scale_factor` +/// 4. Flatten last two dims and zero out padding positions +pub fn pack_text_embeds( + text_hidden_states: &Tensor, + sequence_lengths: &Tensor, + padding_side: &str, + scale_factor: f32, +) -> candle_core::Result { + let eps = 1e-6f64; + let (batch_size, seq_len, hidden_dim, num_layers) = text_hidden_states.dims4()?; + let device = text_hidden_states.device(); + + // Create padding mask [B, seq_len, 1, 1] + let token_indices = Tensor::arange(0u32, seq_len as u32, device)? + .to_dtype(DType::F32)? + .unsqueeze(0)?; // [1, seq_len] + + let mask = match padding_side { + "left" => { + // Valid tokens are from (seq_len - sequence_length) to end + let start_indices = Tensor::full(seq_len as f32, (batch_size, 1), device)? + .broadcast_sub(&sequence_lengths.unsqueeze(1)?)?; // [B, 1] + token_indices.broadcast_ge(&start_indices)? // [B, seq_len] + } + "right" => { + // Valid tokens are from 0 to sequence_length - 1 + token_indices.broadcast_lt(&sequence_lengths.unsqueeze(1)?)? // [B, seq_len] + } + _ => candle_core::bail!("padding_side must be 'left' or 'right'"), + }; + // mask: [B, seq_len] -> [B, seq_len, 1, 1] + let mask_f = mask.to_dtype(DType::F32)?.unsqueeze(2)?.unsqueeze(3)?; + + // Work in F32 for numerical stability + let x = text_hidden_states.to_dtype(DType::F32)?; + + // Masked hidden states (zero out padding) + let masked_x = x.broadcast_mul(&mask_f)?; + + // Compute masked mean: sum over (seq_len, hidden_dim) / num_valid_positions + // num_valid_positions = sequence_lengths * hidden_dim + let num_valid = sequence_lengths + .to_dtype(DType::F32)? + .affine(hidden_dim as f64, 0.0)? + .reshape((batch_size, 1, 1, 1))?; + let sum_x = masked_x.sum(1)?.sum(1)?; // [B, num_layers] + let sum_x = sum_x.unsqueeze(1)?.unsqueeze(1)?; // [B, 1, 1, num_layers] + let num_valid_eps = (num_valid + eps)?; + let masked_mean = sum_x.broadcast_div(&num_valid_eps)?; + + // Compute masked min/max + // For min: fill padding with +inf, then amin + // For max: fill padding with -inf, then amax + let inv_mask = mask_f.affine(-1.0, 1.0)?; // 1 where padding, 0 where valid + let inf_fill = inv_mask.affine(f32::MAX as f64, 0.0)?; + let neg_inf_fill = inv_mask.affine(f32::MIN as f64, 0.0)?; + + let x_for_min = x.broadcast_add(&inf_fill)?; + let x_for_max = x.broadcast_add(&neg_inf_fill)?; + + // amin/amax over dims 1 and 2 (seq_len, hidden_dim), keeping [B, 1, 1, num_layers] + let x_min = x_for_min.flatten(1, 2)?.min(1)?.unsqueeze(1)?.unsqueeze(1)?; + let x_max = x_for_max.flatten(1, 2)?.max(1)?.unsqueeze(1)?.unsqueeze(1)?; + + // Normalize: (x - mean) / (max - min + eps) * scale_factor + let range = (x_max.broadcast_sub(&x_min)? + eps)?; + let normalized = x + .broadcast_sub(&masked_mean)? + .broadcast_div(&range)? + .affine(scale_factor as f64, 0.0)?; + + // Flatten last two dims: [B, seq_len, hidden_dim, num_layers] -> [B, seq_len, hidden_dim * num_layers] + let packed = normalized.flatten(2, 3)?; + + // Zero out padding positions + let mask_flat = mask + .to_dtype(DType::F32)? + .unsqueeze(2)? // [B, seq_len, 1] + .broadcast_as((batch_size, seq_len, hidden_dim * num_layers))? + .contiguous()?; + + packed.broadcast_mul(&mask_flat) +} + +/// Pack text embeddings using per-token RMS normalization (V2 / LTX-2.3). +/// +/// This is the `FeatureExtractorV2._norm_and_concat` method from the Python reference. +/// Unlike V1 which normalizes per-batch-per-layer, V2 normalizes per-token: +/// 1. Compute RMS per token per layer: `rms = sqrt(mean(x^2, dim=hidden))` +/// 2. Normalize: `x / (rms + eps)` +/// 3. Rescale: `x * sqrt(out_dim / embedding_dim)` +/// 4. Flatten last two dims and zero out padding +/// +/// Input: `[B, seq_len, hidden_dim, num_layers]` +/// Output: `[B, seq_len, hidden_dim * num_layers]` +#[allow(dead_code)] +pub fn pack_text_embeds_v2( + text_hidden_states: &Tensor, + sequence_lengths: &Tensor, + padding_side: &str, + out_dim: usize, +) -> candle_core::Result { + let eps = 1e-6f64; + let (batch_size, seq_len, hidden_dim, _num_layers) = text_hidden_states.dims4()?; + let device = text_hidden_states.device(); + + // Create padding mask [B, seq_len] + let token_indices = Tensor::arange(0u32, seq_len as u32, device)? + .to_dtype(DType::F32)? + .unsqueeze(0)?; // [1, seq_len] + + let mask = match padding_side { + "left" => { + let start_indices = Tensor::full(seq_len as f32, (batch_size, 1), device)? + .broadcast_sub(&sequence_lengths.unsqueeze(1)?)?; + token_indices.broadcast_ge(&start_indices)? + } + "right" => { + token_indices.broadcast_lt(&sequence_lengths.unsqueeze(1)?)? + } + _ => candle_core::bail!("padding_side must be 'left' or 'right'"), + }; + + // Work in F32 + let x = text_hidden_states.to_dtype(DType::F32)?; + + // Per-token RMS norm: variance = mean(x^2, dim=hidden_dim), per token per layer + // x: [B, seq_len, hidden_dim, num_layers] + // variance: [B, seq_len, 1, num_layers] + let variance = x.sqr()?.mean_keepdim(2)?; + let rms = (variance + eps)?.sqrt()?; + let normed = x.broadcast_div(&rms)?; + + // Rescale: x * sqrt(out_dim / embedding_dim) + let rescale = (out_dim as f64 / hidden_dim as f64).sqrt(); + let normed = normed.affine(rescale, 0.0)?; + + // Flatten: [B, seq_len, hidden_dim, num_layers] -> [B, seq_len, hidden_dim * num_layers] + let packed = normed.flatten(2, 3)?; + + // Zero out padding positions + let mask_f = mask + .to_dtype(DType::F32)? + .unsqueeze(2)? + .broadcast_as((batch_size, seq_len, hidden_dim * _num_layers))? + .contiguous()?; + + packed.broadcast_mul(&mask_f) +} + +// --------------------------------------------------------------------------- +// Modified Gemma-3 model that returns all hidden states +// --------------------------------------------------------------------------- + +/// Gemma-3 model modified to return hidden states from all layers. +/// +/// Based on `candle_transformers::models::gemma3::Model` but the forward +/// pass collects and returns all intermediate hidden states instead of +/// just the final logits. +struct Gemma3AllHidden { + embed_tokens: candle_nn::Embedding, + layers: Vec, + hidden_size: usize, + sliding_window: usize, + dtype: DType, + device: Device, +} + +impl Gemma3AllHidden { + fn new(use_flash_attn: bool, cfg: &gemma3::Config, vb: VarBuilder) -> candle_core::Result { + // google/gemma-3-12b-pt uses "language_model.model." prefix + let vb_m = if vb.contains_tensor("language_model.model.embed_tokens.weight") { + vb.pp("language_model").pp("model") + } else { + vb.pp("model") + }; + let embed_tokens = + candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?; + let mut layers = Vec::with_capacity(cfg.num_hidden_layers); + let vb_l = vb_m.pp("layers"); + for layer_idx in 0..cfg.num_hidden_layers { + let sliding_window = layer_idx % cfg.sliding_window_pattern != 0; + let layer = Gemma3DecoderLayer::new( + use_flash_attn, + cfg, + vb_l.pp(layer_idx), + sliding_window.then_some(cfg.sliding_window), + )?; + layers.push(layer); + } + Ok(Self { + embed_tokens, + layers, + hidden_size: cfg.hidden_size, + sliding_window: cfg.sliding_window, + dtype: vb.dtype(), + device: vb.device().clone(), + }) + } + + /// Forward pass that returns hidden states from ALL layers. + /// + /// Returns a Vec of `num_hidden_layers + 1` tensors: + /// - `[0]`: embedding output (before any transformer layer) + /// - `[1..=N]`: output of each transformer layer + /// + /// Each tensor has shape `[B, seq_len, hidden_size]`. + fn forward_all_hidden( + &mut self, + input_ids: &Tensor, + seqlen_offset: usize, + padding_mask: Option<&Tensor>, + ) -> candle_core::Result> { + let (b_size, seq_len) = input_ids.dims2()?; + let mut xs = self.embed_tokens.forward(input_ids)?; + xs = (xs * (self.hidden_size as f64).sqrt())?; + + let mut all_hidden = Vec::with_capacity(self.layers.len() + 1); + all_hidden.push(xs.clone()); + + // Convert padding mask [B, L] (1=valid, 0=pad) to additive form [B, 1, 1, L] + // where padding positions get -inf (added to attention weights before softmax) + let padding_attn_mask = if let Some(pm) = padding_mask { + // (mask - 1) gives -1 for padding, 0 for valid + // Multiply by large value to get -inf-like for padding + let additive = pm + .to_dtype(DType::F32)? + .affine(1.0, -1.0)? // 1→0, 0→-1 + .affine(1e9, 0.0)? // 0→0, -1→-1e9 + .unsqueeze(1)? // [B, 1, L] + .unsqueeze(1)?; // [B, 1, 1, L] + Some(additive.to_dtype(self.dtype)?) + } else { + None + }; + + // Create causal attention masks + let (attention_mask, sliding_attention_mask) = if seq_len <= 1 { + (None, None) + } else { + let causal = prepare_decoder_attention_mask( + b_size, + seq_len, + seqlen_offset, + None, + self.dtype, + &self.device, + )?; + let sliding_causal = prepare_decoder_attention_mask( + b_size, + seq_len, + seqlen_offset, + Some(self.sliding_window), + self.dtype, + &self.device, + )?; + // Combine causal masks with padding mask + let mask = match &padding_attn_mask { + Some(pm) => causal.broadcast_add(pm)?, + None => causal, + }; + let sliding_mask = match &padding_attn_mask { + Some(pm) => sliding_causal.broadcast_add(pm)?, + None => sliding_causal, + }; + (Some(mask), Some(sliding_mask)) + }; + + let num_layers = self.layers.len(); + for i in 0..num_layers { + let layer = &mut self.layers[i]; + let mask = if layer.sliding_window.is_some() { + &sliding_attention_mask + } else { + &attention_mask + }; + xs = layer.forward(&xs, mask.as_ref(), seqlen_offset)?; + all_hidden.push(xs.clone()); + } + + Ok(all_hidden) + } + + fn clear_kv_cache(&mut self) { + for layer in self.layers.iter_mut() { + layer.clear_kv_cache(); + } + } +} + +// --------------------------------------------------------------------------- +// Internal Gemma-3 components (duplicated from candle-transformers because +// the upstream types are not public and we need mutable access for KV cache) +// --------------------------------------------------------------------------- + +#[derive(Debug, Clone)] +struct GemmaRmsNorm { + weight: Tensor, + eps: f64, +} + +impl GemmaRmsNorm { + fn new(dim: usize, eps: f64, vb: VarBuilder) -> candle_core::Result { + let weight = vb.get(dim, "weight")?; + Ok(Self { weight, eps }) + } +} + +impl Module for GemmaRmsNorm { + fn forward(&self, x: &Tensor) -> candle_core::Result { + let x_dtype = x.dtype(); + let internal_dtype = match x_dtype { + DType::F16 | DType::BF16 => DType::F32, + d => d, + }; + let hidden_size = x.dim(D::Minus1)?; + let x = x.to_dtype(internal_dtype)?; + let norm_x = (x.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?; + let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?; + x_normed + .to_dtype(x_dtype)? + .broadcast_mul(&(&self.weight + 1.0)?) + } +} + +#[derive(Debug, Clone)] +struct GemmaRotaryEmbedding { + sin: Tensor, + cos: Tensor, +} + +impl GemmaRotaryEmbedding { + fn new(dtype: DType, cfg: &gemma3::Config, dev: &Device, sliding_window: Option) -> candle_core::Result { + let dim = cfg.head_dim; + let max_seq_len = cfg.max_position_embeddings; + let rope_freq = if sliding_window.is_some() { + cfg.rope_local_base_freq + } else { + cfg.rope_theta + }; + let inv_freq: Vec<_> = (0..dim) + .step_by(2) + .map(|i| 1f32 / rope_freq.powf(i as f64 / dim as f64) as f32) + .collect(); + let inv_freq_len = inv_freq.len(); + let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?; + let t = Tensor::arange(0u32, max_seq_len as u32, dev)? + .to_dtype(dtype)? + .reshape((max_seq_len, 1))?; + let freqs = t.matmul(&inv_freq)?; + Ok(Self { + sin: freqs.sin()?, + cos: freqs.cos()?, + }) + } + + fn apply_rotary_emb_qkv( + &self, + q: &Tensor, + k: &Tensor, + seqlen_offset: usize, + ) -> candle_core::Result<(Tensor, Tensor)> { + let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?; + let cos = self.cos.narrow(0, seqlen_offset, seq_len)?; + let sin = self.sin.narrow(0, seqlen_offset, seq_len)?; + let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?; + let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?; + Ok((q_embed, k_embed)) + } +} + +#[derive(Debug, Clone)] +struct GemmaMLP { + gate_proj: candle_nn::Linear, + up_proj: candle_nn::Linear, + down_proj: candle_nn::Linear, + act_fn: candle_nn::Activation, +} + +impl GemmaMLP { + fn new(cfg: &gemma3::Config, vb: VarBuilder) -> candle_core::Result { + let hidden_sz = cfg.hidden_size; + let intermediate_sz = cfg.intermediate_size; + let gate_proj = candle_nn::linear_b(hidden_sz, intermediate_sz, false, vb.pp("gate_proj"))?; + let up_proj = candle_nn::linear_b(hidden_sz, intermediate_sz, false, vb.pp("up_proj"))?; + let down_proj = candle_nn::linear_b(intermediate_sz, hidden_sz, false, vb.pp("down_proj"))?; + Ok(Self { + gate_proj, + up_proj, + down_proj, + act_fn: cfg.hidden_activation, + }) + } +} + +impl Module for GemmaMLP { + fn forward(&self, xs: &Tensor) -> candle_core::Result { + let lhs = xs.apply(&self.gate_proj)?.apply(&self.act_fn)?; + let rhs = xs.apply(&self.up_proj)?; + (lhs * rhs)?.apply(&self.down_proj) + } +} + +#[derive(Debug, Clone)] +enum GemmaKvCache { + Normal(candle_nn::kv_cache::KvCache), + Rotating(candle_nn::kv_cache::RotatingKvCache), +} + +#[derive(Debug, Clone)] +struct GemmaAttention { + q_proj: candle_nn::Linear, + k_proj: candle_nn::Linear, + v_proj: candle_nn::Linear, + o_proj: candle_nn::Linear, + q_norm: GemmaRmsNorm, + k_norm: GemmaRmsNorm, + num_heads: usize, + num_kv_heads: usize, + num_kv_groups: usize, + head_dim: usize, + attn_logit_softcapping: Option, + rotary_emb: std::sync::Arc, + kv_cache: GemmaKvCache, +} + +impl GemmaAttention { + fn new( + rotary_emb: std::sync::Arc, + cfg: &gemma3::Config, + sliding_window: Option, + vb: VarBuilder, + ) -> candle_core::Result { + let hidden_sz = cfg.hidden_size; + let num_heads = cfg.num_attention_heads; + let num_kv_heads = cfg.num_key_value_heads; + let num_kv_groups = num_heads / num_kv_heads; + let head_dim = cfg.head_dim; + let bias = cfg.attention_bias; + let q_proj = candle_nn::linear_b(hidden_sz, num_heads * head_dim, bias, vb.pp("q_proj"))?; + let k_proj = candle_nn::linear_b(hidden_sz, num_kv_heads * head_dim, bias, vb.pp("k_proj"))?; + let v_proj = candle_nn::linear_b(hidden_sz, num_kv_heads * head_dim, bias, vb.pp("v_proj"))?; + let o_proj = candle_nn::linear_b(num_heads * head_dim, hidden_sz, bias, vb.pp("o_proj"))?; + let q_norm = GemmaRmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("q_norm"))?; + let k_norm = GemmaRmsNorm::new(head_dim, cfg.rms_norm_eps, vb.pp("k_norm"))?; + let kv_cache = if let Some(sw) = sliding_window { + GemmaKvCache::Rotating(candle_nn::kv_cache::RotatingKvCache::new(2, sw)) + } else { + GemmaKvCache::Normal(candle_nn::kv_cache::KvCache::new(2, cfg.max_position_embeddings)) + }; + Ok(Self { + q_proj, + k_proj, + v_proj, + o_proj, + q_norm, + k_norm, + num_heads, + num_kv_heads, + num_kv_groups, + head_dim, + attn_logit_softcapping: cfg.attn_logit_softcapping, + rotary_emb, + kv_cache, + }) + } + + fn forward( + &mut self, + xs: &Tensor, + attention_mask: Option<&Tensor>, + seqlen_offset: usize, + ) -> candle_core::Result { + let (b_sz, q_len, _) = xs.dims3()?; + + let query_states = self.q_proj.forward(xs)?; + let key_states = self.k_proj.forward(xs)?; + let value_states = self.v_proj.forward(xs)?; + + let query_states = query_states + .reshape((b_sz, q_len, self.num_heads, self.head_dim))? + .transpose(1, 2)?; + let key_states = key_states + .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))? + .transpose(1, 2)?; + let value_states = value_states + .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))? + .transpose(1, 2)?; + let query_states = self.q_norm.forward(&query_states)?; + let key_states = self.k_norm.forward(&key_states)?; + + let (query_states, key_states) = + self.rotary_emb.apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?; + + // KV cache's slice_set requires contiguous tensors + let key_states = key_states.contiguous()?; + let value_states = value_states.contiguous()?; + let (key_states, value_states) = match &mut self.kv_cache { + GemmaKvCache::Normal(cache) => cache.append(&key_states, &value_states)?, + GemmaKvCache::Rotating(cache) => cache.append(&key_states, &value_states)?, + }; + + let key_states = candle_transformers::utils::repeat_kv(key_states, self.num_kv_groups)? + .contiguous()?; + let value_states = candle_transformers::utils::repeat_kv(value_states, self.num_kv_groups)? + .contiguous()?; + + let scale = 1f64 / f64::sqrt(self.head_dim as f64); + let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?; + + let attn_weights = match self.attn_logit_softcapping { + None => attn_weights, + Some(sc) => ((attn_weights / sc)?.tanh()? * sc)?, + }; + + let attn_weights = match attention_mask { + None => attn_weights, + Some(mask) => attn_weights.broadcast_add(mask)?, + }; + let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?; + let attn_output = attn_weights.matmul(&value_states)?; + + attn_output + .transpose(1, 2)? + .reshape((b_sz, q_len, ()))? + .apply(&self.o_proj) + } + + fn clear_kv_cache(&mut self) { + match &mut self.kv_cache { + GemmaKvCache::Normal(c) => c.reset(), + GemmaKvCache::Rotating(c) => c.reset(), + } + } +} + +struct Gemma3DecoderLayer { + self_attn: GemmaAttention, + mlp: GemmaMLP, + input_layernorm: GemmaRmsNorm, + pre_feedforward_layernorm: GemmaRmsNorm, + post_feedforward_layernorm: GemmaRmsNorm, + post_attention_layernorm: GemmaRmsNorm, + sliding_window: Option, +} + +impl Gemma3DecoderLayer { + fn new( + use_flash_attn: bool, + cfg: &gemma3::Config, + vb: VarBuilder, + sliding_window: Option, + ) -> candle_core::Result { + let _ = use_flash_attn; // Not used in encoder mode (full sequence, no causal needed for hidden state extraction) + let rotary_emb = std::sync::Arc::new(GemmaRotaryEmbedding::new( + vb.dtype(), + cfg, + vb.device(), + sliding_window, + )?); + let self_attn = GemmaAttention::new(rotary_emb, cfg, sliding_window, vb.pp("self_attn"))?; + let mlp = GemmaMLP::new(cfg, vb.pp("mlp"))?; + let input_layernorm = + GemmaRmsNorm::new(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?; + let pre_feedforward_layernorm = GemmaRmsNorm::new( + cfg.hidden_size, + cfg.rms_norm_eps, + vb.pp("pre_feedforward_layernorm"), + )?; + let post_feedforward_layernorm = GemmaRmsNorm::new( + cfg.hidden_size, + cfg.rms_norm_eps, + vb.pp("post_feedforward_layernorm"), + )?; + let post_attention_layernorm = GemmaRmsNorm::new( + cfg.hidden_size, + cfg.rms_norm_eps, + vb.pp("post_attention_layernorm"), + )?; + Ok(Self { + self_attn, + mlp, + input_layernorm, + pre_feedforward_layernorm, + post_feedforward_layernorm, + post_attention_layernorm, + sliding_window, + }) + } + + fn forward( + &mut self, + xs: &Tensor, + attention_mask: Option<&Tensor>, + seqlen_offset: usize, + ) -> candle_core::Result { + let residual = xs; + let xs = self.input_layernorm.forward(xs)?; + let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?; + let xs = xs.apply(&self.post_attention_layernorm)?; + let xs = (xs + residual)?; + let residual = &xs; + let xs = xs.apply(&self.pre_feedforward_layernorm)?; + let xs = xs.apply(&self.mlp)?; + let xs = xs.apply(&self.post_feedforward_layernorm)?; + residual + xs + } + + fn clear_kv_cache(&mut self) { + self.self_attn.clear_kv_cache(); + } +} + +/// Prepare decoder attention mask (causal + optional sliding window). +fn prepare_decoder_attention_mask( + b_size: usize, + tgt_len: usize, + seqlen_offset: usize, + sliding_window: Option, + dtype: DType, + device: &Device, +) -> candle_core::Result { + let mask: Vec<_> = if let Some(sliding_window) = sliding_window { + (0..tgt_len) + .flat_map(|i| { + (0..tgt_len).map(move |j| { + if i < j || j + sliding_window < i { + f32::NEG_INFINITY + } else { + 0. + } + }) + }) + .collect() + } else { + (0..tgt_len) + .flat_map(|i| (0..tgt_len).map(move |j| if i < j { f32::NEG_INFINITY } else { 0f32 })) + .collect() + }; + let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), device)?; + let mask = if seqlen_offset > 0 { + let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, device)?; + Tensor::cat(&[&mask0, &mask], D::Minus1)? + } else { + mask + }; + mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))? + .to_dtype(dtype) +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device, Tensor}; + + #[test] + fn test_pack_text_embeds_shape() { + let device = Device::Cpu; + let b = 2; + let seq_len = 16; + let hidden_dim = 8; + let num_layers = 4; + + let hidden = Tensor::randn( + 0f32, 1f32, + (b, seq_len, hidden_dim, num_layers), + &device, + ).unwrap(); + let seq_lengths = Tensor::new(&[12.0f32, 16.0], &device).unwrap(); + + let packed = pack_text_embeds(&hidden, &seq_lengths, "left", 8.0).unwrap(); + assert_eq!(packed.dims(), &[b, seq_len, hidden_dim * num_layers]); + } + + #[test] + fn test_pack_text_embeds_padding_zeroed() { + let device = Device::Cpu; + let b = 1; + let seq_len = 8; + let hidden_dim = 4; + let num_layers = 2; + + let hidden = Tensor::ones( + (b, seq_len, hidden_dim, num_layers), + DType::F32, + &device, + ).unwrap(); + // Only last 4 tokens are valid (left padding) + let seq_lengths = Tensor::new(&[4.0f32], &device).unwrap(); + + let packed = pack_text_embeds(&hidden, &seq_lengths, "left", 8.0).unwrap(); + let vals: Vec = packed.flatten_all().unwrap().to_vec1().unwrap(); + + // First 4 positions (padding) should be zero + for i in 0..(4 * hidden_dim * num_layers) { + assert_eq!(vals[i], 0.0, "Padding position {} should be zero", i); + } + } + + #[test] + fn test_pack_text_embeds_right_padding() { + let device = Device::Cpu; + let hidden = Tensor::ones((1, 8, 4, 2), DType::F32, &device).unwrap(); + // First 6 tokens valid, last 2 padding + let seq_lengths = Tensor::new(&[6.0f32], &device).unwrap(); + + let packed = pack_text_embeds(&hidden, &seq_lengths, "right", 8.0).unwrap(); + let vals: Vec = packed.flatten_all().unwrap().to_vec1().unwrap(); + + // Last 2 positions (padding) should be zero + let packed_dim = 4 * 2; + for i in (6 * packed_dim)..(8 * packed_dim) { + assert_eq!(vals[i], 0.0, "Padding position {} should be zero", i); + } + } + + #[test] + fn test_gemma3_12b_config() { + let cfg = gemma3_12b_config(); + assert_eq!(cfg.hidden_size, 3840); + assert_eq!(cfg.num_hidden_layers, 48); + assert_eq!(cfg.num_attention_heads, 16); + assert_eq!(cfg.num_key_value_heads, 8); + assert_eq!(cfg.head_dim, 256); + assert_eq!(cfg.intermediate_size, 15360); + assert_eq!(cfg.vocab_size, 262_208); + assert_eq!(cfg.sliding_window, 1024); + assert_eq!(cfg.sliding_window_pattern, 6); // 5 local : 1 global + } +} diff --git a/cake-core/src/models/ltx2/ltx2.rs b/cake-core/src/models/ltx2/ltx2.rs new file mode 100644 index 00000000..f6028a78 --- /dev/null +++ b/cake-core/src/models/ltx2/ltx2.rs @@ -0,0 +1,915 @@ +use anyhow::Result; +use async_trait::async_trait; +use candle_core::{DType, Device, IndexOp, Tensor}; +use image::{ImageBuffer, Rgb}; +use log::info; +use std::path::PathBuf; +use std::collections::HashMap; + +use super::gemma::Ltx2Gemma; +use super::gemma_encoder::{gemma3_12b_config, Gemma3TextEncoder}; +use super::ltx2_shardable::Ltx2Shardable; +use super::transformer::Ltx2Transformer; +use super::vae_forwarder::Ltx2Vae; +use super::vocoder::Ltx2Vocoder; +use super::vendored::config::{Ltx2SchedulerConfig, Ltx2TransformerConfig, Ltx2VaeConfig}; +use super::vendored::model::LTXModel; +use super::vendored::pipeline::{ + build_video_positions, denormalize_latents, pack_latents, unpack_latents, +}; +use super::vendored::scheduler::{euler_step, Ltx2Scheduler}; +use crate::cake::{Context, Forwarder}; +use crate::models::{Generator, VideoGenerator}; +use crate::video::VideoOutput; +use crate::ImageGenerationArgs; + +/// LTX-2 model (19B audio+video generation). +/// +/// Architecture: +/// - Asymmetric dual-stream DiT transformer (14B video + 5B audio) +/// - Gemma-3 12B text encoder +/// - Video VAE decoder (native 4K support) +/// - Audio vocoder (synchronized with video) +/// +/// Supports split transformer topology for distributed inference: +/// ```yaml +/// win5090: +/// host: "worker1:10128" +/// layers: +/// - "ltx2-transformer.0-23" # First 24 blocks (~17GB) +/// # Master keeps blocks 24-47 + connector + VAE + Gemma +/// ``` +pub struct Ltx2 { + /// Connector forwarder (runs locally on master GPU) + gemma_connector: Box, + /// Gemma-3 12B text encoder (GGUF on GPU or safetensors on CPU) + gemma_encoder: Option, + /// Remote transformer forwarder (full model or block range) + transformer: Box, + /// Local transformer blocks (for split mode — the master's block range) + local_transformer: Option, + vae: Box, + #[allow(dead_code)] + vocoder: Box, + /// Per-channel latent normalization parameters (from VAE safetensors) + latents_mean: Vec, + latents_std: Vec, + context: Context, +} + +#[async_trait] +impl Generator for Ltx2 { + type Shardable = Ltx2Shardable; + const MODEL_NAME: &'static str = "ltx-2"; + + async fn load(context: &mut Context) -> Result>> { + info!("Loading LTX-2 components..."); + + // Text connector (runs locally on master GPU) + let gemma_connector: Box = + if let Some((_name, node)) = context.topology.get_node_for_layer("ltx2-gemma") { + info!("ltx2-gemma (connector) will be served by {}", &node.host); + Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx2-gemma", + context.args.cluster_key.as_deref(), + ) + .await?, + ) + } else { + Ltx2Gemma::load_model(context)? + }; + + // Transformer — check for full or block-range topology + let (transformer, local_transformer) = Self::load_transformer(context).await?; + + // VAE — load locally to get latents_mean/std + let (vae, latents_mean, latents_std): (Box, Vec, Vec) = + if let Some((_name, node)) = context.topology.get_node_for_layer("ltx2-vae") { + info!("ltx2-vae will be served by {}", &node.host); + let client = Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx2-vae", + context.args.cluster_key.as_deref(), + ) + .await?, + ); + // Remote VAE — use identity normalization as fallback + (client, vec![0.0; 128], vec![1.0; 128]) + } else { + Ltx2Vae::load_with_stats(context)? + }; + + // Vocoder + let vocoder: Box = + if let Some((_name, node)) = context.topology.get_node_for_layer("ltx2-vocoder") { + info!("ltx2-vocoder will be served by {}", &node.host); + Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx2-vocoder", + context.args.cluster_key.as_deref(), + ) + .await?, + ) + } else { + Ltx2Vocoder::load_model(context)? + }; + + // Gemma-3 12B encoder (GGUF on GPU if available, else safetensors on CPU) + let gemma_encoder = match Self::try_load_gemma_encoder(context) { + Ok(enc) => { + info!("Gemma-3 12B encoder loaded — text prompts supported!"); + Some(enc) + } + Err(e) => { + log::warn!( + "Gemma-3 encoder not available: {}. \ + Pre-computed embeddings must be provided.", + e + ); + None + } + }; + + info!("LTX-2 components loaded"); + + Ok(Some(Box::new(Self { + gemma_connector, + gemma_encoder, + transformer, + local_transformer, + vae, + vocoder, + latents_mean, + latents_std, + context: context.clone(), + }))) + } +} + +impl Ltx2 { + /// Load the transformer, handling both full-model and block-range topologies. + /// + /// Returns (remote_forwarder, local_transformer_option): + /// - Full model on worker: (Client, None) + /// - Full model local: (Ltx2Transformer, None) + /// - Block range on worker: (Client for remote blocks, Some(LTXModel for local blocks)) + async fn load_transformer( + context: &mut Context, + ) -> Result<(Box, Option)> { + // Check for full transformer on a worker + if let Some((_name, node)) = context.topology.get_node_for_layer("ltx2-transformer") { + info!("ltx2-transformer (full) will be served by {}", &node.host); + let client = Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx2-transformer", + context.args.cluster_key.as_deref(), + ) + .await?, + ); + return Ok((client, None)); + } + + // Check for block-range assignments + // Find any layer name matching "ltx2-transformer.N-M" + let block_range_layer = context + .topology + .all_worker_layers() + .into_iter() + .find(|name| name.starts_with("ltx2-transformer.")); + + if let Some(ref remote_layer) = block_range_layer { + let (_name, node) = context + .topology + .get_node_for_layer(remote_layer) + .ok_or_else(|| anyhow::anyhow!("No node found for layer {}", remote_layer))?; + + info!("{} will be served by {}", remote_layer, &node.host); + + // Parse remote block range + let suffix = remote_layer + .strip_prefix("ltx2-transformer.") + .unwrap(); + let parts: Vec<&str> = suffix.split('-').collect(); + let remote_start: usize = parts[0].parse()?; + let remote_end: usize = parts[1].parse::()? + 1; + + let client: Box = Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + remote_layer, + context.args.cluster_key.as_deref(), + ) + .await?, + ); + + // Load the remaining blocks locally on the master + let config = Ltx2TransformerConfig::default(); + let num_layers = config.num_layers; + + // Local gets the complement of remote. + // For split transformer, master should have first blocks (with setup). + let (local_start, local_end) = if remote_start == 0 { + (remote_end, num_layers) + } else { + (0, remote_start) + }; + + if local_start != 0 { + log::warn!( + "Master has blocks {}-{} without setup. \ + Put the HIGHER block range on the worker for best performance.", + local_start, + local_end - 1 + ); + } + + info!( + "Loading local transformer blocks {}-{} on master GPU", + local_start, + local_end - 1 + ); + + // Load local blocks via Ltx2Transformer resolver (handles HF cache) + let local_model = { + let (cfg, weights_path) = + Ltx2Transformer::resolve_config_and_weights(context)?; + let weight_files = find_local_weight_files(&weights_path)?; + // LTX-2 weights are BF16 — load as BF16 to avoid conversion artifacts + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &weight_files, + DType::BF16, + &context.device, + )? + }; + LTXModel::new_block_range(cfg, vb, local_start, Some(local_end))? + }; + + return Ok((client, Some(local_model))); + } + + // No topology entry — load full model locally + info!("Loading full LTX-2 transformer locally"); + let transformer = Ltx2Transformer::load_model(context)?; + Ok((transformer, None)) + } + + /// Load Gemma-3 12B encoder. + /// + /// Prefers quantized GGUF on GPU (if `--ltx-gemma-gguf` is set), + /// falls back to full-precision safetensors on CPU. + fn try_load_gemma_encoder(ctx: &Context) -> Result { + use hf_hub::api::sync::ApiBuilder; + use hf_hub::Cache; + + let gemma_repo = "google/gemma-3-12b-pt"; + + // Resolve HF API for tokenizer (needed by both paths) + let mut cache_path = PathBuf::from(&ctx.args.model); + cache_path.push("hub"); + let api = if cache_path.exists() { + ApiBuilder::from_cache(Cache::new(cache_path)).build()? + } else { + let mut builder = ApiBuilder::new(); + if let Ok(token) = std::env::var("HF_TOKEN") { + builder = builder.with_token(Some(token)); + } + builder.build()? + }; + let model_api = api.model(gemma_repo.to_string()); + let tokenizer_path = model_api.get("tokenizer.json")?; + + // Try GGUF path first (quantized on GPU) + if let Some(ref gguf_path) = ctx.args.ltx_args.ltx_gemma_gguf { + let gguf = PathBuf::from(gguf_path); + if gguf.exists() { + info!("Loading quantized Gemma-3 from GGUF on GPU..."); + return Gemma3TextEncoder::load_gguf( + &gguf, + &tokenizer_path, + &ctx.device, + ); + } else { + log::warn!("GGUF path does not exist: {:?}, falling back to safetensors", gguf); + } + } + + // Fall back to full-precision safetensors on CPU + let config_path = model_api.get("config.json")?; + let config_str = std::fs::read_to_string(&config_path)?; + let gemma_config: candle_transformers::models::gemma3::Config = + serde_json::from_str(&config_str).unwrap_or_else(|_| gemma3_12b_config()); + + let model_paths = if let Ok(index_file) = model_api.get("model.safetensors.index.json") { + let index_str = std::fs::read_to_string(&index_file)?; + let index: serde_json::Value = serde_json::from_str(&index_str)?; + let weight_map = index["weight_map"] + .as_object() + .ok_or_else(|| anyhow::anyhow!("Invalid safetensors index"))?; + + let mut shard_files: Vec = weight_map + .values() + .filter_map(|v| v.as_str().map(String::from)) + .collect(); + shard_files.sort(); + shard_files.dedup(); + + let mut paths = Vec::new(); + for shard in &shard_files { + paths.push(model_api.get(shard)?); + } + paths + } else { + vec![model_api.get("model.safetensors")?] + }; + + info!("Loading Gemma-3 12B on CPU (F32)..."); + Gemma3TextEncoder::load( + &model_paths, + &tokenizer_path, + &gemma_config, + DType::F32, + &Device::Cpu, + ) + } +} + +/// Find weight files from a path (for local master loading). +fn find_local_weight_files(path: &PathBuf) -> Result> { + if path.extension().map_or(false, |e| e == "safetensors") && path.exists() { + return Ok(vec![path.clone()]); + } + if path.is_dir() { + let mut shards = Vec::new(); + for entry in std::fs::read_dir(path)? { + let p = entry?.path(); + if let Some(name) = p.file_name().and_then(|n| n.to_str()) { + if name.starts_with("diffusion_pytorch_model") + && name.ends_with(".safetensors") + && !name.contains("index") + { + shards.push(p); + } + } + } + if !shards.is_empty() { + shards.sort(); + return Ok(shards); + } + } + if let Some(parent) = path.parent() { + let mut shards = Vec::new(); + for entry in std::fs::read_dir(parent)? { + let p = entry?.path(); + if let Some(name) = p.file_name().and_then(|n| n.to_str()) { + if name.starts_with("diffusion_pytorch_model") + && name.ends_with(".safetensors") + && !name.contains("index") + { + shards.push(p); + } + } + } + if !shards.is_empty() { + shards.sort(); + return Ok(shards); + } + } + Ok(vec![path.clone()]) +} + +#[async_trait] +impl VideoGenerator for Ltx2 { + async fn generate_video(&mut self, args: &ImageGenerationArgs) -> Result { + let ImageGenerationArgs { + image_prompt: _, + image_seed, + guidance_scale, + .. + } = args; + + // Copy all ltx_args values out to avoid borrow conflicts with &mut self later + let height = self.context.args.ltx_args.ltx_height; + let width = self.context.args.ltx_args.ltx_width; + let num_frames = self.context.args.ltx_args.ltx_num_frames; + let num_steps = self.context.args.ltx_args.ltx_num_steps.unwrap_or(30); + let frame_rate = self.context.args.ltx_args.ltx_fps; + let stg_scale_arg = self.context.args.ltx_args.ltx_stg_scale; + let stg_block_arg = self.context.args.ltx_args.ltx_stg_block; + let rescale_arg = self.context.args.ltx_args.ltx_rescale; + let guidance_scale = guidance_scale.unwrap_or(4.0) as f32; + + if let Some(seed) = image_seed { + self.context.device.set_seed(*seed)?; + } + + let trans_config = Ltx2TransformerConfig::default(); + let vae_config = Ltx2VaeConfig::default(); + let sched_config = Ltx2SchedulerConfig::default(); + + info!( + "Generating LTX-2 video: {}x{}, {} frames, {} steps, guidance_scale={:.1}", + width, height, num_frames, num_steps, guidance_scale + ); + + // 1. Encode prompt with Gemma-3 → send packed embeddings to connector + info!("Encoding prompt..."); + let prompt_text = if args.image_prompt.is_empty() { + "a beautiful video" + } else { + &args.image_prompt + }; + + let (packed_embeds, text_mask) = if let Some(ref mut encoder) = self.gemma_encoder { + info!("Encoding text with Gemma-3: \"{}\"", prompt_text); + let (embeds, mask) = encoder.encode(prompt_text)?; + // Transfer from CPU to GPU for network serialization + let embeds = embeds + .to_device(&self.context.device)? + .to_dtype(DType::BF16)?; + let mask = mask.to_device(&self.context.device)?; + (embeds, mask) + } else { + // Fallback: dummy packed embeddings (for testing without Gemma weights) + log::warn!("Using dummy text embeddings (Gemma-3 not loaded)"); + let seq_len = 1024usize; + let packed_dim = trans_config.caption_channels * 49; // 3840 * 49 = 188160 + let dummy = Tensor::randn( + 0f32, + 1f32, + (1, seq_len, packed_dim), + &self.context.device, + )? + .to_dtype(DType::BF16)?; + let mask = Tensor::ones((1, seq_len), DType::F32, &self.context.device)?; + (dummy, mask) + }; + + // Send packed embeddings to connector (local) + let prompt_embeds = Ltx2Gemma::encode( + &mut self.gemma_connector, + packed_embeds, + Some(text_mask), + &mut self.context, + ) + .await? + .to_dtype(DType::BF16)?; + + let ctx_seq_len = prompt_embeds.dim(1)?; + let context_mask = Tensor::ones((1, ctx_seq_len), DType::F32, &self.context.device)? + .to_dtype(DType::BF16)?; + + // Prepare unconditional context for classifier-free guidance + // The uncond embedding (encoding "" through Gemma + connector) is always + // the same for a given model, so we cache it to disk after computing once. + let do_cfg = guidance_scale > 1.0; + let (uncond_embeds, uncond_mask) = if do_cfg { + info!("Preparing unconditional embeddings for CFG (guidance_scale={:.1})", guidance_scale); + + let cache_path = Self::uncond_cache_path(&self.context); + if let Some((cached_embeds, cached_mask)) = Self::load_uncond_cache(&cache_path, &self.context.device) { + info!("Loaded cached unconditional embeddings from {:?}", cache_path); + (Some(cached_embeds), Some(cached_mask)) + } else { + let (neg_packed, neg_mask) = if let Some(ref mut encoder) = self.gemma_encoder { + info!("Encoding empty string for unconditional embeddings..."); + let (embeds, mask) = encoder.encode("")?; + let embeds = embeds + .to_device(&self.context.device)? + .to_dtype(DType::BF16)?; + let mask = mask.to_device(&self.context.device)?; + (embeds, mask) + } else { + let seq_len = 1024usize; + let packed_dim = trans_config.caption_channels * 49; + let dummy = Tensor::zeros( + (1, seq_len, packed_dim), + DType::BF16, + &self.context.device, + )?; + let mask = Tensor::zeros((1, seq_len), DType::F32, &self.context.device)?; + (dummy, mask) + }; + + let neg_embeds = Ltx2Gemma::encode( + &mut self.gemma_connector, + neg_packed, + Some(neg_mask), + &mut self.context, + ) + .await? + .to_dtype(DType::BF16)?; + + let neg_ctx_len = neg_embeds.dim(1)?; + let neg_ctx_mask = Tensor::ones((1, neg_ctx_len), DType::F32, &self.context.device)? + .to_dtype(DType::BF16)?; + + // Cache for next time + Self::save_uncond_cache(&cache_path, &neg_embeds, &neg_ctx_mask); + + (Some(neg_embeds), Some(neg_ctx_mask)) + } + } else { + (None, None) + }; + + // 2. Prepare latents + let latent_h = height / vae_config.spatial_compression_ratio; + let latent_w = width / vae_config.spatial_compression_ratio; + let latent_f = (num_frames - 1) / vae_config.temporal_compression_ratio + 1; + let in_channels = trans_config.in_channels; + + // LTX-2 weights are BF16 — keep latents in BF16 throughout to avoid F16 precision loss + let latents_5d = Tensor::randn( + 0f32, + 1f32, + (1, in_channels, latent_f, latent_h, latent_w), + &self.context.device, + )? + .to_dtype(DType::BF16)?; + + // NOTE: Python LTX2Pipeline does NOT normalize initial noise. + // Normalization only happens when img2vid latents are provided. + // For txt2vid, initial noise is standard normal, and only + // denormalize_latents is applied at the end before VAE decode. + let latents_mean = + Tensor::new(self.latents_mean.as_slice(), &self.context.device)?; + let latents_std = + Tensor::new(self.latents_std.as_slice(), &self.context.device)?; + + // Pack latents: [B, C, F, H, W] -> [B, S, C] (patch_size=1) + let mut latents = pack_latents(&latents_5d)?; + + // 3. Build video positions for RoPE + let positions = build_video_positions( + 1, // batch_size + latent_f, + latent_h, + latent_w, + vae_config.temporal_compression_ratio, + vae_config.spatial_compression_ratio, + frame_rate, + &self.context.device, + )?; + + // 4. Prepare scheduler + let num_tokens = latent_f * latent_h * latent_w; + let scheduler = Ltx2Scheduler::new(sched_config); + let sigmas = scheduler.execute(num_steps, num_tokens); + + info!( + "Denoising: {} steps, {} tokens, sigma range {:.4}..{:.4}", + num_steps, + num_tokens, + sigmas.first().unwrap_or(&0.0), + sigmas.last().unwrap_or(&0.0), + ); + + // 5. Denoising loop + let is_split = self.local_transformer.is_some(); + + // STG config: LTX-2.3 defaults + let stg_scale = stg_scale_arg.unwrap_or(1.0); + let stg_block: usize = stg_block_arg.unwrap_or(28); + let rescale_scale = rescale_arg.unwrap_or(0.7); + let do_stg = stg_scale > 0.0; + let stg_skip_blocks: Vec = if do_stg { vec![stg_block] } else { vec![] }; + + if do_stg { + info!( + "STG enabled: scale={:.1}, block={}, rescale={:.2}", + stg_scale, stg_block, rescale_scale + ); + } + + for step in 0..num_steps { + let start_time = std::time::Instant::now(); + + let sigma = sigmas[step]; + let sigma_next = sigmas[step + 1]; + + let sigma_t = Tensor::full(sigma, (1,), &self.context.device)? + .to_dtype(self.context.dtype)?; + let timestep_t = Tensor::full(sigma, (1,), &self.context.device)? + .to_dtype(self.context.dtype)?; + + // Conditional forward pass (no STG perturbation) + let cond_velocity = if is_split { + self.forward_split_transformer( + &latents, &sigma_t, ×tep_t, &positions, + &prompt_embeds, &context_mask, &[], + ).await? + } else { + Ltx2Transformer::forward_packed( + &mut self.transformer, + latents.to_dtype(self.context.dtype)?, + sigma_t.clone(), timestep_t.clone(), positions.clone(), + prompt_embeds.clone(), context_mask.clone(), + &mut self.context, + ).await?.to_dtype(DType::F32)? + }; + + // Apply guidance (CFG + STG) + let mut velocity = cond_velocity.clone(); + + // CFG: pred = cond + (cfg_scale - 1) * (cond - uncond) + if do_cfg { + let uncond_ctx = uncond_embeds.as_ref().unwrap(); + let uncond_mask = uncond_mask.as_ref().unwrap(); + + let uncond_velocity = if is_split { + self.forward_split_transformer( + &latents, &sigma_t, ×tep_t, &positions, + uncond_ctx, uncond_mask, &[], + ).await? + } else { + Ltx2Transformer::forward_packed( + &mut self.transformer, + latents.to_dtype(self.context.dtype)?, + sigma_t.clone(), timestep_t.clone(), positions.clone(), + uncond_ctx.clone(), uncond_mask.clone(), + &mut self.context, + ).await?.to_dtype(DType::F32)? + }; + + let cfg_diff = (&cond_velocity - &uncond_velocity)?; + velocity = (&velocity + cfg_diff.affine((guidance_scale - 1.0) as f64, 0.0)?)?; + } + + // STG: pred += stg_scale * (cond - perturbed) + if do_stg { + let stg_velocity = if is_split { + self.forward_split_transformer( + &latents, &sigma_t, ×tep_t, &positions, + &prompt_embeds, &context_mask, &stg_skip_blocks, + ).await? + } else { + // For non-split mode, STG not yet supported + // (would need a separate forward_packed variant) + cond_velocity.clone() + }; + + let stg_diff = (&cond_velocity - &stg_velocity)?; + velocity = (&velocity + stg_diff.affine(stg_scale as f64, 0.0)?)?; + } + + // Rescale: prevent oversaturation from aggressive guidance + if rescale_scale > 0.0 && (do_cfg || do_stg) { + let cond_std: f32 = cond_velocity.to_dtype(DType::F32)?.flatten_all()? + .var(0)?.to_scalar::()?.sqrt(); + let pred_std: f32 = velocity.to_dtype(DType::F32)?.flatten_all()? + .var(0)?.to_scalar::()?.sqrt(); + if pred_std > 1e-8 { + let factor = rescale_scale as f64 * (cond_std / pred_std) as f64 + + (1.0 - rescale_scale as f64); + velocity = velocity.affine(factor, 0.0)?; + } + } + + // Euler step (keep in BF16 to match transformer weight precision) + latents = euler_step(&latents.to_dtype(DType::F32)?, &velocity, sigma, sigma_next)? + .to_dtype(DType::BF16)?; + + let dt = start_time.elapsed().as_secs_f32(); + info!( + "step {}/{} done, sigma={:.4}, {:.2}s", + step + 1, num_steps, sigma, dt + ); + } + + // 6. Unpack latents: [B, S, C] -> [B, C, F, H, W] + let latents_5d = unpack_latents( + &latents.to_dtype(DType::F32)?, + latent_f, + latent_h, + latent_w, + )?; + + // 7. Denormalize latents + let latents_5d = denormalize_latents( + &latents_5d, + &latents_mean, + &latents_std, + vae_config.scaling_factor, + )? + .to_dtype(DType::BF16)?; + + // Debug: check latent statistics before VAE + // 8. Decode with VAE + info!("Decoding with VAE..."); + let decoded = + Ltx2Vae::decode(&mut self.vae, latents_5d, &mut self.context).await?; + + // 9. Convert video frames to images + let frames = video_tensor_to_images(&decoded)?; + info!("Generated {} frames", frames.len()); + + Ok(VideoOutput::new( + frames, + frame_rate, + width as u32, + height as u32, + )) + } +} + +impl Ltx2 { + /// Forward pass through split transformer. + /// + /// Flow (master has first blocks with setup, worker has last blocks with finalize): + /// 1. Master: setup (proj_in + adaln + caption + RoPE) + /// 2. Master: run local blocks (0-23) + /// 3. Send hidden states + metadata to worker + /// 4. Worker: run remote blocks (24-47) + finalize + /// 5. Worker returns velocity prediction + async fn forward_split_transformer( + &mut self, + latents: &Tensor, + _sigma: &Tensor, + timestep: &Tensor, + positions: &Tensor, + context: &Tensor, + context_mask: &Tensor, + stg_skip_blocks: &[usize], + ) -> Result { + let local = self + .local_transformer + .as_ref() + .expect("split mode requires local_transformer"); + + // LTX-2 weights are BF16 — convert all inputs to BF16 to match + let latents = latents.to_dtype(DType::BF16)?; + let timestep = ×tep.to_dtype(DType::BF16)?; + let positions = &positions.to_dtype(DType::F32)?; // RoPE always F32 + let context = &context.to_dtype(DType::BF16)?; + + // 1. Setup: proj_in + adaln + caption projection + RoPE (local) + let (hidden, temb, embedded_ts, pe, ctx_projected, prompt_temb) = + local.forward_setup(&latents, timestep, positions, context)?; + + // 2. Run local blocks (with STG if applicable) + let context_mask_bf16 = context_mask.to_dtype(DType::BF16)?; + let x = local.forward_blocks_with_stg( + &hidden, + &temb, + &pe, + &ctx_projected, + Some(&context_mask_bf16), + prompt_temb.as_ref(), + stg_skip_blocks, + )?; + + // 3. Send to remote worker for remaining blocks + finalize + let result = Ltx2Transformer::forward_blocks_packed( + &mut self.transformer, + x, + temb, + pe.0, + pe.1, + ctx_projected, + context_mask.clone(), + embedded_ts, + prompt_temb, + stg_skip_blocks, + &mut self.context, + ) + .await?; + + Ok(result.to_dtype(DType::F32)?) + } + + /// Path for cached unconditional embeddings (deterministic per model repo). + fn uncond_cache_path(ctx: &Context) -> PathBuf { + let ltx_repo = ctx.args.ltx_args.ltx_repo(); + // Hash repo name + GGUF path (quantized outputs differ from F32) + let hash = { + use sha2::Digest; + let mut hasher = sha2::Sha256::new(); + hasher.update(ltx_repo.as_bytes()); + if let Some(ref gguf) = ctx.args.ltx_args.ltx_gemma_gguf { + hasher.update(b":gguf:"); + hasher.update(gguf.as_bytes()); + } + hex::encode(&hasher.finalize()[..8]) + }; + let cache_dir = dirs::cache_dir() + .unwrap_or_else(std::env::temp_dir) + .join("cake") + .join("uncond_cache"); + cache_dir.join(format!("uncond_{}.safetensors", hash)) + } + + /// Load cached unconditional embeddings from disk. + fn load_uncond_cache(path: &PathBuf, device: &Device) -> Option<(Tensor, Tensor)> { + if !path.exists() { + return None; + } + match candle_core::safetensors::load(path, device) { + Ok(tensors) => { + let embeds = tensors.get("uncond_embeds")?.clone(); + let mask = tensors.get("uncond_mask")?.clone(); + Some((embeds, mask)) + } + Err(e) => { + log::warn!("Failed to load uncond cache from {:?}: {}", path, e); + None + } + } + } + + /// Save unconditional embeddings to disk cache. + fn save_uncond_cache(path: &PathBuf, embeds: &Tensor, mask: &Tensor) { + if let Some(parent) = path.parent() { + let _ = std::fs::create_dir_all(parent); + } + // Move tensors to CPU for saving + let save_result = (|| -> Result<()> { + let embeds_cpu = embeds.to_device(&Device::Cpu)?; + let mask_cpu = mask.to_device(&Device::Cpu)?; + let tensors: HashMap = HashMap::from([ + ("uncond_embeds".to_string(), embeds_cpu), + ("uncond_mask".to_string(), mask_cpu), + ]); + candle_core::safetensors::save(&tensors, path)?; + info!("Cached unconditional embeddings to {:?}", path); + Ok(()) + })(); + if let Err(e) = save_result { + log::warn!("Failed to save uncond cache: {}", e); + } + } +} + +/// Convert a decoded video tensor `[B, C, T, H, W]` to a list of RGB images. +fn video_tensor_to_images(video: &Tensor) -> Result, Vec>>> { + let mut result = Vec::new(); + + let video = ((video.clamp(-1f32, 1f32)? + 1.0)? * 127.5)? + .to_dtype(DType::U8)? + .to_device(&Device::Cpu)?; + + let bsize = video.dim(0)?; + for batch in 0..bsize { + let batch_video = video.i(batch)?; + let (channels, num_frames, height, width) = batch_video.dims4()?; + if channels != 3 { + anyhow::bail!("Expected 3 channels, got {}", channels); + } + + for frame in 0..num_frames { + let frame_tensor = batch_video.i((.., frame, .., ..))?; + let frame_tensor = frame_tensor.permute((1, 2, 0))?.flatten_all()?; + let pixels = frame_tensor.to_vec1::()?; + + let image: ImageBuffer, Vec> = + ImageBuffer::from_raw(width as u32, height as u32, pixels) + .ok_or_else(|| anyhow::anyhow!("Error creating image buffer"))?; + result.push(image); + } + } + + Ok(result) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_video_tensor_to_images_basic() { + let device = Device::Cpu; + let video = Tensor::zeros((1, 3, 2, 4, 4), DType::F32, &device).unwrap(); + let frames = video_tensor_to_images(&video).unwrap(); + assert_eq!(frames.len(), 2); + assert_eq!(frames[0].width(), 4); + assert_eq!(frames[0].height(), 4); + assert_eq!(frames[0].get_pixel(0, 0)[0], 127); + } + + #[test] + fn test_video_tensor_to_images_clamping() { + let device = Device::Cpu; + let video = Tensor::full(2.0f32, (1, 3, 1, 2, 2), &device).unwrap(); + let frames = video_tensor_to_images(&video).unwrap(); + assert_eq!(frames.len(), 1); + assert_eq!(frames[0].get_pixel(0, 0)[0], 255); + } + + #[test] + fn test_video_tensor_to_images_multi_batch() { + let device = Device::Cpu; + let video = Tensor::zeros((2, 3, 3, 4, 4), DType::F32, &device).unwrap(); + let frames = video_tensor_to_images(&video).unwrap(); + assert_eq!(frames.len(), 6); + } +} diff --git a/cake-core/src/models/ltx2/ltx2_shardable.rs b/cake-core/src/models/ltx2/ltx2_shardable.rs new file mode 100644 index 00000000..19104c4d --- /dev/null +++ b/cake-core/src/models/ltx2/ltx2_shardable.rs @@ -0,0 +1,90 @@ +use crate::cake::{Context, Forwarder}; +use super::gemma::Ltx2Gemma; +use super::transformer::Ltx2Transformer; +use super::vae_forwarder::Ltx2Vae; +use super::vocoder::Ltx2Vocoder; +use async_trait::async_trait; +use candle_core::Tensor; +use std::fmt::{Debug, Display, Formatter}; + +/// Dispatches layer names to the appropriate LTX-2 component: +/// - `"ltx2-transformer"` → Dual-stream DiT (14B video + 5B audio) +/// - `"ltx2-gemma"` → Gemma-3 12B text encoder +/// - `"ltx2-vae"` → Video VAE decoder +/// - `"ltx2-vocoder"` → Audio vocoder +#[derive(Debug)] +pub struct Ltx2Shardable { + forwarder: Box, + layer_name: String, +} + +impl Display for Ltx2Shardable { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!(f, "{} (local)", &self.layer_name) + } +} + +#[async_trait] +impl Forwarder for Ltx2Shardable { + fn load(name: String, ctx: &Context) -> anyhow::Result> + where + Self: Sized, + { + let model: Box = if name == "ltx2-transformer" + || name.starts_with("ltx2-transformer.") + { + Ltx2Transformer::load(name.clone(), ctx)? + } else { + match name.as_str() { + "ltx2-gemma" => Ltx2Gemma::load(name.clone(), ctx)?, + "ltx2-vae" => Ltx2Vae::load(name.clone(), ctx)?, + "ltx2-vocoder" => Ltx2Vocoder::load(name.clone(), ctx)?, + _ => anyhow::bail!("LTX-2 component name not recognized: {}", name), + } + }; + + Ok(Box::new(Self { + forwarder: model, + layer_name: name, + })) + } + + async fn forward( + &self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + self.forwarder.forward(x, index_pos, block_idx, ctx).await + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + self.forwarder + .forward_mut(x, index_pos, block_idx, ctx) + .await + } + + async fn forward_batch( + &mut self, + x: &Tensor, + batch: Vec<(String, usize, usize)>, + ctx: &mut Context, + ) -> anyhow::Result { + self.forwarder.forward_batch(x, batch, ctx).await + } + + fn layer_name(&self) -> &str { + &self.layer_name + } + + fn ident(&self) -> &str { + &self.layer_name + } +} diff --git a/cake-core/src/models/ltx2/mod.rs b/cake-core/src/models/ltx2/mod.rs new file mode 100644 index 00000000..65e80b8d --- /dev/null +++ b/cake-core/src/models/ltx2/mod.rs @@ -0,0 +1,20 @@ +//! LTX-2 model implementation (19B audio+video generation). +//! +//! Component-based topology (same pattern as LTX-Video / HunyuanVideo): +//! - `ltx2-transformer` — Asymmetric dual-stream DiT (14B video + 5B audio) +//! - `ltx2-gemma` — Gemma-3 12B text encoder +//! - `ltx2-vae` — Video VAE decoder +//! - `ltx2-vocoder` — Audio vocoder + +pub mod vendored; + +mod ltx2; +mod ltx2_shardable; +mod gemma; +pub(crate) mod gemma_encoder; +mod quantized_gemma; +mod transformer; +mod vae_forwarder; +mod vocoder; + +pub use ltx2::*; diff --git a/cake-core/src/models/ltx2/quantized_gemma.rs b/cake-core/src/models/ltx2/quantized_gemma.rs new file mode 100644 index 00000000..55849673 --- /dev/null +++ b/cake-core/src/models/ltx2/quantized_gemma.rs @@ -0,0 +1,468 @@ +//! Quantized Gemma-3 model for all-hidden-states extraction. +//! +//! Adapted from `candle_transformers::models::quantized_gemma3` to: +//! 1. Return hidden states from ALL layers (not just final logits) +//! 2. Support padding masks (needed for left-padded text encoding) +//! 3. Fix the sliding window pattern bug +//! +//! Used by `Gemma3TextEncoder::load_gguf()` as an alternative to +//! the full-precision safetensors path. GGUF Q4_K_M of Gemma-3-12B +//! is ~7.4 GB and fits on a 24 GB GPU alongside the LTX-2 connector + VAE. + +use candle_core::quantized::gguf_file; +use candle_core::quantized::QTensor; +use candle_core::{DType, Device, Module, Result, Tensor, D}; +use candle_nn::Embedding; + +const DEFAULT_SLIDING_WINDOW_TYPE: usize = 6; +const DEFAULT_ROPE_FREQUENCY: f32 = 1_000_000.; +const DEFAULT_ROPE_FREQUENCY_SLIDING: f32 = 10_000.; + +/// Max sequence length for RoPE precomputation. +/// We only use this for encoding 1024-token prompts, not for generation, +/// so we cap at 1024 instead of the model's full 131072 context window. +/// This saves ~6.4 GB of GPU memory (48 layers × 134 MB per RoPE table). +const ENCODER_MAX_SEQ_LEN: usize = 1024; + +#[derive(Debug, Clone)] +struct QMatMul { + inner: candle_core::quantized::QMatMul, +} + +impl QMatMul { + fn from_qtensor(qtensor: QTensor) -> Result { + let inner = candle_core::quantized::QMatMul::from_qtensor(qtensor)?; + Ok(Self { inner }) + } + + fn forward(&self, xs: &Tensor) -> Result { + self.inner.forward(xs) + } +} + +#[derive(Debug, Clone)] +struct RmsNorm { + weight: Tensor, + eps: f64, +} + +impl RmsNorm { + fn from_qtensor(weight: QTensor, eps: f64) -> Result { + let weight = weight.dequantize(&weight.device())?; + Ok(Self { weight, eps }) + } +} + +impl Module for RmsNorm { + fn forward(&self, x: &Tensor) -> Result { + candle_nn::ops::rms_norm(x, &self.weight, self.eps as f32) + } +} + +#[derive(Debug, Clone)] +struct Mlp { + feed_forward_gate: QMatMul, + feed_forward_up: QMatMul, + feed_forward_down: QMatMul, +} + +impl Module for Mlp { + fn forward(&self, xs: &Tensor) -> Result { + let gate = self.feed_forward_gate.forward(xs)?; + let up = self.feed_forward_up.forward(xs)?; + let silu = candle_nn::ops::silu(&gate)?; + let gated = (silu * up)?; + self.feed_forward_down.forward(&gated) + } +} + +#[derive(Debug, Clone)] +struct RotaryEmbedding { + sin: Tensor, + cos: Tensor, +} + +impl RotaryEmbedding { + fn new(head_dim: usize, rope_frequency: f32, max_seq_len: usize, device: &Device) -> Result { + let theta: Vec<_> = (0..head_dim) + .step_by(2) + .map(|i| 1f32 / rope_frequency.powf(i as f32 / head_dim as f32)) + .collect(); + let theta = Tensor::new(theta.as_slice(), device)?; + let idx_theta = Tensor::arange(0, max_seq_len as u32, device)? + .to_dtype(DType::F32)? + .reshape((max_seq_len, 1))? + .matmul(&theta.reshape((1, theta.elem_count()))?)?; + let cos = idx_theta.cos()?; + let sin = idx_theta.sin()?; + Ok(Self { sin, cos }) + } + + fn apply_rotary_emb_qkv( + &self, + q: &Tensor, + k: &Tensor, + index_pos: usize, + ) -> Result<(Tensor, Tensor)> { + let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?; + let cos = self.cos.narrow(0, index_pos, seq_len)?; + let sin = self.sin.narrow(0, index_pos, seq_len)?; + let q_embed = candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?; + let k_embed = candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?; + Ok((q_embed, k_embed)) + } +} + +#[derive(Debug, Clone)] +struct LayerWeights { + attention_wq: QMatMul, + attention_wk: QMatMul, + attention_wv: QMatMul, + attention_wo: QMatMul, + attention_q_norm: RmsNorm, + attention_k_norm: RmsNorm, + attention_norm: RmsNorm, + post_attention_norm: RmsNorm, + ffn_norm: RmsNorm, + post_ffn_norm: RmsNorm, + mlp: Mlp, + n_head: usize, + n_kv_head: usize, + head_dim: usize, + q_dim: usize, + sliding_window_size: Option, + rotary_embedding: std::sync::Arc, + neg_inf: Tensor, + kv_cache: Option<(Tensor, Tensor)>, +} + +impl LayerWeights { + /// Build attention mask combining causal mask with optional padding mask. + /// + /// Returns a binary mask (1=attend, 0=block) of shape `[B, 1, seq_len, seq_len+index_pos]`. + fn mask( + &self, + b_sz: usize, + seq_len: usize, + index_pos: usize, + padding_mask: Option<&Tensor>, + device: &Device, + ) -> Result { + // Causal mask (with optional sliding window) + let causal: Vec = if let Some(sw) = self.sliding_window_size { + (0..seq_len) + .flat_map(|i| { + (0..seq_len).map(move |j| { + if i < j || j + sw < i { 0u32 } else { 1u32 } + }) + }) + .collect() + } else { + (0..seq_len) + .flat_map(|i| (0..seq_len).map(move |j| if i < j { 0u32 } else { 1u32 })) + .collect() + }; + let causal = Tensor::from_slice(&causal, (seq_len, seq_len), device)?; + let causal = if index_pos > 0 { + let zeros = Tensor::zeros((seq_len, index_pos), DType::U32, device)?; + Tensor::cat(&[&zeros, &causal], D::Minus1)? + } else { + causal + }; + // [B, 1, seq_len, total_len] + let mut mask = causal.expand((b_sz, 1, seq_len, seq_len + index_pos))?; + + // Combine with padding mask if provided + if let Some(pm) = padding_mask { + // pm: [B, seq_len] with 1=valid, 0=padding + // Expand to [B, 1, 1, seq_len] — keys that are padding should not be attended to + let pm_u32 = pm.to_dtype(DType::U32)? + .unsqueeze(1)? // [B, 1, seq_len] + .unsqueeze(1)?; // [B, 1, 1, seq_len] + mask = mask.broadcast_mul(&pm_u32)?; + } + + Ok(mask) + } + + fn forward_attn( + &mut self, + x: &Tensor, + mask: Option<&Tensor>, + index_pos: usize, + ) -> Result { + let (b_sz, seq_len, _) = x.dims3()?; + + let q = self.attention_wq.forward(x)?; + let k = self.attention_wk.forward(x)?; + let v = self.attention_wv.forward(x)?; + + let q = q + .reshape((b_sz, seq_len, self.n_head, self.head_dim))? + .transpose(1, 2)?; + let k = k + .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))? + .transpose(1, 2)?; + let v = v + .reshape((b_sz, seq_len, self.n_kv_head, self.head_dim))? + .transpose(1, 2)?; + + let q = self.attention_q_norm.forward(&q.contiguous()?)?; + let k = self.attention_k_norm.forward(&k.contiguous()?)?; + + let (q, k) = self.rotary_embedding.apply_rotary_emb_qkv(&q, &k, index_pos)?; + + let (k, v) = match &self.kv_cache { + None => (k, v), + Some((k_cache, v_cache)) => { + if index_pos == 0 { + (k, v) + } else { + let k = Tensor::cat(&[k_cache, &k], 2)?; + let v = Tensor::cat(&[v_cache, &v], 2)?; + (k, v) + } + } + }; + self.kv_cache = Some((k.clone(), v.clone())); + + // Repeat KV for GQA + let k = candle_transformers::utils::repeat_kv(k, self.n_head / self.n_kv_head)?; + let v = candle_transformers::utils::repeat_kv(v, self.n_head / self.n_kv_head)?; + + let scale = 1.0 / (self.head_dim as f64).sqrt(); + let mut attn_weights = (q.matmul(&k.transpose(2, 3)?)? * scale)?; + + if let Some(mask) = mask { + let mask = mask.broadcast_as(attn_weights.shape())?; + let neg_inf = self.neg_inf.broadcast_as(attn_weights.dims())?; + attn_weights = mask.eq(0u32)?.where_cond(&neg_inf, &attn_weights)?; + } + + let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?; + let attn_output = attn_weights.matmul(&v)?; + + attn_output + .transpose(1, 2)? + .reshape((b_sz, seq_len, self.q_dim))? + .apply(&|t: &Tensor| self.attention_wo.forward(t)) + } + + fn clear_kv_cache(&mut self) { + self.kv_cache = None; + } +} + +/// Quantized Gemma-3 model that returns hidden states from all layers. +/// +/// This is the quantized equivalent of `Gemma3AllHidden` in `gemma_encoder.rs`. +/// Loads from GGUF format and runs on GPU with quantized weights (~7.4 GB for Q4_K_M). +#[derive(Debug, Clone)] +pub(crate) struct Gemma3QuantizedAllHidden { + tok_embeddings: Embedding, + embedding_length: usize, + layers: Vec, +} + +impl Gemma3QuantizedAllHidden { + pub fn from_gguf( + ct: gguf_file::Content, + reader: &mut R, + device: &Device, + ) -> Result { + // Detect architecture prefix + let prefix = ["gemma3", "gemma2", "gemma", "gemma-embedding"] + .iter() + .find(|p| { + ct.metadata + .contains_key(&format!("{}.attention.head_count", p)) + }) + .copied() + .unwrap_or("gemma3"); + + let md_get = |s: &str| { + let key = format!("{prefix}.{s}"); + match ct.metadata.get(&key) { + None => candle_core::bail!("cannot find {key} in metadata"), + Some(v) => Ok(v), + } + }; + + let head_count = md_get("attention.head_count")?.to_u32()? as usize; + let head_count_kv = md_get("attention.head_count_kv")?.to_u32()? as usize; + let block_count = md_get("block_count")?.to_u32()? as usize; + let embedding_length = md_get("embedding_length")?.to_u32()? as usize; + let key_length = md_get("attention.key_length")?.to_u32()? as usize; + let rms_norm_eps = md_get("attention.layer_norm_rms_epsilon")?.to_f32()? as f64; + let sliding_window_size = md_get("attention.sliding_window")?.to_u32()? as usize; + + let sliding_window_type = md_get("attention.sliding_window_type") + .and_then(|m| Ok(m.to_u32()? as usize)) + .unwrap_or(DEFAULT_SLIDING_WINDOW_TYPE); + + let rope_freq_base = md_get("rope.freq_base") + .and_then(|m| m.to_f32()) + .unwrap_or(DEFAULT_ROPE_FREQUENCY); + + let rope_freq_base_sliding = md_get("rope.local_freq_base") + .and_then(|m| m.to_f32()) + .unwrap_or(DEFAULT_ROPE_FREQUENCY_SLIDING); + + let q_dim = head_count * key_length; + let neg_inf = Tensor::new(f32::NEG_INFINITY, device)?; + + // Load token embeddings (dequantized to F16 to save 2 GB vs F32) + let tok_embeddings = ct.tensor(reader, "token_embd.weight", device)?; + let tok_embeddings = tok_embeddings.dequantize(device)?.to_dtype(DType::F16)?; + + // Pre-compute shared RoPE tables (only 2 distinct frequencies) + let rope_global = std::sync::Arc::new( + RotaryEmbedding::new(key_length, rope_freq_base, ENCODER_MAX_SEQ_LEN, device)? + ); + let rope_sliding = std::sync::Arc::new( + RotaryEmbedding::new(key_length, rope_freq_base_sliding, ENCODER_MAX_SEQ_LEN, device)? + ); + + let mut layers = Vec::with_capacity(block_count); + for layer_idx in 0..block_count { + let pfx = format!("blk.{layer_idx}"); + + let attention_wq = ct.tensor(reader, &format!("{pfx}.attn_q.weight"), device)?; + let attention_wk = ct.tensor(reader, &format!("{pfx}.attn_k.weight"), device)?; + let attention_wv = ct.tensor(reader, &format!("{pfx}.attn_v.weight"), device)?; + let attention_wo = ct.tensor(reader, &format!("{pfx}.attn_output.weight"), device)?; + + let attention_q_norm = RmsNorm::from_qtensor( + ct.tensor(reader, &format!("{pfx}.attn_q_norm.weight"), device)?, + rms_norm_eps, + )?; + let attention_k_norm = RmsNorm::from_qtensor( + ct.tensor(reader, &format!("{pfx}.attn_k_norm.weight"), device)?, + rms_norm_eps, + )?; + let attention_norm = RmsNorm::from_qtensor( + ct.tensor(reader, &format!("{pfx}.attn_norm.weight"), device)?, + rms_norm_eps, + )?; + let post_attention_norm = RmsNorm::from_qtensor( + ct.tensor(reader, &format!("{pfx}.post_attention_norm.weight"), device)?, + rms_norm_eps, + )?; + let ffn_norm = RmsNorm::from_qtensor( + ct.tensor(reader, &format!("{pfx}.ffn_norm.weight"), device)?, + rms_norm_eps, + )?; + let post_ffn_norm = RmsNorm::from_qtensor( + ct.tensor(reader, &format!("{pfx}.post_ffw_norm.weight"), device)?, + rms_norm_eps, + )?; + + let mlp = Mlp { + feed_forward_gate: QMatMul::from_qtensor( + ct.tensor(reader, &format!("{pfx}.ffn_gate.weight"), device)?, + )?, + feed_forward_up: QMatMul::from_qtensor( + ct.tensor(reader, &format!("{pfx}.ffn_up.weight"), device)?, + )?, + feed_forward_down: QMatMul::from_qtensor( + ct.tensor(reader, &format!("{pfx}.ffn_down.weight"), device)?, + )?, + }; + + // Fixed sliding window pattern: layer_idx % N != 0 means sliding window + // (upstream candle has a bug using (layer_idx + 1) % N > 0) + let is_sliding = layer_idx % sliding_window_type != 0; + let sw = is_sliding.then_some(sliding_window_size); + let rotary_embedding = if is_sliding { + rope_sliding.clone() + } else { + rope_global.clone() + }; + + layers.push(LayerWeights { + attention_wq: QMatMul::from_qtensor(attention_wq)?, + attention_wk: QMatMul::from_qtensor(attention_wk)?, + attention_wv: QMatMul::from_qtensor(attention_wv)?, + attention_wo: QMatMul::from_qtensor(attention_wo)?, + attention_q_norm, + attention_k_norm, + attention_norm, + post_attention_norm, + ffn_norm, + post_ffn_norm, + mlp, + n_head: head_count, + n_kv_head: head_count_kv, + head_dim: key_length, + q_dim, + sliding_window_size: sw, + rotary_embedding, + neg_inf: neg_inf.clone(), + kv_cache: None, + }); + } + + log::info!( + "Quantized Gemma-3 loaded: {} layers, {}d, {} heads ({}kv), head_dim={}", + block_count, embedding_length, head_count, head_count_kv, key_length, + ); + + Ok(Self { + tok_embeddings: Embedding::new(tok_embeddings, embedding_length), + embedding_length, + layers, + }) + } + + /// Forward pass returning hidden states from ALL layers. + /// + /// Returns `num_layers + 1` tensors: [embedding, layer_0, ..., layer_N]. + /// Each tensor is `[B, seq_len, hidden_size]`. + pub fn forward_all_hidden( + &mut self, + x: &Tensor, + index_pos: usize, + padding_mask: Option<&Tensor>, + ) -> Result> { + let (b_sz, seq_len) = x.dims2()?; + + let mut layer_in = self.tok_embeddings.forward(x)?.to_dtype(DType::F32)?; + layer_in = (layer_in * (self.embedding_length as f64).sqrt())?; + + let mut all_hidden = Vec::with_capacity(self.layers.len() + 1); + all_hidden.push(layer_in.clone()); + + for layer in self.layers.iter_mut() { + let attention_mask = if seq_len == 1 { + None + } else { + Some(layer.mask(b_sz, seq_len, index_pos, padding_mask, x.device())?) + }; + + // Attention block + let residual = &layer_in; + let x = layer.attention_norm.forward(&layer_in)?; + let x = layer.forward_attn(&x, attention_mask.as_ref(), index_pos)?; + let x = layer.post_attention_norm.forward(&x)?; + let x = (x + residual)?; + + // Feed-forward block + let residual = &x; + let x = layer.ffn_norm.forward(&x)?; + let x = layer.mlp.forward(&x)?; + let x = layer.post_ffn_norm.forward(&x)?; + let x = (x + residual)?; + + all_hidden.push(x.clone()); + layer_in = x; + } + + Ok(all_hidden) + } + + pub fn clear_kv_cache(&mut self) { + for layer in self.layers.iter_mut() { + layer.clear_kv_cache(); + } + } +} diff --git a/cake-core/src/models/ltx2/transformer.rs b/cake-core/src/models/ltx2/transformer.rs new file mode 100644 index 00000000..b190a457 --- /dev/null +++ b/cake-core/src/models/ltx2/transformer.rs @@ -0,0 +1,544 @@ +use anyhow::Result; +use async_trait::async_trait; +use candle_core::{DType, Tensor}; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use log::info; +use std::path::PathBuf; + +use crate::cake::{Context, Forwarder}; +use crate::models::sd::{pack_tensors, unpack_tensors}; + +use super::vendored::config::Ltx2TransformerConfig; +use super::vendored::model::LTXModel; + +/// LTX-2 dual-stream DiT transformer Forwarder. +/// +/// Supports two modes: +/// 1. Full model: layer name `"ltx2-transformer"` — runs all 48 blocks + setup + finalize +/// 2. Block range: layer name `"ltx2-transformer.N-M"` — runs blocks N through M only +/// +/// Full model packed tensor format: +/// 0: video_latent [B, T, in_channels] +/// 1: sigma [B] +/// 2: timesteps [B] +/// 3: positions [B, 3, T] +/// 4: context [B, L, cross_attention_dim] +/// 5: context_mask [B, L] +/// +/// Block range packed tensor format: +/// 0: hidden [B, T, video_dim] +/// 1: temb [B, 1, adaln_params, video_dim] +/// 2: pe_cos [B, H, T, d_head/2] +/// 3: pe_sin [B, H, T, d_head/2] +/// 4: context [B, L, video_dim] (already through caption projection) +/// 5: context_mask [B, L] +/// 6: embedded_ts [B, 1, video_dim] (for finalize, if this shard includes it) +#[derive(Debug)] +pub struct Ltx2Transformer { + name: String, + model: LTXModel, + /// true when running only a block range (not the full model) + is_block_range: bool, + /// Actual dtype of loaded weights (BF16 for LTX-2) + model_dtype: DType, +} + +impl std::fmt::Display for Ltx2Transformer { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{} (local)", &self.name) + } +} + +/// Parse block range from layer name like "ltx2-transformer.0-23". +/// Returns (start, end_exclusive) or None for full model. +fn parse_block_range(name: &str) -> Option<(usize, usize)> { + let suffix = name.strip_prefix("ltx2-transformer.")?; + let parts: Vec<&str> = suffix.split('-').collect(); + if parts.len() == 2 { + let start: usize = parts[0].parse().ok()?; + let end: usize = parts[1].parse().ok()?; + Some((start, end + 1)) // inclusive to exclusive + } else { + None + } +} + +impl Ltx2Transformer { + /// Load as a full model (all blocks + setup + finalize). + pub fn load_model(ctx: &Context) -> Result> { + let (config, weights_path) = Self::resolve_config_and_weights(ctx)?; + + info!("Loading LTX-2 transformer from {:?}...", weights_path); + + let weight_files = find_weight_files(&weights_path)?; + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors(&weight_files, ctx.dtype, &ctx.device)? + }; + + let model = LTXModel::new(config, vb)?; + + info!("LTX-2 transformer loaded!"); + + Ok(Box::new(Self { + name: "ltx2-transformer".to_string(), + model, + is_block_range: false, + model_dtype: DType::BF16, + })) + } + + /// Load a block range (e.g., blocks 0-23). + #[allow(dead_code)] + pub fn load_block_range( + name: String, + ctx: &Context, + block_start: usize, + block_end: usize, + ) -> Result> { + let (config, weights_path) = Self::resolve_config_and_weights(ctx)?; + + info!( + "Loading LTX-2 transformer blocks {}-{} from {:?}...", + block_start, + block_end - 1, + weights_path + ); + + let weight_files = find_weight_files(&weights_path)?; + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors(&weight_files, ctx.dtype, &ctx.device)? + }; + + let model = LTXModel::new_block_range(config, vb, block_start, Some(block_end))?; + + Ok(Box::new(Self { + name, + model, + is_block_range: true, + model_dtype: DType::BF16, + })) + } + + pub(crate) fn resolve_config_and_weights(ctx: &Context) -> Result<(Ltx2TransformerConfig, PathBuf)> { + let ltx_args = &ctx.args.ltx_args; + + // If explicit transformer path given, use it directly + if let Some(ref p) = ltx_args.ltx_transformer { + let path = PathBuf::from(p); + return Ok((Ltx2TransformerConfig::default(), path)); + } + + // Try direct path first: --model points to a directory containing transformer/ + let model_dir = PathBuf::from(&ctx.args.model); + let direct_transformer = model_dir.join("transformer"); + if direct_transformer.is_dir() { + let config = Self::load_config_from_dir(&direct_transformer); + let weights = Self::find_weights_in_dir(&direct_transformer)?; + return Ok((config, weights)); + } + + // Fall back to HF cache resolution + let repo = ltx_args.ltx_repo(); + let cache_path = model_dir.join("hub"); + let api = if model_dir.is_dir() && cache_path.is_dir() { + // Model-local cache (e.g., /path/to/LTX-2/hub/) — only when --model is a real directory + ApiBuilder::from_cache(Cache::new(cache_path)).build()? + } else { + // Use default HF cache (~/.cache/huggingface/hub) + let mut builder = ApiBuilder::new(); + if let Ok(token) = std::env::var("HF_TOKEN") { + builder = builder.with_token(Some(token)); + } + builder.build()? + }; + let model_api = api.model(repo); + + let config = if let Ok(config_path) = model_api.get("transformer/config.json") { + let config_str = std::fs::read_to_string(&config_path)?; + match serde_json::from_str::(&config_str) { + Ok(cfg) => { + info!("Loaded transformer config from {:?}", config_path); + cfg + } + Err(e) => { + log::warn!("Failed to parse transformer config.json: {}, using defaults", e); + Ltx2TransformerConfig::default() + } + } + } else { + Ltx2TransformerConfig::default() + }; + + // Resolve weights — try single file first, then find index for sharded models + let weights_path = + if let Ok(path) = model_api.get("transformer/diffusion_pytorch_model.safetensors") { + path + } else { + // Sharded model — get the index, parse shard filenames, download each shard + let index_path = model_api + .get("transformer/diffusion_pytorch_model.safetensors.index.json")?; + let index_str = std::fs::read_to_string(&index_path)?; + let index: serde_json::Value = serde_json::from_str(&index_str)?; + + // Extract unique shard filenames from weight_map values + let mut shard_names: Vec = Vec::new(); + if let Some(weight_map) = index.get("weight_map").and_then(|m| m.as_object()) { + for v in weight_map.values() { + if let Some(name) = v.as_str() { + if !shard_names.contains(&name.to_string()) { + shard_names.push(name.to_string()); + } + } + } + } + shard_names.sort(); + info!("Downloading {} transformer weight shards from HF...", shard_names.len()); + + for shard in &shard_names { + let hf_path = format!("transformer/{}", shard); + info!(" downloading {}...", hf_path); + model_api.get(&hf_path)?; + } + + // Return the directory containing the downloaded shards + index_path.parent().unwrap().to_path_buf() + }; + + Ok((config, weights_path)) + } + + fn load_config_from_dir(dir: &PathBuf) -> Ltx2TransformerConfig { + let config_path = dir.join("config.json"); + if config_path.exists() { + if let Ok(s) = std::fs::read_to_string(&config_path) { + if let Ok(cfg) = serde_json::from_str::(&s) { + info!("Loaded transformer config from {:?}", config_path); + return cfg; + } + } + } + info!("Using default transformer config"); + Ltx2TransformerConfig::default() + } + + fn find_weights_in_dir(dir: &PathBuf) -> Result { + // Single file + let single = dir.join("diffusion_pytorch_model.safetensors"); + if single.exists() { + return Ok(single); + } + // Sharded — return the directory (find_weight_files will scan it) + let index = dir.join("diffusion_pytorch_model.safetensors.index.json"); + if index.exists() { + return Ok(dir.clone()); + } + // Look for any safetensors file + for entry in std::fs::read_dir(dir)? { + let p = entry?.path(); + if p.extension().map_or(false, |e| e == "safetensors") { + return Ok(p); + } + } + anyhow::bail!("No safetensors files found in {:?}", dir) + } + + /// Pack tensors for full-model network transport and call the forwarder. + #[allow(clippy::too_many_arguments)] + pub async fn forward_packed( + forwarder: &mut Box, + video_latent: Tensor, + sigma: Tensor, + timesteps: Tensor, + positions: Tensor, + context: Tensor, + context_mask: Tensor, + ctx: &mut Context, + ) -> Result { + let packed = pack_tensors( + vec![video_latent, sigma, timesteps, positions, context, context_mask], + &ctx.device, + )?; + forwarder.forward_mut(&packed, 0, 0, ctx).await + } + + /// Pack tensors for block-range network transport and call the forwarder. + /// + /// Sends pre-computed hidden states + metadata instead of raw latents. + #[allow(clippy::too_many_arguments)] + pub async fn forward_blocks_packed( + forwarder: &mut Box, + hidden: Tensor, + temb: Tensor, + pe_cos: Tensor, + pe_sin: Tensor, + context: Tensor, + context_mask: Tensor, + embedded_ts: Tensor, + prompt_temb: Option, + stg_skip_blocks: &[usize], + ctx: &mut Context, + ) -> Result { + let mut tensors = vec![hidden, temb, pe_cos, pe_sin, context, context_mask, embedded_ts]; + if let Some(pt) = prompt_temb { + tensors.push(pt); + } + // Encode STG skip blocks as a 1D F32 tensor (block indices as floats) + if !stg_skip_blocks.is_empty() { + let stg_vals: Vec = stg_skip_blocks.iter().map(|&b| b as f32).collect(); + tensors.push(Tensor::new(stg_vals, &ctx.device)?); + } + let packed = pack_tensors(tensors, &ctx.device)?; + // block_idx: 1 = normal block-range, 2 = block-range with STG + let block_idx = if stg_skip_blocks.is_empty() { 1 } else { 2 }; + forwarder.forward_mut(&packed, 0, block_idx, ctx).await + } + + /// Reference to the inner model (for master-side local execution). + #[allow(dead_code)] + pub fn model(&self) -> <XModel { + &self.model + } +} + +#[async_trait] +impl Forwarder for Ltx2Transformer { + fn load(name: String, ctx: &Context) -> Result> { + let (config, weights_path) = Self::resolve_config_and_weights(ctx)?; + + // LTX-2 weights are natively BF16 — loading as F16 causes NaN + let model_dtype = DType::BF16; + + let is_block_range; + let model = if let Some((start, end)) = parse_block_range(&name) { + info!( + "Loading LTX-2 transformer blocks {}-{} from {:?} (dtype={:?})...", + start, + end - 1, + weights_path, + model_dtype, + ); + is_block_range = true; + let weight_files = find_weight_files(&weights_path)?; + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &weight_files, + model_dtype, + &ctx.device, + )? + }; + LTXModel::new_block_range(config, vb, start, Some(end))? + } else { + info!("Loading full LTX-2 transformer from {:?} (dtype={:?})...", weights_path, model_dtype); + is_block_range = false; + let weight_files = find_weight_files(&weights_path)?; + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &weight_files, + model_dtype, + &ctx.device, + )? + }; + LTXModel::new(config, vb)? + }; + + info!("LTX-2 transformer loaded!"); + + Ok(Box::new(Self { + name, + model, + is_block_range, + model_dtype, + })) + } + + async fn forward( + &self, + x: &Tensor, + _index_pos: usize, + block_idx: usize, + _ctx: &mut Context, + ) -> Result { + let t0 = std::time::Instant::now(); + let unpacked = unpack_tensors(x)?; + + // block_idx == 1 or 2 signals block-range format (2 = with STG) + if self.is_block_range || block_idx == 1 || block_idx == 2 { + // Block-range format: [hidden, temb, pe_cos, pe_sin, context, context_mask, embedded_ts, prompt_temb?, stg_blocks?] + let dt = self.model_dtype; + let hidden = unpacked[0].to_dtype(dt)?; + let temb = unpacked[1].to_dtype(dt)?; + let pe_cos = unpacked[2].to_dtype(dt)?; + let pe_sin = unpacked[3].to_dtype(dt)?; + let context = unpacked[4].to_dtype(dt)?; + let context_mask = unpacked[5].to_dtype(dt)?; + // Determine how many optional tensors follow the 7 base tensors. + // For block_idx==2 (STG), the LAST tensor is always stg_blocks. + // Base: [hidden, temb, pe_cos, pe_sin, context, context_mask, embedded_ts] = 7 + // Optional: prompt_temb (index 7), stg_blocks (last, only when block_idx==2) + let has_stg = block_idx == 2; + let num_base = 7; + let num_optional_after = unpacked.len() - num_base; + // If STG, last optional is stg_blocks. prompt_temb exists if there's more than just stg. + let (prompt_temb, stg_skip_blocks) = if has_stg { + let stg_tensor = &unpacked[unpacked.len() - 1]; + let stg_vals: Vec = stg_tensor.to_vec1()?; + let stg_blocks: Vec = stg_vals.iter().map(|&v| v as usize).collect(); + // prompt_temb at index 7 if there are 2+ optional tensors (prompt_temb + stg) + let pt = if num_optional_after >= 2 { + Some(unpacked[7].to_dtype(dt)?) + } else { + None + }; + (pt, stg_blocks) + } else { + let pt = if unpacked.len() > 7 { + Some(unpacked[7].to_dtype(dt)?) + } else { + None + }; + (pt, vec![]) + }; + let embedded_ts = if unpacked.len() > 6 { + Some(unpacked[6].to_dtype(dt)?) + } else { + None + }; + + info!( + "LTX-2 transformer blocks forwarding (unpack: {}ms, hidden: {:?}{})", + t0.elapsed().as_millis(), + hidden.shape(), + if stg_skip_blocks.is_empty() { String::new() } else { format!(", stg_skip={:?}", stg_skip_blocks) } + ); + + let pe = (pe_cos, pe_sin); + let result = self.model.forward_blocks_only_with_stg( + &hidden, + &temb, + &pe, + &context, + Some(&context_mask), + embedded_ts.as_ref(), + prompt_temb.as_ref(), + &stg_skip_blocks, + )?; + + info!("LTX-2 transformer blocks done in {}ms", t0.elapsed().as_millis()); + Ok(result) + } else { + // Full model format: [video_latent, sigma, timesteps, positions, context, context_mask] + let dt = self.model_dtype; + let video_latent = unpacked[0].to_dtype(dt)?; + let sigma = unpacked[1].to_dtype(dt)?; + let timesteps = unpacked[2].to_dtype(dt)?; + let positions = unpacked[3].to_dtype(DType::F32)?; + let context = unpacked[4].to_dtype(dt)?; + let context_mask = unpacked[5].to_dtype(dt)?; + + info!( + "LTX-2 transformer forwarding (unpack: {}ms, latent: {:?})", + t0.elapsed().as_millis(), + video_latent.shape() + ); + + let result = self.model.forward_video( + &video_latent, + &sigma, + ×teps, + &positions, + &context, + Some(&context_mask), + )?; + + info!("LTX-2 transformer done in {}ms", t0.elapsed().as_millis()); + Ok(result) + } + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> Result { + self.forward(x, index_pos, block_idx, ctx).await + } + + fn layer_name(&self) -> &str { + &self.name + } +} + +/// Find all safetensors weight files from a path. +/// +/// If `path` is a single .safetensors file, returns just that file. +/// If `path` is a directory, scans for all diffusion_pytorch_model*.safetensors files. +fn find_weight_files(path: &PathBuf) -> Result> { + // Single safetensors file + if path.extension().map_or(false, |e| e == "safetensors") && path.exists() { + return Ok(vec![path.clone()]); + } + + // Directory: scan for shards + if path.is_dir() { + let mut shards = Vec::new(); + for entry in std::fs::read_dir(path)? { + let entry = entry?; + let p = entry.path(); + if let Some(name) = p.file_name().and_then(|n| n.to_str()) { + if name.starts_with("diffusion_pytorch_model") + && name.ends_with(".safetensors") + && !name.contains("index") + { + shards.push(p); + } + } + } + if !shards.is_empty() { + shards.sort(); + info!("Found {} transformer weight shards", shards.len()); + return Ok(shards); + } + } + + // Try parent directory scan (for paths pointing to specific shard files) + if let Some(parent) = path.parent() { + let mut shards = Vec::new(); + for entry in std::fs::read_dir(parent)? { + let entry = entry?; + let p = entry.path(); + if let Some(name) = p.file_name().and_then(|n| n.to_str()) { + if name.starts_with("diffusion_pytorch_model") + && name.ends_with(".safetensors") + && !name.contains("index") + { + shards.push(p); + } + } + } + if !shards.is_empty() { + shards.sort(); + info!("Found {} transformer weight shards", shards.len()); + return Ok(shards); + } + } + + Ok(vec![path.clone()]) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_parse_block_range() { + assert_eq!(parse_block_range("ltx2-transformer"), None); + assert_eq!(parse_block_range("ltx2-transformer.0-23"), Some((0, 24))); + assert_eq!(parse_block_range("ltx2-transformer.24-47"), Some((24, 48))); + assert_eq!(parse_block_range("ltx2-transformer.0-47"), Some((0, 48))); + assert_eq!(parse_block_range("ltx2-transformer.abc"), None); + assert_eq!(parse_block_range("ltx2-vae"), None); + } +} diff --git a/cake-core/src/models/ltx2/vae_forwarder.rs b/cake-core/src/models/ltx2/vae_forwarder.rs new file mode 100644 index 00000000..bf97d364 --- /dev/null +++ b/cake-core/src/models/ltx2/vae_forwarder.rs @@ -0,0 +1,264 @@ +use anyhow::Result; +use async_trait::async_trait; +use candle_core::{DType, Tensor}; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use log::info; +use std::path::PathBuf; + +use crate::cake::{Context, Forwarder}; +use crate::models::sd::{pack_tensors, unpack_tensors}; + +// LTX-2 VAE decoder reuses the same building blocks as LTX-Video, +// but the encoder is architecturally different (AutoencoderKLLTX2Video). +// We only need the decoder for generation, so we load it directly. +use crate::models::ltx_video::vendored::vae::{AutoencoderKLLtxVideoConfig, LtxVideoDecoder3d}; + +/// LTX-2 Video VAE Forwarder (decoder-only). +/// +/// Layer name: `"ltx2-vae"` +/// +/// The LTX-2 VAE (`AutoencoderKLLTX2Video`) has a different encoder architecture +/// from LTX-Video, but shares the same decoder building blocks. Since video +/// generation only needs decode (latents → pixels), we skip the encoder entirely. +#[derive(Debug)] +pub struct Ltx2Vae { + name: String, + decoder: LtxVideoDecoder3d, + /// Per-channel latent normalization mean (loaded from safetensors). + pub latents_mean: Vec, + /// Per-channel latent normalization std (loaded from safetensors). + pub latents_std: Vec, +} + +impl std::fmt::Display for Ltx2Vae { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{} (local)", &self.name) + } +} + +impl Ltx2Vae { + fn vae_config(is_ltx23: bool) -> AutoencoderKLLtxVideoConfig { + if is_ltx23 { + // LTX-2.3 VAE: 4 up_blocks with different channel dims and strides + AutoencoderKLLtxVideoConfig { + block_out_channels: vec![256, 512, 1024, 2048], + decoder_block_out_channels: vec![256, 512, 512, 1024], + layers_per_block: vec![4, 6, 6, 2, 2], + decoder_layers_per_block: vec![4, 6, 4, 2, 2], + latent_channels: 128, + patch_size: 4, + patch_size_t: 1, + timestep_conditioning: false, + decoder_spatiotemporal_scaling: vec![true, true, true, true], + decoder_inject_noise: vec![false, false, false, false, false], + decoder_upsample_residual: vec![true, true, true, true], + decoder_upsample_factor: vec![2, 2, 1, 2], + // Per-block strides (un-reversed, matching decoder_block_out_channels order): + // After reversal: block0=(2,2,2), block1=(2,2,2), block2=(2,1,1), block3=(1,2,2) + decoder_strides: vec![(1, 2, 2), (2, 1, 1), (2, 2, 2), (2, 2, 2)], + ..Default::default() + } + } else { + // LTX-2 VAE: 3 up_blocks, same as LTX-Video structure + AutoencoderKLLtxVideoConfig { + block_out_channels: vec![256, 512, 1024, 2048], + decoder_block_out_channels: vec![256, 512, 1024], + layers_per_block: vec![4, 6, 6, 2, 2], + decoder_layers_per_block: vec![5, 5, 5, 5], + latent_channels: 128, + patch_size: 4, + patch_size_t: 1, + timestep_conditioning: false, + ..Default::default() + } + } + } + + fn resolve_weights(ctx: &Context) -> Result { + let ltx_args = &ctx.args.ltx_args; + if let Some(ref p) = ltx_args.ltx_vae { + return Ok(PathBuf::from(p)); + } + + // Try direct path: --model points to directory containing vae/ + let model_dir = PathBuf::from(&ctx.args.model); + let direct = model_dir.join("vae/diffusion_pytorch_model.safetensors"); + if direct.exists() { + return Ok(direct); + } + + // Fall back to HF cache + let repo = ltx_args.ltx_repo(); + let cache_path = model_dir.join("hub"); + let api = if model_dir.is_dir() && cache_path.is_dir() { + ApiBuilder::from_cache(Cache::new(cache_path)).build()? + } else { + let mut builder = ApiBuilder::new(); + if let Ok(token) = std::env::var("HF_TOKEN") { + builder = builder.with_token(Some(token)); + } + builder.build()? + }; + let model_api = api.model(repo); + Ok(model_api.get("vae/diffusion_pytorch_model.safetensors")?) + } + + fn load_inner(name: String, ctx: &Context) -> Result { + let weights_path = Self::resolve_weights(ctx)?; + let is_ltx23 = ctx.args.ltx_args.is_ltx23(); + info!("Loading LTX-2{} VAE (decoder-only) from {:?}...", + if is_ltx23 { ".3" } else { "" }, weights_path); + + // LTX-2 VAE weights are BF16 — load as BF16 to avoid conversion artifacts + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &[weights_path], + DType::BF16, + &ctx.device, + )? + }; + + let config = Self::vae_config(is_ltx23); + + // Load latents_mean and latents_std from safetensors (registered buffers) + let latents_mean: Vec = vb + .get(config.latent_channels, "latents_mean")? + .to_dtype(DType::F32)? + .to_vec1()?; + let latents_std: Vec = vb + .get(config.latent_channels, "latents_std")? + .to_dtype(DType::F32)? + .to_vec1()?; + info!( + "VAE latents_mean range: [{:.4}, {:.4}], latents_std range: [{:.4}, {:.4}]", + latents_mean.iter().cloned().fold(f32::INFINITY, f32::min), + latents_mean.iter().cloned().fold(f32::NEG_INFINITY, f32::max), + latents_std.iter().cloned().fold(f32::INFINITY, f32::min), + latents_std.iter().cloned().fold(f32::NEG_INFINITY, f32::max), + ); + + // Load decoder directly — skip encoder (different architecture in LTX-2) + let decoder = if !config.decoder_strides.is_empty() { + LtxVideoDecoder3d::new_with_strides( + config.latent_channels, + config.out_channels, + &config.decoder_block_out_channels, + &config.decoder_strides, + &config.decoder_layers_per_block, + config.patch_size, + config.patch_size_t, + config.resnet_eps, + config.decoder_causal, + &config.decoder_inject_noise, + config.timestep_conditioning, + &config.decoder_upsample_residual, + &config.decoder_upsample_factor, + vb.pp("decoder"), + )? + } else { + LtxVideoDecoder3d::new( + config.latent_channels, + config.out_channels, + &config.decoder_block_out_channels, + &config.decoder_spatiotemporal_scaling, + &config.decoder_layers_per_block, + config.patch_size, + config.patch_size_t, + config.resnet_eps, + config.decoder_causal, + &config.decoder_inject_noise, + config.timestep_conditioning, + &config.decoder_upsample_residual, + &config.decoder_upsample_factor, + vb.pp("decoder"), + )? + }; + + info!("LTX-2 VAE decoder loaded!"); + + Ok(Self { + name, + decoder, + latents_mean, + latents_std, + }) + } + + #[allow(dead_code)] + pub fn load_model(ctx: &Context) -> Result> { + Ok(Box::new(Self::load_inner("ltx2-vae".to_string(), ctx)?)) + } + + /// Load VAE and return (forwarder, latents_mean, latents_std). + pub fn load_with_stats(ctx: &Context) -> Result<(Box, Vec, Vec)> { + let vae = Self::load_inner("ltx2-vae".to_string(), ctx)?; + let mean = vae.latents_mean.clone(); + let std = vae.latents_std.clone(); + Ok((Box::new(vae), mean, std)) + } + + /// Decode latents through the VAE (no timestep conditioning for LTX-2). + pub async fn decode( + forwarder: &mut Box, + latents: Tensor, + ctx: &mut Context, + ) -> Result { + let tensors = vec![ + Tensor::from_slice(&[0f32], 1, &ctx.device)?, // direction: 0.0 = decode + latents, + ]; + let packed = pack_tensors(tensors, &ctx.device)?; + forwarder.forward_mut(&packed, 0, 0, ctx).await + } +} + +#[async_trait] +impl Forwarder for Ltx2Vae { + fn load(name: String, ctx: &Context) -> Result> { + Ok(Box::new(Self::load_inner(name, ctx)?)) + } + + async fn forward( + &self, + x: &Tensor, + _index_pos: usize, + _block_idx: usize, + _ctx: &mut Context, + ) -> Result { + let unpacked = unpack_tensors(x)?; + let direction_vec: Vec = unpacked[0].to_vec1()?; + let direction = direction_vec[0]; + // VAE weights are BF16 — convert input to match + let input = unpacked[1].to_dtype(DType::BF16)?; + + if direction == 1.0 { + anyhow::bail!( + "LTX-2 VAE encoding not supported — encoder architecture differs from decoder. \ + Use LTX-Video VAE for encoding." + ); + } + + let timestep = if unpacked.len() > 2 { + Some(unpacked[2].to_dtype(DType::BF16)?) + } else { + None + }; + let decoded = self.decoder.forward(&input, timestep.as_ref(), false)?; + Ok(decoded) + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> Result { + self.forward(x, index_pos, block_idx, ctx).await + } + + fn layer_name(&self) -> &str { + &self.name + } +} diff --git a/cake-core/src/models/ltx2/vendored/adaln.rs b/cake-core/src/models/ltx2/vendored/adaln.rs new file mode 100644 index 00000000..0024ff23 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/adaln.rs @@ -0,0 +1,169 @@ +//! Adaptive Layer Norm (AdaLN) for LTX-2. +//! +//! Timestep → sinusoidal embedding → SiLU → Linear → per-block modulation params. + +use candle_core::{DType, Result, Tensor}; +use candle_nn::{Linear, Module, VarBuilder}; + +/// Sinusoidal timestep embedding (PixArt-Alpha style). +#[derive(Debug)] +struct Timesteps { + dim: usize, + flip_sin_to_cos: bool, + downscale_freq_shift: f64, +} + +impl Timesteps { + fn new(dim: usize) -> Self { + Self { + dim, + flip_sin_to_cos: true, + downscale_freq_shift: 0.0, + } + } + + fn forward(&self, t: &Tensor) -> Result { + let device = t.device(); + let half_dim = self.dim / 2; + + // exp(-log(10000) * i / half_dim) for i in 0..half_dim + let exponent: Vec = (0..half_dim) + .map(|i| { + let freq = -(10000.0f64.ln()) * (i as f64) + / ((half_dim as f64) - self.downscale_freq_shift); + freq.exp() as f32 + }) + .collect(); + + let freqs = Tensor::new(exponent, device)?; // [half_dim] + let t = t.to_dtype(DType::F32)?; + + // t: [B] or [B, T], freqs: [half_dim] + // Outer product: [B, half_dim] + let args = if t.rank() == 1 { + t.unsqueeze(1)?.broadcast_mul(&freqs.unsqueeze(0)?)? + } else { + // [B, T] -> [B, T, half_dim] + t.unsqueeze(t.rank())?.broadcast_mul( + &freqs + .reshape(std::iter::repeat(1).take(t.rank()).chain([half_dim]).collect::>())?, + )? + }; + + let (cos, sin) = if self.flip_sin_to_cos { + (args.cos()?, args.sin()?) + } else { + (args.sin()?, args.cos()?) + }; + + Tensor::cat(&[cos, sin], args.rank() - 1) + } +} + +/// Two-layer MLP for timestep projection. +#[derive(Debug)] +struct TimestepEmbedding { + linear_1: Linear, + linear_2: Linear, +} + +impl TimestepEmbedding { + fn new(in_channels: usize, time_embed_dim: usize, vb: VarBuilder) -> Result { + let linear_1 = candle_nn::linear(in_channels, time_embed_dim, vb.pp("linear_1"))?; + let linear_2 = candle_nn::linear(time_embed_dim, time_embed_dim, vb.pp("linear_2"))?; + Ok(Self { linear_1, linear_2 }) + } + + fn forward(&self, x: &Tensor) -> Result { + let x = self.linear_1.forward(x)?; + let x = candle_nn::ops::silu(&x)?; + self.linear_2.forward(&x) + } +} + +/// PixArt-Alpha combined timestep + size embeddings. +#[derive(Debug)] +struct PixArtAlphaCombinedTimestepSizeEmbeddings { + timestep: Timesteps, + time_proj: TimestepEmbedding, +} + +impl PixArtAlphaCombinedTimestepSizeEmbeddings { + fn new(embedding_dim: usize, vb: VarBuilder) -> Result { + let timestep = Timesteps::new(256); + let time_proj = TimestepEmbedding::new(256, embedding_dim, vb.pp("timestep_embedder"))?; + Ok(Self { + timestep, + time_proj, + }) + } + + fn forward(&self, t: &Tensor) -> Result { + let t_emb = self.timestep.forward(t)?; + // Timesteps produces F32 (sinusoidal); convert to weight dtype before Linear + let weight_dtype = self.time_proj.linear_1.weight().dtype(); + let t_emb = t_emb.to_dtype(weight_dtype)?; + self.time_proj.forward(&t_emb) + } +} + +/// AdaLayerNormSingle: timestep → embedding → SiLU → Linear → per-block params. +/// +/// Returns `(modulation_params, embedded_timestep)`. +/// `modulation_params` shape: `[B, embedding_coefficient * dim]`. +#[derive(Debug)] +pub struct AdaLayerNormSingle { + emb: PixArtAlphaCombinedTimestepSizeEmbeddings, + linear: Linear, +} + +impl AdaLayerNormSingle { + pub fn new( + embedding_dim: usize, + embedding_coefficient: usize, + vb: VarBuilder, + ) -> Result { + let emb = PixArtAlphaCombinedTimestepSizeEmbeddings::new(embedding_dim, vb.pp("emb"))?; + let linear = candle_nn::linear( + embedding_dim, + embedding_coefficient * embedding_dim, + vb.pp("linear"), + )?; + Ok(Self { emb, linear }) + } + + /// Returns `(modulation_params, raw_embedded_timestep)`. + pub fn forward(&self, timestep: &Tensor) -> Result<(Tensor, Tensor)> { + let embedded = self.emb.forward(timestep)?; + let params = candle_nn::ops::silu(&embedded)?; + let params = self.linear.forward(¶ms)?; + Ok((params, embedded)) + } +} + +/// Caption/text projection: Linear → GELU → Linear. +#[derive(Debug)] +pub struct TextProjection { + linear_1: Linear, + linear_2: Linear, +} + +impl TextProjection { + pub fn new( + caption_channels: usize, + inner_dim: usize, + vb: VarBuilder, + ) -> Result { + let linear_1 = candle_nn::linear(caption_channels, inner_dim, vb.pp("linear_1"))?; + let linear_2 = candle_nn::linear(inner_dim, inner_dim, vb.pp("linear_2"))?; + Ok(Self { linear_1, linear_2 }) + } +} + +impl Module for TextProjection { + fn forward(&self, xs: &Tensor) -> Result { + let x = self.linear_1.forward(xs)?; + let x = x.gelu()?; + self.linear_2.forward(&x) + } +} diff --git a/cake-core/src/models/ltx2/vendored/attention.rs b/cake-core/src/models/ltx2/vendored/attention.rs new file mode 100644 index 00000000..31007d39 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/attention.rs @@ -0,0 +1,298 @@ +//! Multi-head attention for LTX-2 transformer. +//! +//! Matches HF diffusers `LTX2Attention` + `LTX2AudioVideoAttnProcessor`. +//! QK-norm is applied across all heads (before head reshape), then RoPE, then reshape. + +use candle_core::{DType, Result, Tensor, D}; +use candle_nn::{Linear, Module, VarBuilder}; + +use super::rope::apply_rotary_emb; + +/// RMSNorm (with learned weight). +#[derive(Debug)] +struct RmsNorm { + weight: Tensor, + eps: f64, +} + +impl RmsNorm { + fn new(dim: usize, eps: f64, vb: VarBuilder) -> Result { + let weight = vb.get(dim, "weight")?; + Ok(Self { weight, eps }) + } +} + +impl Module for RmsNorm { + fn forward(&self, xs: &Tensor) -> Result { + let dtype = xs.dtype(); + let xs = xs.to_dtype(DType::F32)?; + let variance = xs.sqr()?.mean_keepdim(D::Minus1)?; + let xs = xs.broadcast_div(&(variance + self.eps)?.sqrt()?)?; + let xs = xs.to_dtype(dtype)?; + xs.broadcast_mul(&self.weight) + } +} + +/// Standalone RMS normalization (no learned weight). +pub fn rms_norm(x: &Tensor, eps: f64) -> Result { + let dtype = x.dtype(); + let x = x.to_dtype(DType::F32)?; + let variance = x.sqr()?.mean_keepdim(D::Minus1)?; + let x = x.broadcast_div(&(variance + eps)?.sqrt()?)?; + x.to_dtype(dtype) +} + +/// LayerNorm without learnable affine parameters (elementwise_affine=False). +/// Subtracts mean and divides by std, matching `nn.LayerNorm(..., elementwise_affine=False)`. +pub fn layer_norm_no_affine(x: &Tensor, eps: f64) -> Result { + let dtype = x.dtype(); + let x = x.to_dtype(DType::F32)?; + let mean = x.mean_keepdim(D::Minus1)?; + let x_centered = x.broadcast_sub(&mean)?; + let variance = x_centered.sqr()?.mean_keepdim(D::Minus1)?; + let x = x_centered.broadcast_div(&(variance + eps)?.sqrt()?)?; + x.to_dtype(dtype) +} + +/// Multi-head attention with QK-norm across heads, split RoPE. +/// +/// Matches HF `LTX2Attention`: +/// - norm_q/norm_k operate on `[B, T, heads*d_head]` (across all heads) +/// - Order: project → norm → RoPE → reshape to heads → SDPA → reshape back → project out +#[derive(Debug)] +pub struct Attention { + to_q: Linear, + to_k: Linear, + to_v: Linear, + to_out: Linear, + norm_q: RmsNorm, // normalizes heads*d_head dim + norm_k: RmsNorm, // normalizes heads*d_head dim + /// LTX-2.3: per-head gating (sigmoid gate on attention output). + /// Linear(inner_dim, heads) -> sigmoid -> gate per head. + to_gate_logits: Option, + heads: usize, + d_head: usize, +} + +impl Attention { + pub fn new( + query_dim: usize, + context_dim: Option, + heads: usize, + d_head: usize, + norm_eps: f64, + gated: bool, + vb: VarBuilder, + ) -> Result { + let inner_dim = heads * d_head; + let kv_dim = context_dim.unwrap_or(query_dim); + + let to_q = candle_nn::linear(query_dim, inner_dim, vb.pp("to_q"))?; + let to_k = candle_nn::linear(kv_dim, inner_dim, vb.pp("to_k"))?; + let to_v = candle_nn::linear(kv_dim, inner_dim, vb.pp("to_v"))?; + let to_out = candle_nn::linear(inner_dim, query_dim, vb.pp("to_out.0"))?; + + // QK norm across full inner dim (heads * d_head) + let norm_q = RmsNorm::new(inner_dim, norm_eps, vb.pp("norm_q"))?; + let norm_k = RmsNorm::new(inner_dim, norm_eps, vb.pp("norm_k"))?; + + // LTX-2.3: per-head gated attention + let to_gate_logits = if gated { + Some(candle_nn::linear(inner_dim, heads, vb.pp("to_gate_logits"))?) + } else { + None + }; + + Ok(Self { + to_q, + to_k, + to_v, + to_out, + norm_q, + norm_k, + to_gate_logits, + heads, + d_head, + }) + } + + /// Forward pass. + /// + /// `x`: query, `[B, T_q, D]` + /// `context`: key/value (None = self-attention), `[B, T_kv, D_kv]` + /// `pe`: RoPE `(cos, sin)` — applied BEFORE head reshape + /// `k_pe`: separate K RoPE (for cross-modal attention) + /// `mask`: attention mask `[B, T_q, T_kv]` (0=masked, 1=attend) + pub fn forward( + &self, + x: &Tensor, + context: Option<&Tensor>, + pe: Option<&(Tensor, Tensor)>, + k_pe: Option<&(Tensor, Tensor)>, + mask: Option<&Tensor>, + ) -> Result { + let (b, t_q, _) = x.dims3()?; + let kv_input = context.unwrap_or(x); + + // 1. Project Q, K, V — [B, T, inner_dim] + let q = self.to_q.forward(x)?; + let k = self.to_k.forward(kv_input)?; + let v = self.to_v.forward(kv_input)?; + + // 2. QK-norm across full inner dim (before head reshape) + let q = self.norm_q.forward(&q)?; + let k = self.norm_k.forward(&k)?; + + // 3. Apply split RoPE (q/k still flat [B, T, inner_dim]) + // cos/sin: [B, H, T, r] — apply_rotary_emb reshapes x per-head internally + let (q, k) = if let Some((cos, sin)) = pe { + let q = apply_rotary_emb(&q, cos, sin)?; + let k = if let Some((k_cos, k_sin)) = k_pe { + apply_rotary_emb(&k, k_cos, k_sin)? + } else { + apply_rotary_emb(&k, cos, sin)? + }; + (q, k) + } else { + (q, k) + }; + + // 4. Reshape to heads: [B, T, H, D_head] + let q = q.reshape((b, t_q, self.heads, self.d_head))?; + let k = k.reshape((b, (), self.heads, self.d_head))?; + let v = v.reshape((b, (), self.heads, self.d_head))?; + + // 5. Transpose to [B, H, T, D_head] for attention + let q = q.transpose(1, 2)?.contiguous()?; + let k = k.transpose(1, 2)?.contiguous()?; + let v = v.transpose(1, 2)?.contiguous()?; + + // 6. Scaled dot-product attention (compute scores in F32 for numerical stability, + // matching PyTorch's F.scaled_dot_product_attention which uses F32 internally) + let input_dtype = q.dtype(); + let scale = (self.d_head as f64).sqrt(); + let q_f32 = q.to_dtype(DType::F32)?; + let k_f32 = k.to_dtype(DType::F32)?; + let attn = q_f32.matmul(&k_f32.transpose(2, 3)?.contiguous()?)?.affine(1.0 / scale, 0.0)?; + + // Apply mask (additive: masked positions get -inf) + let attn = if let Some(mask) = mask { + // mask: [B, T_q, T_kv] (1=attend, 0=masked) -> [B, 1, T_q, T_kv] + let mask = mask.unsqueeze(1)?.to_dtype(DType::F32)?; + // (1 - mask) * -1e9 gives 0 for attend positions, -1e9 for masked + let additive_mask = mask.affine(-1.0, 1.0)?.affine(-1e9, 0.0)?; + attn.broadcast_add(&additive_mask)? + } else { + attn + }; + + let attn = candle_nn::ops::softmax_last_dim(&attn)?; + let v_f32 = v.to_dtype(DType::F32)?; + let out = attn.matmul(&v_f32)?.to_dtype(input_dtype)?; // [B, H, T_q, D_head] + + // 7. Apply per-head gating (LTX-2.3) + let out = if let Some(ref gate_proj) = self.to_gate_logits { + // Compute gate from query input: [B, T_q, inner_dim] -> [B, T_q, H] + let gate = gate_proj.forward(x)?; + let gate = (candle_nn::ops::sigmoid(&gate)? * 2.0)?; + // gate: [B, T_q, H] -> [B, H, T_q, 1] to broadcast with [B, H, T_q, D_head] + let gate = gate.transpose(1, 2)?.unsqueeze(3)?; + out.broadcast_mul(&gate)? + } else { + out + }; + + // 8. Transpose back and flatten: [B, T_q, H*D_head] + let out = out.transpose(1, 2)?.contiguous()?; + let out = out.flatten_from(2)?; + + // 9. Project out + self.to_out.forward(&out) + } + + /// STG forward: skip Q/K attention, pass V straight through. + /// + /// Computes `to_out(to_v(kv_input))` with gating but no attention. + pub fn forward_skip_attn( + &self, + x: &Tensor, + context: Option<&Tensor>, + ) -> Result { + let kv_input = context.unwrap_or(x); + + // Only V projection — skip Q, K, RoPE, softmax + let v = self.to_v.forward(kv_input)?; + + // Apply per-head gating (LTX-2.3) — gate is computed from query input + let out = if let Some(ref gate_proj) = self.to_gate_logits { + let (b, _t_q, _) = x.dims3()?; + let gate = gate_proj.forward(x)?; + let gate = (candle_nn::ops::sigmoid(&gate)? * 2.0)?; + // Reshape v to [B, H, T, D_head] then apply gate + let v = v.reshape((b, (), self.heads, self.d_head))?; + let v = v.transpose(1, 2)?.contiguous()?; + let gate = gate.transpose(1, 2)?.unsqueeze(3)?; + let out = v.broadcast_mul(&gate)?; + let out = out.transpose(1, 2)?.contiguous()?; + out.flatten_from(2)? + } else { + v + }; + + self.to_out.forward(&out) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device, Tensor}; + + #[test] + fn test_attention_self_attn_shape() { + let device = Device::Cpu; + let dim = 32; + let heads = 2; + let d_head = 16; + + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let attn = Attention::new(dim, None, heads, d_head, 1e-6, false, vb).unwrap(); + + let x = Tensor::randn(0f32, 1f32, (1, 8, dim), &device).unwrap(); + let out = attn.forward(&x, None, None, None, None).unwrap(); + assert_eq!(out.dims(), &[1, 8, dim]); + } + + #[test] + fn test_attention_cross_attn_shape() { + let device = Device::Cpu; + let q_dim = 32; + let kv_dim = 64; + let heads = 2; + let d_head = 16; + + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let attn = Attention::new(q_dim, Some(kv_dim), heads, d_head, 1e-6, false, vb).unwrap(); + + let x = Tensor::randn(0f32, 1f32, (1, 8, q_dim), &device).unwrap(); + let ctx = Tensor::randn(0f32, 1f32, (1, 12, kv_dim), &device).unwrap(); + let out = attn.forward(&x, Some(&ctx), None, None, None).unwrap(); + assert_eq!(out.dims(), &[1, 8, q_dim]); + } + + #[test] + fn test_rms_norm_unit_variance() { + let device = Device::Cpu; + let x = Tensor::new(&[1.0f32, 2.0, 3.0, 4.0], &device) + .unwrap() + .reshape((1, 1, 4)) + .unwrap(); + let normed = rms_norm(&x, 1e-6).unwrap(); + // RMS norm: x / sqrt(mean(x^2)) + // mean(x^2) = (1+4+9+16)/4 = 7.5, sqrt = 2.7386 + let vals: Vec = normed.flatten_all().unwrap().to_vec1().unwrap(); + let rms = (7.5f32).sqrt(); + assert!((vals[0] - 1.0 / rms).abs() < 1e-5); + assert!((vals[3] - 4.0 / rms).abs() < 1e-5); + } +} diff --git a/cake-core/src/models/ltx2/vendored/config.rs b/cake-core/src/models/ltx2/vendored/config.rs new file mode 100644 index 00000000..54e0ba87 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/config.rs @@ -0,0 +1,362 @@ +//! LTX-2 model configuration. + +use serde::{Deserialize, Serialize}; + +/// Which modalities the model processes. +#[derive(Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize)] +pub enum Ltx2ModelType { + AudioVideo, + VideoOnly, + AudioOnly, +} + +impl Ltx2ModelType { + pub fn is_video_enabled(self) -> bool { + matches!(self, Self::AudioVideo | Self::VideoOnly) + } + pub fn is_audio_enabled(self) -> bool { + matches!(self, Self::AudioVideo | Self::AudioOnly) + } +} + +/// Full transformer configuration for LTX-2. +/// +/// Can be loaded from the HF `transformer/config.json` via serde with aliases. +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(default)] +pub struct Ltx2TransformerConfig { + #[serde(default = "default_video_only")] + pub model_type: Ltx2ModelType, + + // Video stream + pub num_attention_heads: usize, + pub attention_head_dim: usize, + pub in_channels: usize, + pub out_channels: usize, + pub cross_attention_dim: usize, + + // Audio stream + #[serde(default = "default_32")] + pub audio_num_attention_heads: usize, + #[serde(default = "default_64")] + pub audio_attention_head_dim: usize, + #[serde(default = "default_128")] + pub audio_in_channels: usize, + #[serde(default = "default_128")] + pub audio_out_channels: usize, + #[serde(default = "default_2048")] + pub audio_cross_attention_dim: usize, + + // Shared + pub num_layers: usize, + pub norm_eps: f64, + pub activation_fn: String, + pub attention_bias: bool, + #[serde(alias = "timestep_scale_multiplier")] + pub timestep_scale_multiplier: f32, + + // RoPE — HF config uses rope_theta, we map it + #[serde(alias = "rope_theta")] + pub positional_embedding_theta: f32, + #[serde(default = "default_max_pos")] + pub positional_embedding_max_pos: Vec, + #[serde(default = "default_audio_max_pos")] + pub audio_positional_embedding_max_pos: Vec, + + // AdaLN + #[serde(default)] + pub cross_attention_adaln: bool, + + // Caption projection + pub caption_channels: usize, + #[serde(default = "default_2048")] + pub audio_caption_channels: usize, + + // LTX-2.3 features + /// Whether attention blocks use learned per-head gating (to_gate_logits). + #[serde(default)] + pub gated_attention: bool, + /// Whether blocks have prompt-specific AdaLN modulation (prompt_scale_shift_table). + #[serde(default)] + pub prompt_modulation: bool, +} + +fn default_video_only() -> Ltx2ModelType { Ltx2ModelType::VideoOnly } +fn default_32() -> usize { 32 } +fn default_64() -> usize { 64 } +fn default_128() -> usize { 128 } +fn default_2048() -> usize { 2048 } +fn default_max_pos() -> Vec { vec![20, 2048, 2048] } +fn default_audio_max_pos() -> Vec { vec![20] } + +impl Default for Ltx2TransformerConfig { + fn default() -> Self { + Self { + model_type: Ltx2ModelType::VideoOnly, + + num_attention_heads: 32, + attention_head_dim: 128, + in_channels: 128, + out_channels: 128, + cross_attention_dim: 4096, + + audio_num_attention_heads: 32, + audio_attention_head_dim: 64, + audio_in_channels: 128, + audio_out_channels: 128, + audio_cross_attention_dim: 2048, + + num_layers: 48, + norm_eps: 1e-6, + activation_fn: "gelu-approximate".to_string(), + attention_bias: true, + timestep_scale_multiplier: 1000.0, + + positional_embedding_theta: 10000.0, + positional_embedding_max_pos: vec![20, 2048, 2048], + audio_positional_embedding_max_pos: vec![20], + + cross_attention_adaln: false, + + // Gemma-3 outputs 3840-dim embeddings (not 4096) + caption_channels: 3840, + audio_caption_channels: 2048, + + gated_attention: false, + prompt_modulation: false, + } + } +} + +impl Ltx2TransformerConfig { + /// Video inner dimension. + pub fn video_inner_dim(&self) -> usize { + self.num_attention_heads * self.attention_head_dim + } + + /// Audio inner dimension. + pub fn audio_inner_dim(&self) -> usize { + self.audio_num_attention_heads * self.audio_attention_head_dim + } + + /// Number of AdaLN parameters per block. + /// 6 base (shift+scale+gate for self-attn and MLP) + 3 if cross_attention_adaln. + pub fn adaln_params(&self) -> usize { + 6 + if self.cross_attention_adaln { 3 } else { 0 } + } + + /// Number of prompt AdaLN parameters per block (LTX-2.3). + /// 2 params: shift + scale (no gate) for prompt modulation. + pub fn prompt_adaln_params(&self) -> usize { + if self.prompt_modulation { 2 } else { 0 } + } +} + +/// LTX-2 scheduler config (separate from the flow-match scheduler used by LTX-Video). +#[derive(Clone, Debug, Serialize, Deserialize)] +pub struct Ltx2SchedulerConfig { + pub base_shift: f32, + pub max_shift: f32, + pub power: f32, + pub stretch_terminal: Option, +} + +impl Default for Ltx2SchedulerConfig { + fn default() -> Self { + Self { + base_shift: 0.95, + max_shift: 2.05, + power: 1.0, + stretch_terminal: Some(0.1), + } + } +} + +/// LTX-2 text connectors config (Gemma → transformer embedding projection). +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(default)] +pub struct Ltx2ConnectorConfig { + pub caption_channels: usize, + pub video_connector_num_layers: usize, + pub video_connector_num_attention_heads: usize, + pub video_connector_attention_head_dim: usize, + pub video_connector_num_learnable_registers: usize, + pub audio_connector_num_layers: usize, + pub audio_connector_num_attention_heads: usize, + pub audio_connector_attention_head_dim: usize, + pub audio_connector_num_learnable_registers: usize, + pub text_proj_in_factor: usize, + pub rope_theta: f32, + pub connector_rope_base_seq_len: usize, + /// Whether connector uses gated attention (LTX-2.3). + pub gated_attention: bool, + /// Whether a separate feature_extractor is used instead of text_proj_in (LTX-2.3). + pub has_feature_extractor: bool, + /// Output dim for the feature extractor (LTX-2.3: 4096 = transformer cross_attention_dim). + /// Only used when has_feature_extractor is true. Defaults to 0 (use video_inner_dim). + pub feature_extractor_out_dim: usize, +} + +impl Default for Ltx2ConnectorConfig { + fn default() -> Self { + Self { + caption_channels: 3840, + video_connector_num_layers: 2, + video_connector_num_attention_heads: 30, + video_connector_attention_head_dim: 128, + video_connector_num_learnable_registers: 128, + audio_connector_num_layers: 2, + audio_connector_num_attention_heads: 30, + audio_connector_attention_head_dim: 128, + audio_connector_num_learnable_registers: 128, + text_proj_in_factor: 49, + rope_theta: 10000.0, + connector_rope_base_seq_len: 4096, + gated_attention: false, + has_feature_extractor: false, + feature_extractor_out_dim: 0, + } + } +} + +impl Ltx2ConnectorConfig { + /// Config for LTX-2.3 (8 connector blocks, 32 heads, gated attention, feature extractor). + pub fn for_ltx23() -> Self { + Self { + video_connector_num_layers: 8, + video_connector_num_attention_heads: 32, + audio_connector_num_layers: 8, + audio_connector_num_attention_heads: 32, + gated_attention: true, + has_feature_extractor: true, + feature_extractor_out_dim: 4096, + ..Default::default() + } + } + + pub fn video_inner_dim(&self) -> usize { + self.video_connector_num_attention_heads * self.video_connector_attention_head_dim + } + + pub fn audio_inner_dim(&self) -> usize { + self.audio_connector_num_attention_heads * self.audio_connector_attention_head_dim + } +} + +/// VAE config shared with LTX-Video. +#[derive(Clone, Debug, Serialize, Deserialize)] +pub struct Ltx2VaeConfig { + pub latent_channels: usize, + pub temporal_compression_ratio: usize, + pub spatial_compression_ratio: usize, + pub scaling_factor: f32, + pub timestep_conditioning: bool, + /// Per-channel mean for latent normalization (128 channels). + pub latents_mean: Vec, + /// Per-channel std for latent normalization (128 channels). + pub latents_std: Vec, +} + +impl Default for Ltx2VaeConfig { + fn default() -> Self { + Self { + latent_channels: 128, + temporal_compression_ratio: 8, + spatial_compression_ratio: 32, + scaling_factor: 1.0, + // LTX-2 VAE does NOT use timestep conditioning (unlike LTX-Video 0.9.x) + timestep_conditioning: false, + // Default: zero mean, unit std (no normalization effect) + // These should be overridden from the model's config.json + latents_mean: vec![0.0; 128], + latents_std: vec![1.0; 128], + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_default_transformer_config() { + let config = Ltx2TransformerConfig::default(); + assert_eq!(config.num_layers, 48); + assert_eq!(config.num_attention_heads, 32); + assert_eq!(config.attention_head_dim, 128); + assert_eq!(config.video_inner_dim(), 4096); + assert_eq!(config.cross_attention_dim, 4096); + assert_eq!(config.caption_channels, 3840); // Gemma-3 output dim + assert_eq!(config.adaln_params(), 6); // no cross_attention_adaln + assert!(config.model_type.is_video_enabled()); + assert!(!config.model_type.is_audio_enabled()); + } + + #[test] + fn test_parse_hf_transformer_config() { + let json = r#"{ + "_class_name": "LTX2VideoTransformer3DModel", + "num_attention_heads": 32, + "attention_head_dim": 128, + "in_channels": 128, + "out_channels": 128, + "cross_attention_dim": 4096, + "num_layers": 48, + "norm_eps": 1e-06, + "activation_fn": "gelu-approximate", + "attention_bias": true, + "caption_channels": 3840, + "rope_theta": 10000.0, + "timestep_scale_multiplier": 1000 + }"#; + let config: Ltx2TransformerConfig = serde_json::from_str(json).unwrap(); + assert_eq!(config.num_layers, 48); + assert_eq!(config.caption_channels, 3840); + assert_eq!(config.positional_embedding_theta, 10000.0); // via alias + assert_eq!(config.timestep_scale_multiplier, 1000.0); + assert_eq!(config.video_inner_dim(), 4096); + } + + #[test] + fn test_default_scheduler_config() { + let config = Ltx2SchedulerConfig::default(); + assert!((config.base_shift - 0.95).abs() < 1e-6); + assert!((config.max_shift - 2.05).abs() < 1e-6); + assert_eq!(config.stretch_terminal, Some(0.1)); + } + + #[test] + fn test_default_vae_config() { + let config = Ltx2VaeConfig::default(); + assert_eq!(config.latent_channels, 128); + assert_eq!(config.temporal_compression_ratio, 8); + assert_eq!(config.spatial_compression_ratio, 32); + assert!(!config.timestep_conditioning); // LTX-2 VAE: no timestep conditioning + assert_eq!(config.latents_mean.len(), 128); + assert_eq!(config.latents_std.len(), 128); + } + + #[test] + fn test_default_connector_config() { + let config = Ltx2ConnectorConfig::default(); + assert_eq!(config.caption_channels, 3840); + assert_eq!(config.video_connector_num_layers, 2); + assert_eq!(config.video_connector_num_learnable_registers, 128); + assert_eq!(config.video_inner_dim(), 3840); // 30 * 128 + } + + #[test] + fn test_audio_video_model_type() { + let av = Ltx2ModelType::AudioVideo; + assert!(av.is_video_enabled()); + assert!(av.is_audio_enabled()); + + let vo = Ltx2ModelType::VideoOnly; + assert!(vo.is_video_enabled()); + assert!(!vo.is_audio_enabled()); + + let ao = Ltx2ModelType::AudioOnly; + assert!(!ao.is_video_enabled()); + assert!(ao.is_audio_enabled()); + } +} diff --git a/cake-core/src/models/ltx2/vendored/connector.rs b/cake-core/src/models/ltx2/vendored/connector.rs new file mode 100644 index 00000000..e40da669 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/connector.rs @@ -0,0 +1,474 @@ +//! LTX-2 Text Connectors — self-attention transformer with learnable registers. +//! +//! Matches HF diffusers `LTX2TextConnectors` + `LTX2ConnectorTransformer1d`. +//! +//! Architecture: +//! 1. Project packed Gemma tokens (3840 * 49 = 188160 → 3840) via linear (no bias) +//! 2. Replace padding tokens with learnable registers +//! 3. Apply 1D RoPE self-attention transformer (2 layers) +//! 4. norm_out (RMSNorm, no learnable weights) +//! +//! Key difference from perceiver: registers replace padding tokens in the SAME +//! sequence (not separate queries). The transformer does pure self-attention. + +use candle_core::{DType, Result, Tensor}; +use candle_nn::{Linear, Module, VarBuilder}; + +use super::attention::{rms_norm, Attention}; +use super::config::Ltx2ConnectorConfig; +use super::feed_forward::FeedForward; +use super::rope::precompute_freqs_cis; + +/// A single 1D transformer block (self-attention + FFN, no cross-attention). +/// +/// Matches `LTX2TransformerBlock1d`. +#[derive(Debug)] +struct ConnectorBlock { + attn1: Attention, + ff: FeedForward, + norm_eps: f64, +} + +impl ConnectorBlock { + fn new( + dim: usize, + heads: usize, + d_head: usize, + norm_eps: f64, + gated: bool, + vb: VarBuilder, + ) -> Result { + let attn1 = Attention::new(dim, None, heads, d_head, norm_eps, gated, vb.pp("attn1"))?; + let ff = FeedForward::new(dim, dim, 4, vb.pp("ff"))?; + Ok(Self { + attn1, + ff, + norm_eps, + }) + } + + fn forward( + &self, + hidden_states: &Tensor, + mask: Option<&Tensor>, + pe: Option<&(Tensor, Tensor)>, + ) -> Result { + // Self-attention + let norm_h = rms_norm(hidden_states, self.norm_eps)?; + let attn_out = self.attn1.forward(&norm_h, None, pe, None, mask)?; + let h = hidden_states.broadcast_add(&attn_out)?; + + // FFN + let norm_h = rms_norm(&h, self.norm_eps)?; + let ff_out = self.ff.forward(&norm_h)?; + h.broadcast_add(&ff_out) + } +} + +/// 1D connector transformer (matches `LTX2ConnectorTransformer1d`). +/// +/// Self-attention transformer with learnable registers that replace padding tokens. +#[derive(Debug)] +struct ConnectorTransformer1d { + learnable_registers: Tensor, // [num_registers, inner_dim] + num_registers: usize, + blocks: Vec, + norm_eps: f64, + // 1D RoPE parameters + inner_dim: usize, + num_heads: usize, + rope_theta: f32, + base_seq_len: usize, +} + +impl ConnectorTransformer1d { + fn new( + num_layers: usize, + num_registers: usize, + heads: usize, + d_head: usize, + norm_eps: f64, + rope_theta: f32, + base_seq_len: usize, + gated: bool, + vb: VarBuilder, + ) -> Result { + let inner_dim = heads * d_head; + + let learnable_registers = vb.get((num_registers, inner_dim), "learnable_registers")?; + + let mut blocks = Vec::with_capacity(num_layers); + for i in 0..num_layers { + blocks.push(ConnectorBlock::new( + inner_dim, + heads, + d_head, + norm_eps, + gated, + vb.pp(format!("transformer_blocks.{i}")), + )?); + } + + Ok(Self { + learnable_registers, + num_registers, + blocks, + norm_eps, + inner_dim, + num_heads: heads, + rope_theta, + base_seq_len, + }) + } + + fn forward( + &self, + hidden_states: &Tensor, + attention_mask: Option<&Tensor>, + ) -> Result<(Tensor, Option)> { + let (batch_size, seq_len, _) = hidden_states.dims3()?; + + // Replace padding with learned registers + let (mut h, new_mask) = self.replace_padding_with_registers( + hidden_states, + attention_mask, + seq_len, + batch_size, + )?; + + // 1D RoPE: build position grid [B, 1, seq_len] with arange(seq_len) + let positions_1d: Vec = (0..seq_len).map(|i| i as f32).collect(); + let pos_t = Tensor::new(positions_1d, h.device())?; + let pos_grid = pos_t + .unsqueeze(0)? // [1, seq_len] + .unsqueeze(0)? // [1, 1, seq_len] + .broadcast_as((batch_size, 1, seq_len))? + .contiguous()?; + let pe = precompute_freqs_cis( + &pos_grid, + self.inner_dim, + self.rope_theta, + &[self.base_seq_len], + self.num_heads, + h.dtype(), + )?; + + // Run transformer blocks + for block in &self.blocks { + h = block.forward(&h, None, Some(&pe))?; + } + + // norm_out (no learnable weights) + let h = rms_norm(&h, self.norm_eps)?; + + Ok((h, new_mask)) + } + + /// Replace padding tokens with learned registers. + /// + /// For each batch element: + /// 1. Extract non-padding tokens (where mask >= threshold) + /// 2. Pad to seq_len with zeros + /// 3. Tile registers to fill sequence + /// 4. Use flipped mask to blend: mask * padded_text + (1-mask) * registers + fn replace_padding_with_registers( + &self, + hidden_states: &Tensor, + attention_mask: Option<&Tensor>, + seq_len: usize, + batch_size: usize, + ) -> Result<(Tensor, Option)> { + let mask = match attention_mask { + Some(m) => m, + None => return Ok((hidden_states.clone(), None)), + }; + + // Binarize mask: >= -9000 means valid token + let threshold = -9000.0f32; + let binary_mask = mask.ge(threshold)?.to_dtype(DType::F32)?; + // binary_mask: [B, L] or [B, 1, 1, L] + let binary_mask = if binary_mask.rank() == 4 { + binary_mask.squeeze(1)?.squeeze(1)? + } else { + binary_mask + }; + + // Tile registers to fill sequence + if seq_len % self.num_registers != 0 { + candle_core::bail!( + "seq_len ({}) must be divisible by num_learnable_registers ({})", + seq_len, + self.num_registers + ); + } + let num_repeats = seq_len / self.num_registers; + let inner_dim = self.learnable_registers.dim(1)?; + + // [num_registers, dim] -> tile -> [seq_len, dim] + let registers = if num_repeats > 1 { + let mut parts = Vec::with_capacity(num_repeats); + for _ in 0..num_repeats { + parts.push(self.learnable_registers.clone()); + } + Tensor::cat(&parts, 0)? + } else { + self.learnable_registers.clone() + }; + let registers = registers + .to_dtype(hidden_states.dtype())?; + + // For each batch: extract non-padded tokens, re-pack, blend with registers + let mut batch_results = Vec::with_capacity(batch_size); + for i in 0..batch_size { + let h_i = hidden_states.get(i)?; // [L, D] + let m_i = binary_mask.get(i)?; // [L] + + // Count valid tokens + let m_vals: Vec = m_i.to_vec1()?; + let valid_count: usize = m_vals.iter().filter(|&&v| v > 0.5).count(); + + // Extract valid tokens + let mut valid_indices = Vec::with_capacity(valid_count); + for (j, &v) in m_vals.iter().enumerate() { + if v > 0.5 { + valid_indices.push(j as u32); + } + } + + let padded = if valid_count > 0 && valid_count < seq_len { + let idx = Tensor::from_vec(valid_indices, (valid_count,), h_i.device())?; + let valid_tokens = h_i.index_select(&idx, 0)?; // [valid_count, D] + // Pad with zeros to seq_len + let pad = Tensor::zeros((seq_len - valid_count, inner_dim), hidden_states.dtype(), h_i.device())?; + Tensor::cat(&[valid_tokens, pad], 0)? + } else { + h_i.clone() + }; + + // Flip mask and use as blend factor + // flipped_mask[j] = mask[L-1-j] + let flip_indices: Vec = (0..seq_len).rev().map(|j| j as u32).collect(); + let flip_idx = Tensor::from_vec(flip_indices, (seq_len,), m_i.device())?; + let flipped_mask = binary_mask.get(i)?.index_select(&flip_idx, 0)?; // [L] + let flipped_mask = flipped_mask.unsqueeze(1)?; // [L, 1] + + // blend: flipped_mask * padded + (1 - flipped_mask) * registers + let one_minus = flipped_mask.affine(-1.0, 1.0)?; + let blended = padded + .to_dtype(hidden_states.dtype())? + .broadcast_mul(&flipped_mask.to_dtype(hidden_states.dtype())?)? + .broadcast_add( + ®isters.broadcast_mul(&one_minus.to_dtype(hidden_states.dtype())?)? + )?; + + batch_results.push(blended.unsqueeze(0)?); + } + + let result = Tensor::cat(&batch_results, 0)?; + + // With registers, attention mask becomes all-zeros (all tokens attend) + let new_mask = Tensor::zeros_like(mask)?; + + Ok((result, Some(new_mask))) + } +} + +/// Full LTX-2 text connectors module. +/// +/// Matches HF `LTX2TextConnectors`: +/// - text_proj_in: Linear(caption_channels * text_proj_in_factor, caption_channels, bias=False) +/// - video_connector: ConnectorTransformer1d +/// - audio_connector: ConnectorTransformer1d +#[derive(Debug)] +pub struct Ltx2TextConnectors { + /// Input projection (LTX-2: text_proj_in, LTX-2.3: feature_extractor.video_aggregate_embed) + text_proj_in: Linear, + video_connector: ConnectorTransformer1d, + #[allow(dead_code)] + audio_connector: Option, +} + +impl Ltx2TextConnectors { + pub fn new(config: &Ltx2ConnectorConfig, has_audio: bool, vb: VarBuilder) -> Result { + let text_dim = config.caption_channels; // 3840 + let proj_in_dim = text_dim * config.text_proj_in_factor; // 3840 * 49 = 188160 + let gated = config.gated_attention; + + // Input projection: packed Gemma tokens → output dim + // LTX-2: text_proj_in (3840*49 → 3840, no bias) + // LTX-2.3: feature_extractor.video_aggregate_embed (3840*49 → 4096, with bias) + let text_proj_in = if config.has_feature_extractor { + let out_dim = if config.feature_extractor_out_dim > 0 { + config.feature_extractor_out_dim // LTX-2.3: 4096 + } else { + config.video_inner_dim() // fallback: 3840 + }; + candle_nn::linear(proj_in_dim, out_dim, vb.pp("feature_extractor.video_aggregate_embed"))? + } else { + candle_nn::linear_no_bias(proj_in_dim, text_dim, vb.pp("text_proj_in"))? + }; + + let video_connector = ConnectorTransformer1d::new( + config.video_connector_num_layers, + config.video_connector_num_learnable_registers, + config.video_connector_num_attention_heads, + config.video_connector_attention_head_dim, + 1e-6, + config.rope_theta, + config.connector_rope_base_seq_len, + gated, + vb.pp("video_connector"), + )?; + + let audio_connector = if has_audio { + Some(ConnectorTransformer1d::new( + config.audio_connector_num_layers, + config.audio_connector_num_learnable_registers, + config.audio_connector_num_attention_heads, + config.audio_connector_attention_head_dim, + 1e-6, + config.rope_theta, + config.connector_rope_base_seq_len, + gated, + vb.pp("audio_connector"), + )?) + } else { + None + }; + + Ok(Self { + text_proj_in, + video_connector, + audio_connector, + }) + } + + /// Process packed Gemma embeddings into video context tokens. + /// + /// `text_embeds`: `[B, L, caption_channels * text_proj_in_factor]` — packed Gemma output + /// `attention_mask`: `[B, L]` — binary mask (1=valid, 0=padding) + /// + /// Returns `(video_embeddings, attention_mask)`: + /// - `video_embeddings`: `[B, L, caption_channels]` + /// - `attention_mask`: `[B, L]` + pub fn forward_video( + &self, + text_embeds: &Tensor, + attention_mask: Option<&Tensor>, + ) -> Result<(Tensor, Option)> { + // Convert binary mask to additive format: (mask - 1) * finfo.max + let additive_mask = attention_mask.map(|m| { + let text_dtype = text_embeds.dtype(); + // (mask - 1) gives -1 for padding, 0 for valid + let shifted = m.affine(1.0, -1.0); // 0 → -1, 1 → 0 + let max_val = match text_dtype { + DType::F32 => f32::MAX as f64, + DType::F16 => 65504.0, + DType::BF16 => 3.39e38, + _ => f32::MAX as f64, + }; + shifted.and_then(|s| { + let shaped = s.reshape((s.dim(0)?, 1, 1, s.dim(1)?))?; + shaped.affine(max_val, 0.0) + }) + }).transpose()?; + + // Project text embeddings + let projected = self.text_proj_in.forward(text_embeds)?; + + // Run video connector + let (video_emb, new_mask) = self.video_connector.forward(&projected, additive_mask.as_ref())?; + + // Apply output mask: zero out padded positions + let (video_emb, out_mask) = if let Some(ref nm) = new_mask { + // (new_mask < 1e-6) gives 1 for ~zero positions (valid after register replacement) + let attn_mask = nm.lt(1e-6f32)?.to_dtype(DType::F32)?; + let attn_mask = if attn_mask.rank() == 4 { + attn_mask.squeeze(1)?.squeeze(1)? + } else { + attn_mask + }; + let mask_3d = attn_mask.unsqueeze(2)?; // [B, L, 1] + let masked_emb = video_emb.broadcast_mul(&mask_3d.to_dtype(video_emb.dtype())?)?; + (masked_emb, Some(attn_mask)) + } else { + (video_emb, None) + }; + + Ok((video_emb, out_mask)) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device, Tensor}; + + #[test] + fn test_connector_transformer_1d_shapes() { + let device = Device::Cpu; + let b = 2; + let seq_len = 128; + let heads = 4; + let d_head = 16; + let inner_dim = heads * d_head; + let num_registers = 64; + + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let ct = ConnectorTransformer1d::new( + 2, // num_layers + num_registers, + heads, + d_head, + 1e-6, + 10000.0, // rope_theta + 4096, // base_seq_len + false, // gated + vb, + ) + .unwrap(); + + let hidden = Tensor::randn(0f32, 1f32, (b, seq_len, inner_dim), &device).unwrap(); + // All-zeros additive mask = no masking + let mask = Tensor::zeros((b, 1, 1, seq_len), DType::F32, &device).unwrap(); + let (out, new_mask) = ct.forward(&hidden, Some(&mask)).unwrap(); + + assert_eq!(out.dims(), &[b, seq_len, inner_dim]); + assert!(new_mask.is_some()); + } + + #[test] + fn test_connector_transformer_1d_no_mask() { + let device = Device::Cpu; + let b = 1; + let seq_len = 64; + let heads = 2; + let d_head = 8; + let inner_dim = heads * d_head; + + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let ct = ConnectorTransformer1d::new(1, 32, heads, d_head, 1e-6, 10000.0, 4096, false, vb) + .unwrap(); + + let hidden = Tensor::randn(0f32, 1f32, (b, seq_len, inner_dim), &device).unwrap(); + let (out, new_mask) = ct.forward(&hidden, None).unwrap(); + + assert_eq!(out.dims(), &[b, seq_len, inner_dim]); + assert!(new_mask.is_none()); + } + + #[test] + fn test_connector_block_shapes() { + let device = Device::Cpu; + let dim = 32; + let heads = 2; + let d_head = 16; + + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let block = ConnectorBlock::new(dim, heads, d_head, 1e-6, false, vb).unwrap(); + + let x = Tensor::randn(0f32, 1f32, (1, 8, dim), &device).unwrap(); + let out = block.forward(&x, None, None).unwrap(); + assert_eq!(out.dims(), x.dims()); + } +} diff --git a/cake-core/src/models/ltx2/vendored/feed_forward.rs b/cake-core/src/models/ltx2/vendored/feed_forward.rs new file mode 100644 index 00000000..d5f5f061 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/feed_forward.rs @@ -0,0 +1,85 @@ +//! FeedForward (GEGLU-style) for LTX-2 transformer blocks. + +use candle_core::{Result, Tensor}; +use candle_nn::{Linear, Module, VarBuilder}; + +/// Approximate GELU: x * 0.5 * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3))) +fn gelu_approx(x: &Tensor) -> Result { + x.gelu() +} + +/// GELU + Linear projection (GELUApprox in Python). +#[derive(Debug)] +struct GeluProjection { + linear: Linear, +} + +impl GeluProjection { + fn new(dim_in: usize, dim_out: usize, vb: VarBuilder) -> Result { + let linear = candle_nn::linear(dim_in, dim_out, vb.pp("proj"))?; + Ok(Self { linear }) + } +} + +impl Module for GeluProjection { + fn forward(&self, xs: &Tensor) -> Result { + let x = self.linear.forward(xs)?; + gelu_approx(&x) + } +} + +/// FeedForward: GELUApprox(dim -> inner_dim) -> Linear(inner_dim -> dim_out) +#[derive(Debug)] +pub struct FeedForward { + gelu_proj: GeluProjection, + out_proj: Linear, +} + +impl FeedForward { + pub fn new(dim: usize, dim_out: usize, mult: usize, vb: VarBuilder) -> Result { + let inner_dim = dim * mult; + let gelu_proj = GeluProjection::new(dim, inner_dim, vb.pp("net.0"))?; + let out_proj = candle_nn::linear(inner_dim, dim_out, vb.pp("net.2"))?; + Ok(Self { + gelu_proj, + out_proj, + }) + } +} + +impl Module for FeedForward { + fn forward(&self, xs: &Tensor) -> Result { + let x = self.gelu_proj.forward(xs)?; + self.out_proj.forward(&x) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device, Tensor}; + + #[test] + fn test_feed_forward_shape() { + let device = Device::Cpu; + let dim = 16; + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let ff = FeedForward::new(dim, dim, 4, vb).unwrap(); + + let x = Tensor::randn(0f32, 1f32, (1, 8, dim), &device).unwrap(); + let out = ff.forward(&x).unwrap(); + assert_eq!(out.dims(), &[1, 8, dim]); + } + + #[test] + fn test_feed_forward_different_out_dim() { + let device = Device::Cpu; + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let ff = FeedForward::new(16, 32, 4, vb).unwrap(); + + let x = Tensor::randn(0f32, 1f32, (2, 4, 16), &device).unwrap(); + let out = ff.forward(&x).unwrap(); + assert_eq!(out.dims(), &[2, 4, 32]); + } +} + diff --git a/cake-core/src/models/ltx2/vendored/mod.rs b/cake-core/src/models/ltx2/vendored/mod.rs new file mode 100644 index 00000000..c7f4b8c2 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/mod.rs @@ -0,0 +1,17 @@ +//! Vendored LTX-2 model code ported from Python (Apache 2.0). +//! +//! Source: +//! +//! This module contains the dual-stream DiT transformer and supporting +//! components for video-only inference. Audio stream support is deferred. + +pub mod config; +pub mod rope; +pub mod attention; +pub mod feed_forward; +pub mod adaln; +pub mod transformer_block; +pub mod model; +pub mod connector; +pub mod scheduler; +pub mod pipeline; diff --git a/cake-core/src/models/ltx2/vendored/model.rs b/cake-core/src/models/ltx2/vendored/model.rs new file mode 100644 index 00000000..2606fa40 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/model.rs @@ -0,0 +1,471 @@ +//! LTXModel — the full LTX-2 transformer. +//! +//! Wraps N `BasicAVTransformerBlock` layers with input/output projections, +//! AdaLN timestep embedding, caption projection, and RoPE. +//! +//! Supports block-range sharding: load only blocks N..M for distributed inference. + +use candle_core::{Result, Tensor}; +use candle_nn::{Linear, Module, VarBuilder}; + +use super::adaln::{AdaLayerNormSingle, TextProjection}; +use super::attention::layer_norm_no_affine; +use super::config::Ltx2TransformerConfig; +use super::rope::precompute_freqs_cis; +use super::transformer_block::BasicAVTransformerBlock; + +/// Velocity-to-denoised conversion: denoised = sample - sigma * velocity. +pub fn to_denoised(sample: &Tensor, sigma: &Tensor, velocity: &Tensor) -> Result { + // sigma needs to broadcast to sample shape + let sigma = sigma.unsqueeze(1)?.unsqueeze(2)?; // [B, 1, 1] + sample.broadcast_sub(&sigma.broadcast_mul(velocity)?) +} + +/// Full LTX-2 transformer model (video-only path). +/// +/// Supports partial block loading via `new_block_range()` for distributed inference. +/// When loaded with a block range, only those blocks are in memory. The setup +/// (proj_in, adaln, caption_projection) and finalize (scale_shift_table, proj_out) +/// are only loaded when block_start == 0 or block_end == num_layers respectively. +#[derive(Debug)] +pub struct LTXModel { + config: Ltx2TransformerConfig, + + // Video components (None when not needed for this block range) + proj_in: Option, + adaln_single: Option, + caption_projection: Option, + scale_shift_table: Option, // [2, video_inner_dim] — final output modulation + + // LTX-2.3: prompt-specific timestep embedding + prompt_adaln_single: Option, + + // Transformer blocks (may be a subset) + blocks: Vec, + /// First block index (0 for full model or first shard) + block_start: usize, + + // Output + proj_out: Option, +} + +impl LTXModel { + /// Load the full model (all blocks + setup + finalize). + pub fn new(config: Ltx2TransformerConfig, vb: VarBuilder) -> Result { + Self::new_block_range(config, vb, 0, None) + } + + /// Load a range of blocks [block_start, block_end). + /// + /// - Setup (proj_in, adaln, caption_projection) is loaded only when block_start == 0. + /// - Finalize (scale_shift_table, proj_out) is loaded only when block_end == num_layers. + /// - For workers that only run a middle range, neither setup nor finalize is loaded. + pub fn new_block_range( + config: Ltx2TransformerConfig, + vb: VarBuilder, + block_start: usize, + block_end: Option, + ) -> Result { + let has_video = config.model_type.is_video_enabled(); + let video_dim = config.video_inner_dim(); + let adaln_params = config.adaln_params(); + let num_layers = config.num_layers; + let block_end = block_end.unwrap_or(num_layers); + + let is_first = block_start == 0; + let is_last = block_end >= num_layers; + + log::info!( + "Loading LTX-2 transformer blocks {}-{} of {} (setup={}, finalize={})", + block_start, block_end - 1, num_layers, is_first, is_last + ); + + // Setup: only load for the first shard + let (proj_in, adaln_single, caption_projection) = if has_video && is_first { + let proj_in = candle_nn::linear(config.in_channels, video_dim, vb.pp("proj_in"))?; + let adaln = AdaLayerNormSingle::new(video_dim, adaln_params, vb.pp("time_embed"))?; + // LTX-2.3: no caption_projection (feature extractor in connector handles this) + let caption = if config.prompt_modulation { + None + } else { + Some(TextProjection::new( + config.caption_channels, + video_dim, + vb.pp("caption_projection"), + )?) + }; + (Some(proj_in), Some(adaln), caption) + } else { + (None, None, None) + }; + + // LTX-2.3: prompt timestep embedding (loaded with setup) + let prompt_adaln_single = if has_video && is_first && config.prompt_modulation { + let prompt_adaln_params = config.prompt_adaln_params(); + Some(AdaLayerNormSingle::new(video_dim, prompt_adaln_params, vb.pp("prompt_time_embed"))?) + } else { + None + }; + + // Finalize: only load for the last shard + let (sst, proj_out) = if has_video && is_last { + let sst = vb.get((2, video_dim), "scale_shift_table")?; + let proj_out = candle_nn::linear(video_dim, config.out_channels, vb.pp("proj_out"))?; + (Some(sst), Some(proj_out)) + } else { + (None, None) + }; + + // Load only the blocks in range + let mut blocks = Vec::with_capacity(block_end - block_start); + for i in block_start..block_end { + let block = BasicAVTransformerBlock::new( + i, + &config, + vb.pp(format!("transformer_blocks.{i}")), + )?; + blocks.push(block); + } + + log::info!("Loaded {} transformer blocks ({}-{})", blocks.len(), block_start, block_end - 1); + + Ok(Self { + config, + proj_in, + adaln_single, + caption_projection, + scale_shift_table: sst, + prompt_adaln_single, + blocks, + block_start, + proj_out, + }) + } + + pub fn config(&self) -> &Ltx2TransformerConfig { + &self.config + } + + /// Whether this model shard includes the setup components (proj_in, adaln, caption). + pub fn has_setup(&self) -> bool { + self.proj_in.is_some() + } + + /// Whether this model shard includes the finalize components (scale_shift_table, proj_out). + pub fn has_finalize(&self) -> bool { + self.proj_out.is_some() + } + + /// Run setup: proj_in + adaln + caption_projection + RoPE. + /// + /// Returns (hidden, temb, embedded_ts, pe, context_projected, prompt_temb). + /// `prompt_temb` is Some only for LTX-2.3 (prompt modulation). + pub fn forward_setup( + &self, + video_latent: &Tensor, + timesteps: &Tensor, + positions: &Tensor, + context: &Tensor, + ) -> Result<(Tensor, Tensor, Tensor, (Tensor, Tensor), Tensor, Option)> { + let proj_in = self.proj_in.as_ref().expect("forward_setup requires proj_in"); + let adaln = self.adaln_single.as_ref().expect("forward_setup requires adaln"); + + let video_dim = self.config.video_inner_dim(); + let adaln_params = self.config.adaln_params(); + + // 1. Project input + let hidden = proj_in.forward(video_latent)?; + + // 2. Timestep embedding → AdaLN params + let scaled_ts = timesteps.affine(self.config.timestep_scale_multiplier as f64, 0.0)?; + let (temb, embedded_ts) = adaln.forward(&scaled_ts)?; + + let (b, _) = temb.dims2()?; + let temb = temb.reshape((b, 1, adaln_params, video_dim))?; + let embedded_ts = embedded_ts.reshape((b, 1, video_dim))?; + + // 2b. LTX-2.3: prompt timestep embedding for prompt modulation + let prompt_temb = if let Some(ref prompt_adaln) = self.prompt_adaln_single { + let prompt_adaln_params = self.config.prompt_adaln_params(); + let (pt, _pt_embedded) = prompt_adaln.forward(&scaled_ts)?; + Some(pt.reshape((b, 1, prompt_adaln_params, video_dim))?) + } else { + None + }; + + // 3. Caption projection (LTX-2 only; LTX-2.3 does this in the connector) + let context = if let Some(ref caption_proj) = self.caption_projection { + caption_proj.forward(context)? + } else { + context.clone() + }; + + // 4. Compute RoPE + let pe = precompute_freqs_cis( + positions, + self.config.num_attention_heads * self.config.attention_head_dim, + self.config.positional_embedding_theta, + &self.config.positional_embedding_max_pos, + self.config.num_attention_heads, + hidden.dtype(), + )?; + + Ok((hidden, temb, embedded_ts, pe, context, prompt_temb)) + } + + /// Run transformer blocks on pre-setup hidden states. + /// + /// `hidden`: [B, T, video_dim] — output of proj_in or previous block range + /// `temb`: [B, 1, adaln_params, video_dim] + /// `pe`: (cos, sin) RoPE + /// `context`: [B, L, video_dim] — already through caption projection + /// `context_mask`: [B, L] + /// `prompt_temb`: [B, 1, 3, video_dim] — prompt modulation (LTX-2.3, None for LTX-2) + pub fn forward_blocks( + &self, + hidden: &Tensor, + temb: &Tensor, + pe: &(Tensor, Tensor), + context: &Tensor, + context_mask: Option<&Tensor>, + prompt_temb: Option<&Tensor>, + ) -> Result { + self.forward_blocks_with_stg(hidden, temb, pe, context, context_mask, prompt_temb, &[]) + } + + /// Run transformer blocks with optional STG perturbation. + /// + /// `stg_skip_blocks`: global block indices where self-attention should be skipped. + pub fn forward_blocks_with_stg( + &self, + hidden: &Tensor, + temb: &Tensor, + pe: &(Tensor, Tensor), + context: &Tensor, + context_mask: Option<&Tensor>, + prompt_temb: Option<&Tensor>, + stg_skip_blocks: &[usize], + ) -> Result { + let mut x = hidden.clone(); + for (i, block) in self.blocks.iter().enumerate() { + let global_idx = self.block_start + i; + let skip = stg_skip_blocks.contains(&global_idx); + x = block.forward_video_only(&x, temb, Some(pe), context, context_mask, prompt_temb, skip)?; + } + Ok(x) + } + + /// Run finalize: final AdaLN modulation + proj_out. + pub fn forward_finalize( + &self, + x: &Tensor, + embedded_ts: &Tensor, + ) -> Result { + let sst = self.scale_shift_table.as_ref().expect("forward_finalize requires scale_shift_table"); + let proj_out = self.proj_out.as_ref().expect("forward_finalize requires proj_out"); + + let sst_4d = sst.unsqueeze(0)?.unsqueeze(0)?; + let et_4d = embedded_ts.unsqueeze(2)?; + let scale_shift = sst_4d + .to_dtype(et_4d.dtype())? + .broadcast_add(&et_4d)?; + let shift = scale_shift.narrow(2, 0, 1)?.squeeze(2)?; + let scale = scale_shift.narrow(2, 1, 1)?.squeeze(2)?; + + // Python uses nn.LayerNorm (mean-subtraction + variance norm), NOT RMSNorm + let x = layer_norm_no_affine(x, self.config.norm_eps)?; + let x = x + .broadcast_mul(&scale.broadcast_add(&Tensor::ones_like(&scale)?)?)? + .broadcast_add(&shift)?; + + proj_out.forward(&x) + } + + /// Full forward pass (video-only mode). Convenience method that calls + /// forward_setup + forward_blocks + forward_finalize. + pub fn forward_video( + &self, + video_latent: &Tensor, + _sigma: &Tensor, + timesteps: &Tensor, + positions: &Tensor, + context: &Tensor, + context_mask: Option<&Tensor>, + ) -> Result { + let t0 = std::time::Instant::now(); + + log::info!( + "Transformer input shapes: video_latent={:?} timesteps={:?} positions={:?} context={:?} dtype={:?} device={:?}", + video_latent.shape(), timesteps.shape(), positions.shape(), context.shape(), + video_latent.dtype(), video_latent.device(), + ); + + let (hidden, temb, embedded_ts, pe, context, prompt_temb) = + self.forward_setup(video_latent, timesteps, positions, context)?; + + log::info!("Transformer setup: {}ms", t0.elapsed().as_millis()); + + let x = self.forward_blocks(&hidden, &temb, &pe, &context, context_mask, prompt_temb.as_ref())?; + let x = self.forward_finalize(&x, &embedded_ts)?; + + log::info!("Transformer forward total: {}ms ({} blocks)", t0.elapsed().as_millis(), self.blocks.len()); + + Ok(x) + } + + /// Forward pass for block-range workers. + /// + /// Input: pre-setup hidden states + metadata (no raw latents). + /// Output: hidden states after running this shard's blocks. + /// If this shard includes finalize, output is the final velocity prediction. + pub fn forward_blocks_only( + &self, + hidden: &Tensor, + temb: &Tensor, + pe: &(Tensor, Tensor), + context: &Tensor, + context_mask: Option<&Tensor>, + embedded_ts: Option<&Tensor>, + prompt_temb: Option<&Tensor>, + ) -> Result { + self.forward_blocks_only_with_stg( + hidden, temb, pe, context, context_mask, embedded_ts, prompt_temb, &[], + ) + } + + /// Forward pass for block-range workers with optional STG perturbation. + pub fn forward_blocks_only_with_stg( + &self, + hidden: &Tensor, + temb: &Tensor, + pe: &(Tensor, Tensor), + context: &Tensor, + context_mask: Option<&Tensor>, + embedded_ts: Option<&Tensor>, + prompt_temb: Option<&Tensor>, + stg_skip_blocks: &[usize], + ) -> Result { + let x = self.forward_blocks_with_stg( + hidden, temb, pe, context, context_mask, prompt_temb, stg_skip_blocks, + )?; + + if self.has_finalize() { + let ets = embedded_ts.expect("forward_blocks_only with finalize needs embedded_ts"); + self.forward_finalize(&x, ets) + } else { + Ok(x) + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device, Tensor}; + + fn small_config() -> Ltx2TransformerConfig { + Ltx2TransformerConfig { + num_attention_heads: 2, + attention_head_dim: 8, + in_channels: 16, + out_channels: 16, + cross_attention_dim: 16, + num_layers: 4, + caption_channels: 32, + ..Default::default() + } + } + + #[test] + fn test_ltx_model_video_forward_shape() { + let device = Device::Cpu; + let config = small_config(); + let vb = candle_nn::VarBuilder::zeros(DType::F32, &device); + let model = LTXModel::new(config.clone(), vb).unwrap(); + + let b = 1; + let seq = 8; + + let video_latent = + Tensor::randn(0f32, 1f32, (b, seq, config.in_channels), &device).unwrap(); + let sigma = Tensor::full(0.5f32, (b,), &device).unwrap(); + let timestep = Tensor::full(0.5f32, (b,), &device).unwrap(); + let positions = Tensor::randn(0f32, 1f32, (b, 3, seq), &device).unwrap(); + let context = + Tensor::randn(0f32, 1f32, (b, 4, config.caption_channels), &device).unwrap(); + + let out = model + .forward_video(&video_latent, &sigma, ×tep, &positions, &context, None) + .unwrap(); + assert_eq!(out.dims(), &[b, seq, config.out_channels]); + } + + #[test] + fn test_block_range_split() { + let device = Device::Cpu; + let config = small_config(); + + // Full model + let vb_full = candle_nn::VarBuilder::zeros(DType::F32, &device); + let full_model = LTXModel::new(config.clone(), vb_full).unwrap(); + + // Split: first half (blocks 0-1) with setup + let vb1 = candle_nn::VarBuilder::zeros(DType::F32, &device); + let first_half = LTXModel::new_block_range(config.clone(), vb1, 0, Some(2)).unwrap(); + assert!(first_half.has_setup()); + assert!(!first_half.has_finalize()); + assert_eq!(first_half.blocks.len(), 2); + + // Split: second half (blocks 2-3) with finalize + let vb2 = candle_nn::VarBuilder::zeros(DType::F32, &device); + let second_half = LTXModel::new_block_range(config.clone(), vb2, 2, Some(4)).unwrap(); + assert!(!second_half.has_setup()); + assert!(second_half.has_finalize()); + assert_eq!(second_half.blocks.len(), 2); + + // Run full model + let b = 1; + let seq = 8; + let video_latent = + Tensor::randn(0f32, 1f32, (b, seq, config.in_channels), &device).unwrap(); + let sigma = Tensor::full(0.5f32, (b,), &device).unwrap(); + let timestep = Tensor::full(0.5f32, (b,), &device).unwrap(); + let positions = Tensor::randn(0f32, 1f32, (b, 3, seq), &device).unwrap(); + let context = + Tensor::randn(0f32, 1f32, (b, 4, config.caption_channels), &device).unwrap(); + + let full_out = full_model + .forward_video(&video_latent, &sigma, ×tep, &positions, &context, None) + .unwrap(); + + // Run split pipeline + let (hidden, temb, embedded_ts, pe, ctx, prompt_temb) = + first_half.forward_setup(&video_latent, ×tep, &positions, &context).unwrap(); + let x = first_half.forward_blocks(&hidden, &temb, &pe, &ctx, None, prompt_temb.as_ref()).unwrap(); + let x = second_half.forward_blocks(&x, &temb, &pe, &ctx, None, prompt_temb.as_ref()).unwrap(); + let split_out = second_half.forward_finalize(&x, &embedded_ts).unwrap(); + + // Results should match (both use zeros weights) + assert_eq!(full_out.dims(), split_out.dims()); + } + + #[test] + fn test_to_denoised() { + let device = Device::Cpu; + let sample = Tensor::new(&[1.0f32, 2.0, 3.0], &device) + .unwrap() + .reshape((1, 1, 3)) + .unwrap(); + let sigma = Tensor::new(&[0.5f32], &device).unwrap(); + let velocity = Tensor::new(&[0.1f32, 0.2, 0.3], &device) + .unwrap() + .reshape((1, 1, 3)) + .unwrap(); + + let denoised = to_denoised(&sample, &sigma, &velocity).unwrap(); + let vals: Vec = denoised.flatten_all().unwrap().to_vec1().unwrap(); + assert!((vals[0] - 0.95).abs() < 1e-5); + assert!((vals[1] - 1.9).abs() < 1e-5); + assert!((vals[2] - 2.85).abs() < 1e-5); + } +} diff --git a/cake-core/src/models/ltx2/vendored/pipeline.rs b/cake-core/src/models/ltx2/vendored/pipeline.rs new file mode 100644 index 00000000..a3e48333 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/pipeline.rs @@ -0,0 +1,234 @@ +//! LTX-2 video generation pipeline. +//! +//! Orchestrates: text encoding → noise init → denoising loop → VAE decode. + +use candle_core::{Device, Result, Tensor}; + +/// Pack latents from `[B, C, F, H, W]` to `[B, S, C]` (patchified tokens). +/// +/// LTX-2 uses patch_size=1 so this is just a reshape/flatten. +pub fn pack_latents(latents: &Tensor) -> Result { + let (b, c, f, h, w) = latents.dims5()?; + // [B, C, F, H, W] -> [B, C, F*H*W] -> [B, F*H*W, C] + let latents = latents.reshape((b, c, f * h * w))?; + latents.transpose(1, 2) +} + +/// Unpack latents from `[B, S, C]` back to `[B, C, F, H, W]`. +pub fn unpack_latents( + latents: &Tensor, + num_frames: usize, + height: usize, + width: usize, +) -> Result { + let (b, _s, c) = latents.dims3()?; + // [B, S, C] -> [B, C, S] -> [B, C, F, H, W] + let latents = latents.transpose(1, 2)?; + latents.reshape((b, c, num_frames, height, width)) +} + +/// Build 3D positional coordinate grid for video tokens. +/// +/// Returns `[B, 3, F*H*W]` where 3 = (time, height, width). +pub fn build_video_positions( + batch_size: usize, + num_frames: usize, + height: usize, + width: usize, + temporal_compression: usize, + spatial_compression: usize, + frame_rate: usize, + device: &Device, +) -> Result { + let total = num_frames * height * width; + + // Build coordinate grids + let mut t_coords = Vec::with_capacity(total); + let mut h_coords = Vec::with_capacity(total); + let mut w_coords = Vec::with_capacity(total); + + let tc = temporal_compression as f32; + let sc = spatial_compression as f32; + let fps = frame_rate as f32; + // causal_offset=1 matches Python's default + let causal_offset = 1.0f32; + + for f in 0..num_frames { + for h in 0..height { + for w in 0..width { + // Temporal: patch boundary [start, end) in pixel space, then midpoint. + // Python: pixel = (latent * tc + causal_offset - tc).clamp(min=0) + // patch_size_t=1, so latent_start=f, latent_end=f+1 + let t_start = (f as f32 * tc + causal_offset - tc).max(0.0); + let t_end = ((f as f32 + 1.0) * tc + causal_offset - tc).max(0.0); + t_coords.push((t_start + t_end) / (2.0 * fps)); + + // Spatial: patch boundary midpoint. + // patch_size=1, so latent_start=h, latent_end=h+1 + // pixel = latent * sc, midpoint = (h*sc + (h+1)*sc) / 2 + h_coords.push((h as f32 + 0.5) * sc); + w_coords.push((w as f32 + 0.5) * sc); + } + } + } + + let t = Tensor::new(t_coords, device)?; + let h = Tensor::new(h_coords, device)?; + let w = Tensor::new(w_coords, device)?; + + // Stack to [3, total] then expand to [B, 3, total] + let grid = Tensor::stack(&[t, h, w], 0)?; // [3, total] + let grid = grid.unsqueeze(0)?; // [1, 3, total] + grid.broadcast_as((batch_size, 3, total))?.contiguous() +} + +/// Normalize latents using per-channel mean/std. +pub fn normalize_latents( + latents: &Tensor, + mean: &Tensor, + std: &Tensor, + scaling_factor: f32, +) -> Result { + let c = latents.dim(1)?; + let mean = mean + .reshape((1, c, 1, 1, 1))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + let std = std + .reshape((1, c, 1, 1, 1))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + let x = latents.broadcast_sub(&mean)?; + x.affine(scaling_factor as f64, 0.0)?.broadcast_div(&std) +} + +/// Denormalize latents (inverse of normalize_latents). +pub fn denormalize_latents( + latents: &Tensor, + mean: &Tensor, + std: &Tensor, + scaling_factor: f32, +) -> Result { + let c = latents.dim(1)?; + let mean = mean + .reshape((1, c, 1, 1, 1))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + let std = std + .reshape((1, c, 1, 1, 1))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + let x = latents.broadcast_mul(&std)?; + x.affine((1.0 / scaling_factor) as f64, 0.0)? + .broadcast_add(&mean) +} + +/// Postprocess video tensor from VAE: [-1,1] → [0,255] uint8. +pub fn postprocess_video(video: &Tensor) -> Result { + let v = video.affine(0.5, 0.5)?; // [-1,1] -> [0,1] + let v = v.clamp(0.0f32, 1.0f32)?; + v.affine(255.0, 0.0) // [0,1] -> [0,255] +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{Device, IndexOp, Tensor}; + + #[test] + fn test_pack_unpack_roundtrip() { + let device = Device::Cpu; + let b = 1; + let c = 4; + let f = 2; + let h = 3; + let w = 3; + + let latents = Tensor::randn(0f32, 1f32, (b, c, f, h, w), &device).unwrap(); + let packed = pack_latents(&latents).unwrap(); + + // packed should be [B, F*H*W, C] + assert_eq!(packed.dims(), &[b, f * h * w, c]); + + let unpacked = unpack_latents(&packed, f, h, w).unwrap(); + assert_eq!(unpacked.dims(), &[b, c, f, h, w]); + + // Values should roundtrip + let orig: Vec = latents.flatten_all().unwrap().to_vec1().unwrap(); + let rt: Vec = unpacked.flatten_all().unwrap().to_vec1().unwrap(); + for (a, b) in orig.iter().zip(rt.iter()) { + assert!((a - b).abs() < 1e-6, "Mismatch: {} vs {}", a, b); + } + } + + #[test] + fn test_build_video_positions_shape() { + let device = Device::Cpu; + let pos = build_video_positions(2, 3, 4, 5, 8, 32, 25, &device).unwrap(); + // [B, 3, F*H*W] + assert_eq!(pos.dims(), &[2, 3, 3 * 4 * 5]); + } + + #[test] + fn test_build_video_positions_first_frame_midpoint_time() { + let device = Device::Cpu; + let pos = build_video_positions(1, 2, 1, 1, 8, 32, 25, &device).unwrap(); + // First frame: t_start = max(0, 0*8+1-8) = 0, t_end = max(0, 1*8+1-8) = 1 + // midpoint = (0 + 1) / (2 * 25) = 0.02 + let t_coords: Vec = pos.i((0, 0, ..)).unwrap().to_vec1().unwrap(); + assert!((t_coords[0] - 0.02).abs() < 1e-6); + // Second frame: t_start = max(0, 1*8+1-8) = 1, t_end = max(0, 2*8+1-8) = 9 + // midpoint = (1 + 9) / (2 * 25) = 0.2 + assert!((t_coords[1] - 0.2).abs() < 1e-6); + } + + #[test] + fn test_build_video_positions_spatial_midpoints() { + let device = Device::Cpu; + let pos = build_video_positions(1, 1, 2, 3, 8, 32, 25, &device).unwrap(); + let h_coords: Vec = pos.i((0, 1, ..)).unwrap().to_vec1().unwrap(); + let w_coords: Vec = pos.i((0, 2, ..)).unwrap().to_vec1().unwrap(); + // h=0: midpoint = 0.5 * 32 = 16.0, h=1: midpoint = 1.5 * 32 = 48.0 + assert!((h_coords[0] - 16.0).abs() < 1e-4); + assert!((h_coords[3] - 48.0).abs() < 1e-4); + // w=0: 16.0, w=1: 48.0, w=2: 80.0 + assert!((w_coords[0] - 16.0).abs() < 1e-4); + assert!((w_coords[1] - 48.0).abs() < 1e-4); + assert!((w_coords[2] - 80.0).abs() < 1e-4); + } + + #[test] + fn test_normalize_denormalize_roundtrip() { + let device = Device::Cpu; + let c = 4; + let latents = Tensor::randn(0f32, 1f32, (1, c, 2, 3, 3), &device).unwrap(); + let mean = Tensor::new(vec![0.1f32, 0.2, 0.3, 0.4], &device).unwrap(); + let std = Tensor::new(vec![1.0f32, 1.5, 0.8, 1.2], &device).unwrap(); + let sf = 1.0; + + let normalized = normalize_latents(&latents, &mean, &std, sf).unwrap(); + let recovered = denormalize_latents(&normalized, &mean, &std, sf).unwrap(); + + let orig: Vec = latents.flatten_all().unwrap().to_vec1().unwrap(); + let rec: Vec = recovered.flatten_all().unwrap().to_vec1().unwrap(); + for (a, b) in orig.iter().zip(rec.iter()) { + assert!((a - b).abs() < 1e-4, "Mismatch: {} vs {}", a, b); + } + } + + #[test] + fn test_postprocess_video_range() { + let device = Device::Cpu; + // Values in [-1, 1] + let video = Tensor::new(&[-1.0f32, 0.0, 0.5, 1.0], &device) + .unwrap() + .reshape((1, 1, 1, 2, 2)) + .unwrap(); + let result = postprocess_video(&video).unwrap(); + let vals: Vec = result.flatten_all().unwrap().to_vec1().unwrap(); + assert!((vals[0] - 0.0).abs() < 1e-4); // -1 -> 0 + assert!((vals[1] - 127.5).abs() < 1e-4); // 0 -> 127.5 + assert!((vals[2] - 191.25).abs() < 1e-4); // 0.5 -> 191.25 + assert!((vals[3] - 255.0).abs() < 1e-4); // 1 -> 255 + } +} diff --git a/cake-core/src/models/ltx2/vendored/rope.rs b/cake-core/src/models/ltx2/vendored/rope.rs new file mode 100644 index 00000000..56692973 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/rope.rs @@ -0,0 +1,283 @@ +//! Rotary Position Embeddings for LTX-2. +//! +//! Matches HF `LTX2AudioVideoRotaryPosEmbed` and `apply_split_rotary_emb`. +//! +//! Split RoPE: frequencies are reshaped per-head `[B, H, T, D_per_head//2]`. +//! Each head's embedding is independently rotated in halves. + +use candle_core::{DType, Device, Result, Tensor}; + +/// Precompute split RoPE (cos, sin) for the given positions grid. +/// +/// `indices_grid`: `[B, n_pos_dims, T]` — positional coordinates per token. +/// For video: n_pos_dims=3 (time, height, width). +/// `dim`: total head dimension = heads * d_head. +/// `theta`: base frequency (default 10000). +/// `max_pos`: max position per dimension (for fractional scaling). +/// `num_heads`: number of attention heads for per-head reshape. +/// +/// Returns `(cos, sin)` each of shape `[B, H, T, D_per_head//2]`. +pub fn precompute_freqs_cis( + indices_grid: &Tensor, + dim: usize, + theta: f32, + max_pos: &[usize], + num_heads: usize, + out_dtype: DType, +) -> Result<(Tensor, Tensor)> { + let device = indices_grid.device(); + let (_b, n_pos_dims, _t) = indices_grid.dims3()?; + + // num_rope_elems = n_pos_dims * 2 + let num_rope_elems = n_pos_dims * 2; + let dim_per_pos = dim / num_rope_elems; + + let freqs = generate_freq_grid(theta, dim_per_pos, device)?; + + // [B, n_pos_dims, T] -> [B, T, n_pos_dims] + let grid = indices_grid.transpose(1, 2)?; + + // Fractional positions: divide by max_pos, then map to [-1, 1] + let max_pos_t = Tensor::new( + max_pos.iter().map(|&m| m as f32).collect::>(), + device, + )?; + let frac_pos = grid.broadcast_div(&max_pos_t)?; + let scaled = frac_pos.affine(2.0, -1.0)?; + + // Outer product: [B, T, n_pos_dims, dim_per_pos] + let scaled_unsq = scaled.unsqueeze(3)?; + let freqs_unsq = freqs.unsqueeze(0)?.unsqueeze(0)?.unsqueeze(0)?; + let freqs_out = scaled_unsq.broadcast_mul(&freqs_unsq)?; + + // transpose(-1, -2): [B, T, dim_per_pos, n_pos_dims] + // flatten(2): [B, T, dim_per_pos * n_pos_dims] = [B, T, dim//2 - maybe] + let freqs_out = freqs_out.transpose(2, 3)?.flatten_from(2)?; + + let cos_raw = freqs_out.cos()?; + let sin_raw = freqs_out.sin()?; + + // Pad to expected_freqs = dim // 2 (PREPEND padding) + let expected_freqs = dim / 2; + let current_freqs = cos_raw.dim(2)?; + let pad_size = expected_freqs - current_freqs; + + let (cos, sin) = if pad_size > 0 { + let b_size = cos_raw.dim(0)?; + let t_size = cos_raw.dim(1)?; + let cos_pad = Tensor::ones((b_size, t_size, pad_size), DType::F32, device)?; + let sin_pad = Tensor::zeros((b_size, t_size, pad_size), DType::F32, device)?; + // PREPEND: [pad, raw] (Python does concatenate([padding, freq], axis=-1)) + ( + Tensor::cat(&[cos_pad, cos_raw], 2)?, + Tensor::cat(&[sin_pad, sin_raw], 2)?, + ) + } else { + (cos_raw, sin_raw) + }; + + // Reshape per-head: [B, T, dim//2] -> [B, T, H, D_per_head//2] -> [B, H, T, D_per_head//2] + let d_per_head_half = expected_freqs / num_heads; + let (b, t, _) = cos.dims3()?; + let cos = cos + .reshape((b, t, num_heads, d_per_head_half))? + .transpose(1, 2)? + .contiguous()?; + let sin = sin + .reshape((b, t, num_heads, d_per_head_half))? + .transpose(1, 2)? + .contiguous()?; + + Ok((cos.to_dtype(out_dtype)?, sin.to_dtype(out_dtype)?)) +} + +/// Apply split RoPE to input tensor. +/// +/// Matches HF `apply_split_rotary_emb`: +/// - cos/sin: `[B, H, T, r]` where r = D_per_head // 2 +/// - x: `[B, T, D]` (flat) — reshaped to `[B, H, T, D_per_head]` internally +/// - Per-head: split D_per_head into [2, r], rotate halves independently. +pub fn apply_rotary_emb(x: &Tensor, cos: &Tensor, sin: &Tensor) -> Result { + let needs_reshape = x.rank() == 3 && cos.rank() == 4; + + let (b, h, t, d_per_head) = if needs_reshape { + let b = cos.dim(0)?; + let h = cos.dim(1)?; + let t = cos.dim(2)?; + let d_per_head = x.dim(2)? / h; + (b, h, t, d_per_head) + } else { + // Already 4D: [B, H, T, D_per_head] + (x.dim(0)?, x.dim(1)?, x.dim(2)?, x.dim(3)?) + }; + + let r = d_per_head / 2; + + // Get x in [B, H, T, D_per_head] shape + let x4d = if needs_reshape { + x.reshape((b, t, h, d_per_head))?.transpose(1, 2)? + } else { + x.clone() + }; + + // Split: [B, H, T, D_per_head] -> [B, H, T, 2, r] + let split_x = x4d.reshape((b, h, t, 2, r))?.to_dtype(DType::F32)?; + let first_x = split_x.narrow(3, 0, 1)?; // [B, H, T, 1, r] + let second_x = split_x.narrow(3, 1, 1)?; // [B, H, T, 1, r] + + // cos/sin: [B, H, T, r] -> [B, H, T, 1, r] + let cos_f = cos.to_dtype(DType::F32)?.unsqueeze(3)?; + let sin_f = sin.to_dtype(DType::F32)?.unsqueeze(3)?; + + // out = split_x * cos (element-wise broadcast) + let out = split_x.broadcast_mul(&cos_f)?; // [B, H, T, 2, r] + + // first_out = first_x * cos - second_x * sin + let first_out = out.narrow(3, 0, 1)?; + let first_out = first_out.broadcast_sub(&sin_f.broadcast_mul(&second_x)?)?; + + // second_out = second_x * cos + first_x * sin + let second_out = out.narrow(3, 1, 1)?; + let second_out = second_out.broadcast_add(&sin_f.broadcast_mul(&first_x)?)?; + + // Concat: [B, H, T, 2, r] + let out = Tensor::cat(&[first_out, second_out], 3)?; + + // Reshape: [B, H, T, 2, r] -> [B, H, T, D_per_head] + let out = out.reshape((b, h, t, d_per_head))?; + + // If we reshaped, convert back: [B, H, T, D] -> [B, T, H, D] -> [B, T, H*D] + let out = if needs_reshape { + out.transpose(1, 2)?.reshape((b, t, h * d_per_head))? + } else { + out + }; + + out.to_dtype(x.dtype()) +} + +/// Generate log-spaced frequency grid: pow(theta, linspace(0, 1, steps)) * pi/2. +fn generate_freq_grid(theta: f32, dim_per_pos: usize, device: &Device) -> Result { + let end = theta as f64; + + let indices: Vec = (0..dim_per_pos) + .map(|i| { + let t = if dim_per_pos > 1 { + i as f64 / (dim_per_pos - 1) as f64 + } else { + 0.0 + }; + let val = end.powf(t) * std::f64::consts::FRAC_PI_2; + val as f32 + }) + .collect(); + + Tensor::from_vec(indices, (dim_per_pos,), device) +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device, Tensor}; + + #[test] + fn test_precompute_freqs_shape() { + let device = Device::Cpu; + let batch = 2; + let seq = 12; + let heads = 32; + let d_head = 128; + let dim = heads * d_head; + let n_pos = 3; + + let grid = Tensor::randn(0f32, 1f32, (batch, n_pos, seq), &device).unwrap(); + let max_pos = vec![20, 2048, 2048]; + + let (cos, sin) = + precompute_freqs_cis(&grid, dim, 10000.0, &max_pos, heads, DType::F32).unwrap(); + + // [B, H, T, D_per_head//2] + assert_eq!(cos.dims(), &[batch, heads, seq, d_head / 2]); + assert_eq!(sin.dims(), &[batch, heads, seq, d_head / 2]); + } + + #[test] + fn test_apply_rotary_emb_identity() { + // cos=1, sin=0 should be identity + let device = Device::Cpu; + let b = 1; + let t = 4; + let h = 2; + let d_head = 8; + let dim = h * d_head; + let r = d_head / 2; + + let x = Tensor::randn(0f32, 1f32, (b, t, dim), &device).unwrap(); + let cos = Tensor::ones((b, h, t, r), DType::F32, &device).unwrap(); + let sin = Tensor::zeros((b, h, t, r), DType::F32, &device).unwrap(); + + let out = apply_rotary_emb(&x, &cos, &sin).unwrap(); + assert_eq!(out.dims(), &[b, t, dim]); + + let x_vals: Vec = x.flatten_all().unwrap().to_vec1().unwrap(); + let o_vals: Vec = out.flatten_all().unwrap().to_vec1().unwrap(); + for (a, b) in x_vals.iter().zip(o_vals.iter()) { + assert!((a - b).abs() < 1e-5, "Identity RoPE failed: {} vs {}", a, b); + } + } + + #[test] + fn test_apply_rotary_emb_rotation() { + let device = Device::Cpu; + // 1 head, d_head=4, r=2 + // x = [1, 0, 0, 0] (first_half=[1,0], second_half=[0,0]) + // cos=0, sin=1: + // first_out = first*cos - sin*second = [0,0] - [0,0] = [0,0] + // second_out = second*cos + sin*first = [0,0] + [1,0] = [1,0] + // result = [0, 0, 1, 0] + let x = Tensor::new(&[1.0f32, 0.0, 0.0, 0.0], &device) + .unwrap() + .reshape((1, 1, 4)) + .unwrap(); + let cos = Tensor::new(&[0.0f32, 0.0], &device) + .unwrap() + .reshape((1, 1, 1, 2)) + .unwrap(); + let sin = Tensor::new(&[1.0f32, 1.0], &device) + .unwrap() + .reshape((1, 1, 1, 2)) + .unwrap(); + + let out = apply_rotary_emb(&x, &cos, &sin).unwrap(); + let vals: Vec = out.flatten_all().unwrap().to_vec1().unwrap(); + assert!((vals[0] - 0.0).abs() < 1e-6); + assert!((vals[1] - 0.0).abs() < 1e-6); + assert!((vals[2] - 1.0).abs() < 1e-6); + assert!((vals[3] - 0.0).abs() < 1e-6); + } + + #[test] + fn test_apply_rotary_emb_4d() { + // Already 4D input: [B, H, T, D_per_head] + let device = Device::Cpu; + let b = 1; + let h = 2; + let t = 3; + let d_head = 8; + let r = d_head / 2; + + let x = Tensor::randn(0f32, 1f32, (b, h, t, d_head), &device).unwrap(); + let cos = Tensor::ones((b, h, t, r), DType::F32, &device).unwrap(); + let sin = Tensor::zeros((b, h, t, r), DType::F32, &device).unwrap(); + + let out = apply_rotary_emb(&x, &cos, &sin).unwrap(); + assert_eq!(out.dims(), &[b, h, t, d_head]); + + // Identity check + let x_vals: Vec = x.flatten_all().unwrap().to_vec1().unwrap(); + let o_vals: Vec = out.flatten_all().unwrap().to_vec1().unwrap(); + for (a, b) in x_vals.iter().zip(o_vals.iter()) { + assert!((a - b).abs() < 1e-5); + } + } +} diff --git a/cake-core/src/models/ltx2/vendored/scheduler.rs b/cake-core/src/models/ltx2/vendored/scheduler.rs new file mode 100644 index 00000000..ef190e39 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/scheduler.rs @@ -0,0 +1,200 @@ +//! LTX-2 scheduler: token-count-dependent sigma shifting. +//! +//! Generates sigma schedules with `flux_time_shift` and optional stretch-to-terminal. + +use candle_core::{Result, Tensor}; + +use super::config::Ltx2SchedulerConfig; + +/// flux_time_shift: exp(mu) / (exp(mu) + (1/t - 1)^sigma) +fn flux_time_shift(mu: f32, sigma_power: f32, t: f32) -> f32 { + let emu = mu.exp(); + if t <= 0.0 || t >= 1.0 { + return t; + } + let base = (1.0 / t - 1.0).powf(sigma_power); + emu / (emu + base) +} + +/// LTX-2 scheduler. +pub struct Ltx2Scheduler { + config: Ltx2SchedulerConfig, +} + +impl Ltx2Scheduler { + pub fn new(config: Ltx2SchedulerConfig) -> Self { + Self { config } + } + + /// Compute sigma schedule for a given number of tokens and steps. + /// + /// Returns `(steps + 1)` sigma values from ~1.0 down to 0.0. + /// Matches Python diffusers FlowMatchEulerDiscreteScheduler: + /// 1. Generate N sigmas (no zero) via linspace + /// 2. Apply flux_time_shift + /// 3. Apply stretch_to_terminal (on N sigmas, without trailing zero) + /// 4. Append 0.0 at the end + pub fn execute(&self, steps: usize, num_tokens: usize) -> Vec { + let shift = self.compute_shift(num_tokens); + + // Generate N sigmas from 1.0 down to 1/steps (no zero) + let mut sigmas: Vec = (0..steps) + .map(|i| 1.0 - (i as f32 / steps as f32)) + .collect(); + + // Apply flux_time_shift + for s in sigmas.iter_mut() { + *s = flux_time_shift(shift, self.config.power, *s); + } + + // Optional stretch to terminal (before appending zero) + if let Some(terminal) = self.config.stretch_terminal { + stretch_to_terminal(&mut sigmas, terminal); + } + + // Append terminal zero + sigmas.push(0.0); + + sigmas + } + + fn compute_shift(&self, num_tokens: usize) -> f32 { + // Dynamic shift: log-linear interpolation between base_shift and max_shift + // based on token count (matches diffusers FlowMatchEulerDiscreteScheduler). + // base_image_seq_len=1024, max_image_seq_len=4096 from scheduler config. + let base_seq = 1024.0f32; + let max_seq = 4096.0f32; + + let m = (self.config.max_shift - self.config.base_shift) + / (max_seq - base_seq); + let b = self.config.base_shift - m * base_seq; + let mu = (num_tokens as f32) * m + b; + mu + } +} + +fn stretch_to_terminal(sigmas: &mut [f32], terminal: f32) { + if sigmas.is_empty() { + return; + } + let last = *sigmas.last().unwrap(); + let one_minus_last = 1.0 - last; + let denom = 1.0 - terminal; + if denom.abs() < 1e-12 { + return; + } + let scale = one_minus_last / denom; + for s in sigmas.iter_mut() { + let one_minus = 1.0 - *s; + *s = 1.0 - (one_minus / scale); + } +} + +/// Euler diffusion step: sample + velocity * dt. +/// +/// `sample`: current latent, `[B, T, D]` +/// `velocity`: model prediction (velocity) +/// `sigma`: current sigma (scalar) +/// `sigma_next`: next sigma (scalar) +pub fn euler_step( + sample: &Tensor, + velocity: &Tensor, + sigma: f32, + sigma_next: f32, +) -> Result { + let dt = sigma_next - sigma; + let scaled = velocity.affine(dt as f64, 0.0)?; + sample.broadcast_add(&scaled) +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{Device, Tensor}; + + #[test] + fn test_flux_time_shift_boundaries() { + // t=0 and t=1 are identity + assert_eq!(flux_time_shift(1.0, 1.0, 0.0), 0.0); + assert_eq!(flux_time_shift(1.0, 1.0, 1.0), 1.0); + } + + #[test] + fn test_flux_time_shift_midpoint() { + // At t=0.5 with mu=0 (exp(0)=1), sigma=1: 1 / (1 + (1/0.5 - 1)^1) = 1/2 = 0.5 + let v = flux_time_shift(0.0, 1.0, 0.5); + assert!((v - 0.5).abs() < 1e-6); + } + + #[test] + fn test_flux_time_shift_positive_mu() { + // Positive mu shifts schedule toward 1 (more denoising at start) + let v_low = flux_time_shift(0.5, 1.0, 0.5); + let v_high = flux_time_shift(2.0, 1.0, 0.5); + assert!(v_high > v_low); + } + + #[test] + fn test_scheduler_produces_correct_length() { + let config = Ltx2SchedulerConfig::default(); + let scheduler = Ltx2Scheduler::new(config); + let sigmas = scheduler.execute(20, 1024); + assert_eq!(sigmas.len(), 21); // steps + 1 + } + + #[test] + fn test_scheduler_monotonically_decreasing() { + let config = Ltx2SchedulerConfig::default(); + let scheduler = Ltx2Scheduler::new(config); + let sigmas = scheduler.execute(30, 2048); + for i in 1..sigmas.len() { + assert!( + sigmas[i] <= sigmas[i - 1], + "Sigma at step {} ({}) > step {} ({})", + i, + sigmas[i], + i - 1, + sigmas[i - 1] + ); + } + // All sigmas should be non-negative + for (i, s) in sigmas.iter().enumerate() { + assert!(*s >= 0.0, "Sigma at step {} ({}) is negative", i, s); + } + // Last sigma should be 0.0 + assert_eq!(*sigmas.last().unwrap(), 0.0); + } + + #[test] + fn test_scheduler_starts_near_one() { + let config = Ltx2SchedulerConfig::default(); + let scheduler = Ltx2Scheduler::new(config); + let sigmas = scheduler.execute(20, 1024); + // First sigma should be close to 1 (shifted) + assert!(sigmas[0] > 0.8); + } + + #[test] + fn test_scheduler_more_tokens_more_shift() { + let config = Ltx2SchedulerConfig::default(); + let scheduler = Ltx2Scheduler::new(config); + let sigmas_small = scheduler.execute(20, 256); + let sigmas_large = scheduler.execute(20, 4096); + // More tokens = more shift = higher initial sigma + assert!(sigmas_large[1] > sigmas_small[1]); + } + + #[test] + fn test_euler_step() { + let device = Device::Cpu; + let sample = Tensor::ones((1, 4, 3), candle_core::DType::F32, &device).unwrap(); + let velocity = Tensor::full(2.0f32, (1, 4, 3), &device).unwrap(); + // dt = sigma_next - sigma = 0.8 - 1.0 = -0.2 + let result = euler_step(&sample, &velocity, 1.0, 0.8).unwrap(); + let val: Vec = result.flatten_all().unwrap().to_vec1().unwrap(); + // sample + velocity * dt = 1.0 + 2.0 * (-0.2) = 0.6 + for v in &val { + assert!((*v - 0.6).abs() < 1e-6, "Expected 0.6, got {}", v); + } + } +} diff --git a/cake-core/src/models/ltx2/vendored/transformer_block.rs b/cake-core/src/models/ltx2/vendored/transformer_block.rs new file mode 100644 index 00000000..5587e481 --- /dev/null +++ b/cake-core/src/models/ltx2/vendored/transformer_block.rs @@ -0,0 +1,329 @@ +//! BasicAVTransformerBlock — the core dual-stream block for LTX-2. +//! +//! Each block has: +//! - Video: self-attn → text cross-attn → FFN (with AdaLN modulation) +//! - Audio: self-attn → text cross-attn → FFN (with AdaLN modulation) +//! - Audio↔Video bidirectional cross-attention +//! +//! In video-only mode, audio components are None. + +use candle_core::{Result, Tensor}; +use candle_nn::{Module, VarBuilder}; + +use super::attention::{rms_norm, Attention}; +use super::config::Ltx2TransformerConfig; +use super::feed_forward::FeedForward; + +/// Per-stream config for one block. +#[allow(dead_code)] +struct StreamConfig { + dim: usize, + heads: usize, + d_head: usize, + context_dim: usize, +} + +/// A single dual-stream transformer block. +#[derive(Debug)] +#[allow(dead_code)] +pub struct BasicAVTransformerBlock { + // Video stream + attn1: Option, // video self-attention + attn2: Option, // video text cross-attention + ff: Option, // video feedforward + scale_shift_table: Option, // [adaln_params, video_dim] + + // LTX-2.3: prompt-specific AdaLN modulation + prompt_scale_shift_table: Option, // [3, video_dim] + + // Audio stream (None in video-only mode) + audio_attn1: Option, + audio_attn2: Option, + audio_ff: Option, + audio_scale_shift_table: Option, + + // Audio↔Video cross-attention (None in unimodal mode) + audio_to_video_attn: Option, + video_to_audio_attn: Option, + scale_shift_table_a2v_ca_audio: Option, + scale_shift_table_a2v_ca_video: Option, + + norm_eps: f64, + adaln_params: usize, +} + +/// Inputs/outputs for one modality stream through a block. +pub struct StreamArgs { + pub x: Tensor, + pub timesteps: Tensor, // [B, adaln_params, dim] pre-split modulation + pub pe: Option<(Tensor, Tensor)>, // RoPE (cos, sin) + pub context: Tensor, // text embeddings + pub context_mask: Option, + pub self_attention_mask: Option, + pub cross_pe: Option<(Tensor, Tensor)>, // cross-modal RoPE + pub enabled: bool, +} + +impl BasicAVTransformerBlock { + pub fn new( + _idx: usize, + config: &Ltx2TransformerConfig, + vb: VarBuilder, + ) -> Result { + let norm_eps = config.norm_eps; + let adaln_params = config.adaln_params(); + + let video_dim = config.video_inner_dim(); + let audio_dim = config.audio_inner_dim(); + let has_video = config.model_type.is_video_enabled(); + let has_audio = config.model_type.is_audio_enabled(); + let gated = config.gated_attention; + + // Video components + let (attn1, attn2, ff, scale_shift_table) = if has_video { + let attn1 = Attention::new( + video_dim, + None, + config.num_attention_heads, + config.attention_head_dim, + norm_eps, + gated, + vb.pp("attn1"), + )?; + let attn2 = Attention::new( + video_dim, + Some(config.cross_attention_dim), + config.num_attention_heads, + config.attention_head_dim, + norm_eps, + gated, + vb.pp("attn2"), + )?; + let ff = FeedForward::new(video_dim, video_dim, 4, vb.pp("ff"))?; + let sst = vb.get((adaln_params, video_dim), "scale_shift_table")?; + (Some(attn1), Some(attn2), Some(ff), Some(sst)) + } else { + (None, None, None, None) + }; + + // LTX-2.3: prompt modulation table (shift + scale, no gate) + let prompt_scale_shift_table = if has_video && config.prompt_modulation { + Some(vb.get((2, video_dim), "prompt_scale_shift_table")?) + } else { + None + }; + + // Audio components + let (audio_attn1, audio_attn2, audio_ff, audio_sst) = if has_audio { + let a1 = Attention::new( + audio_dim, + None, + config.audio_num_attention_heads, + config.audio_attention_head_dim, + norm_eps, + gated, + vb.pp("audio_attn1"), + )?; + let a2 = Attention::new( + audio_dim, + Some(config.audio_cross_attention_dim), + config.audio_num_attention_heads, + config.audio_attention_head_dim, + norm_eps, + gated, + vb.pp("audio_attn2"), + )?; + let ff = FeedForward::new(audio_dim, audio_dim, 4, vb.pp("audio_ff"))?; + let sst = vb.get((adaln_params, audio_dim), "audio_scale_shift_table")?; + (Some(a1), Some(a2), Some(ff), Some(sst)) + } else { + (None, None, None, None) + }; + + // Cross-modal attention + let (a2v, v2a, sst_a2v_audio, sst_a2v_video) = if has_video && has_audio { + let a2v = Attention::new( + video_dim, + Some(audio_dim), + config.audio_num_attention_heads, + config.audio_attention_head_dim, + norm_eps, + gated, + vb.pp("audio_to_video_attn"), + )?; + let v2a = Attention::new( + audio_dim, + Some(video_dim), + config.audio_num_attention_heads, + config.audio_attention_head_dim, + norm_eps, + gated, + vb.pp("video_to_audio_attn"), + )?; + let sst_audio = vb.get((5, audio_dim), "audio_a2v_cross_attn_scale_shift_table")?; + let sst_video = vb.get((5, video_dim), "video_a2v_cross_attn_scale_shift_table")?; + (Some(a2v), Some(v2a), Some(sst_audio), Some(sst_video)) + } else { + (None, None, None, None) + }; + + Ok(Self { + attn1, + attn2, + ff, + scale_shift_table, + prompt_scale_shift_table, + audio_attn1, + audio_attn2, + audio_ff, + audio_scale_shift_table: audio_sst, + audio_to_video_attn: a2v, + video_to_audio_attn: v2a, + scale_shift_table_a2v_ca_audio: sst_a2v_audio, + scale_shift_table_a2v_ca_video: sst_a2v_video, + norm_eps, + adaln_params, + }) + } + + /// Extract AdaLN modulation values from scale_shift_table + timestep. + /// + /// `sst`: `[N, dim]` + /// `timestep`: `[B, 1, N, dim]` (pre-reshaped) + /// `indices`: range of params to extract + /// + /// Returns tuple of tensors, each `[B, 1, dim]`. + fn get_ada_values( + sst: &Tensor, + timestep: &Tensor, + start: usize, + end: usize, + ) -> Result> { + let count = end - start; + // sst[start..end]: [count, dim] -> [1, 1, count, dim] + let sst_slice = sst.narrow(0, start, count)?.unsqueeze(0)?.unsqueeze(0)?; + + // timestep[:, :, start..end, :]: [B, 1, count, dim] + let ts_slice = timestep.narrow(2, start, count)?; + + // Add: [B, 1, count, dim] + let combined = sst_slice + .to_dtype(ts_slice.dtype())? + .broadcast_add(&ts_slice)?; + + // Unbind along dim 2 -> count tensors of [B, 1, dim] + let mut result = Vec::with_capacity(count); + for i in 0..count { + result.push(combined.narrow(2, i, 1)?.squeeze(2)?); + } + Ok(result) + } + + /// Forward pass for video-only mode. + /// + /// `video`: current video hidden states + /// `timesteps`: pre-computed AdaLN modulation, `[B, 1, adaln_params, dim]` + /// `pe`: RoPE (cos, sin) + /// `context`: text embeddings + /// `context_mask`: attention mask for text + /// `prompt_temb`: prompt timestep embedding for prompt modulation (LTX-2.3), `[B, 1, 3, dim]` + /// `skip_self_attn`: if true, bypass self-attention Q/K (STG perturbation) + pub fn forward_video_only( + &self, + video: &Tensor, + timesteps: &Tensor, + pe: Option<&(Tensor, Tensor)>, + context: &Tensor, + context_mask: Option<&Tensor>, + prompt_temb: Option<&Tensor>, + skip_self_attn: bool, + ) -> Result { + let sst = self + .scale_shift_table + .as_ref() + .expect("video scale_shift_table required"); + let attn1 = self.attn1.as_ref().unwrap(); + let attn2 = self.attn2.as_ref().unwrap(); + let ff = self.ff.as_ref().unwrap(); + + // Get modulation params: [shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp] + let ada_msa = Self::get_ada_values(sst, timesteps, 0, 3)?; + let (shift_msa, scale_msa, gate_msa) = (&ada_msa[0], &ada_msa[1], &ada_msa[2]); + + // Self-attention with AdaLN + let norm_x = rms_norm(video, self.norm_eps)?; + let norm_x = norm_x + .broadcast_mul(&scale_msa.broadcast_add(&Tensor::ones_like(scale_msa)?)?)? + .broadcast_add(shift_msa)?; + + // STG: skip Q/K attention, pass V through directly + let attn_out = if skip_self_attn { + attn1.forward_skip_attn(&norm_x, None)? + } else { + attn1.forward(&norm_x, None, pe, None, None)? + }; + let vx = video.broadcast_add(&attn_out.broadcast_mul(gate_msa)?)?; + + // Text cross-attention with AdaLN + let norm_vx = rms_norm(&vx, self.norm_eps)?; + + // Cross-attention AdaLN: modulate query input (LTX-2.3) + // Guard on actual temb tensor dim (not stored field) to handle config mismatches + let has_ca_adaln = self.adaln_params > 6 && timesteps.dim(2)? > 6; + let (norm_vx, gate_ca) = if has_ca_adaln { + let ada_ca = Self::get_ada_values(sst, timesteps, 6, 9)?; + let (shift_ca, scale_ca, gate) = (&ada_ca[0], &ada_ca[1], ada_ca[2].clone()); + let modulated = norm_vx + .broadcast_mul(&scale_ca.broadcast_add(&Tensor::ones_like(scale_ca)?)?)? + .broadcast_add(shift_ca)?; + (modulated, Some(gate)) + } else { + (norm_vx, None) + }; + + // LTX-2.3: prompt modulation — modulate CONTEXT (key/value) for cross-attention + // Python: encoder_hidden_states = context * (1 + scale_kv) + shift_kv + let ca_context = if let (Some(psst), Some(pt)) = (&self.prompt_scale_shift_table, prompt_temb) { + let prompt_ada = Self::get_ada_values(psst, pt, 0, 2)?; + let (p_shift, p_scale) = (&prompt_ada[0], &prompt_ada[1]); + context + .broadcast_mul(&p_scale.broadcast_add(&Tensor::ones_like(p_scale)?)?)? + .broadcast_add(p_shift)? + } else { + context.clone() + }; + + // Expand context_mask from [B, L] to [B, T_q, L] for cross-attention + let t_q = norm_vx.dim(1)?; + let expanded_mask = context_mask.map(|m| { + m.unsqueeze(1) + .and_then(|m| m.broadcast_as((m.dim(0)?, t_q, m.dim(2)?))) + .and_then(|m| m.contiguous()) + }).transpose()?; + let ca_out = attn2.forward(&norm_vx, Some(&ca_context), None, None, expanded_mask.as_ref())?; + + // Apply cross-attention gate (LTX-2.3) + let ca_out = if let Some(ref gate) = gate_ca { + ca_out.broadcast_mul(gate)? + } else { + ca_out + }; + let vx = vx.broadcast_add(&ca_out)?; + + // FFN with AdaLN + let ada_mlp = Self::get_ada_values(sst, timesteps, 3, 6)?; + let (shift_mlp, scale_mlp, gate_mlp) = (&ada_mlp[0], &ada_mlp[1], &ada_mlp[2]); + + let norm_vx = rms_norm(&vx, self.norm_eps)?; + let norm_vx = norm_vx + .broadcast_mul(&scale_mlp.broadcast_add(&Tensor::ones_like(scale_mlp)?)?)? + .broadcast_add(shift_mlp)?; + + let ff_out = ff.forward(&norm_vx)?; + + let vx = vx.broadcast_add(&ff_out.broadcast_mul(gate_mlp)?)?; + + Ok(vx) + } + +} diff --git a/cake-core/src/models/ltx2/vocoder.rs b/cake-core/src/models/ltx2/vocoder.rs new file mode 100644 index 00000000..a2a0cab5 --- /dev/null +++ b/cake-core/src/models/ltx2/vocoder.rs @@ -0,0 +1,62 @@ +use anyhow::Result; +use async_trait::async_trait; +use candle_core::Tensor; + +use crate::cake::{Context, Forwarder}; + +/// LTX-2 audio vocoder Forwarder. +/// +/// Layer name: `"ltx2-vocoder"` +/// +/// Converts latent audio representations to waveform audio, +/// synchronized with the generated video. +#[derive(Debug)] +pub struct Ltx2Vocoder { + name: String, +} + +impl std::fmt::Display for Ltx2Vocoder { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{} (local)", &self.name) + } +} + +impl Ltx2Vocoder { + pub fn load_model(_ctx: &Context) -> Result> { + log::warn!("LTX-2 vocoder: vendored model code not yet implemented"); + Ok(Box::new(Self { + name: "ltx2-vocoder".to_string(), + })) + } +} + +#[async_trait] +impl Forwarder for Ltx2Vocoder { + fn load(name: String, _ctx: &Context) -> Result> { + Ok(Box::new(Self { name })) + } + + async fn forward( + &self, + _x: &Tensor, + _index_pos: usize, + _block_idx: usize, + _ctx: &mut Context, + ) -> Result { + anyhow::bail!("LTX-2 vocoder forward not yet implemented") + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> Result { + self.forward(x, index_pos, block_idx, ctx).await + } + + fn layer_name(&self) -> &str { + &self.name + } +} diff --git a/cake-core/src/models/ltx_video/ltx_video.rs b/cake-core/src/models/ltx_video/ltx_video.rs new file mode 100644 index 00000000..2f1dc90d --- /dev/null +++ b/cake-core/src/models/ltx_video/ltx_video.rs @@ -0,0 +1,472 @@ +use crate::cake::{Context, Forwarder}; +use crate::models::ltx_video::ltx_video_shardable::LtxVideoShardable; +use crate::models::ltx_video::t5::LtxT5; +use crate::models::ltx_video::transformer::LtxTransformer; +use crate::models::ltx_video::vae_forwarder::LtxVae; +use crate::models::{Generator, VideoGenerator}; +use crate::video::VideoOutput; +use crate::ImageGenerationArgs; +use anyhow::{Error as E, Result}; +use async_trait::async_trait; +use candle_core::{DType, Device, IndexOp, Tensor}; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use image::{ImageBuffer, Rgb}; +use log::info; +use std::path::PathBuf; +use tokenizers::Tokenizer; + +use super::vendored::configs::get_config_by_version; +use super::vendored::scheduler::FlowMatchEulerDiscreteScheduler; +use super::vendored::t2v_pipeline::{self, LtxPipeline}; + +pub struct LtxVideo { + t5_tokenizer: Tokenizer, + t5_encoder: Box, + transformer: Box, + vae: Box, + context: Context, +} + +#[async_trait] +impl Generator for LtxVideo { + type Shardable = LtxVideoShardable; + const MODEL_NAME: &'static str = "ltx-video"; + + async fn load(context: &mut Context) -> Result>> { + let ltx_args = &context.args.ltx_args; + let ltx_repo = ltx_args.ltx_repo(); + + // Load T5 tokenizer + info!("Loading T5 tokenizer..."); + let t5_tokenizer_path = if let Some(ref p) = ltx_args.ltx_t5_tokenizer { + PathBuf::from(p) + } else { + // LTX ships spiece.model; use tokenizer.json from the repo or T5-XXL fallback + resolve_hf_file(<x_repo, "tokenizer/tokenizer.json", &context.args.model) + .or_else(|_| { + resolve_hf_file( + "google/t5-v1_1-xxl", + "tokenizer.json", + &context.args.model, + ) + })? + }; + let t5_tokenizer = Tokenizer::from_file(&t5_tokenizer_path).map_err(E::msg)?; + info!("T5 tokenizer loaded!"); + + // T5 encoder + info!("Loading T5 encoder..."); + let t5_encoder: Box = + if let Some((node_name, node)) = context.topology.get_node_for_layer("ltx-t5") { + info!("node {node_name} will serve ltx-t5"); + Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx-t5", + context.args.cluster_key.as_deref(), + ) + .await?, + ) + } else { + info!("T5 encoder will be served locally"); + LtxT5::load_model(context)? + }; + info!("T5 encoder ready!"); + + // VAE + info!("Loading LTX VAE..."); + let vae: Box = + if let Some((node_name, node)) = context.topology.get_node_for_layer("ltx-vae") { + info!("node {node_name} will serve ltx-vae"); + Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx-vae", + context.args.cluster_key.as_deref(), + ) + .await?, + ) + } else { + info!("LTX VAE will be served locally"); + LtxVae::load_model(context)? + }; + info!("LTX VAE ready!"); + + // Transformer + info!("Loading LTX transformer..."); + let transformer: Box = if let Some((node_name, node)) = + context.topology.get_node_for_layer("ltx-transformer") + { + info!("node {node_name} will serve ltx-transformer"); + Box::new( + crate::cake::Client::new( + context.device.clone(), + &node.host, + "ltx-transformer", + context.args.cluster_key.as_deref(), + ) + .await?, + ) + } else { + info!("LTX transformer will be served locally"); + LtxTransformer::load_model(context)? + }; + info!("LTX transformer ready!"); + + Ok(Some(Box::new(Self { + t5_tokenizer, + t5_encoder, + transformer, + vae, + context: context.clone(), + }))) + } +} + +#[async_trait] +impl VideoGenerator for LtxVideo { + async fn generate_video( + &mut self, + args: &ImageGenerationArgs, + ) -> Result { + let ImageGenerationArgs { + image_prompt, + image_seed, + .. + } = args; + + let ltx_args = &self.context.args.ltx_args; + let version = <x_args.ltx_version; + let full_config = get_config_by_version(version); + let inference = &full_config.inference; + + let height = ltx_args.ltx_height; + let width = ltx_args.ltx_width; + let num_frames = ltx_args.ltx_num_frames; + let num_steps = ltx_args.ltx_num_steps.unwrap_or(inference.num_inference_steps); + let frame_rate = ltx_args.ltx_fps; + let guidance_scale = inference.guidance_scale; + + if let Some(seed) = image_seed { + self.context.device.set_seed(*seed)?; + } + + info!( + "Generating LTX video: {}x{}, {} frames, {} steps, guidance={}, version={}", + width, height, num_frames, num_steps, guidance_scale, version + ); + + // Transformer config for pack/unpack + let tcfg = LtxTransformer::pipeline_config(version); + let vae_spatial = full_config.vae.spatial_compression_ratio; + let vae_temporal = full_config.vae.temporal_compression_ratio; + let patch_size = tcfg.patch_size; + let patch_size_t = tcfg.patch_size_t; + + // 1. Encode prompt with T5 + info!("Encoding prompt with T5..."); + let t5_tokens = self + .t5_tokenizer + .encode(image_prompt.as_str(), true) + .map_err(E::msg)?; + let t5_token_ids = t5_tokens.get_ids().to_vec(); + let t5_input = + Tensor::new(t5_token_ids.as_slice(), &self.context.device)?.unsqueeze(0)?; + let prompt_embeds = LtxT5::encode(&mut self.t5_encoder, t5_input.clone(), &mut self.context) + .await? + .to_dtype(self.context.dtype)?; + info!("T5 encoding done: {:?}", prompt_embeds.shape()); + + // Create attention mask (all 1s for actual tokens) + let seq_len = prompt_embeds.dim(1)?; + let prompt_mask = Tensor::ones((1, seq_len), DType::F32, &self.context.device)? + .to_dtype(self.context.dtype)?; + + // 2. Prepare latents + let latent_h = height / vae_spatial; + let latent_w = width / vae_spatial; + let latent_f = (num_frames - 1) / vae_temporal + 1; + let num_channels = tcfg.in_channels; // 128 + + let latents_5d = Tensor::randn( + 0f32, + 1f32, + (1, num_channels, latent_f, latent_h, latent_w), + &self.context.device, + )? + .to_dtype(self.context.dtype)?; + + // Pack latents: [B, C, F, H, W] -> [B, S, D] + let mut latents = + LtxPipeline::pack_latents(&latents_5d, patch_size, patch_size_t)?; + + // 3. Prepare RoPE video coordinates + let video_coords = self.prepare_video_coords( + latent_f, + latent_h, + latent_w, + vae_temporal, + vae_spatial, + frame_rate, + )?; + + // 4. Prepare scheduler + let video_seq_len = latent_f * latent_h * latent_w; + + // Get timesteps from config or compute sigmas + let timesteps: Vec = if let Some(ref ts) = inference.timesteps { + ts.clone() + } else { + // Linspace from 1.0 to 1/num_steps + let mut ts = Vec::with_capacity(num_steps); + for i in 0..num_steps { + ts.push(1.0 - (i as f32) / (num_steps as f32)); + } + ts + }; + + // Compute mu for time shifting + let sched_cfg = &full_config.scheduler; + let base_seq = sched_cfg.base_image_seq_len.unwrap_or(256); + let max_seq = sched_cfg.max_image_seq_len.unwrap_or(4096); + let base_shift = sched_cfg.base_shift.unwrap_or(0.5); + let max_shift = sched_cfg.max_shift.unwrap_or(1.15); + let mu = t2v_pipeline::calculate_shift( + video_seq_len, + base_seq, + max_seq, + base_shift as f32, + max_shift as f32, + ); + + // Initialize scheduler and set timesteps + let mut scheduler = FlowMatchEulerDiscreteScheduler::new(full_config.scheduler.clone())?; + let sigmas: Vec = timesteps.clone(); + scheduler.set_timesteps( + None, + &self.context.device, + Some(&sigmas), + Some(mu), + None, + )?; + // Get timesteps as f32 vector + let schedule: Vec = scheduler.timesteps.to_vec1()?; + + info!( + "Denoising: {} steps, mu={:.4}, seq_len={}", + schedule.len(), + mu, + video_seq_len + ); + + // 5. Denoising loop + for (step, &t) in schedule.iter().enumerate() { + let start_time = std::time::Instant::now(); + + let b = latents.dim(0)?; + let timestep_t = + Tensor::full(t as f32, (b,), &self.context.device)? + .to_dtype(self.context.dtype)?; + + let noise_pred = LtxTransformer::forward_packed( + &mut self.transformer, + latents.to_dtype(self.context.dtype)?, + prompt_embeds.clone(), + timestep_t, + prompt_mask.clone(), + video_coords.clone(), + latent_f, + latent_h, + latent_w, + &mut self.context, + ) + .await? + .to_dtype(DType::F32)?; + + // Euler step + let step_output = scheduler.step(&noise_pred, t, &latents, None)?; + latents = step_output.prev_sample; + + let dt = start_time.elapsed().as_secs_f32(); + info!("step {}/{} done, {:.2}s", step + 1, schedule.len(), dt); + } + + // 6. Unpack latents: [B, S, D] -> [B, C, F, H, W] + let latents_5d = LtxPipeline::unpack_latents( + &latents, + latent_f, + latent_h, + latent_w, + patch_size, + patch_size_t, + )?; + + // 7. Denormalize latents + let vae_config = &full_config.vae; + let latents_mean = + Tensor::new(vae_config.latents_mean.as_slice(), &self.context.device)? + .to_dtype(DType::F32)?; + let latents_std = + Tensor::new(vae_config.latents_std.as_slice(), &self.context.device)? + .to_dtype(DType::F32)?; + let latents_5d = LtxPipeline::denormalize_latents( + &latents_5d.to_dtype(DType::F32)?, + &latents_mean, + &latents_std, + vae_config.scaling_factor as f32, + )? + .to_dtype(self.context.dtype)?; + + // 8. Decode with VAE + info!("Decoding with VAE..."); + let decode_timestep = inference + .decode_timestep + .as_ref() + .and_then(|v| v.first().copied()); + let decode_noise_scale = inference + .decode_noise_scale + .as_ref() + .and_then(|v| v.first().copied()); + + // Optionally add noise for timestep-conditioned decoding + let (latents_for_decode, vae_timestep) = if let Some(dt) = decode_timestep { + let dns = decode_noise_scale.unwrap_or(dt); + let noise = Tensor::randn(0f32, 1f32, latents_5d.dims(), &self.context.device)? + .to_dtype(self.context.dtype)?; + let scale = + Tensor::full(dns, latents_5d.dims(), &self.context.device)? + .to_dtype(self.context.dtype)?; + let one_minus = scale.affine(-1.0, 1.0)?; + let noised = latents_5d.mul(&one_minus)?.add(&noise.mul(&scale)?)?; + let ts = Tensor::full(dt, (1,), &self.context.device)? + .to_dtype(self.context.dtype)?; + (noised, Some(ts)) + } else { + (latents_5d, None) + }; + + let decoded = LtxVae::decode( + &mut self.vae, + latents_for_decode, + vae_timestep, + &mut self.context, + ) + .await?; + + // 9. Convert video frames to images + let frames = self.video_tensor_to_images(&decoded)?; + info!("Generated {} frames", frames.len()); + + Ok(VideoOutput::new( + frames, + frame_rate, + width as u32, + height as u32, + )) + } +} + +impl LtxVideo { + /// Prepare 3D RoPE coordinates for the video latent grid. + fn prepare_video_coords( + &self, + latent_f: usize, + latent_h: usize, + latent_w: usize, + vae_temporal: usize, + vae_spatial: usize, + frame_rate: usize, + ) -> Result { + let device = &self.context.device; + + let grid_f = Tensor::arange(0u32, latent_f as u32, device)?.to_dtype(DType::F32)?; + let grid_h = Tensor::arange(0u32, latent_h as u32, device)?.to_dtype(DType::F32)?; + let grid_w = Tensor::arange(0u32, latent_w as u32, device)?.to_dtype(DType::F32)?; + + let f = grid_f + .reshape((latent_f, 1, 1))? + .broadcast_as((latent_f, latent_h, latent_w))?; + let h = grid_h + .reshape((1, latent_h, 1))? + .broadcast_as((latent_f, latent_h, latent_w))?; + let w = grid_w + .reshape((1, 1, latent_w))? + .broadcast_as((latent_f, latent_h, latent_w))?; + + // Stack [3, F, H, W] -> flatten -> [3, seq] -> transpose -> [seq, 3] -> [1, seq, 3] + let coords = Tensor::stack(&[f.contiguous()?, h.contiguous()?, w.contiguous()?], 0)? + .flatten_from(1)? + .transpose(0, 1)? + .unsqueeze(0)?; + + // Apply causal fix and spatial scaling + let vf = coords.i((.., .., 0))?; + let vh = coords.i((.., .., 1))?; + let vw = coords.i((.., .., 2))?; + + let ts_ratio = vae_temporal as f64; + let sp_ratio = vae_spatial as f64; + + // CAUSAL FIX: (L * temporal_ratio + 1 - temporal_ratio).clamp(0) / frame_rate + let vf = vf + .affine(ts_ratio, 1.0 - ts_ratio)? + .clamp(0.0f32, 1000.0f32)? + .affine(1.0 / (frame_rate as f64), 0.0)?; + + // SPATIAL SCALE: L * spatial_ratio + let vh = vh.affine(sp_ratio, 0.0)?; + let vw = vw.affine(sp_ratio, 0.0)?; + + let video_coords = + Tensor::stack(&[vf, vh, vw], candle_core::D::Minus1)?; + + Ok(video_coords) + } + + /// Convert a decoded video tensor [B, C, T, H, W] to a list of RGB images (one per frame). + fn video_tensor_to_images( + &self, + video: &Tensor, + ) -> Result, Vec>>> { + let mut result = Vec::new(); + + // Video output is in [-1, 1] range, convert to [0, 255] + let video = ((video.clamp(-1f32, 1f32)? + 1.0)? * 127.5)? + .to_dtype(DType::U8)? + .to_device(&Device::Cpu)?; + + let bsize = video.dim(0)?; + for batch in 0..bsize { + let batch_video = video.i(batch)?; // [C, T, H, W] + let (channels, num_frames, height, width) = batch_video.dims4()?; + if channels != 3 { + anyhow::bail!("Expected 3 channels, got {}", channels); + } + + for frame in 0..num_frames { + let frame_tensor = batch_video.i((.., frame, .., ..))?; // [C, H, W] + let frame_tensor = frame_tensor.permute((1, 2, 0))?.flatten_all()?; + let pixels = frame_tensor.to_vec1::()?; + + let image: ImageBuffer, Vec> = + ImageBuffer::from_raw(width as u32, height as u32, pixels) + .ok_or_else(|| anyhow::anyhow!("Error creating image buffer"))?; + result.push(image); + } + } + + Ok(result) + } +} + +fn resolve_hf_file(repo: &str, file: &str, cache_dir: &str) -> Result { + let mut cache_path = PathBuf::from(cache_dir); + cache_path.push("hub"); + let cache = Cache::new(cache_path); + let api = ApiBuilder::from_cache(cache).build()?; + let filename = api.model(repo.to_string()).get(file)?; + Ok(filename) +} diff --git a/cake-core/src/models/ltx_video/ltx_video_shardable.rs b/cake-core/src/models/ltx_video/ltx_video_shardable.rs new file mode 100644 index 00000000..7f0b0b7b --- /dev/null +++ b/cake-core/src/models/ltx_video/ltx_video_shardable.rs @@ -0,0 +1,78 @@ +use crate::cake::{Context, Forwarder}; +use super::t5::LtxT5; +use super::transformer::LtxTransformer; +use super::vae_forwarder::LtxVae; +use async_trait::async_trait; +use candle_core::Tensor; +use std::fmt::{Debug, Display, Formatter}; + +#[derive(Debug)] +pub struct LtxVideoShardable { + forwarder: Box, + layer_name: String, +} + +impl Display for LtxVideoShardable { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!(f, "{} (local)", &self.layer_name) + } +} + +#[async_trait] +impl Forwarder for LtxVideoShardable { + fn load(name: String, ctx: &Context) -> anyhow::Result> + where + Self: Sized, + { + let model: Box = match name.as_str() { + "ltx-transformer" => LtxTransformer::load(name.clone(), ctx)?, + "ltx-t5" => LtxT5::load(name.clone(), ctx)?, + "ltx-vae" => LtxVae::load(name.clone(), ctx)?, + _ => anyhow::bail!("LTX-Video component name not recognized: {}", name), + }; + + Ok(Box::new(Self { + forwarder: model, + layer_name: name, + })) + } + + async fn forward( + &self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + self.forwarder.forward(x, index_pos, block_idx, ctx).await + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + self.forwarder + .forward_mut(x, index_pos, block_idx, ctx) + .await + } + + async fn forward_batch( + &mut self, + x: &Tensor, + batch: Vec<(String, usize, usize)>, + ctx: &mut Context, + ) -> anyhow::Result { + self.forwarder.forward_batch(x, batch, ctx).await + } + + fn layer_name(&self) -> &str { + &self.layer_name + } + + fn ident(&self) -> &str { + &self.layer_name + } +} diff --git a/cake-core/src/models/ltx_video/mod.rs b/cake-core/src/models/ltx_video/mod.rs new file mode 100644 index 00000000..5a982552 --- /dev/null +++ b/cake-core/src/models/ltx_video/mod.rs @@ -0,0 +1,9 @@ +pub mod vendored; + +mod ltx_video; +mod ltx_video_shardable; +mod t5; +mod transformer; +mod vae_forwarder; + +pub use ltx_video::*; diff --git a/cake-core/src/models/ltx_video/t5.rs b/cake-core/src/models/ltx_video/t5.rs new file mode 100644 index 00000000..33e02181 --- /dev/null +++ b/cake-core/src/models/ltx_video/t5.rs @@ -0,0 +1,137 @@ +use crate::cake::{Context, Forwarder}; +use async_trait::async_trait; +use candle_core::Tensor; +use candle_transformers::models::t5::{self, T5EncoderModel}; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use log::info; +use std::fmt::{Debug, Display, Formatter}; +use std::path::PathBuf; + +const T5_XXL_REPO: &str = "google/t5-v1_1-xxl"; + +#[derive(Debug)] +pub struct LtxT5 { + model: T5EncoderModel, +} + +impl Display for LtxT5 { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!(f, "ltx-t5 (local)") + } +} + +#[async_trait] +impl Forwarder for LtxT5 { + fn load(_name: String, ctx: &Context) -> anyhow::Result> + where + Self: Sized, + { + Self::load_model(ctx) + } + + async fn forward( + &self, + _x: &Tensor, + _index_pos: usize, + _block_idx: usize, + _ctx: &mut Context, + ) -> anyhow::Result { + anyhow::bail!("T5 encoder requires forward_mut (has KV cache)") + } + + async fn forward_mut( + &mut self, + x: &Tensor, + _index_pos: usize, + _block_idx: usize, + _ctx: &mut Context, + ) -> anyhow::Result { + info!("LTX T5 encoder forwarding..."); + Ok(self.model.forward(x)?) + } + + fn layer_name(&self) -> &str { + "ltx-t5" + } +} + +impl LtxT5 { + /// Resolve a file from the LTX model repo or T5-XXL repo via HuggingFace cache. + fn resolve_hf_file( + repo: &str, + file: &str, + cache_dir: &str, + ) -> anyhow::Result { + let mut cache_path = PathBuf::from(cache_dir); + cache_path.push("hub"); + let cache = Cache::new(cache_path); + let api = ApiBuilder::from_cache(cache).build()?; + let filename = api.model(repo.to_string()).get(file)?; + Ok(filename) + } + + pub fn load_model(ctx: &Context) -> anyhow::Result> { + let ltx_args = &ctx.args.ltx_args; + + // Load T5 config from the LTX model repo (or T5-XXL fallback) + let config_path = if let Some(ref p) = ltx_args.ltx_t5_config { + PathBuf::from(p) + } else { + // LTX-Video ships T5 config in the main repo + let ltx_repo = ltx_args.ltx_repo(); + Self::resolve_hf_file(<x_repo, "text_encoder/config.json", &ctx.args.model) + .or_else(|_| { + Self::resolve_hf_file(T5_XXL_REPO, "config.json", &ctx.args.model) + })? + }; + + info!("Loading T5 config from {:?}...", config_path); + let config: t5::Config = serde_json::from_reader(std::fs::File::open(&config_path)?)?; + + // Load T5 weights (potentially sharded) + let weight_files = if let Some(ref p) = ltx_args.ltx_t5 { + p.split(',').map(|s| PathBuf::from(s.trim())).collect() + } else { + let ltx_repo = ltx_args.ltx_repo(); + Self::get_t5_weight_files(<x_repo, &ctx.args.model)? + }; + + info!("Loading T5 encoder from {:?}...", weight_files); + + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors(&weight_files, ctx.dtype, &ctx.device)? + }; + let model = T5EncoderModel::load(vb, &config)?; + + info!("T5 encoder loaded!"); + + Ok(Box::new(Self { model })) + } + + fn get_t5_weight_files(repo: &str, cache_dir: &str) -> anyhow::Result> { + let mut cache_path = PathBuf::from(cache_dir); + cache_path.push("hub"); + let cache = Cache::new(cache_path); + let api = ApiBuilder::from_cache(cache).build()?; + let model_api = api.model(repo.to_string()); + + // Try single file first + if let Ok(path) = model_api.get("text_encoder/model.safetensors") { + return Ok(vec![path]); + } + + // Fall back to 2-shard format + let shard1 = model_api.get("text_encoder/model-00001-of-00002.safetensors")?; + let shard2 = model_api.get("text_encoder/model-00002-of-00002.safetensors")?; + Ok(vec![shard1, shard2]) + } + + pub async fn encode( + forwarder: &mut Box, + tokens: Tensor, + ctx: &mut Context, + ) -> anyhow::Result { + forwarder.forward_mut(&tokens, 0, 0, ctx).await + } +} diff --git a/cake-core/src/models/ltx_video/transformer.rs b/cake-core/src/models/ltx_video/transformer.rs new file mode 100644 index 00000000..5c112483 --- /dev/null +++ b/cake-core/src/models/ltx_video/transformer.rs @@ -0,0 +1,215 @@ +use crate::cake::{Context, Forwarder}; +use crate::models::sd::{pack_tensors, unpack_tensors}; +use async_trait::async_trait; +use candle_core::{DType, Tensor}; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use log::info; +use std::fmt::{Debug, Display, Formatter}; +use std::path::PathBuf; + +use super::vendored::configs::get_config_by_version; +use super::vendored::ltx_transformer::LtxVideoTransformer3DModel; +use super::vendored::t2v_pipeline::TransformerConfig; + +#[derive(Debug)] +pub struct LtxTransformer { + model: LtxVideoTransformer3DModel, +} + +impl Display for LtxTransformer { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!(f, "ltx-transformer (local)") + } +} + +#[async_trait] +impl Forwarder for LtxTransformer { + fn load(_name: String, ctx: &Context) -> anyhow::Result> + where + Self: Sized, + { + Self::load_model(ctx) + } + + async fn forward( + &self, + x: &Tensor, + _index_pos: usize, + _block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + let unpacked = unpack_tensors(x)?; + // Packed format: [hidden_states, encoder_hidden_states, timestep, + // encoder_attention_mask, video_coords, + // dims_tensor(num_frames, height, width)] + let hidden_states = unpacked[0].to_dtype(ctx.dtype)?; + let encoder_hidden_states = unpacked[1].to_dtype(ctx.dtype)?; + let timestep = unpacked[2].to_dtype(ctx.dtype)?; + let encoder_attention_mask = unpacked[3].to_dtype(ctx.dtype)?; + let video_coords = unpacked[4].to_dtype(DType::F32)?; + let dims: Vec = unpacked[5].to_vec1()?; + let num_frames = dims[0] as usize; + let height = dims[1] as usize; + let width = dims[2] as usize; + + info!("LTX transformer forwarding..."); + + let result = self.model.forward( + &hidden_states, + &encoder_hidden_states, + ×tep, + Some(&encoder_attention_mask), + num_frames, + height, + width, + None, + Some(&video_coords), + None, + )?; + + Ok(result) + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + self.forward(x, index_pos, block_idx, ctx).await + } + + fn layer_name(&self) -> &str { + "ltx-transformer" + } +} + +impl LtxTransformer { + pub fn load_model(ctx: &Context) -> anyhow::Result> { + let ltx_args = &ctx.args.ltx_args; + let version = <x_args.ltx_version; + let config = get_config_by_version(version); + + let weights_path = if let Some(ref p) = ltx_args.ltx_transformer { + PathBuf::from(p) + } else { + let repo = ltx_args.ltx_repo(); + let mut cache_path = PathBuf::from(&ctx.args.model); + cache_path.push("hub"); + let cache = Cache::new(cache_path); + let api = ApiBuilder::from_cache(cache).build()?; + let model_api = api.model(repo); + + // Try single file first, then sharded + if let Ok(path) = model_api.get("transformer/diffusion_pytorch_model.safetensors") { + path + } else { + // Try sharded format + let index_path = model_api + .get("transformer/diffusion_pytorch_model.safetensors.index.json")?; + let _index: serde_json::Value = + serde_json::from_reader(std::fs::File::open(&index_path)?)?; + // Just return the first shard path - loading will handle all + index_path + .parent() + .unwrap() + .join("diffusion_pytorch_model-00001-of-00002.safetensors") + } + }; + + info!( + "Loading LTX transformer (version {}) from {:?}...", + version, weights_path + ); + + // Handle sharded weights + let weight_files = Self::find_weight_files(&weights_path)?; + + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &weight_files, + ctx.dtype, + &ctx.device, + )? + }; + + let model = LtxVideoTransformer3DModel::new(&config.transformer, vb)?; + + info!("LTX transformer loaded!"); + + Ok(Box::new(Self { model })) + } + + fn find_weight_files(path: &PathBuf) -> anyhow::Result> { + // If the path is a single safetensors file, use it + if path.extension().map_or(false, |e| e == "safetensors") && path.exists() { + return Ok(vec![path.clone()]); + } + + // Check for sharded format in the same directory + if let Some(parent) = path.parent() { + let mut shards = Vec::new(); + for entry in std::fs::read_dir(parent)? { + let entry = entry?; + let p = entry.path(); + if let Some(name) = p.file_name().and_then(|n| n.to_str()) { + if name.starts_with("diffusion_pytorch_model") + && name.ends_with(".safetensors") + && !name.contains("index") + { + shards.push(p); + } + } + } + if !shards.is_empty() { + shards.sort(); + return Ok(shards); + } + } + + Ok(vec![path.clone()]) + } + + pub fn pipeline_config(version: &str) -> TransformerConfig { + let config = get_config_by_version(version); + TransformerConfig { + in_channels: config.transformer.in_channels, + patch_size: config.transformer.patch_size, + patch_size_t: config.transformer.patch_size_t, + num_layers: config.transformer.num_layers, + } + } + + /// Pack tensors for network transport and call the forwarder. + #[allow(clippy::too_many_arguments)] + pub async fn forward_packed( + forwarder: &mut Box, + hidden_states: Tensor, + encoder_hidden_states: Tensor, + timestep: Tensor, + encoder_attention_mask: Tensor, + video_coords: Tensor, + num_frames: usize, + height: usize, + width: usize, + ctx: &mut Context, + ) -> anyhow::Result { + let dims = Tensor::from_vec( + vec![num_frames as f32, height as f32, width as f32], + 3, + &ctx.device, + )?; + let tensors = vec![ + hidden_states, + encoder_hidden_states, + timestep, + encoder_attention_mask, + video_coords, + dims, + ]; + let packed = pack_tensors(tensors, &ctx.device)?; + forwarder.forward_mut(&packed, 0, 0, ctx).await + } +} diff --git a/cake-core/src/models/ltx_video/vae_forwarder.rs b/cake-core/src/models/ltx_video/vae_forwarder.rs new file mode 100644 index 00000000..bb5bf3f6 --- /dev/null +++ b/cake-core/src/models/ltx_video/vae_forwarder.rs @@ -0,0 +1,140 @@ +use crate::cake::{Context, Forwarder}; +use crate::models::sd::{pack_tensors, unpack_tensors}; +use async_trait::async_trait; +use candle_core::Tensor; +use hf_hub::api::sync::ApiBuilder; +use hf_hub::Cache; +use log::info; +use std::fmt::{Debug, Display, Formatter}; +use std::path::PathBuf; + +use super::vendored::configs::get_config_by_version; +use super::vendored::vae::AutoencoderKLLtxVideo; + +#[derive(Debug)] +pub struct LtxVae { + model: AutoencoderKLLtxVideo, +} + +impl Display for LtxVae { + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + write!(f, "ltx-vae (local)") + } +} + +#[async_trait] +impl Forwarder for LtxVae { + fn load(_name: String, ctx: &Context) -> anyhow::Result> + where + Self: Sized, + { + Self::load_model(ctx) + } + + async fn forward( + &self, + x: &Tensor, + _index_pos: usize, + _block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + info!("LTX VAE forwarding..."); + + let unpacked = unpack_tensors(x)?; + + // Protocol: [direction, data, optional_timestep] + // direction: 1.0 = encode, 0.0 = decode + let direction_vec: Vec = unpacked[0].to_vec1()?; + let direction = *direction_vec.first().expect("Error retrieving direction"); + + let input = unpacked[1].to_dtype(ctx.dtype)?; + + if direction == 1.0 { + // Encode + let encoded = self.model.encoder.forward(&input, false)?; + let dist = + super::vendored::vae::DiagonalGaussianDistribution::new(&encoded)?; + Ok(dist.mode()?) + } else { + // Decode + let timestep = if unpacked.len() > 2 { + Some(unpacked[2].to_dtype(ctx.dtype)?) + } else { + None + }; + + let decoded = self.model.decoder.forward( + &input, + timestep.as_ref(), + false, + )?; + Ok(decoded) + } + } + + async fn forward_mut( + &mut self, + x: &Tensor, + index_pos: usize, + block_idx: usize, + ctx: &mut Context, + ) -> anyhow::Result { + self.forward(x, index_pos, block_idx, ctx).await + } + + fn layer_name(&self) -> &str { + "ltx-vae" + } +} + +impl LtxVae { + pub fn load_model(ctx: &Context) -> anyhow::Result> { + let ltx_args = &ctx.args.ltx_args; + let version = <x_args.ltx_version; + let config = get_config_by_version(version); + + let weights_path = if let Some(ref p) = ltx_args.ltx_vae { + PathBuf::from(p) + } else { + let repo = ltx_args.ltx_repo(); + let mut cache_path = PathBuf::from(&ctx.args.model); + cache_path.push("hub"); + let cache = Cache::new(cache_path); + let api = ApiBuilder::from_cache(cache).build()?; + let model_api = api.model(repo); + model_api.get("vae/diffusion_pytorch_model.safetensors")? + }; + + info!("Loading LTX VAE from {:?}...", weights_path); + + let vb = unsafe { + candle_nn::VarBuilder::from_mmaped_safetensors( + &[weights_path], + ctx.dtype, + &ctx.device, + )? + }; + let model = AutoencoderKLLtxVideo::new(config.vae, vb)?; + + info!("LTX VAE loaded!"); + + Ok(Box::new(Self { model })) + } + + pub async fn decode( + forwarder: &mut Box, + latents: Tensor, + timestep: Option, + ctx: &mut Context, + ) -> anyhow::Result { + let mut tensors = vec![ + Tensor::from_slice(&[0f32], 1, &ctx.device)?, + latents, + ]; + if let Some(t) = timestep { + tensors.push(t); + } + let packed = pack_tensors(tensors, &ctx.device)?; + forwarder.forward_mut(&packed, 0, 0, ctx).await + } +} diff --git a/cake-core/src/models/ltx_video/vendored/configs.rs b/cake-core/src/models/ltx_video/vendored/configs.rs new file mode 100644 index 00000000..0eb813f7 --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/configs.rs @@ -0,0 +1,325 @@ +//! Official LTX-Video configurations and presets. +//! Based on official configs from tp/LTX-Video/configs/ +//! Supports versions 0.9.5+ + +use super::ltx_transformer::LtxVideoTransformer3DModelConfig; +use super::scheduler::FlowMatchEulerDiscreteSchedulerConfig; +use super::vae::AutoencoderKLLtxVideoConfig; +use serde::{Deserialize, Serialize}; + +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct LTXVInferenceConfig { + pub guidance_scale: f32, + pub num_inference_steps: usize, + pub stg_scale: f32, + pub rescaling_scale: f32, + pub stochastic_sampling: bool, + pub skip_block_list: Vec, + pub timesteps: Option>, + pub decode_timestep: Option>, + pub decode_noise_scale: Option>, +} + +impl Default for LTXVInferenceConfig { + fn default() -> Self { + Self { + guidance_scale: 3.0, + num_inference_steps: 40, + stg_scale: 1.0, + rescaling_scale: 0.7, + stochastic_sampling: false, + skip_block_list: vec![], + timesteps: None, + decode_timestep: None, + decode_noise_scale: None, + } + } +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct LTXVFullConfig { + pub inference: LTXVInferenceConfig, + pub transformer: LtxVideoTransformer3DModelConfig, + pub vae: AutoencoderKLLtxVideoConfig, + pub scheduler: FlowMatchEulerDiscreteSchedulerConfig, +} + +/// Returns the full configuration for a given version string. +/// Supports 0.9.5+ only. +pub fn get_config_by_version(version: &str) -> LTXVFullConfig { + match version { + // 0.9.5 + "0.9.5" | "0.9.5-2b" => presets::v0_9_5_2b(), + + // 0.9.6 + "0.9.6-dev" | "0.9.6-2b-dev" => presets::v0_9_6_dev_2b(), + "0.9.6-distilled" | "0.9.6-2b-distilled" => presets::v0_9_6_distilled_2b(), + + // 0.9.8 2B + "0.9.8-2b-distilled" | "0.9.8-distilled" => presets::v0_9_8_distilled_2b(), + + // 0.9.8 13B + "0.9.8-13b-dev" => presets::v0_9_8_dev_13b(), + "0.9.8-13b-distilled" | "0.9.8-13b" => presets::v0_9_8_distilled_13b(), + + // Default to 0.9.5 + _ => presets::v0_9_5_2b(), + } +} + +use super::scheduler::TimeShiftType; + +pub mod presets { + use super::*; + + /// Common VAE config for 0.9.5+ + /// Based on OURS_VAE_CONFIG from diffusers_config_mapping.py: + /// - dims: 3 + /// - latent_channels: 128 + /// - blocks: [res_x(4), compress_all, res_x_y, res_x(3), compress_all, res_x_y, res_x(3), compress_all, res_x(3), res_x(4)] + /// - norm_layer: "pixel_norm" + /// - patch_size: 4 + /// - latent_log_var: "uniform" + /// - causal_decoder: false + fn common_vae_config() -> AutoencoderKLLtxVideoConfig { + AutoencoderKLLtxVideoConfig { + block_out_channels: vec![128, 256, 512, 1024, 2048], + layers_per_block: vec![4, 6, 6, 2, 2], + latent_channels: 128, + patch_size: 4, + timestep_conditioning: true, + ..Default::default() + } + } + + /// Common scheduler config for 0.9.5+ + /// Based on OURS_SCHEDULER_CONFIG: + /// - num_train_timesteps: 1000 + /// - shifting: "SD3" + /// - target_shift_terminal: 0.1 + fn common_scheduler_config() -> FlowMatchEulerDiscreteSchedulerConfig { + // Official LTX-Video uses SD3 resolution-dependent shifting with target_shift_terminal=0.1 + // For now we use a fixed shift approximation based on typical latent sizes + // TODO: Implement proper SD3 resolution-dependent shifting + FlowMatchEulerDiscreteSchedulerConfig { + num_train_timesteps: 1000, + shift: 1.0, + use_dynamic_shifting: false, // LTX uses manual mu + base_shift: Some(0.95), + max_shift: Some(2.05), + base_image_seq_len: Some(1024), + max_image_seq_len: Some(4096), + invert_sigmas: false, + shift_terminal: Some(0.1), + use_karras_sigmas: false, + use_exponential_sigmas: false, + use_beta_sigmas: false, + time_shift_type: TimeShiftType::Exponential, + stochastic_sampling: false, + } + } + + /// 2B transformer config (28 layers) + /// Based on OURS_TRANSFORMER_CONFIG from diffusers_config_mapping.py: + /// - num_layers: 28 + /// - num_attention_heads: 32 + /// - attention_head_dim: 64 + /// - cross_attention_dim: 2048 + /// - caption_channels: 4096 + /// - in_channels/out_channels: 128 + /// - qk_norm: "rms_norm" + /// - positional_embedding_type: "rope" + /// - positional_embedding_theta: 10000.0 + /// - positional_embedding_max_pos: [20, 2048, 2048] + /// - timestep_scale_multiplier: 1000 + fn transformer_2b_config() -> LtxVideoTransformer3DModelConfig { + LtxVideoTransformer3DModelConfig { + num_layers: 28, + num_attention_heads: 32, + attention_head_dim: 64, + cross_attention_dim: 2048, + caption_channels: 4096, + ..Default::default() + } + } + + /// 13B transformer config (48 layers) + /// Larger model with: + /// - num_layers: 48 + /// - attention_head_dim: 128 + /// - cross_attention_dim: 4096 + fn transformer_13b_config() -> LtxVideoTransformer3DModelConfig { + LtxVideoTransformer3DModelConfig { + num_layers: 48, + num_attention_heads: 32, + attention_head_dim: 128, + cross_attention_dim: 4096, + caption_channels: 4096, + ..Default::default() + } + } + + /// ltxv-2b-0.9.5.yaml + pub fn v0_9_5_2b() -> LTXVFullConfig { + LTXVFullConfig { + inference: LTXVInferenceConfig { + guidance_scale: 3.0, + num_inference_steps: 40, + stg_scale: 1.0, + rescaling_scale: 0.7, + stochastic_sampling: false, + skip_block_list: vec![19], + timesteps: None, + decode_timestep: None, + decode_noise_scale: None, + }, + transformer: transformer_2b_config(), + vae: common_vae_config(), + scheduler: common_scheduler_config(), + } + } + + /// ltxv-2b-0.9.6-dev.yaml + pub fn v0_9_6_dev_2b() -> LTXVFullConfig { + LTXVFullConfig { + inference: LTXVInferenceConfig { + guidance_scale: 3.0, + num_inference_steps: 40, + stg_scale: 1.0, + rescaling_scale: 0.7, + stochastic_sampling: false, + skip_block_list: vec![19], + timesteps: None, + decode_timestep: None, + decode_noise_scale: None, + }, + transformer: transformer_2b_config(), + vae: common_vae_config(), + scheduler: common_scheduler_config(), + } + } + + /// ltxv-2b-0.9.6-distilled.yaml + pub fn v0_9_6_distilled_2b() -> LTXVFullConfig { + LTXVFullConfig { + inference: LTXVInferenceConfig { + guidance_scale: 1.0, + num_inference_steps: 8, + stg_scale: 0.0, + rescaling_scale: 1.0, + stochastic_sampling: true, + skip_block_list: vec![], + timesteps: None, + decode_timestep: None, + decode_noise_scale: None, + }, + transformer: transformer_2b_config(), + vae: common_vae_config(), + scheduler: common_scheduler_config(), + } + } + + /// ltxv-2b-0.9.8-distilled.yaml (first_pass config) + pub fn v0_9_8_distilled_2b() -> LTXVFullConfig { + LTXVFullConfig { + inference: LTXVInferenceConfig { + guidance_scale: 1.0, + num_inference_steps: 7, + stg_scale: 0.0, + rescaling_scale: 1.0, + stochastic_sampling: false, + skip_block_list: vec![], + timesteps: Some(vec![1.0000, 0.9937, 0.9875, 0.9812, 0.9750, 0.9094, 0.7250]), + decode_timestep: Some(vec![0.05]), + decode_noise_scale: Some(vec![0.025]), + }, + transformer: transformer_2b_config(), + vae: common_vae_config(), + scheduler: common_scheduler_config(), + } + } + + /// ltxv-13b-0.9.8-dev.yaml (first_pass config) + pub fn v0_9_8_dev_13b() -> LTXVFullConfig { + LTXVFullConfig { + inference: LTXVInferenceConfig { + // Uses dynamic guidance, we use peak value + guidance_scale: 8.0, + num_inference_steps: 30, + stg_scale: 4.0, + rescaling_scale: 0.5, + stochastic_sampling: false, + // First skip_block_list from guidance schedule + skip_block_list: vec![11, 25, 35, 39], + timesteps: None, + decode_timestep: None, + decode_noise_scale: None, + }, + transformer: transformer_13b_config(), + vae: common_vae_config(), + scheduler: common_scheduler_config(), + } + } + + /// ltxv-13b-0.9.8-distilled.yaml (first_pass config) + pub fn v0_9_8_distilled_13b() -> LTXVFullConfig { + LTXVFullConfig { + inference: LTXVInferenceConfig { + guidance_scale: 1.0, + num_inference_steps: 7, + stg_scale: 0.0, + rescaling_scale: 1.0, + stochastic_sampling: false, + skip_block_list: vec![42], + timesteps: Some(vec![1.0000, 0.9937, 0.9875, 0.9812, 0.9750, 0.9094, 0.7250]), + decode_timestep: Some(vec![0.05]), + decode_noise_scale: Some(vec![0.025]), + }, + transformer: transformer_13b_config(), + vae: common_vae_config(), + scheduler: common_scheduler_config(), + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_v0_9_5_2b_config() { + let config = get_config_by_version("0.9.5"); + assert_eq!(config.transformer.num_layers, 28); + assert_eq!(config.inference.guidance_scale, 3.0); + assert_eq!(config.inference.num_inference_steps, 40); + assert_eq!(config.inference.skip_block_list, vec![19]); + } + + #[test] + fn test_v0_9_8_distilled_2b_config() { + let config = get_config_by_version("0.9.8-2b-distilled"); + assert_eq!(config.transformer.num_layers, 28); + assert_eq!(config.inference.guidance_scale, 1.0); + assert_eq!(config.inference.stg_scale, 0.0); + } + + #[test] + fn test_v0_9_8_13b_distilled_config() { + let config = get_config_by_version("0.9.8-13b-distilled"); + assert_eq!(config.transformer.num_layers, 48); + assert_eq!(config.transformer.attention_head_dim, 128); + assert_eq!(config.transformer.cross_attention_dim, 4096); + assert_eq!(config.inference.skip_block_list, vec![42]); + } + + #[test] + fn test_vae_config_5_blocks() { + let config = get_config_by_version("0.9.5"); + assert_eq!(config.vae.block_out_channels.len(), 5); + assert_eq!( + config.vae.block_out_channels, + vec![128, 256, 512, 1024, 2048] + ); + assert_eq!(config.vae.layers_per_block, vec![4, 6, 6, 2, 2]); + } +} diff --git a/cake-core/src/models/ltx_video/vendored/loader.rs b/cake-core/src/models/ltx_video/vendored/loader.rs new file mode 100644 index 00000000..cc710046 --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/loader.rs @@ -0,0 +1,655 @@ +//! Safetensors weight loading with mapping support +//! +//! This module provides comprehensive weight loading utilities for loading +//! model weights from safetensors files, with support for: +//! +//! - Single file loading +//! - Sharded model loading with automatic detection via `model.safetensors.index.json` +//! - JSON config parsing for VAE/DiT configurations +//! - Python → Rust name mapping (exact, prefix, suffix) +//! - Tensor name validation + +use candle_core::{DType, Device, Result, Tensor}; +use candle_nn::VarBuilder; +use serde::{Deserialize, Serialize, de::DeserializeOwned}; +use std::collections::{HashMap, HashSet}; +use std::path::{Path, PathBuf}; + +// ============================================================================= +// Error Types +// ============================================================================= + +/// Errors that can occur during weight loading +#[derive(Debug, thiserror::Error)] +pub enum LoaderError { + #[error("Failed to read file: {path}")] + FileRead { + path: String, + #[source] + source: std::io::Error, + }, + + #[error("Failed to parse JSON config: {path}")] + JsonParse { + path: String, + #[source] + source: serde_json::Error, + }, + + #[error("Missing shard files: {missing:?}")] + MissingShards { missing: Vec }, + + #[error("No safetensors files found in directory: {path}")] + NoSafetensorsFound { path: String }, + + #[error("Missing required tensors: {missing:?}")] + MissingTensors { missing: Vec }, + + #[error("Invalid safetensors file: {path}")] + InvalidSafetensors { + path: String, + #[source] + source: safetensors::SafeTensorError, + }, + + #[error("Candle error: {0}")] + Candle(#[from] candle_core::Error), +} + +// ============================================================================= +// Name Mapping Types +// ============================================================================= + +/// Types of name mapping transformations +#[derive(Debug, Clone)] +enum MappingRule { + /// Exact match replacement + Exact { from: String, to: String }, + /// Prefix replacement (strip prefix and optionally add new one) + Prefix { + from_prefix: String, + to_prefix: String, + }, + /// Suffix replacement + Suffix { + from_suffix: String, + to_suffix: String, + }, +} + +impl MappingRule { + /// Apply this mapping rule to a name, returning the mapped name if applicable + fn apply(&self, name: &str) -> Option { + match self { + MappingRule::Exact { from, to } => { + if name == from { + Some(to.clone()) + } else { + None + } + } + MappingRule::Prefix { + from_prefix, + to_prefix, + } => { + if name.starts_with(from_prefix) { + Some(format!("{}{}", to_prefix, &name[from_prefix.len()..])) + } else { + None + } + } + MappingRule::Suffix { + from_suffix, + to_suffix, + } => { + if name.ends_with(from_suffix) { + let base = &name[..name.len() - from_suffix.len()]; + Some(format!("{}{}", base, to_suffix)) + } else { + None + } + } + } + } +} + +// ============================================================================= +// Safetensors Index (model.safetensors.index.json) +// ============================================================================= + +/// Represents the parsed contents of model.safetensors.index.json +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct SafetensorsIndex { + /// Maps tensor names to their shard file names + pub weight_map: HashMap, + /// Optional metadata about the model + #[serde(default)] + pub metadata: Option, +} + +/// Metadata from the index.json file +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct IndexMetadata { + /// Format of the weights (usually "safetensors") + #[serde(default)] + pub format: Option, + /// Total size of all weights in bytes + #[serde(default)] + pub total_size: Option, + /// Model type if specified + #[serde(default)] + pub model_type: Option, +} + +impl SafetensorsIndex { + /// Load and parse an index.json file + pub fn load(path: impl AsRef) -> std::result::Result { + let path = path.as_ref(); + let content = std::fs::read_to_string(path).map_err(|e| LoaderError::FileRead { + path: path.display().to_string(), + source: e, + })?; + + serde_json::from_str(&content).map_err(|e| LoaderError::JsonParse { + path: path.display().to_string(), + source: e, + }) + } + + /// Get the list of unique shard files referenced in the weight map + pub fn shard_files(&self) -> Vec { + let files: HashSet<_> = self.weight_map.values().collect(); + let mut result: Vec<_> = files.into_iter().cloned().collect(); + result.sort(); + result + } + + /// Get the file name that contains a specific tensor + pub fn get_file_for_tensor(&self, tensor_name: &str) -> Option<&str> { + self.weight_map.get(tensor_name).map(|s| s.as_str()) + } + + /// Check if this index is for a sharded model + pub fn is_sharded(&self) -> bool { + self.shard_files().len() > 1 + } + + /// Get all tensor names in the index + pub fn tensor_names(&self) -> Vec<&str> { + self.weight_map.keys().map(|s| s.as_str()).collect() + } +} + +// ============================================================================= +// Weight Loader +// ============================================================================= + +/// Weight loader with support for sharded safetensors and name mapping +pub struct WeightLoader { + /// Device to load weights onto + device: Device, + /// Data type for weights + dtype: DType, + /// Name mapping rules (applied in order) + mapping_rules: Vec, + /// Whether to use strict mode (error on missing tensors) + strict_mode: bool, +} + +impl WeightLoader { + /// Create a new weight loader + pub fn new(device: Device, dtype: DType) -> Self { + Self { + device, + dtype, + mapping_rules: Vec::new(), + strict_mode: false, + } + } + + /// Add an exact name mapping rule + /// + /// This is useful when Python model uses different naming conventions + /// than the Rust implementation. + /// + /// # Example + /// ``` + /// use candle_core::{Device, DType}; + /// use candle_video::loader::WeightLoader; + /// + /// let loader = WeightLoader::new(Device::Cpu, DType::F32) + /// .add_mapping("model.diffusion_model", "diffusion_model"); + /// ``` + pub fn add_mapping(mut self, from: impl Into, to: impl Into) -> Self { + self.mapping_rules.push(MappingRule::Exact { + from: from.into(), + to: to.into(), + }); + self + } + + /// Add a prefix mapping rule + /// + /// Strips the `from_prefix` and optionally prepends `to_prefix`. + /// + /// # Example + /// ``` + /// use candle_core::{Device, DType}; + /// use candle_video::loader::WeightLoader; + /// + /// // Remove "model." prefix from all tensor names + /// let loader = WeightLoader::new(Device::Cpu, DType::F32) + /// .add_prefix_mapping("model.", ""); + /// ``` + pub fn add_prefix_mapping( + mut self, + from_prefix: impl Into, + to_prefix: impl Into, + ) -> Self { + self.mapping_rules.push(MappingRule::Prefix { + from_prefix: from_prefix.into(), + to_prefix: to_prefix.into(), + }); + self + } + + /// Add a suffix mapping rule + /// + /// Replaces `from_suffix` with `to_suffix` at the end of tensor names. + /// + /// # Example + /// ``` + /// use candle_core::{Device, DType}; + /// use candle_video::loader::WeightLoader; + /// + /// // Map PyTorch LayerNorm naming to Rust conventions + /// let loader = WeightLoader::new(Device::Cpu, DType::F32) + /// .add_suffix_mapping(".gamma", ".weight") + /// .add_suffix_mapping(".beta", ".bias"); + /// ``` + pub fn add_suffix_mapping( + mut self, + from_suffix: impl Into, + to_suffix: impl Into, + ) -> Self { + self.mapping_rules.push(MappingRule::Suffix { + from_suffix: from_suffix.into(), + to_suffix: to_suffix.into(), + }); + self + } + + /// Set strict mode for tensor loading + /// + /// In strict mode, loading will fail if any expected tensors are missing. + pub fn with_strict_mode(mut self, strict: bool) -> Self { + self.strict_mode = strict; + self + } + + /// Check if strict mode is enabled + pub fn is_strict_mode(&self) -> bool { + self.strict_mode + } + + /// Check if a mapping exists for the given name + pub fn has_mapping(&self, name: &str) -> bool { + self.mapping_rules + .iter() + .any(|rule| rule.apply(name).is_some()) + } + + /// Apply all mapping rules to a tensor name + /// + /// Rules are applied in order. If a rule matches, its result is used + /// as input for subsequent rules. + pub fn map_name(&self, name: &str) -> String { + let mut current = name.to_string(); + + for rule in &self.mapping_rules { + if let Some(mapped) = rule.apply(¤t) { + current = mapped; + } + } + + current + } + + /// Load weights from a single safetensors file + pub fn load_single(&self, path: impl AsRef) -> Result> { + let path = path.as_ref(); + let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[path], self.dtype, &self.device)? }; + Ok(vb) + } + + /// Load weights from multiple sharded safetensors files + pub fn load_sharded(&self, paths: &[PathBuf]) -> Result> { + let paths: Vec<&Path> = paths.iter().map(|p| p.as_path()).collect(); + let vb = unsafe { VarBuilder::from_mmaped_safetensors(&paths, self.dtype, &self.device)? }; + Ok(vb) + } + + /// Load weights from a directory with automatic shard detection + /// + /// This method will: + /// 1. Look for `model.safetensors.index.json` for sharded models + /// 2. Fall back to looking for a single `model.safetensors` + /// 3. Fall back to scanning for any `.safetensors` files + /// + /// If `strict_mode` is enabled and an index.json is found, it will + /// verify that all referenced shard files exist. + pub fn load_from_directory( + &self, + dir: impl AsRef, + ) -> std::result::Result, LoaderError> { + let dir = dir.as_ref(); + + // First, check for index.json (sharded model) + let index_path = dir.join("model.safetensors.index.json"); + if index_path.exists() { + let index = SafetensorsIndex::load(&index_path)?; + let shard_files = index.shard_files(); + + // Verify all shard files exist + let mut missing = Vec::new(); + let mut paths = Vec::new(); + + for shard in &shard_files { + let shard_path = dir.join(shard); + if !shard_path.exists() { + missing.push(shard.clone()); + } else { + paths.push(shard_path); + } + } + + if !missing.is_empty() { + return Err(LoaderError::MissingShards { missing }); + } + + return self.load_sharded(&paths).map_err(LoaderError::from); + } + + // Check for single model.safetensors + let single_path = dir.join("model.safetensors"); + if single_path.exists() { + return self.load_single(&single_path).map_err(LoaderError::from); + } + + // Fall back to scanning for .safetensors files + let files = find_sharded_files(dir, "").map_err(|e| LoaderError::FileRead { + path: dir.display().to_string(), + source: std::io::Error::other(e.to_string()), + })?; + + if files.is_empty() { + return Err(LoaderError::NoSafetensorsFound { + path: dir.display().to_string(), + }); + } + + if files.len() == 1 { + self.load_single(&files[0]).map_err(LoaderError::from) + } else { + self.load_sharded(&files).map_err(LoaderError::from) + } + } + + /// Get the data type used by this loader + pub fn dtype(&self) -> DType { + self.dtype + } + + /// Get the device used by this loader + pub fn device(&self) -> &Device { + &self.device + } + + /// Get a tensor by name with optional mapping + /// + /// Note: This is a placeholder. In practice, you need to know the shape + /// to call VarBuilder::get. This method should be used with shape information. + pub fn get_tensor>( + &self, + vb: &VarBuilder, + shape: S, + name: &str, + ) -> Result { + let mapped_name = self.map_name(name); + vb.get(shape, &mapped_name) + } + + /// Load all tensors from a safetensors file into a HashMap + pub fn load_all_tensors(&self, path: impl AsRef) -> Result> { + use candle_core::safetensors::load; + let tensors = load(path, &self.device)?; + Ok(tensors) + } +} + +// ============================================================================= +// Utility Functions +// ============================================================================= + +/// Helper to find all sharded safetensors files in a directory +/// +/// Files are sorted alphabetically to ensure consistent ordering. +pub fn find_sharded_files(dir: impl AsRef, prefix: &str) -> Result> { + use std::fs; + let dir = dir.as_ref(); + let mut files = Vec::new(); + + for entry in fs::read_dir(dir)? { + let entry = entry?; + let path = entry.path(); + if let Some(name) = path.file_name().and_then(|n| n.to_str()) { + if name.starts_with(prefix) && name.ends_with(".safetensors") { + files.push(path); + } + } + + + + + + } + } + + files.sort(); + Ok(files) +} + +/// Load a JSON configuration file and deserialize it +/// +/// # Example +/// ```no_run +/// use candle_video::loader::load_model_config; +/// use candle_video::config::VaeConfig; +/// +/// let config: VaeConfig = load_model_config("path/to/config.json").unwrap(); +/// ``` +pub fn load_model_config( + path: impl AsRef, +) -> std::result::Result { + let path = path.as_ref(); + let content = std::fs::read_to_string(path).map_err(|e| LoaderError::FileRead { + path: path.display().to_string(), + source: e, + })?; + + serde_json::from_str(&content).map_err(|e| LoaderError::JsonParse { + path: path.display().to_string(), + source: e, + }) +} + +/// Validate that all expected tensors are present in the loaded weights +/// +/// Returns a list of missing tensor names. +/// +/// # Example +/// ``` +/// use candle_video::loader::validate_tensor_names; +/// +/// let expected = vec!["weight1".to_string(), "weight2".to_string()]; +/// let actual = vec!["weight1"]; +/// +/// let missing = validate_tensor_names(&expected, &actual); +/// assert_eq!(missing, vec!["weight2".to_string()]); +/// ``` +pub fn validate_tensor_names(expected: &[String], actual: &[&str]) -> Vec { + let actual_set: HashSet<_> = actual.iter().cloned().collect(); + + expected + .iter() + .filter(|name| !actual_set.contains(name.as_str())) + .cloned() + .collect() +} + +/// List all tensor names in a safetensors file +/// +/// This is useful for debugging and validation. +pub fn list_tensor_names(path: impl AsRef) -> std::result::Result, LoaderError> { + let path = path.as_ref(); + let data = std::fs::read(path).map_err(|e| LoaderError::FileRead { + path: path.display().to_string(), + source: e, + })?; + + let tensors = safetensors::SafeTensors::deserialize(&data).map_err(|e| { + LoaderError::InvalidSafetensors { + path: path.display().to_string(), + source: e, + } + })?; + + Ok(tensors.names().into_iter().map(|s| s.to_string()).collect()) +} + +/// Get tensor metadata (dtype, shape) without loading the actual data +pub fn get_tensor_info( + path: impl AsRef, +) -> std::result::Result, LoaderError> { + let path = path.as_ref(); + let data = std::fs::read(path).map_err(|e| LoaderError::FileRead { + path: path.display().to_string(), + source: e, + })?; + + let tensors = safetensors::SafeTensors::deserialize(&data).map_err(|e| { + LoaderError::InvalidSafetensors { + path: path.display().to_string(), + source: e, + } + })?; + + let mut info = HashMap::new(); + for name in tensors.names() { + if let Ok(view) = tensors.tensor(name) { + info.insert( + name.to_string(), + TensorInfo { + dtype: format!("{:?}", view.dtype()), + shape: view.shape().to_vec(), + }, + ); + } + } + + Ok(info) +} + +/// Information about a tensor (without the actual data) +#[derive(Debug, Clone)] +pub struct TensorInfo { + /// Data type as a string + pub dtype: String, + /// Shape of the tensor + pub shape: Vec, +} + +// ============================================================================= +// Tests +// ============================================================================= + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_weight_loader_creation() { + let loader = WeightLoader::new(Device::Cpu, DType::F32); + assert_eq!(loader.dtype, DType::F32); + } + + #[test] + fn test_name_mapping_exact() { + let loader = WeightLoader::new(Device::Cpu, DType::F32) + .add_mapping("model.diffusion_model", "diffusion_model"); + + assert_eq!(loader.map_name("model.diffusion_model"), "diffusion_model"); + // Unmapped names should return as-is + assert_eq!(loader.map_name("other.name"), "other.name"); + } + + #[test] + fn test_name_mapping_prefix() { + let loader = WeightLoader::new(Device::Cpu, DType::F32).add_prefix_mapping("model.", ""); + + assert_eq!( + loader.map_name("model.transformer.weight"), + "transformer.weight" + ); + // Non-matching prefix + assert_eq!(loader.map_name("other.weight"), "other.weight"); + } + + #[test] + fn test_name_mapping_suffix() { + let loader = + WeightLoader::new(Device::Cpu, DType::F32).add_suffix_mapping(".gamma", ".weight"); + + assert_eq!(loader.map_name("layer_norm.gamma"), "layer_norm.weight"); + } + + #[test] + fn test_name_mapping_chain() { + let loader = WeightLoader::new(Device::Cpu, DType::F32) + .add_prefix_mapping("model.", "") + .add_suffix_mapping(".gamma", ".weight"); + + // Both rules should apply in sequence + assert_eq!( + loader.map_name("model.layer_norm.gamma"), + "layer_norm.weight" + ); + } + + #[test] + fn test_validate_tensor_names() { + let expected = vec!["a".to_string(), "b".to_string(), "c".to_string()]; + let actual = vec!["a", "b"]; + + let missing = validate_tensor_names(&expected, &actual); + assert_eq!(missing, vec!["c".to_string()]); + } + + #[test] + fn test_safetensors_index_shard_files() { + let mut weight_map = HashMap::new(); + weight_map.insert("a".to_string(), "shard1.safetensors".to_string()); + weight_map.insert("b".to_string(), "shard1.safetensors".to_string()); + weight_map.insert("c".to_string(), "shard2.safetensors".to_string()); + + let index = SafetensorsIndex { + weight_map, + metadata: None, + }; + + let shards = index.shard_files(); + assert_eq!(shards.len(), 2); + assert!(shards.contains(&"shard1.safetensors".to_string())); + assert!(shards.contains(&"shard2.safetensors".to_string())); + } +} diff --git a/cake-core/src/models/ltx_video/vendored/ltx_transformer.rs b/cake-core/src/models/ltx_video/vendored/ltx_transformer.rs new file mode 100644 index 00000000..bf405f14 --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/ltx_transformer.rs @@ -0,0 +1,1302 @@ +//! Rust 2024 + candle port of transformer_ltx.py (LTX-Video transformer core). +//! +//! Notes: +//! - This is a self-contained module intended to compile and mirror the structure +//! of the provided Python file. +//! - Some components imported in Python are implemented here minimally to match +//! the tensor contracts used in the file (e.g., AdaLayerNormSingle, PixArtAlphaTextProjection). + +use super::t2v_pipeline::{TransformerConfig, VideoTransformer3D}; +use candle_core::{D, DType, Device, IndexOp, Result, Tensor}; +use candle_nn as nn; +use nn::{Module, VarBuilder}; + +#[derive(Clone, Debug)] +pub struct Transformer2DModelOutput { + pub sample: Tensor, +} + +use serde::{Deserialize, Serialize}; + +/// Configuration for LtxVideoTransformer3DModel +#[derive(Clone, Debug, Serialize, Deserialize)] +pub struct LtxVideoTransformer3DModelConfig { + pub in_channels: usize, + pub out_channels: usize, + pub patch_size: usize, + pub patch_size_t: usize, + pub num_attention_heads: usize, + pub attention_head_dim: usize, + pub cross_attention_dim: usize, + pub num_layers: usize, + pub qk_norm: String, + pub norm_elementwise_affine: bool, + pub norm_eps: f64, + pub caption_channels: usize, + pub attention_bias: bool, + pub attention_out_bias: bool, +} + +impl Default for LtxVideoTransformer3DModelConfig { + fn default() -> Self { + Self { + in_channels: 128, + out_channels: 128, + patch_size: 1, // 0.9.5 uses patch_size 1? Json says 1. + patch_size_t: 1, + num_attention_heads: 32, // 2048 hidden size / 64 + attention_head_dim: 64, + cross_attention_dim: 2048, + num_layers: 28, + qk_norm: "rms_norm_across_heads".to_string(), + norm_elementwise_affine: false, + norm_eps: 1e-6, + caption_channels: 4096, + attention_bias: true, + attention_out_bias: true, + } + } +} + +/// LayerNorm without affine parameters (elementwise_affine=False). +#[derive(Clone, Debug)] +pub struct LayerNormNoParams { + eps: f64, +} + +impl LayerNormNoParams { + pub fn new(eps: f64) -> Self { + Self { eps } + } + + pub fn forward(&self, xs: &Tensor) -> Result { + let last_dim = xs.dim(D::Minus1)?; + let mean = (xs.sum_keepdim(D::Minus1)? / (last_dim as f64))?; + let xc = xs.broadcast_sub(&mean)?; + let var = (xc.sqr()?.sum_keepdim(D::Minus1)? / (last_dim as f64))?; + let denom = (var + self.eps)?.sqrt()?; + xc.broadcast_div(&denom) + } +} + +/// RMSNorm with optional affine weight (elementwise_affine=True/False). +#[derive(Clone, Debug)] +pub struct RmsNorm { + weight: Option, + eps: f64, +} + +impl RmsNorm { + pub fn new(dim: usize, eps: f64, elementwise_affine: bool, vb: VarBuilder) -> Result { + let weight = if elementwise_affine { + Some(vb.get(dim, "weight")?) + } else { + None + }; + Ok(Self { weight, eps }) + } + + pub fn forward(&self, xs: &Tensor) -> Result { + let dtype = xs.dtype(); + let xs_f32 = xs.to_dtype(DType::F32)?; + let dim = xs_f32.dim(D::Minus1)? as f64; + let ms = xs_f32 + .sqr()? + .sum_keepdim(D::Minus1)? + .affine(1.0 / dim, 0.0)?; + let denom = ms.affine(1.0, self.eps)?.sqrt()?; + let ys_f32 = xs_f32.broadcast_div(&denom)?; + let mut ys = ys_f32.to_dtype(dtype)?; + if let Some(w) = &self.weight { + // Broadcast weight over leading dims. + let rank = ys.rank(); + let mut shape = vec![1usize; rank]; + shape[rank - 1] = w.dims1()?; + let w = w.reshape(shape)?; + ys = ys.broadcast_mul(&w)?; + } + Ok(ys) + } +} + +// Helper for GEGLU feed-forward structure usually found in diffusers +// Helper for GELU (approximate) feed-forward projection (Layer 0 of FeedForward) +#[derive(Clone, Debug)] +struct GeluProjection { + proj: nn::Linear, +} + +impl GeluProjection { + fn new(dim_in: usize, dim_out: usize, vb: VarBuilder) -> Result { + let proj = nn::linear(dim_in, dim_out, vb.pp("proj"))?; + Ok(Self { proj }) + } + + fn forward(&self, xs: &Tensor) -> Result { + let x = self.proj.forward(xs)?; + gelu_approximate(&x) + } +} + +impl Module for GeluProjection { + fn forward(&self, xs: &Tensor) -> Result { + self.forward(xs) + } +} + +// FeedForward container matching "net" structure with GEGLU +#[derive(Clone, Debug)] +pub struct FeedForward { + net_0: GeluProjection, + net_2: nn::Linear, +} + +impl FeedForward { + pub fn new(dim: usize, vb: VarBuilder) -> Result { + // net.0: GeluProjection (Linear + Gelu) + // net.2: Linear + let hidden = dim * 4; + + let net_0 = GeluProjection::new(dim, hidden, vb.pp("net.0"))?; + let net_2 = nn::linear(hidden, dim, vb.pp("net.2"))?; + + Ok(Self { net_0, net_2 }) + } + + pub fn forward(&self, xs: &Tensor) -> Result { + let x = self.net_0.forward(xs)?; + self.net_2.forward(&x) + } +} + +/// Minimal PixArtAlphaTextProjection: linear projection to model inner dim. +#[derive(Clone, Debug)] +pub struct PixArtAlphaTextProjection { + linear_1: nn::Linear, + linear_2: nn::Linear, +} + +impl PixArtAlphaTextProjection { + pub fn new(in_features: usize, hidden_size: usize, vb: VarBuilder) -> Result { + let linear_1 = nn::linear(in_features, hidden_size, vb.pp("linear_1"))?; + let linear_2 = nn::linear(hidden_size, hidden_size, vb.pp("linear_2"))?; + Ok(Self { linear_1, linear_2 }) + } + + pub fn forward(&self, xs: &Tensor) -> Result { + let x = self.linear_1.forward(xs)?; + let x = gelu_approximate(&x)?; + self.linear_2.forward(&x) + } +} + +/// Timestep embedding with two linear layers and SiLU. +#[derive(Clone, Debug)] +pub struct TimestepEmbedding { + linear_1: nn::Linear, + linear_2: nn::Linear, +} + +impl TimestepEmbedding { + pub fn new(in_channels: usize, time_embed_dim: usize, vb: VarBuilder) -> Result { + let linear_1 = nn::linear(in_channels, time_embed_dim, vb.pp("linear_1"))?; + let linear_2 = nn::linear(time_embed_dim, time_embed_dim, vb.pp("linear_2"))?; + Ok(Self { linear_1, linear_2 }) + } + + pub fn forward(&self, xs: &Tensor) -> Result { + let x = self.linear_1.forward(xs)?; + let x = x.silu()?; + self.linear_2.forward(&x) + } +} + +pub fn gelu_approximate(x: &Tensor) -> Result { + // Upcast to F32 for math stability + let x_f32 = x.to_dtype(DType::F32)?; + let x_cube = x_f32.sqr()?.broadcast_mul(&x_f32)?; + let inner = x_f32.broadcast_add(&x_cube.affine(0.044715, 0.0)?)?; + let scale = (2.0f64 / std::f64::consts::PI).sqrt() as f32; + let tanh_input = inner.affine(scale as f64, 0.0)?; + let tanh_out = tanh_input.tanh()?; + let gelu = x_f32 + .broadcast_mul(&tanh_out.affine(1.0, 1.0)?)? + .affine(0.5, 0.0)?; + gelu.to_dtype(x.dtype()) +} + +/// PixArtAlphaCombinedTimestepSizeEmbeddings +#[derive(Clone, Debug)] +pub struct PixArtAlphaCombinedTimestepSizeEmbeddings { + timestep_embedder: TimestepEmbedding, +} + +impl PixArtAlphaCombinedTimestepSizeEmbeddings { + pub fn new(embedding_dim: usize, vb: VarBuilder) -> Result { + let timestep_embedder = + TimestepEmbedding::new(256, embedding_dim, vb.pp("timestep_embedder"))?; + Ok(Self { timestep_embedder }) + } + + pub fn forward(&self, timestep: &Tensor) -> Result { + // time_proj produces 256 dimensions, flip_sin_to_cos=true + let timesteps_proj = get_timestep_embedding(timestep, 256, true)?; + self.timestep_embedder.forward(×teps_proj) + } +} + +/// AdaLayerNormSingle: (PixArtAlphaCombinedTimestepSizeEmbeddings + Linear) +#[derive(Clone, Debug)] +pub struct AdaLayerNormSingle { + emb: PixArtAlphaCombinedTimestepSizeEmbeddings, + linear: nn::Linear, +} + +impl AdaLayerNormSingle { + pub fn new(dim: usize, vb: VarBuilder) -> Result { + let emb = PixArtAlphaCombinedTimestepSizeEmbeddings::new(dim, vb.pp("emb"))?; + let linear = nn::linear(dim, 6 * dim, vb.pp("linear"))?; + Ok(Self { emb, linear }) + } + + pub fn forward(&self, timestep: &Tensor) -> Result<(Tensor, Tensor)> { + let embedded_timestep = self.emb.forward(timestep)?; + let x = embedded_timestep.silu()?; + let x = self.linear.forward(&x)?; + Ok((x, embedded_timestep)) + } +} + +/// This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. +fn get_timestep_embedding( + timesteps: &Tensor, + embedding_dim: usize, + flip_sin_to_cos: bool, +) -> Result { + let device = timesteps.device(); + let original_dtype = timesteps.dtype(); + + // Always use F32 for sinusoidal embedding math + let dtype = DType::F32; + + let n = timesteps.dim(0)?; + let half = embedding_dim / 2; + + let t = timesteps.to_dtype(dtype)?; // [N] + let t = t.unsqueeze(1)?; // [N, 1] + + let inv_freq: Vec<_> = (0..half) + .map(|i| 1.0 / 10000f32.powf(i as f32 / (half as f32))) + .collect(); + let inv_freq = Tensor::new(inv_freq.as_slice(), device)?.to_dtype(dtype)?; + let freqs = t.broadcast_mul(&inv_freq.unsqueeze(0)?)?; // [N, half] + + let sin = freqs.sin()?; + let cos = freqs.cos()?; + + let emb = if flip_sin_to_cos { + Tensor::cat(&[cos, sin], D::Minus1)? + } else { + Tensor::cat(&[sin, cos], D::Minus1)? + }; + + if embedding_dim % 2 == 1 { + let pad = Tensor::zeros((n, 1), dtype, device)?; + Tensor::cat(&[emb, pad], D::Minus1)?.to_dtype(original_dtype) + } else { + emb.to_dtype(original_dtype) + } +} + +/// apply_rotary_emb from the Python file: +/// - x: [B, S, C] +/// - freqs: (cos, sin) each [B, S, C] +pub fn apply_rotary_emb(x: &Tensor, cos: &Tensor, sin: &Tensor) -> Result { + let dtype = x.dtype(); + // Upcast to F32 for rotation math stability + let x_f32 = x.to_dtype(DType::F32)?; + let cos = cos.to_dtype(DType::F32)?; + let sin = sin.to_dtype(DType::F32)?; + + let (b, s, c) = x_f32.dims3()?; + if c % 2 != 0 { + candle_core::bail!("apply_rotary_emb expects last dim even, got {c}"); + } + let half = c / 2; + + // x -> [B, S, half, 2] + let x2 = x_f32.reshape((b, s, half, 2))?; + let x_real = x2.i((.., .., .., 0))?; + let x_imag = x2.i((.., .., .., 1))?; + + // [-imag, real] interleave back. + let x_rot = Tensor::stack(&[x_imag.neg()?, x_real.clone()], D::Minus1)?.reshape((b, s, c))?; + + let out = x_f32 + .broadcast_mul(&cos)? + .broadcast_add(&x_rot.broadcast_mul(&sin)?)?; + out.to_dtype(dtype) +} + +#[derive(Clone, Debug)] +pub struct LtxVideoRotaryPosEmbed { + dim: usize, + base_num_frames: usize, + base_height: usize, + base_width: usize, + patch_size: usize, + patch_size_t: usize, + theta: f64, +} + +impl LtxVideoRotaryPosEmbed { + pub fn new( + dim: usize, + base_num_frames: usize, + base_height: usize, + base_width: usize, + patch_size: usize, + patch_size_t: usize, + theta: f64, + ) -> Self { + Self { + dim, + base_num_frames, + base_height, + base_width, + patch_size, + patch_size_t, + theta, + } + } + + fn prepare_video_coords( + &self, + batch_size: usize, + num_frames: usize, + height: usize, + width: usize, + rope_interpolation_scale: Option<(f64, f64, f64)>, + device: &Device, + ) -> Result { + // Compute coords in F32 for precision, convert to model dtype later + let dtype = DType::F32; + + let grid_h = Tensor::arange(0u32, height as u32, device)?.to_dtype(dtype)?; // [H] + let grid_w = Tensor::arange(0u32, width as u32, device)?.to_dtype(dtype)?; // [W] + let grid_f = Tensor::arange(0u32, num_frames as u32, device)?.to_dtype(dtype)?; // [F] + + // meshgrid ij: + // f: [F,H,W], h: [F,H,W], w: [F,H,W] + let f = grid_f + .reshape((num_frames, 1, 1))? + .broadcast_as((num_frames, height, width))?; + let h = grid_h + .reshape((1, height, 1))? + .broadcast_as((num_frames, height, width))?; + let w = grid_w + .reshape((1, 1, width))? + .broadcast_as((num_frames, height, width))?; + + // stack -> [3,F,H,W] + let mut grid = Tensor::stack(&[f, h, w], 0)?; // [3,F,H,W] + // [B,3,F,H,W] + grid = grid + .unsqueeze(0)? + .broadcast_as((batch_size, 3, num_frames, height, width))?; + + if let Some((sf, sh, sw)) = rope_interpolation_scale { + // grid[:,0:1] *= sf * patch_size_t / base_num_frames + let f_scale = (sf * self.patch_size_t as f64 / self.base_num_frames as f64) as f32; + let h_scale = (sh * self.patch_size as f64 / self.base_height as f64) as f32; + let w_scale = (sw * self.patch_size as f64 / self.base_width as f64) as f32; + + let gf = grid + .i((.., 0..1, .., .., ..))? + .affine(f_scale as f64, 0.0)?; + let gh = grid + .i((.., 1..2, .., .., ..))? + .affine(h_scale as f64, 0.0)?; + let gw = grid + .i((.., 2..3, .., .., ..))? + .affine(w_scale as f64, 0.0)?; + grid = Tensor::cat(&[gf, gh, gw], 1)?; + } + + // flatten dims 2..4 => seq, transpose(1,2): [B, seq, 3] + let seq = num_frames * height * width; + let grid = grid + .reshape((batch_size, 3, seq))? + .transpose(1, 2)? + .contiguous()?; + Ok(grid) + } + + /// Returns (cos, sin), both shaped [B, seq, dim]. + pub fn forward( + &self, + hidden_states: &Tensor, + num_frames: usize, + height: usize, + width: usize, + rope_interpolation_scale: Option<(f64, f64, f64)>, + video_coords: Option<&Tensor>, + ) -> Result<(Tensor, Tensor)> { + let device = hidden_states.device(); + let batch_size = hidden_states.dim(0)?; + + let grid = if let Some(coords) = video_coords { + // Expect [B, seq, 3] and normalize by base sizes. + let (b, seq, c) = coords.dims3()?; + if b != batch_size || c != 3 { + candle_core::bail!("video_coords must be [B, seq, 3], got [{b}, {seq}, {c}]"); + } + let base_f = (self.base_num_frames as f64) as f32; + let base_h = (self.base_height as f64) as f32; + let base_w = (self.base_width as f64) as f32; + + let cf = coords.i((.., .., 0))?.affine(1.0 / base_f as f64, 0.0)?; + let ch = coords.i((.., .., 1))?.affine(1.0 / base_h as f64, 0.0)?; + let cw = coords.i((.., .., 2))?.affine(1.0 / base_w as f64, 0.0)?; + Tensor::stack(&[cf, ch, cw], D::Minus1)? + } else { + self.prepare_video_coords( + batch_size, + num_frames, + height, + width, + rope_interpolation_scale, + device, + )? + }; + + // freqs: theta ** linspace(log(start,theta), log(end,theta), dim//6) + // In the file: start=1.0, end=theta => exponents go 0..1. + let steps = self.dim / 6; + let dtype = DType::F32; // Use F32 for coordinate math + + let lin = if steps <= 1 { + Tensor::zeros((1,), dtype, device)? + } else { + // linspace [0, 1], inclusive + let idx = Tensor::arange(0u32, steps as u32, device)?.to_dtype(dtype)?; + idx.affine(1.0 / ((steps - 1) as f64), 0.0)? + }; + + let theta_ln = (self.theta.ln()) as f32; + let freqs = (lin.affine(theta_ln as f64, 0.0)?).exp()?; // exp(lin * ln(theta)) => theta**lin + let freqs = freqs.affine(std::f64::consts::PI / 2.0, 0.0)?; // * pi/2 + + // freqs = freqs * (grid.unsqueeze(-1) * 2 - 1) + // grid: [B, seq, 3] -> [B, seq, 3, 1] + let grid = grid.to_dtype(dtype)?; + let grid_scaled = grid.unsqueeze(D::Minus1)?.affine(2.0, -1.0)?; // *2 -1 + let freqs = grid_scaled.broadcast_mul(&freqs.reshape((1, 1, 1, steps))?)?; // [B,seq,3,steps] + let freqs = freqs + .transpose(D::Minus1, D::Minus2)? + .contiguous()? + .flatten_from(2)?; // [B,seq,3*steps] + + // Manually implement repeat_interleave(2, D::Minus1) for cos/sin + fn repeat_interleave_2(t: &Tensor) -> Result { + let t_unsq = t.unsqueeze(D::Minus1)?; // [..., C, 1] + let t_rep = Tensor::cat(&[t_unsq.clone(), t_unsq], D::Minus1)?; // [..., C, 2] + let shape = t.dims(); + let new_last = shape[shape.len() - 1] * 2; + let mut new_shape: Vec = shape[..shape.len() - 1].to_vec(); + new_shape.push(new_last); + t_rep.reshape(new_shape) + } + + let mut cos = repeat_interleave_2(&freqs.cos()?)?; + let mut sin = repeat_interleave_2(&freqs.sin()?)?; + + let rem = self.dim % 6; + if rem != 0 { + let (b, seq, _) = cos.dims3()?; + let cos_pad = Tensor::ones((b, seq, rem), dtype, device)?; + let sin_pad = Tensor::zeros((b, seq, rem), dtype, device)?; + cos = Tensor::cat(&[cos_pad, cos], D::Minus1)?; + sin = Tensor::cat(&[sin_pad, sin], D::Minus1)?; + } + + Ok((cos, sin)) + } +} + +#[allow(dead_code)] +#[derive(Clone, Debug)] +pub struct LtxAttention { + heads: usize, + head_dim: usize, + inner_dim: usize, + inner_kv_dim: usize, + cross_attention_dim: usize, + + norm_q: RmsNorm, + norm_k: RmsNorm, + + to_q: nn::Linear, + to_k: nn::Linear, + to_v: nn::Linear, + + to_out: nn::Linear, + dropout: nn::Dropout, +} + +impl LtxAttention { + #[allow(clippy::too_many_arguments)] + pub fn new( + query_dim: usize, + heads: usize, + kv_heads: usize, + dim_head: usize, + dropout: f64, + bias: bool, + cross_attention_dim: Option, + out_bias: bool, + qk_norm: &str, + vb: VarBuilder, + ) -> Result { + if qk_norm != "rms_norm_across_heads" { + candle_core::bail!("Only 'rms_norm_across_heads' is supported as qk_norm."); + } + + let inner_dim = dim_head * heads; + let inner_kv_dim = dim_head * kv_heads; + let cross_attention_dim = cross_attention_dim.unwrap_or(query_dim); + + // Python uses eps=1e-5 and elementwise_affine=True for these. + let norm_q = RmsNorm::new(inner_dim, 1e-5, true, vb.pp("norm_q"))?; + let norm_k = RmsNorm::new(inner_kv_dim, 1e-5, true, vb.pp("norm_k"))?; + + let to_q = nn::linear_b(query_dim, inner_dim, bias, vb.pp("to_q"))?; + let to_k = nn::linear_b(cross_attention_dim, inner_kv_dim, bias, vb.pp("to_k"))?; + let to_v = nn::linear_b(cross_attention_dim, inner_kv_dim, bias, vb.pp("to_v"))?; + + let to_out = nn::linear_b(inner_dim, query_dim, out_bias, vb.pp("to_out").pp("0"))?; + let dropout = nn::Dropout::new(dropout as f32); + + Ok(Self { + heads, + head_dim: dim_head, + inner_dim, + inner_kv_dim, + cross_attention_dim, + norm_q, + norm_k, + to_q, + to_k, + to_v, + to_out, + dropout, + }) + } + + fn prepare_attention_mask( + &self, + attention_mask: &Tensor, + q_len: usize, + k_len: usize, + ) -> Result { + // The Python file relies on AttentionModuleMixin.prepare_attention_mask. + // Here we support the shapes that are consistent with the file usage: + // - [B, 1, k_len] bias -> expand to [B, heads, q_len, k_len] + // - [B, heads, q_len, k_len] already prepared + match attention_mask.rank() { + 2 => { + let (b, kk) = attention_mask.dims2()?; + if kk != k_len { + candle_core::bail!( + "Expected attention_mask [B,k_len]=[{},{}], got [{},{}]", + b, + k_len, + b, + kk + ); + } + // Convert 0/1 mask from tokenizer (where 1 is keep, 0 is mask) + // to additive offset (-10000.0 for mask, 0.0 for keep) + let mask = (1.0 - attention_mask.to_dtype(DType::F32)?)? * -10000.0; + // [B, k_len] -> [B, 1, 1, k_len] + let m = mask?.unsqueeze(1)?.unsqueeze(1)?; + + m.broadcast_as((b, self.heads, q_len, k_len))?.contiguous() + } + + 3 => { + let (b, one, kk) = attention_mask.dims3()?; + if one != 1 || kk != k_len { + candle_core::bail!( + "Expected attention_mask [B,1,k_len]=[{},1,{}], got [{},{},{}]", + b, + k_len, + b, + one, + kk + ); + } + let m = attention_mask.unsqueeze(2)?; // [B,1,1,k_len] + m.broadcast_as((b, self.heads, q_len, k_len))?.contiguous() + } + 4 => Ok(attention_mask.clone()), + other => candle_core::bail!("Unsupported attention_mask rank {other}"), + } + } + + /// Mirrors LTXVideoAttnProcessor.__call__ behavior from the Python file. + pub fn forward( + &self, + hidden_states: &Tensor, // [B, S, query_dim] + encoder_hidden_states: Option<&Tensor>, // [B, K, cross_dim] or None + attention_mask: Option<&Tensor>, // optional bias/mask + image_rotary_emb: Option<(&Tensor, &Tensor)>, // (cos, sin) + ) -> Result { + let (b, q_len, _) = hidden_states.dims3()?; + let enc = encoder_hidden_states.unwrap_or(hidden_states); + let (_, k_len, _) = enc.dims3()?; + + let _attn_mask = if let Some(mask) = attention_mask { + Some(self.prepare_attention_mask(mask, q_len, k_len)?) + } else { + None + }; + + // Project + let mut q = self.to_q.forward(hidden_states)?; // [B,S,inner_dim] + let mut k = self.to_k.forward(enc)?; // [B,K,inner_kv_dim] + let v = self.to_v.forward(enc)?; // [B,K,inner_kv_dim] + + // QK RMSNorm + q = self.norm_q.forward(&q)?; + k = self.norm_k.forward(&k)?; + + // RoPE on Q,K if provided + if let Some((cos, sin)) = image_rotary_emb { + q = apply_rotary_emb(&q, cos, sin)?; + k = apply_rotary_emb(&k, cos, sin)?; + } + + // Reshape to heads: [B, S, heads, head_dim] + let q = q.reshape((b, q_len, self.heads, self.head_dim))?; + let k = k.reshape((b, k_len, self.heads, self.head_dim))?; + let v = v.reshape((b, k_len, self.heads, self.head_dim))?; + + let dtype = q.dtype(); + let scale = 1f32 / (self.head_dim as f32).sqrt(); + + // Check if we can use Flash Attention + #[allow(unused_mut)] + let mut use_flash = false; + #[cfg(feature = "flash-attn")] + { + // Flash Attention doesn't support masks easily + if _attn_mask.is_none() && q.device().is_cuda() { + use_flash = true; + } + } + + let out = if use_flash { + #[cfg(feature = "flash-attn")] + { + // candle_flash_attn expects [B, seq, heads, head_dim] which matches our current shape + let q_bf = q.to_dtype(DType::BF16)?; + let k_bf = k.to_dtype(DType::BF16)?; + let v_bf = v.to_dtype(DType::BF16)?; + + let out = candle_flash_attn::flash_attn(&q_bf, &k_bf, &v_bf, scale, false)?; + + // Result is [B, seq, heads, head_dim]. + // We need it to be [B, heads, seq, head_dim] to match common post-processing below + out.transpose(1, 2)?.to_dtype(dtype)? + } + #[cfg(not(feature = "flash-attn"))] + { + unreachable!() + } + } else { + // Manual attention path + let q_f32 = q.transpose(1, 2)?.contiguous()?.to_dtype(DType::F32)?; // [B, heads, seq, head_dim] + let k_f32 = k.transpose(1, 2)?.contiguous()?.to_dtype(DType::F32)?; + let v_f32 = v.transpose(1, 2)?.contiguous()?.to_dtype(DType::F32)?; + + let att = q_f32.matmul(&k_f32.transpose(D::Minus1, D::Minus2)?)?; + let att = (att * (scale as f64))?; + + // Add mask if present + let att = match _attn_mask { + Some(ref mask) => att.broadcast_add(&mask.to_dtype(DType::F32)?)?, + None => att, + }; + + // Softmax - already in F32 + let (b_sz, h_sz, q_l, k_l) = att.dims4()?; + let att = att.reshape((b_sz * h_sz * q_l, k_l))?; + let att = nn::ops::softmax(&att, D::Minus1)?; + let att = att.reshape((b_sz, h_sz, q_l, k_l))?; + + // out = att @ v + let out_f32 = att.matmul(&v_f32)?; + out_f32.to_dtype(dtype)? + }; + + // Back to [B, S, heads, head_dim] -> flatten -> [B,S,inner_dim] + let out = out.transpose(1, 2)?.contiguous()?; + let out = out.reshape((b, q_len, self.inner_dim))?; + + // Output projection + dropout + let out = self.to_out.forward(&out)?; + self.dropout.forward(&out, false) + } +} + +#[derive(Clone, Debug)] +pub struct LtxVideoTransformerBlock { + norm1: RmsNorm, + attn1: LtxAttention, + norm2: RmsNorm, + attn2: LtxAttention, + ff: FeedForward, + scale_shift_table: Tensor, // [6, dim] +} + +impl LtxVideoTransformerBlock { + #[allow(clippy::too_many_arguments)] + pub fn new( + dim: usize, + num_attention_heads: usize, + attention_head_dim: usize, + cross_attention_dim: usize, + qk_norm: &str, + attention_bias: bool, + attention_out_bias: bool, + eps: f64, + elementwise_affine: bool, + vb: VarBuilder, + ) -> Result { + let norm1 = RmsNorm::new(dim, eps, elementwise_affine, vb.pp("norm1"))?; + let attn1 = LtxAttention::new( + dim, + num_attention_heads, + num_attention_heads, + attention_head_dim, + 0.0, + attention_bias, + None, + attention_out_bias, + qk_norm, + vb.pp("attn1"), + )?; + let norm2 = RmsNorm::new(dim, eps, elementwise_affine, vb.pp("norm2"))?; + let attn2 = LtxAttention::new( + dim, + num_attention_heads, + num_attention_heads, + attention_head_dim, + 0.0, + attention_bias, + Some(cross_attention_dim), + attention_out_bias, + qk_norm, + vb.pp("attn2"), + )?; + + let ff = FeedForward::new(dim, vb.pp("ff"))?; + + // Parameter: torch.randn(6, dim) / dim**0.5 + // In candle: we store as a trainable tensor; initialization is delegated to checkpoint loading. + let scale_shift_table = vb.get((6, dim), "scale_shift_table")?; + + Ok(Self { + norm1, + attn1, + norm2, + attn2, + ff, + scale_shift_table, + }) + } + + pub fn forward( + &self, + hidden_states: &Tensor, // [B, S, dim] + encoder_hidden_states: &Tensor, // [B, K, dim] + temb: &Tensor, // [B, T, 6*dim] + image_rotary_emb: Option<(&Tensor, &Tensor)>, + encoder_attention_mask: Option<&Tensor>, + ) -> Result { + let b = hidden_states.dim(0)?; + let norm_hidden = self.norm1.forward(hidden_states)?; + + // ada_values = scale_shift_table[None,None] + temb.reshape(B, T, 6, dim) + // ada_values = scale_shift_table[None,None] + temb.reshape(B, T, 6, dim) + let (b_temb, temb_last) = temb.dims2()?; + if b_temb != b { + candle_core::bail!( + "temb batch size {} mismatch hidden_states batch size {}", + b_temb, + b + ); + } + + if temb_last % 6 != 0 { + candle_core::bail!("temb last dim must be divisible by 6, got {temb_last}"); + } + let dim = temb_last / 6; + let t = 1; // temb is [B, 6*dim], so T=1 effectively + let temb_reshaped = temb.reshape((b, t, 6, dim))?; + + let table = self + .scale_shift_table + .unsqueeze(0)? + .unsqueeze(0)? + .broadcast_as((b, t, 6, dim))?; + let ada = table.broadcast_add(&temb_reshaped)?; // [B,T,6,dim] + + let shift_msa = ada.i((.., .., 0, ..))?; + let scale_msa = ada.i((.., .., 1, ..))?; + let gate_msa = ada.i((.., .., 2, ..))?; + let shift_mlp = ada.i((.., .., 3, ..))?; + let scale_mlp = ada.i((.., .., 4, ..))?; + let gate_mlp = ada.i((.., .., 5, ..))?; + + // norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + // Align shapes: norm_hidden is [B,S,dim], while shift/scale are [B,T,dim]. + // In the Python file, T corresponds to the second dimension after time embedding; typically T==1. + // We broadcast T over S when T==1. + let scale_msa = scale_msa; + let shift_msa = shift_msa; + let gate_msa = gate_msa; + let scale_mlp = scale_mlp; + let shift_mlp = shift_mlp; + let gate_mlp = gate_mlp; + + let norm_hidden = { + let one = Tensor::ones_like(&scale_msa)?; + let s = one.broadcast_add(&scale_msa)?; // 1+scale + // If T==1, expand to [B,S,dim] + let s = if s.dim(1)? == 1 { + s.broadcast_as((b, hidden_states.dim(1)?, s.dim(2)?))? + } else { + s + }; + let sh = if shift_msa.dim(1)? == 1 { + shift_msa.broadcast_as((b, hidden_states.dim(1)?, shift_msa.dim(2)?))? + } else { + shift_msa + }; + norm_hidden.broadcast_mul(&s)?.broadcast_add(&sh)? + }; + + // Self-attn (encoder_hidden_states=None) with RoPE + let attn1 = self + .attn1 + .forward(&norm_hidden, None, None, image_rotary_emb)?; + let gate_msa = if gate_msa.dim(1)? == 1 { + gate_msa.broadcast_as((b, hidden_states.dim(1)?, gate_msa.dim(2)?))? + } else { + gate_msa + }; + let mut hs = hidden_states.broadcast_add(&attn1.broadcast_mul(&gate_msa)?)?; + + // Cross-attn + let attn2 = self.attn2.forward( + &hs, + Some(encoder_hidden_states), + encoder_attention_mask, + None, + )?; + hs = hs.broadcast_add(&attn2)?; + + // MLP + let norm2 = self.norm2.forward(&hs)?; + let norm2 = { + let one = Tensor::ones_like(&scale_mlp)?; + let s = one.broadcast_add(&scale_mlp)?; + let s = if s.dim(1)? == 1 { + s.broadcast_as((b, hs.dim(1)?, s.dim(2)?))? + } else { + s + }; + let sh = if shift_mlp.dim(1)? == 1 { + shift_mlp.broadcast_as((b, hs.dim(1)?, shift_mlp.dim(2)?))? + } else { + shift_mlp + }; + norm2.broadcast_mul(&s)?.broadcast_add(&sh)? + }; + let ff = self.ff.forward(&norm2)?; + let gate_mlp = if gate_mlp.dim(1)? == 1 { + gate_mlp.broadcast_as((b, hs.dim(1)?, gate_mlp.dim(2)?))? + } else { + gate_mlp + }; + hs = hs.broadcast_add(&ff.broadcast_mul(&gate_mlp)?)?; + + Ok(hs) + } +} + +#[derive(Clone, Debug)] +pub struct LtxVideoTransformer3DModel { + proj_in: nn::Linear, + scale_shift_table: Tensor, // [2, inner_dim] + time_embed: AdaLayerNormSingle, + caption_projection: PixArtAlphaTextProjection, + rope: LtxVideoRotaryPosEmbed, + transformer_blocks: Vec, + norm_out: LayerNormNoParams, + + proj_out: nn::Linear, + pipeline_config: TransformerConfig, + skip_block_list: Vec, +} + +impl LtxVideoTransformer3DModel { + #[allow(clippy::too_many_arguments)] + pub fn new(config: &LtxVideoTransformer3DModelConfig, vb: VarBuilder) -> Result { + let out_channels = if config.out_channels == 0 { + config.in_channels + } else { + config.out_channels + }; + let inner_dim = config.num_attention_heads * config.attention_head_dim; + + let proj_in = nn::linear(config.in_channels, inner_dim, vb.pp("proj_in"))?; + + let scale_shift_table = vb.get((2, inner_dim), "scale_shift_table")?; + + let time_embed = AdaLayerNormSingle::new(inner_dim, vb.pp("time_embed"))?; + let caption_projection = PixArtAlphaTextProjection::new( + config.caption_channels, + inner_dim, + vb.pp("caption_projection"), + )?; + + let rope = LtxVideoRotaryPosEmbed::new( + inner_dim, + 20, + 2048, + 2048, + config.patch_size, + config.patch_size_t, + 10000.0, + ); + + let mut transformer_blocks = Vec::with_capacity(config.num_layers); + for layer_idx in 0..config.num_layers { + transformer_blocks.push(LtxVideoTransformerBlock::new( + inner_dim, + config.num_attention_heads, + config.attention_head_dim, + config.cross_attention_dim, + &config.qk_norm, + config.attention_bias, + config.attention_out_bias, + config.norm_eps, + config.norm_elementwise_affine, + vb.pp("transformer_blocks").pp(layer_idx.to_string()), + )?); + } + + let norm_out = LayerNormNoParams::new(1e-6); + let proj_out = nn::linear(inner_dim, out_channels, vb.pp("proj_out"))?; + + Ok(Self { + proj_in, + scale_shift_table, + time_embed, + caption_projection, + rope, + transformer_blocks, + norm_out, + proj_out, + pipeline_config: TransformerConfig { + in_channels: config.in_channels, + patch_size: config.patch_size, + patch_size_t: config.patch_size_t, + num_layers: config.num_layers, + }, + skip_block_list: Vec::new(), + }) + } + + pub fn set_skip_block_list(&mut self, list: Vec) { + self.skip_block_list = list; + } + + #[allow(clippy::too_many_arguments)] + pub fn forward( + &self, + hidden_states: &Tensor, // [B, S, in_channels] + encoder_hidden_states: &Tensor, // [B, K, caption_channels] + timestep: &Tensor, // int tensor, shape [B] or [B,1]... + encoder_attention_mask: Option<&Tensor>, // [B,K] (1 valid, 0 pad) or already bias + num_frames: usize, + height: usize, + width: usize, + rope_interpolation_scale: Option<(f64, f64, f64)>, + video_coords: Option<&Tensor>, + skip_layer_mask: Option<&Tensor>, + ) -> Result { + let (_b, _s, _c) = hidden_states.dims3()?; + + // Convert inputs to model dtype (BF16) if needed + let model_dtype = self.proj_in.weight().dtype(); + let hidden_states = hidden_states.to_dtype(model_dtype)?; + let encoder_hidden_states = encoder_hidden_states.to_dtype(model_dtype)?; + + let hidden_states = self.proj_in.forward(&hidden_states)?; + + let timestep = timestep.flatten_all()?.to_dtype(model_dtype)?; // ensure [B] or flat, in BF16 + + // 1. AdaLayerNormSingle (Timesteps -> PixArtEmbed -> Silu -> Linear) + let (temb, embedded_timestep) = self.time_embed.forward(×tep)?; // [B, 6*dim], [B, dim] + + let encoder_hidden_states = self.caption_projection.forward(&encoder_hidden_states)?; + + // convert encoder_attention_mask to a bias + let encoder_attention_mask = if let Some(mask) = encoder_attention_mask { + if mask.rank() == 2 { + let mask_f = mask.to_dtype(hidden_states.dtype())?; + // (1 - mask) * -10000.0 + let bias = (mask_f.affine(-1.0, 1.0)? * (-10000.0))?; + Some(bias.unsqueeze(1)?) + } else { + Some(mask.clone()) + } + } else { + None + }; + let encoder_attention_mask = encoder_attention_mask.as_ref(); + + let (cos, sin) = self.rope.forward( + &hidden_states, + num_frames, + height, + width, + rope_interpolation_scale, + video_coords, + )?; + + // Pass embedded_timestep as conditioning? LTX-Video adds it to temb or similar? + // Wait, logic in `forward` block of transformer_ltx.py: + // temb is passed to blocks. embedded_timestep is not used explicitly in blocks for this model version? + // Let's check block forward. Block uses `temb`. + // The `AdaLayerNormSingle` returns (temb, embedded_timestep), but `LtxVideoTransformerBlock` + // seems to take `temb`. + // `LtxVideoTransformerBlock::forward` signature: `temb: &Tensor`. + + let mut hidden_states = hidden_states; + let image_rotary_emb = Some((&cos, &sin)); + + for (index, block) in self.transformer_blocks.iter().enumerate() { + if self.skip_block_list.contains(&index) { + continue; + } + + let original_hidden_states = if skip_layer_mask.is_some() { + Some(hidden_states.clone()) + } else { + None + }; + + hidden_states = block.forward( + &hidden_states, + &encoder_hidden_states, + &temb, + image_rotary_emb, + encoder_attention_mask, + )?; + + if let (Some(mask), Some(orig)) = (skip_layer_mask, original_hidden_states) { + // mask shape: [num_layers, batch] + // FIX: mask=1 means SKIP layer (use original), mask=0 means APPLY layer (keep processed) + let m = mask.narrow(0, index, 1)?.flatten_all()?; + let b_size = hidden_states.dim(0)?; + let m = m.reshape((b_size, 1, 1))?.to_dtype(hidden_states.dtype())?; + let one_minus_m = m.affine(-1.0, 1.0)?; + // When m=1 (skip): use orig. When m=0 (apply): use hidden_states + hidden_states = hidden_states + .broadcast_mul(&one_minus_m)? // m=0 -> keep processed hidden_states + .broadcast_add(&orig.broadcast_mul(&m)?)?; // m=1 -> add original (skip) + } + } + + // Final modulation: scale_shift_table[None,None] + embedded_timestep[:, :, None] + let b = hidden_states.dim(0)?; + let inner_dim = hidden_states.dim(2)?; + + // scale_shift_table: [2, inner_dim] -> cast to dtype of embedded_timestep + let table = self.scale_shift_table.to_dtype(embedded_timestep.dtype())?; + + // table: [1, 1, 2, inner_dim] + let table = table.unsqueeze(0)?.unsqueeze(0)?; + + // embedded_timestep: [B, T=1, inner_dim] (usually T=1 after pool or similar? AdaLayerNormSingle returns [B, inner_dim]?) + // AdaLayerNormSingle returns `emb` which is [B, inner_dim]. + // We need to check dims. + // If embedded_timestep is [B, D]. + let emb = embedded_timestep.unsqueeze(1)?.unsqueeze(2)?; // [B, 1, 1, D] + + // broadcast add: table + emb + // [1,1,2,D] + [B,1,1,D] -> [B,1,2,D] + let scale_shift = table.broadcast_add(&emb)?; + + let shift = scale_shift.i((.., .., 0, ..))?; // [B, 1, D] + let scale = scale_shift.i((.., .., 1, ..))?; // [B, 1, D] + + let mut hidden_states = self.norm_out.forward(&hidden_states)?; + + // (1 + scale) * x + shift + let one = Tensor::ones_like(&scale)?; + let ss = one.broadcast_add(&scale)?; + + // Broadcast scale/shift to [B, S, D] + // S is dim 1 of hidden_states + let s_dim = hidden_states.dim(1)?; + let ss = ss.broadcast_as((b, s_dim, inner_dim))?; + let sh = shift.broadcast_as((b, s_dim, inner_dim))?; + + hidden_states = hidden_states.broadcast_mul(&ss)?.broadcast_add(&sh)?; + + let hidden_states = self.proj_out.forward(&hidden_states)?; + + // Residual connection? In Python: + // output = self.proj_out(self.norm_out(hidden_states)) + // return output + residual (if configured? No usually strict functional) + // Check if `hidden_states` (input) is added? + // LTX models usually treat it as noise prediction (epsilon or v-prediction). + // Let's assume just return output. + Ok(hidden_states) + } +} + +impl VideoTransformer3D for LtxVideoTransformer3DModel { + fn config(&self) -> &TransformerConfig { + &self.pipeline_config + } + + fn set_skip_block_list(&mut self, list: Vec) { + self.set_skip_block_list(list); + } + + fn forward( + &mut self, + hidden_states: &Tensor, + encoder_hidden_states: &Tensor, + timestep: &Tensor, + encoder_attention_mask: &Tensor, + num_frames: usize, + height: usize, + width: usize, + rope_interpolation_scale: Option<(f32, f32, f32)>, + video_coords: Option<&Tensor>, + skip_layer_mask: Option<&Tensor>, + ) -> Result { + // cast scale to f64 + let scale = rope_interpolation_scale.map(|s| (s.0 as f64, s.1 as f64, s.2 as f64)); + + // Call inherent forward + // Note: inherent forward takes &self, trait takes &mut self (which coerces to &self). + LtxVideoTransformer3DModel::forward( + self, + hidden_states, + encoder_hidden_states, + timestep, + Some(encoder_attention_mask), + num_frames, + height, + width, + scale, + video_coords, + skip_layer_mask, + ) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use candle_core::{DType, Device}; + use candle_nn::VarBuilder; + + #[test] + fn test_skip_block_list_logic() -> candle_core::Result<()> { + let device = Device::Cpu; + let config = LtxVideoTransformer3DModelConfig { + num_layers: 3, + ..Default::default() + }; + + // Use zeros/ones for weights to track passes if needed, but here we just check if it runs + let vb = VarBuilder::zeros(DType::F32, &device); + let mut model = LtxVideoTransformer3DModel::new(&config, vb.pp("transformer"))?; + + // Initial state: no skips + assert_eq!(model.skip_block_list.len(), 0); + + // Set skips + model.set_skip_block_list(vec![1]); + assert_eq!(model.skip_block_list, vec![1]); + + // In a real test we'd verify the output differs or some side effect, + // but for now, we ensure it compiles and the logic is present. + Ok(()) + } + + #[test] + fn test_skip_layer_mask() -> candle_core::Result<()> { + let device = Device::Cpu; + let config = LtxVideoTransformer3DModelConfig { + num_layers: 2, + attention_head_dim: 16, + num_attention_heads: 2, + cross_attention_dim: 32, + caption_channels: 32, + in_channels: 32, + ..Default::default() + }; + + let vb = VarBuilder::zeros(DType::F32, &device); + let model = LtxVideoTransformer3DModel::new(&config, vb.pp("transformer"))?; + + let b = 2; + let s = 16; + let hidden_states = Tensor::ones( + (b, s, config.attention_head_dim * config.num_attention_heads), + DType::F32, + &device, + )?; + let encoder_hidden_states = + Tensor::zeros((b, 1, config.caption_channels), DType::F32, &device)?; + let timestep = Tensor::zeros((b,), DType::F32, &device)?; + + // Mask: Layer 0 skipped for batch 0, Layer 1 skipped for batch 1 + // [num_layers, batch] + let mask_data = vec![ + 0.0f32, 1.0f32, // Layer 0: skip batch 0, keep batch 1 + 1.0f32, 0.0f32, // Layer 1: keep batch 0, skip batch 1 + ]; + let mask = Tensor::from_vec(mask_data, (2, b), &device)?; + + let out = model.forward( + &hidden_states, + &encoder_hidden_states, + ×tep, + None, + 1, + 1, + 1, + None, + None, + Some(&mask), + )?; + + assert_eq!(out.dims3()?, (b, s, 128)); // 128 is out_channels by default? No, let's check + // By default out_channels = in_channels if not specified. + // LTXV model has out_channels = 128 by default in config. + + Ok(()) + } +} diff --git a/cake-core/src/models/ltx_video/vendored/mod.rs b/cake-core/src/models/ltx_video/vendored/mod.rs new file mode 100644 index 00000000..b2afc5dd --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/mod.rs @@ -0,0 +1,20 @@ +//! Vendored from https://github.com/FerrisMind/candle-video (Apache 2.0, by FerrisMind) +//! with minimal modifications (import path adaptation, nightly feature removal). + +#[allow(dead_code, unused_imports, clippy::too_many_arguments, clippy::type_complexity)] +pub mod configs; +#[allow(dead_code, unused_imports, clippy::too_many_arguments)] +pub mod ltx_transformer; +#[allow(dead_code, unused_imports, clippy::too_many_arguments)] +pub mod scheduler; +#[allow(dead_code, unused_imports, clippy::too_many_arguments)] +pub mod t2v_pipeline; +#[allow(dead_code, unused_imports, clippy::too_many_arguments, clippy::type_complexity)] +pub mod vae; + +pub use configs::*; +pub use ltx_transformer::*; +pub use scheduler::FlowMatchEulerDiscreteScheduler; +pub use scheduler::FlowMatchEulerDiscreteSchedulerConfig; +pub use vae::AutoencoderKLLtxVideo; +pub use vae::AutoencoderKLLtxVideoConfig; diff --git a/cake-core/src/models/ltx_video/vendored/scheduler.rs b/cake-core/src/models/ltx_video/vendored/scheduler.rs new file mode 100644 index 00000000..a6386898 --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/scheduler.rs @@ -0,0 +1,669 @@ +//! FlowMatchEulerDiscreteScheduler (Euler, discrete) ported from the attached Python implementation. +//! +//! Note: This is a standalone scheduler implementation (no Diffusers ConfigMixin/SchedulerMixin layer). +//! It keeps the same math and branching as the source file. + +use super::t2v_pipeline::{Scheduler, SchedulerConfig, TimestepsSpec}; +use candle_core::{DType, Device, Result, Tensor, bail}; +use statrs::distribution::{Beta, ContinuousCDF}; + +#[derive(Debug, Clone, Copy, PartialEq, Eq, serde::Serialize, serde::Deserialize)] +pub enum TimeShiftType { + Exponential, + Linear, +} + +#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)] +pub struct FlowMatchEulerDiscreteSchedulerConfig { + pub num_train_timesteps: usize, + pub shift: f32, + pub use_dynamic_shifting: bool, + + pub base_shift: Option, + pub max_shift: Option, + pub base_image_seq_len: Option, + pub max_image_seq_len: Option, + + pub invert_sigmas: bool, + pub shift_terminal: Option, + + pub use_karras_sigmas: bool, + pub use_exponential_sigmas: bool, + pub use_beta_sigmas: bool, + + pub time_shift_type: TimeShiftType, + pub stochastic_sampling: bool, +} + +impl Default for FlowMatchEulerDiscreteSchedulerConfig { + fn default() -> Self { + Self { + num_train_timesteps: 1000, + shift: 1.0, + // Official Lightricks config from LTX-Video 0.9.5 + use_dynamic_shifting: false, + base_shift: Some(0.5), + max_shift: Some(1.15), + base_image_seq_len: Some(256), + max_image_seq_len: Some(4096), + invert_sigmas: false, + shift_terminal: None, + use_karras_sigmas: false, + use_exponential_sigmas: false, + use_beta_sigmas: false, + time_shift_type: TimeShiftType::Exponential, + stochastic_sampling: false, + } + } +} + +#[derive(Debug, Clone)] +pub struct FlowMatchEulerDiscreteSchedulerOutput { + pub prev_sample: Tensor, +} + +#[derive(Debug)] +pub struct FlowMatchEulerDiscreteScheduler { + pub config: FlowMatchEulerDiscreteSchedulerConfig, + + // Stored as tensors for convenient device/dtype conversion. + pub timesteps: Tensor, // shape [n] (not appended) + sigmas: Tensor, // shape [n+1] (terminal appended) + timesteps_cpu: Vec, + sigmas_cpu: Vec, // includes terminal appended + + sigma_min: f32, + sigma_max: f32, + + step_index: Option, + begin_index: Option, + num_inference_steps: Option, +} + +impl FlowMatchEulerDiscreteScheduler { + pub fn new(config: FlowMatchEulerDiscreteSchedulerConfig) -> Result { + if config.use_beta_sigmas as u32 + + config.use_exponential_sigmas as u32 + + config.use_karras_sigmas as u32 + > 1 + { + bail!( + "Only one of use_beta_sigmas/use_exponential_sigmas/use_karras_sigmas can be enabled." + ); + } + + // Equivalent to: + // timesteps = np.linspace(1, N, N, dtype=float32)[::-1] + // sigmas = timesteps / N + let n = config.num_train_timesteps; + let mut ts: Vec = (1..=n).map(|v| v as f32).collect(); + ts.reverse(); + + let mut sigmas: Vec = ts.iter().map(|t| t / n as f32).collect(); + + // If not dynamic shifting: apply fixed shift at init (as in Python). + if !config.use_dynamic_shifting { + sigmas = sigmas + .into_iter() + .map(|s| { + let shift = config.shift; + shift * s / (1.0 + (shift - 1.0) * s) + }) + .collect(); + ts = sigmas.iter().map(|s| s * n as f32).collect(); + } else { + // Python keeps unshifted schedule here and does shifting in set_timesteps(mu=...) + ts = sigmas.iter().map(|s| s * n as f32).collect(); + } + + // Store on CPU by default. + let device = Device::Cpu; + let timesteps_t = Tensor::from_vec(ts.clone(), (ts.len(),), &device)?; + let sigmas_t = Tensor::from_vec(sigmas.clone(), (sigmas.len(),), &device)?; + + let sigma_min = *sigmas.last().unwrap_or(&0.0); + let sigma_max = *sigmas.first().unwrap_or(&1.0); + + // Note: during init, Python does NOT append terminal sigma; this is done in set_timesteps. + // But we keep a consistent internal representation: append terminal in sigmas/sigmas_cpu. + let mut sigmas_cpu = sigmas.clone(); + sigmas_cpu.push(0.0); + let sigmas_with_terminal = + Tensor::cat(&[sigmas_t, Tensor::zeros((1,), DType::F32, &device)?], 0)?; + + Ok(Self { + config, + timesteps: timesteps_t, + sigmas: sigmas_with_terminal, + timesteps_cpu: ts, + sigmas_cpu, + sigma_min, + sigma_max, + step_index: None, + begin_index: None, + num_inference_steps: None, + }) + } + + pub fn shift(&self) -> f32 { + self.config.shift + } + + pub fn step_index(&self) -> Option { + self.step_index + } + + pub fn begin_index(&self) -> Option { + self.begin_index + } + + pub fn set_begin_index(&mut self, begin_index: usize) { + self.begin_index = Some(begin_index); + } + + pub fn set_shift(&mut self, shift: f32) { + self.config.shift = shift; + } + + fn sigma_to_t(&self, sigma: f32) -> f32 { + sigma * self.config.num_train_timesteps as f32 + } + + fn time_shift_scalar(&self, mu: f32, sigma: f32, t: f32) -> f32 { + match self.config.time_shift_type { + TimeShiftType::Exponential => { + // exp(mu) / (exp(mu) + (1/t - 1)^sigma) + let emu = mu.exp(); + let base = (1.0 / t - 1.0).powf(sigma); + emu / (emu + base) + } + TimeShiftType::Linear => { + // mu / (mu + (1/t - 1)^sigma) + let base = (1.0 / t - 1.0).powf(sigma); + mu / (mu + base) + } + } + } + + fn stretch_shift_to_terminal_vec(&self, t: &mut [f32]) -> Result<()> { + let shift_terminal = match self.config.shift_terminal { + Some(v) => v, + None => return Ok(()), + }; + if t.is_empty() { + return Ok(()); + } + let one_minus_last = 1.0 - t[t.len() - 1]; + let denom = 1.0 - shift_terminal; + if denom.abs() < 1e-12 { + bail!("shift_terminal too close to 1.0, would divide by zero."); + } + let scale_factor = one_minus_last / denom; + for v in t.iter_mut() { + let one_minus_z = 1.0 - *v; + *v = 1.0 - (one_minus_z / scale_factor); + } + Ok(()) + } + + fn linspace(start: f32, end: f32, steps: usize) -> Vec { + if steps == 0 { + return vec![]; + } + if steps == 1 { + return vec![start]; + } + let denom = (steps - 1) as f32; + (0..steps) + .map(|i| start + (end - start) * (i as f32) / denom) + .collect() + } + + fn convert_to_karras(&self, in_sigmas: &[f32], num_inference_steps: usize) -> Vec { + let sigma_min = in_sigmas.last().copied().unwrap_or(self.sigma_min); + let sigma_max = in_sigmas.first().copied().unwrap_or(self.sigma_max); + + let rho: f32 = 7.0; + let ramp = Self::linspace(0.0, 1.0, num_inference_steps); + + let min_inv_rho = sigma_min.powf(1.0 / rho); + let max_inv_rho = sigma_max.powf(1.0 / rho); + + ramp.into_iter() + .map(|r| (max_inv_rho + r * (min_inv_rho - max_inv_rho)).powf(rho)) + .collect() + } + + fn convert_to_exponential(&self, in_sigmas: &[f32], num_inference_steps: usize) -> Vec { + let sigma_min = in_sigmas.last().copied().unwrap_or(self.sigma_min); + let sigma_max = in_sigmas.first().copied().unwrap_or(self.sigma_max); + + let start = sigma_max.ln(); + let end = sigma_min.ln(); + let logs = Self::linspace(start, end, num_inference_steps); + logs.into_iter().map(|v| v.exp()).collect() + } + + fn convert_to_beta( + &self, + in_sigmas: &[f32], + num_inference_steps: usize, + alpha: f64, + beta: f64, + ) -> Result> { + let sigma_min = in_sigmas.last().copied().unwrap_or(self.sigma_min); + let sigma_max = in_sigmas.first().copied().unwrap_or(self.sigma_max); + + // ppf for timesteps in: 1 - linspace(0, 1, steps) + let ts = Self::linspace(0.0, 1.0, num_inference_steps) + .into_iter() + .map(|v| 1.0 - v as f64) + .collect::>(); + + let dist = Beta::new(alpha, beta).map_err(|e| candle_core::Error::msg(format!("{e:?}")))?; + + let mut out = Vec::with_capacity(num_inference_steps); + for t in ts { + let ppf = dist.inverse_cdf(t); // matches scipy.stats.beta.ppf + let s = sigma_min as f64 + ppf * ((sigma_max - sigma_min) as f64); + out.push(s as f32); + } + Ok(out) + } + + pub fn set_timesteps( + &mut self, + num_inference_steps: Option, + device: &Device, + sigmas: Option<&[f32]>, + mu: Option, + timesteps: Option<&[f32]>, + ) -> Result<()> { + if self.config.use_dynamic_shifting && mu.is_none() { + bail!("mu must be provided when use_dynamic_shifting = true."); + } + + if sigmas + .zip(timesteps) + .is_some_and(|(s, t)| s.len() != t.len()) + { + bail!("sigmas and timesteps must have the same length."); + } + + let mut num_inference_steps = num_inference_steps; + if let Some(n) = num_inference_steps { + if sigmas.is_some_and(|s| s.len() != n) { + bail!("sigmas length must match num_inference_steps."); + } + if timesteps.is_some_and(|t| t.len() != n) { + bail!("timesteps length must match num_inference_steps."); + } + } else { + // Infer from provided sigmas/timesteps. + if let Some(s) = sigmas { + num_inference_steps = Some(s.len()); + } else if let Some(t) = timesteps { + num_inference_steps = Some(t.len()); + } else { + bail!( + "num_inference_steps must be provided if neither sigmas nor timesteps are provided." + ); + } + } + let num_inference_steps = num_inference_steps.unwrap(); + self.num_inference_steps = Some(num_inference_steps); + + // 1) Prepare default timesteps/sigmas arrays (Vec). + let is_timesteps_provided = timesteps.is_some(); + let mut ts_vec: Option> = timesteps.map(|t| t.to_vec()); + + let mut sigmas_vec: Vec = if let Some(s) = sigmas { + s.to_vec() + } else { + // if timesteps is None => construct timesteps linearly in t-space + let timesteps_vec = match ts_vec.take() { + Some(v) => v, + None => { + let start = self.sigma_to_t(self.sigma_max); + let end = self.sigma_to_t(self.sigma_min); + Self::linspace(start, end, num_inference_steps) + } + }; + let s = timesteps_vec + .iter() + .map(|t| *t / self.config.num_train_timesteps as f32) + .collect::>(); + ts_vec = Some(timesteps_vec); + s + }; + + // 2) Perform shifting (dynamic or fixed) + if let Some(mu) = mu { + // Use exponential time shift (SD3 style) + sigmas_vec = sigmas_vec + .into_iter() + .map(|t| self.time_shift_scalar(mu, 1.0, t)) + .collect(); + } else if self.config.use_dynamic_shifting { + bail!("mu must be provided when use_dynamic_shifting = true."); + } else { + // Use standard linear/rational shift + let shift = self.config.shift; + sigmas_vec = sigmas_vec + .into_iter() + .map(|s| shift * s / (1.0 + (shift - 1.0) * s)) + .collect(); + } + + // 3) Optional stretch to terminal + if self.config.shift_terminal.is_some() { + self.stretch_shift_to_terminal_vec(&mut sigmas_vec)?; + } + + // 4) Optional conversion to karras/exponential/beta + if self.config.use_karras_sigmas { + sigmas_vec = self.convert_to_karras(&sigmas_vec, num_inference_steps); + } else if self.config.use_exponential_sigmas { + sigmas_vec = self.convert_to_exponential(&sigmas_vec, num_inference_steps); + } else if self.config.use_beta_sigmas { + sigmas_vec = self.convert_to_beta(&sigmas_vec, num_inference_steps, 0.6, 0.6)?; + } + + // 5) timesteps tensor + let mut timesteps_vec: Vec = if is_timesteps_provided { + ts_vec.unwrap_or_else(|| { + sigmas_vec + .iter() + .map(|s| s * self.config.num_train_timesteps as f32) + .collect() + }) + } else { + sigmas_vec + .iter() + .map(|s| s * self.config.num_train_timesteps as f32) + .collect() + }; + + // 6) Optional invert sigmas + append terminal sigma + if self.config.invert_sigmas { + for v in sigmas_vec.iter_mut() { + *v = 1.0 - *v; + } + timesteps_vec = sigmas_vec + .iter() + .map(|s| s * self.config.num_train_timesteps as f32) + .collect(); + sigmas_vec.push(1.0); + } else { + sigmas_vec.push(0.0); + } + + self.sigmas_cpu = sigmas_vec.clone(); + self.timesteps_cpu = timesteps_vec.clone(); + + self.sigmas = Tensor::from_vec(sigmas_vec, (self.sigmas_cpu.len(),), device)?; + self.timesteps = Tensor::from_vec(timesteps_vec, (self.timesteps_cpu.len(),), device)?; + + // Reset indices like in Python. + self.step_index = None; + self.begin_index = None; + + Ok(()) + } + + pub fn index_for_timestep( + &self, + timestep: f32, + schedule_timesteps: Option<&[f32]>, + ) -> Result { + let st = schedule_timesteps.unwrap_or(&self.timesteps_cpu); + let mut indices = Vec::new(); + for (i, &v) in st.iter().enumerate() { + if (v - timestep).abs() < 1e-6 { + indices.push(i); + } + } + if indices.is_empty() { + bail!("timestep not found in schedule_timesteps."); + } + let pos = if indices.len() > 1 { 1 } else { 0 }; + Ok(indices[pos]) + } + + fn init_step_index(&mut self, timestep: f32) -> Result<()> { + if self.begin_index.is_none() { + self.step_index = Some(self.index_for_timestep(timestep, None)?); + } else { + self.step_index = self.begin_index; + } + Ok(()) + } + + /// Forward process in flow-matching: sample <- sigma * noise + (1 - sigma) * sample + pub fn scale_noise( + &self, + sample: &Tensor, + timestep: &Tensor, + noise: Option<&Tensor>, + ) -> Result { + let device = sample.device(); + + // timestep is expected to be 1D (batch). For scalar, allow rank 0. + let ts: Vec = match timestep.rank() { + 0 => vec![timestep.to_scalar::()?], + 1 => timestep.to_vec1::()?, + r => bail!("timestep must be rank 0 or 1, got rank={r}"), + }; + + // Resolve indices the same way as Python (begin_index/step_index rules). + let mut step_indices = Vec::with_capacity(ts.len()); + if self.begin_index.is_none() { + for &t in ts.iter() { + step_indices.push(self.index_for_timestep(t, Some(&self.timesteps_cpu))?); + } + } else if let Some(si) = self.step_index { + step_indices.extend(std::iter::repeat(si).take(ts.len())); + } else { + let bi = self.begin_index.unwrap_or(0); + step_indices.extend(std::iter::repeat(bi).take(ts.len())); + } + + // Gather sigmas and reshape/broadcast to sample rank. + let gathered = step_indices + .into_iter() + .map(|idx| self.sigmas_cpu[idx]) + .collect::>(); + + let mut sigma = + Tensor::from_vec(gathered, (ts.len(),), device)?.to_dtype(sample.dtype())?; + while sigma.rank() < sample.rank() { + sigma = sigma.unsqueeze(sigma.rank())?; + } + + let noise = match noise { + Some(n) => n.clone(), + None => Tensor::randn(0f32, 1f32, sample.shape(), device)?.to_dtype(sample.dtype())?, + }; + + let one_minus_sigma = sigma.affine(-1.0, 1.0)?; + let a = sigma.broadcast_mul(&noise)?; + let b = one_minus_sigma.broadcast_mul(sample)?; + a.broadcast_add(&b) + } + + /// One Euler step. + pub fn step( + &mut self, + model_output: &Tensor, + timestep: f32, + sample: &Tensor, + per_token_timesteps: Option<&Tensor>, + ) -> Result { + if self.step_index.is_none() { + self.init_step_index(timestep)?; + } + + // Upcast to f32 (Python does: sample = sample.to(torch.float32)). + let mut sample_f = sample.to_dtype(DType::F32)?; + + let device = sample_f.device(); + + let (current_sigma, next_sigma, dt) = if let Some(per_token_ts) = per_token_timesteps { + // per_token_sigmas = per_token_timesteps / num_train_timesteps + let per_token_sigmas = + per_token_ts.affine(1.0 / self.config.num_train_timesteps as f64, 0.0)?; + + // sigmas = self.sigmas[:, None, None] + let sigmas_t = self + .sigmas + .to_device(device)? + .to_dtype(per_token_sigmas.dtype())? + .unsqueeze(1)? + .unsqueeze(2)?; + + // lower_mask = sigmas < per_token_sigmas[None] - 1e-6 + let threshold = per_token_sigmas.unsqueeze(0)?.affine(1.0, -1e-6)?; + let lower_mask = sigmas_t.broadcast_lt(&threshold)?; // bool-like (u8) tensor + let lower_mask_f = lower_mask.to_dtype(per_token_sigmas.dtype())?; + + // lower_sigmas = lower_mask * sigmas + let lower_sigmas = lower_mask_f.broadcast_mul(&sigmas_t)?; + + // lower_sigmas, _ = lower_sigmas.max(dim=0) + let lower_sigmas = lower_sigmas.max(0)?; // reduce over sigma dimension -> shape like per_token_sigmas + + // current_sigma = per_token_sigmas[..., None] + // next_sigma = lower_sigmas[..., None] + let current_sigma = per_token_sigmas.unsqueeze(per_token_sigmas.rank())?; + let next_sigma = lower_sigmas.unsqueeze(lower_sigmas.rank())?; + + // dt = current_sigma - next_sigma + let dt = current_sigma.broadcast_sub(&next_sigma)?; + (current_sigma, next_sigma, dt) + } else { + let idx = self.step_index.expect("step_index must be initialized"); + let sigma = self.sigmas_cpu[idx]; + let sigma_next = self.sigmas_cpu[idx + 1]; + + // In Python (non per-token): dt = sigma_next - sigma + let dt = sigma_next - sigma; + + let current_sigma = Tensor::new(sigma, device)?.to_dtype(DType::F32)?; + let next_sigma = Tensor::new(sigma_next, device)?.to_dtype(DType::F32)?; + let dt = Tensor::new(dt, device)?.to_dtype(DType::F32)?; + (current_sigma, next_sigma, dt) + }; + + let prev_sample = if self.config.stochastic_sampling { + // x0 = sample - current_sigma * model_output + let cs = current_sigma + .broadcast_as(sample_f.shape())? + .to_dtype(DType::F32)?; + let x0 = + sample_f.broadcast_sub(&cs.broadcast_mul(&model_output.to_dtype(DType::F32)?)?)?; + + // noise = randn_like(sample) + let noise = Tensor::randn(0f32, 1f32, sample_f.shape(), device)?; + + // prev_sample = (1 - next_sigma) * x0 + next_sigma * noise + let ns = next_sigma + .broadcast_as(sample_f.shape())? + .to_dtype(DType::F32)?; + let one_minus_ns = ns.affine(-1.0, 1.0)?; + let a = one_minus_ns.broadcast_mul(&x0)?; + let b = ns.broadcast_mul(&noise)?; + a.broadcast_add(&b)? + } else { + // prev_sample = sample + dt * model_output + let dt = dt.broadcast_as(sample_f.shape())?.to_dtype(DType::F32)?; + let scaled = model_output.to_dtype(DType::F32)?.broadcast_mul(&dt)?; + sample_f = sample_f.broadcast_add(&scaled)?; + sample_f + }; + + // Increment step index. + if let Some(si) = self.step_index.as_mut() { + *si += 1; + } + + // PRECISION FIX: Keep result in F32 to prevent error accumulation over multiple steps. + // The pipeline will convert to model dtype only when needed for transformer forward. + // Previously: prev_sample.to_dtype(model_output.dtype())? for non per-token case + // Now: Always return F32 to maintain precision throughout denoising loop. + + Ok(FlowMatchEulerDiscreteSchedulerOutput { prev_sample }) + } + + pub fn timesteps(&self) -> &Tensor { + &self.timesteps + } + + pub fn sigmas(&self) -> &Tensor { + &self.sigmas + } + + pub fn len(&self) -> usize { + self.config.num_train_timesteps + } + + pub fn is_empty(&self) -> bool { + self.config.num_train_timesteps == 0 + } +} + +impl Scheduler for FlowMatchEulerDiscreteScheduler { + fn config(&self) -> &SchedulerConfig { + // We need to return a reference to SchedulerConfig. + // Since FlowMatchEulerDiscreteSchedulerConfig doesn't match exactly, + // and trait returns reference, we either need to store SchedulerConfig + // or change trait to return Cow or Clone. + // For now, let's assume we can't change the trait (it returns &). + // Hack: return a static default or store it. + // The LtxPipeline uses this config mainly for `calculate_shift`. + // Let's rely on LtxPipeline using its own defaults if we don't change this, + // OR add a field to struct. + // Simplest: use a lazy_static or constant if possible, or just unimplemented if not strictly used dynamic. + // Converting: + // base_image_seq_len: 256 + // max_image_seq_len: 4096 + // base_shift: 0.5 + // max_shift: 1.15 + + // BETTER: allow implementing struct to own the config. + // But for now, I'll store a `SchedulerConfig` inside `FlowMatchEulerDiscreteScheduler`? + // No, that changes the struct definition. + + // Let's implement it by adding a phantom static or leaking? No. + // Let's just create a static instance for now as LTX uses fixed params. + static DEFAULT_CONFIG: std::sync::OnceLock = std::sync::OnceLock::new(); + DEFAULT_CONFIG.get_or_init(SchedulerConfig::default) + } + + fn order(&self) -> usize { + 1 + } + + fn set_timesteps(&mut self, spec: TimestepsSpec, device: &Device, mu: f32) -> Result> { + let (num, ts, sig) = match spec { + TimestepsSpec::Steps(n) => (Some(n), None, None), + TimestepsSpec::Timesteps(t) => ( + None, + Some(t.iter().map(|&x| x as f32).collect::>()), + None, + ), + TimestepsSpec::Sigmas(s) => (None, None, Some(s)), + }; + + self.set_timesteps(num, device, sig.as_deref(), Some(mu), ts.as_deref())?; + let t = self.timesteps.to_vec1::()?; + Ok(t.into_iter().map(|x| x as i64).collect()) + } + + fn step(&mut self, noise_pred: &Tensor, timestep: i64, latents: &Tensor) -> Result { + // We cast timestep to f32 as underlying scheduler expects f32 (usually) or i64? + // Scheduler::step takes timestep: f32. + let ts = timestep as f32; + let out = self.step(noise_pred, ts, latents, None)?; + Ok(out.prev_sample) + } +} diff --git a/cake-core/src/models/ltx_video/vendored/t2v_pipeline.rs b/cake-core/src/models/ltx_video/vendored/t2v_pipeline.rs new file mode 100644 index 00000000..8fb3912c --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/t2v_pipeline.rs @@ -0,0 +1,1074 @@ +use candle_core::{D, DType, Device, IndexOp, Result, Tensor}; + +#[derive(Debug, Clone)] +pub struct SchedulerConfig { + pub base_image_seq_len: usize, + pub max_image_seq_len: usize, + pub base_shift: f32, + pub max_shift: f32, +} + +impl Default for SchedulerConfig { + fn default() -> Self { + Self { + base_image_seq_len: 256, + max_image_seq_len: 4096, + base_shift: 0.5, + max_shift: 1.15, + } + } +} + +pub enum TimestepsSpec { + Steps(usize), + Timesteps(Vec), + Sigmas(Vec), +} + +pub trait Scheduler { + fn config(&self) -> &SchedulerConfig; + fn order(&self) -> usize; + + /// Должен сохранить внутренний schedule и вернуть timesteps (в torch это scheduler.timesteps). + fn set_timesteps(&mut self, spec: TimestepsSpec, device: &Device, mu: f32) -> Result>; + + /// x_t -> x_{t-1} + fn step(&mut self, noise_pred: &Tensor, timestep: i64, latents: &Tensor) -> Result; +} + +pub trait Tokenizer { + /// Должен вернуть: + /// - input_ids: [B, L] (обычно i64) + /// - attention_mask: [B, L] (0/1) + fn encode_batch(&self, prompts: &[String], max_length: usize) -> Result<(Tensor, Tensor)>; + + fn model_max_length(&self) -> usize; +} + +pub trait TextEncoder { + fn dtype(&self) -> DType; + + /// Возвращает hidden states: [B, L, D] + fn forward(&mut self, input_ids: &Tensor) -> Result; +} + +#[derive(Debug, Clone)] +pub struct TransformerConfig { + pub in_channels: usize, + pub patch_size: usize, + pub patch_size_t: usize, + pub num_layers: usize, +} + +pub trait VideoTransformer3D { + fn config(&self) -> &TransformerConfig; + + /// Аналог transformer(...)[0] в python. + #[allow(clippy::too_many_arguments)] + fn forward( + &mut self, + hidden_states: &Tensor, + encoder_hidden_states: &Tensor, + timestep: &Tensor, + encoder_attention_mask: &Tensor, + num_frames: usize, + height: usize, + width: usize, + rope_interpolation_scale: Option<(f32, f32, f32)>, + video_coords: Option<&Tensor>, + skip_layer_mask: Option<&Tensor>, + ) -> Result; + + fn set_skip_block_list(&mut self, list: Vec); +} + +#[derive(Debug, Clone)] +pub struct VaeConfig { + pub scaling_factor: f32, + pub timestep_conditioning: bool, +} + +pub trait VaeLtxVideo { + fn dtype(&self) -> DType; + fn spatial_compression_ratio(&self) -> usize; + fn temporal_compression_ratio(&self) -> usize; + fn config(&self) -> &VaeConfig; + + /// latents_mean/std предполагаются shape [C] + fn latents_mean(&self) -> &Tensor; + fn latents_std(&self) -> &Tensor; + + /// Декод: [B, C, F, H, W] -> видео (тензор) + fn decode(&self, latents: &Tensor, timestep: Option<&Tensor>) -> Result; +} + +pub trait VideoProcessor { + /// В оригинале postprocess_video умеет возвращать PIL/np; здесь оставляем тензор. + fn postprocess_video(&self, video: &Tensor) -> Result; +} + +#[derive(Debug, Clone)] +pub enum PromptInput { + Single(String), + Batch(Vec), +} + +impl PromptInput { + fn into_vec(self) -> Vec { + match self { + PromptInput::Single(s) => vec![s], + PromptInput::Batch(v) => v, + } + } +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum OutputType { + Latent, + Tensor, +} + +#[derive(Debug, Clone)] +pub struct LtxPipelineOutput { + pub frames: Tensor, +} + +pub struct LtxVideoProcessor { + pub config: VaeConfig, +} + +impl LtxVideoProcessor { + pub fn new(config: VaeConfig) -> Self { + Self { config } + } +} + +impl VideoProcessor for LtxVideoProcessor { + fn postprocess_video(&self, video: &Tensor) -> Result { + // v is in [-1, 1] usually from VAE + // Postprocess: (v + 1.0) / 2.0 -> [0, 1] + let video = video.affine(0.5, 0.5)?; + let video = video.clamp(0.0f32, 1.0f32)?; + // scale to 0-255 + let video = video.affine(255.0, 0.0)?; + Ok(video) + } +} + +// Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift +pub fn calculate_shift( + image_seq_len: usize, + base_seq_len: usize, + max_seq_len: usize, + base_shift: f32, + max_shift: f32, +) -> f32 { + let m = (max_shift - base_shift) / ((max_seq_len - base_seq_len) as f32); + let b = base_shift - m * (base_seq_len as f32); + (image_seq_len as f32) * m + b +} + +fn linspace(start: f32, end: f32, steps: usize) -> Vec { + if steps == 0 { + return vec![]; + } + if steps == 1 { + return vec![start]; + } + let denom = (steps - 1) as f32; + (0..steps) + .map(|i| start + (end - start) * (i as f32) / denom) + .collect() +} + +pub fn retrieve_timesteps( + scheduler: &mut dyn Scheduler, + num_inference_steps: Option, + device: &Device, + timesteps: Option>, + sigmas: Option>, + mu: f32, +) -> Result<(Vec, usize)> { + if timesteps.is_some() && sigmas.is_some() { + candle_core::bail!("Only one of `timesteps` or `sigmas` can be passed."); + } + + let schedule = if let Some(ts) = timesteps { + scheduler.set_timesteps(TimestepsSpec::Timesteps(ts), device, mu)? + } else if let Some(s) = sigmas { + scheduler.set_timesteps(TimestepsSpec::Sigmas(s), device, mu)? + } else { + let steps = num_inference_steps.unwrap_or(50); + scheduler.set_timesteps(TimestepsSpec::Steps(steps), device, mu)? + }; + + let n = schedule.len(); + Ok((schedule, n)) +} + +fn std_over_dims_except0_keepdim(x: &Tensor) -> Result { + // torch: x.std(dim=list(range(1, x.ndim)), keepdim=True) + // Здесь: flatten [B, ...] -> [B, N], var over dim=1 keepdim => [B,1], затем reshape -> [B,1,1,...] + let rank = x.rank(); + if rank < 2 { + candle_core::bail!("std_over_dims_except0_keepdim expects rank >= 2, got {rank}"); + } + let b = x.dim(0)?; + let flat = x.flatten_from(1)?; + let var = flat.var_keepdim(1)?; // unbiased variance + let std = var.sqrt()?; + let mut shape = Vec::with_capacity(rank); + shape.push(b); + shape.extend(std::iter::repeat(1usize).take(rank - 1)); + std.reshape(shape) +} + +// Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg +pub fn rescale_noise_cfg( + noise_cfg: &Tensor, + noise_pred_text: &Tensor, + guidance_rescale: f32, +) -> Result { + // std_text/std_cfg keepdim across dims 1..N + let std_text = std_over_dims_except0_keepdim(noise_pred_text)?; + let std_cfg = std_over_dims_except0_keepdim(noise_cfg)?; + + let ratio = std_text.broadcast_div(&std_cfg)?; + let noise_pred_rescaled = noise_cfg.broadcast_mul(&ratio)?; + + // noise_cfg = guidance_rescale * noise_pred_rescaled + (1-guidance_rescale)*noise_cfg + let a = noise_pred_rescaled.affine(guidance_rescale as f64, 0.0)?; + let b = noise_cfg.affine((1.0 - guidance_rescale) as f64, 0.0)?; + a.broadcast_add(&b) +} + +pub struct LtxPipeline<'a> { + pub scheduler: Box, + pub vae: Box, + pub text_encoder: Box, + pub tokenizer: Box, + pub transformer: Box, + pub video_processor: Box, + + pub tokenizer_max_length: usize, + + pub vae_spatial_compression_ratio: usize, + pub vae_temporal_compression_ratio: usize, + pub transformer_spatial_patch_size: usize, + pub transformer_temporal_patch_size: usize, + + // runtime state (аналог properties в python) + pub guidance_scale: f32, + pub guidance_rescale: f32, + pub stg_scale: f32, + pub num_timesteps: usize, + pub current_timestep: Option, + pub interrupt: bool, +} + +impl<'a> LtxPipeline<'a> { + pub fn new( + scheduler: Box, + vae: Box, + text_encoder: Box, + tokenizer: Box, + transformer: Box, + video_processor: Box, + ) -> Self { + let vae_spatial = vae.spatial_compression_ratio(); + let vae_temporal = vae.temporal_compression_ratio(); + let tcfg = transformer.config().clone(); + let max_len = tokenizer.model_max_length(); + + Self { + scheduler, + vae, + text_encoder, + tokenizer, + transformer, + video_processor, + tokenizer_max_length: max_len, + vae_spatial_compression_ratio: vae_spatial, + vae_temporal_compression_ratio: vae_temporal, + transformer_spatial_patch_size: tcfg.patch_size, + transformer_temporal_patch_size: tcfg.patch_size_t, + guidance_scale: 1.0, + guidance_rescale: 0.0, + stg_scale: 1.0, + num_timesteps: 0, + current_timestep: None, + interrupt: false, + } + } + + pub fn do_spatio_temporal_guidance(&self) -> bool { + self.stg_scale > 0.0 + } + + pub fn do_classifier_free_guidance(&self) -> bool { + self.guidance_scale > 1.0 + } + + #[allow(clippy::too_many_arguments)] + pub fn check_inputs( + &self, + prompt: Option<&PromptInput>, + height: usize, + width: usize, + prompt_embeds: Option<&Tensor>, + negative_prompt_embeds: Option<&Tensor>, + prompt_attention_mask: Option<&Tensor>, + negative_prompt_attention_mask: Option<&Tensor>, + ) -> Result<()> { + if height % 32 != 0 || width % 32 != 0 { + candle_core::bail!( + "`height` and `width` must be divisible by 32, got {height} and {width}" + ); + } + + if prompt.is_some() && prompt_embeds.is_some() { + candle_core::bail!("Cannot forward both `prompt` and `prompt_embeds`."); + } + if prompt.is_none() && prompt_embeds.is_none() { + candle_core::bail!("Provide either `prompt` or `prompt_embeds`."); + } + + if prompt_embeds.is_some() && prompt_attention_mask.is_none() { + candle_core::bail!( + "Must provide `prompt_attention_mask` when specifying `prompt_embeds`." + ); + } + if negative_prompt_embeds.is_some() && negative_prompt_attention_mask.is_none() { + candle_core::bail!( + "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`." + ); + } + + if prompt_embeds + .zip(negative_prompt_embeds) + .is_some_and(|(p, n)| p.dims() != n.dims()) + { + candle_core::bail!( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape." + ); + } + if prompt_attention_mask + .zip(negative_prompt_attention_mask) + .is_some_and(|(p, n)| p.dims() != n.dims()) + { + candle_core::bail!( + "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape." + ); + } + + Ok(()) + } + + fn get_t5_prompt_embeds( + &mut self, + prompt: &[String], + num_videos_per_prompt: usize, + max_sequence_length: usize, + device: &Device, + dtype: DType, + ) -> Result<(Tensor, Tensor)> { + let batch_size = prompt.len(); + let (input_ids, attention_mask) = + self.tokenizer.encode_batch(prompt, max_sequence_length)?; + let input_ids = input_ids.to_device(device)?; + let attention_mask = attention_mask.to_device(device)?; + + let prompt_embeds = self.text_encoder.forward(&input_ids)?; + let prompt_embeds = prompt_embeds.to_device(device)?.to_dtype(dtype)?; + + // repeat(1, num_videos_per_prompt, 1) then view => [B*num_videos, L, D] + let dims = prompt_embeds.dims(); + if dims.len() != 3 { + candle_core::bail!("text_encoder output must be rank-3 [B,L,D], got {:?}", dims); + } + let seq_len = dims[1]; + let hidden = dims[2]; + + let pe = prompt_embeds.repeat((1usize, num_videos_per_prompt, 1usize))?; + let pe = pe.reshape((batch_size * num_videos_per_prompt, seq_len, hidden))?; + + // return raw [B, L] 0/1 mask + let am = attention_mask.to_dtype(dtype)?.to_device(device)?; + Ok((pe, am)) + } + + #[allow(clippy::too_many_arguments)] + pub fn encode_prompt( + &mut self, + prompt: PromptInput, + negative_prompt: Option, + do_classifier_free_guidance: bool, + num_videos_per_prompt: usize, + prompt_embeds: Option, + negative_prompt_embeds: Option, + prompt_attention_mask: Option, + negative_prompt_attention_mask: Option, + max_sequence_length: usize, + device: &Device, + dtype: DType, + ) -> Result<(Tensor, Tensor, Tensor, Tensor)> { + let prompt_vec = prompt.clone().into_vec(); + let batch_size = if let Some(ref pe) = prompt_embeds { + pe.dim(0)? + } else { + prompt_vec.len() + }; + + let (prompt_embeds, prompt_attention_mask) = + if let (Some(pe), Some(pm)) = (prompt_embeds, prompt_attention_mask) { + (pe, pm) + } else { + self.get_t5_prompt_embeds( + &prompt_vec, + num_videos_per_prompt, + max_sequence_length, + device, + dtype, + )? + }; + + let (negative_prompt_embeds, negative_prompt_attention_mask) = + if do_classifier_free_guidance && negative_prompt_embeds.is_none() { + let neg = match negative_prompt { + Some(p) => p, + None => PromptInput::Single(String::new()), + }; + let mut neg_vec = neg.into_vec(); + if neg_vec.len() == 1 && batch_size > 1 { + neg_vec = vec![neg_vec[0].clone(); batch_size]; + } + if neg_vec.len() != batch_size { + candle_core::bail!( + "negative_prompt batch mismatch: expected {batch_size}, got {}", + neg_vec.len() + ); + } + self.get_t5_prompt_embeds( + &neg_vec, + num_videos_per_prompt, + max_sequence_length, + device, + dtype, + )? + } else { + let ne = + negative_prompt_embeds.unwrap_or_else(|| prompt_embeds.zeros_like().unwrap()); + let nm = negative_prompt_attention_mask + .unwrap_or_else(|| prompt_attention_mask.zeros_like().unwrap()); + (ne, nm) + }; + + Ok(( + prompt_embeds, + prompt_attention_mask, + negative_prompt_embeds, + negative_prompt_attention_mask, + )) + } + + pub fn pack_latents( + latents: &Tensor, + patch_size: usize, + patch_size_t: usize, + ) -> Result { + // [B,C,F,H,W] -> [B, S, D] + let dims = latents.dims(); + if dims.len() != 5 { + candle_core::bail!("pack_latents expects [B,C,F,H,W], got {:?}", dims); + } + let (b, c, f, h, w) = (dims[0], dims[1], dims[2], dims[3], dims[4]); + + if f % patch_size_t != 0 || h % patch_size != 0 || w % patch_size != 0 { + candle_core::bail!("latents shape not divisible by patch sizes"); + } + + let f2 = f / patch_size_t; + let h2 = h / patch_size; + let w2 = w / patch_size; + + // [B, C, F2, pt, H2, p, W2, p] + let x = latents.reshape(vec![b, c, f2, patch_size_t, h2, patch_size, w2, patch_size])?; + // permute -> [B, F2, H2, W2, C, pt, p, p] + let x = x.permute(vec![0, 2, 4, 6, 1, 3, 5, 7])?; + // flatten last 4 dims => [B, F2, H2, W2, D] + let x = x.flatten_from(4)?; + let d = x.dim(4)?; + // reshape [B, S, D], S=F2*H2*W2 + let s = f2 * h2 * w2; + x.reshape((b, s, d)) + } + + pub fn unpack_latents( + latents: &Tensor, + num_frames: usize, + height: usize, + width: usize, + patch_size: usize, + patch_size_t: usize, + ) -> Result { + // [B,S,D] -> [B,C,F,H,W] + let dims = latents.dims(); + if dims.len() != 3 { + candle_core::bail!("unpack_latents expects [B,S,D], got {:?}", dims); + } + let b = dims[0]; + let d = dims[2]; + + let denom = patch_size_t * patch_size * patch_size; + if d % denom != 0 { + candle_core::bail!("D is not divisible by (pt*p*p)"); + } + let c = d / denom; + + // [B, F2, H2, W2, C, pt, p, p] + let x = latents.reshape(vec![ + b, + num_frames, + height, + width, + c, + patch_size_t, + patch_size, + patch_size, + ])?; + // [B, C, F2, pt, H2, p, W2, p] + let x = x.permute(vec![0, 4, 1, 5, 2, 6, 3, 7])?.contiguous()?; + // merge last two p => W, merge H, merge F + let x = x.reshape(( + b, + c, + num_frames * patch_size_t, + height * patch_size, + width * patch_size, + ))?; + Ok(x) + } + + pub fn normalize_latents( + latents: &Tensor, + mean: &Tensor, + std: &Tensor, + scaling_factor: f32, + ) -> Result { + let c = latents.dim(1)?; + let mean = mean + .reshape((1usize, c, 1usize, 1usize, 1usize))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + let std = std + .reshape((1usize, c, 1usize, 1usize, 1usize))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + + let x = latents.broadcast_sub(&mean)?; + let x = x.affine(scaling_factor as f64, 0.0)?.broadcast_div(&std)?; + Ok(x) + } + + pub fn denormalize_latents( + latents: &Tensor, + mean: &Tensor, + std: &Tensor, + scaling_factor: f32, + ) -> Result { + let c = latents.dim(1)?; + let mean = mean + .reshape((1usize, c, 1usize, 1usize, 1usize))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + let std = std + .reshape((1usize, c, 1usize, 1usize, 1usize))? + .to_device(latents.device())? + .to_dtype(latents.dtype())?; + + let x = latents.broadcast_mul(&std)?; + let x = x + .affine((1.0 / scaling_factor) as f64, 0.0)? + .broadcast_add(&mean)?; + Ok(x) + } + + #[allow(clippy::too_many_arguments)] + pub fn prepare_latents( + &self, + batch_size: usize, + num_channels_latents: usize, + height: usize, + width: usize, + num_frames: usize, + dtype: DType, + device: &Device, + latents: Option, + ) -> Result { + if let Some(l) = latents { + return l.to_device(device)?.to_dtype(dtype); + } + + let h = height / self.vae_spatial_compression_ratio; + let w = width / self.vae_spatial_compression_ratio; + let f = (num_frames - 1) / self.vae_temporal_compression_ratio + 1; + + let shape = (batch_size, num_channels_latents, f, h, w); + let latents = Tensor::randn(0f32, 1f32, shape, device)?.to_dtype(dtype)?; + let latents = Self::pack_latents( + &latents, + self.transformer_spatial_patch_size, + self.transformer_temporal_patch_size, + )?; + Ok(latents) + } + + #[allow(clippy::too_many_arguments)] + pub fn call( + &mut self, + prompt: Option, + negative_prompt: Option, + height: usize, + width: usize, + num_frames: usize, + frame_rate: usize, + num_inference_steps: usize, + timesteps: Option>, + sigmas_provided: Option>, + guidance_scale: f32, + guidance_rescale: f32, + stg_scale: f32, + num_videos_per_prompt: usize, + latents: Option, + prompt_embeds: Option, + prompt_attention_mask: Option, + negative_prompt_embeds: Option, + negative_prompt_attention_mask: Option, + decode_timestep: Vec, + decode_noise_scale: Option>, + output_type: OutputType, + max_sequence_length: usize, + skip_block_list: Option>, + device: &Device, + ) -> Result { + self.check_inputs( + prompt.as_ref(), + height, + width, + prompt_embeds.as_ref(), + negative_prompt_embeds.as_ref(), + prompt_attention_mask.as_ref(), + negative_prompt_attention_mask.as_ref(), + )?; + + self.guidance_scale = guidance_scale; + self.guidance_rescale = guidance_rescale; + self.stg_scale = stg_scale; + self.interrupt = false; + self.current_timestep = None; + + // Set skip blocks from presets (distilled models) + // Note: In some versions this list is vec![42] (2B distilled) or others. + // We get it from the calling side usually (t2v script or example), + // but here we ensure the transformer knows it. + // For now, we assume it's provided in the parameters or via a separate setter if needed. + // However, the trait-based architecture suggests we should pass it before or during call. + // We add it to the call logic if it's not already handled. + + // batch_size + let batch_size = match (&prompt, &prompt_embeds) { + (Some(PromptInput::Single(_)), _) => 1usize, + (Some(PromptInput::Batch(v)), _) => v.len(), + (None, Some(pe)) => pe.dim(0)?, + _ => candle_core::bail!("Invalid prompt/prompt_embeds combination"), + }; + let effective_batch = batch_size * num_videos_per_prompt; + + // Apply skip blocks to transformer + // In LTXV, skip_block_list can be used for: + // 1. Permanent skipping (Distilled models): applied here if stg_scale is 0. + // 2. STG masking (Dev models): applied per-pass if stg_scale > 0. + if let Some(ref list) = skip_block_list { + if !self.do_spatio_temporal_guidance() { + self.transformer.set_skip_block_list(list.clone()); + } else { + self.transformer.set_skip_block_list(vec![]); + } + } + + // text embeddings + let dtype = self.text_encoder.dtype(); + let prompt_in = prompt + .clone() + .unwrap_or_else(|| PromptInput::Single(String::new())); + let (mut p_emb, mut p_mask, n_emb, n_mask) = self.encode_prompt( + prompt_in, + negative_prompt, + self.do_classifier_free_guidance(), + num_videos_per_prompt, + prompt_embeds, + negative_prompt_embeds, + prompt_attention_mask, + negative_prompt_attention_mask, + max_sequence_length, + device, + dtype, + )?; + + // Store individual embeds for sequential CFG + let prompt_embeds_cond = p_emb.clone(); + let prompt_mask_cond = p_mask.clone(); + let prompt_embeds_uncond = n_emb.clone(); + let prompt_mask_uncond = n_mask.clone(); + + if self.do_classifier_free_guidance() { + p_emb = Tensor::cat(&[n_emb, p_emb], 0)?; + p_mask = Tensor::cat(&[n_mask, p_mask], 0)?; + } + + // latents + let num_channels_latents = self.transformer.config().in_channels; + let mut latents = self.prepare_latents( + effective_batch, + num_channels_latents, + height, + width, + num_frames, + DType::F32, + device, + latents, + )?; + + // timesteps/sigmas/mu + let latent_num_frames = (num_frames - 1) / self.vae_temporal_compression_ratio + 1; + let latent_height = height / self.vae_spatial_compression_ratio; + let latent_width = width / self.vae_spatial_compression_ratio; + + let video_sequence_length = latent_num_frames * latent_height * latent_width; + + // Check if sigmas were provided before moving + let has_custom_sigmas = sigmas_provided.is_some(); + + let sigmas = if sigmas_provided.is_none() && timesteps.is_none() { + Some(linspace( + 1.0, + 1.0 / (num_inference_steps as f32), + num_inference_steps, + )) + } else { + sigmas_provided + }; + + let scfg = self.scheduler.config().clone(); + let mu = if has_custom_sigmas { + 0.0 // No additional shift for distilled timesteps + } else { + calculate_shift( + video_sequence_length, + scfg.base_image_seq_len, + scfg.max_image_seq_len, + scfg.base_shift, + scfg.max_shift, + ) + }; + + if !has_custom_sigmas { + println!( + " Calculated SD3 shift (mu): {:.4} for {} tokens", + mu, video_sequence_length + ); + } else { + println!(" Using custom distilled sigmas (mu=0.0)"); + } + + let (ts, _nsteps_effective) = retrieve_timesteps( + self.scheduler.as_mut(), + Some(num_inference_steps), + device, + timesteps, + sigmas, + mu, + )?; + self.num_timesteps = ts.len(); + + let num_warmup_steps = ts + .len() + .saturating_sub(num_inference_steps * self.scheduler.order()); + + // 5. RoPE coordinates and scaling + let ts_ratio = self.vae_temporal_compression_ratio as f32; + let sp_ratio = self.vae_spatial_compression_ratio as f32; + + let grid_f = + Tensor::arange(0u32, latent_num_frames as u32, device)?.to_dtype(DType::F32)?; + let grid_h = Tensor::arange(0u32, latent_height as u32, device)?.to_dtype(DType::F32)?; + let grid_w = Tensor::arange(0u32, latent_width as u32, device)?.to_dtype(DType::F32)?; + + let f = grid_f.reshape((latent_num_frames, 1, 1))?.broadcast_as(( + latent_num_frames, + latent_height, + latent_width, + ))?; + let h = grid_h.reshape((1, latent_height, 1))?.broadcast_as(( + latent_num_frames, + latent_height, + latent_width, + ))?; + let w = grid_w.reshape((1, 1, latent_width))?.broadcast_as(( + latent_num_frames, + latent_height, + latent_width, + ))?; + + // [3, F, H, W] -> flatten(1) -> [3, seq] -> transpose -> [seq, 3] -> [1, seq, 3] + let video_coords = Tensor::stack(&[f, h, w], 0)? + .flatten_from(1)? + .transpose(0, 1)? + .unsqueeze(0)?; + + let vf = video_coords.i((.., .., 0))?; + let vh = video_coords.i((.., .., 1))?; + let vw = video_coords.i((.., .., 2))?; + + // CAUSAL FIX: (L * 8 + 1 - 8).clamp(0) / frame_rate + let vf = vf + .affine(ts_ratio as f64, (1.0 - ts_ratio) as f64)? + .clamp(0.0f32, 1000.0f32)? + .affine(1.0 / (frame_rate as f64), 0.0)?; + + // SPATIAL SCALE: L * 32 + let vh = vh.affine(sp_ratio as f64, 0.0)?; + let vw = vw.affine(sp_ratio as f64, 0.0)?; + + let video_coords = Tensor::stack(&[vf, vh, vw], D::Minus1)?.broadcast_as(( + effective_batch, + video_sequence_length, + 3, + ))?; + + let num_conds = if self.do_classifier_free_guidance() && self.do_spatio_temporal_guidance() + { + 3 + } else if self.do_classifier_free_guidance() || self.do_spatio_temporal_guidance() { + 2 + } else { + 1 + }; + let _video_coords_batch = Tensor::cat(&vec![video_coords.clone(); num_conds], 0)?; + + // denoising loop + for (i, &t) in ts.iter().enumerate() { + if self.interrupt { + continue; + } + + self.current_timestep = Some(t); + + println!("Step {}/{}: t={}", i + 1, ts.len(), t); + + // Guidance Logic (CFG and/or STG) + // We use Sequential CFG style to save memory, running passes one by one. + let noise_pred = + if self.do_classifier_free_guidance() || self.do_spatio_temporal_guidance() { + let b = latents.dim(0)?; + let timestep_t = Tensor::full(t as f32, (b,), device)?; + let latents_input = latents.to_dtype(dtype)?; + + // 1. Unconditional pass (if CFG active) + let noise_uncond = if self.do_classifier_free_guidance() { + Some(self.transformer.forward( + &latents_input, + &prompt_embeds_uncond, + ×tep_t, + &prompt_mask_uncond, + latent_num_frames, + latent_height, + latent_width, + None, + Some(&video_coords), + None, + )?) + } else { + None + }; + + // 2. Conditional pass (Required for both CFG and STG) + let noise_text = self.transformer.forward( + &latents_input, + &prompt_embeds_cond, + ×tep_t, + &prompt_mask_cond, + latent_num_frames, + latent_height, + latent_width, + None, + Some(&video_coords), + None, + )?; + + // 3. Perturbed pass (if STG active) + let noise_perturbed = if self.do_spatio_temporal_guidance() { + let num_layers = self.transformer.config().num_layers; + // FIX: default=0 means apply all layers, 1=skip + let mut mask_data = vec![0.0f32; num_layers * b]; + if let Some(ref layers_to_skip) = skip_block_list { + for &layer_idx in layers_to_skip { + if layer_idx < num_layers { + for batch_idx in 0..b { + mask_data[layer_idx * b + batch_idx] = 1.0; // 1 = skip + } + } + } + } + let stg_mask = Tensor::from_vec(mask_data, (num_layers, b), device)?; + + Some(self.transformer.forward( + &latents_input, + &prompt_embeds_cond, + ×tep_t, + &prompt_mask_cond, + latent_num_frames, + latent_height, + latent_width, + None, + Some(&video_coords), + Some(&stg_mask), + )?) + } else { + None + }; + + // Mix results + let noise_text = noise_text.to_dtype(DType::F32)?; + let mut combined = noise_text.clone(); + + if let Some(uncond) = noise_uncond { + let uncond = uncond.to_dtype(DType::F32)?; + let diff_cfg = noise_text.broadcast_sub(&uncond)?; + combined = uncond + .broadcast_add(&diff_cfg.affine(self.guidance_scale as f64, 0.0)?)?; + + if self.guidance_rescale > 0.0 { + combined = + rescale_noise_cfg(&combined, &noise_text, self.guidance_rescale)?; + } + } + + if let Some(perturbed) = noise_perturbed { + let perturbed = perturbed.to_dtype(DType::F32)?; + let diff_stg = noise_text.broadcast_sub(&perturbed)?; + combined = combined + .broadcast_add(&diff_stg.affine(self.stg_scale as f64, 0.0)?)?; + } + + combined + } else { + // No guidance: single forward pass + let b = latents.dim(0)?; + let timestep_t = Tensor::full(t as f32, (b,), device)?; + let latents_input = latents.to_dtype(p_emb.dtype())?; + + self.transformer + .forward( + &latents_input, + &p_emb, + ×tep_t, + &p_mask, + latent_num_frames, + latent_height, + latent_width, + None, + Some(&video_coords), + None, + )? + .to_dtype(DType::F32)? + }; + + latents = self.scheduler.step(&noise_pred, t, &latents)?; + + if i == ts.len() - 1 + || ((i + 1) > num_warmup_steps && (i + 1) % self.scheduler.order() == 0) + { + // progress_bar.update() — опущено + } + } + + if output_type == OutputType::Latent { + return Ok(LtxPipelineOutput { frames: latents }); + } + + // decode branch + println!(" Decoding latents with VAE..."); + let mut latents = Self::unpack_latents( + &latents, + latent_num_frames, + latent_height, + latent_width, + self.transformer_spatial_patch_size, + self.transformer_temporal_patch_size, + )?; + + latents = Self::denormalize_latents( + &latents, + self.vae.latents_mean(), + self.vae.latents_std(), + self.vae.config().scaling_factor, + )?; + + latents = latents.to_dtype(p_emb.dtype())?; + + let timestep_opt: Option; + if !self.vae.config().timestep_conditioning { + timestep_opt = None; + } else { + // В оригинале decode_timestep/scale размножаются до batch_size (prompt batch), + // но на практике латенты имеют effective_batch = batch_size*num_videos_per_prompt. + // Здесь ожидаем decode_timestep длины 1 либо effective_batch. + let dt = if decode_timestep.len() == 1 { + vec![decode_timestep[0]; effective_batch] + } else { + decode_timestep + }; + if dt.len() != effective_batch { + candle_core::bail!( + "decode_timestep must have len 1 or effective_batch={effective_batch}" + ); + } + + let dns = match decode_noise_scale { + None => dt.clone(), + Some(v) if v.len() == 1 => vec![v[0]; effective_batch], + Some(v) => v, + }; + if dns.len() != effective_batch { + candle_core::bail!( + "decode_noise_scale must have len 1 or effective_batch={effective_batch}" + ); + } + + let timestep = + Tensor::from_vec(dt, (effective_batch,), device)?.to_dtype(latents.dtype())?; + let scale = Tensor::from_vec(dns, (effective_batch,), device)? + .to_dtype(latents.dtype())? + .reshape((effective_batch, 1usize, 1usize, 1usize, 1usize))?; + + let noise = + Tensor::randn(0f32, 1f32, latents.dims(), device)?.to_dtype(latents.dtype())?; + + // latents = (1 - scale)*latents + scale*noise + let one_minus = scale.affine(-1.0, 1.0)?; // 1 - scale + let a = latents.broadcast_mul(&one_minus)?; + let b = noise.broadcast_mul(&scale)?; + latents = a.broadcast_add(&b)?; + + timestep_opt = Some(timestep); + } + + latents = latents.to_dtype(self.vae.dtype())?; + + let video = self.vae.decode(&latents, timestep_opt.as_ref())?; + let video = self.video_processor.postprocess_video(&video)?; + + Ok(LtxPipelineOutput { frames: video }) + } +} diff --git a/cake-core/src/models/ltx_video/vendored/vae.rs b/cake-core/src/models/ltx_video/vendored/vae.rs new file mode 100644 index 00000000..b0421166 --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/vae.rs @@ -0,0 +1,2428 @@ +// Rust 2024 +// Скомпилируется как crate (lib). Структура: один файл src/lib.rs. +// +// Реализовано: +// - CausalConv3d через сумму Conv2d по временной оси (kt срезов веса) +// - RMSNorm "channels-first" как permute -> rmsnorm(last-dim) -> permute back +// - ResnetBlock3d, Down/Up blocks, Encoder3d, Decoder3d +// - DiagonalGaussianDistribution (sample/mode) +// - AutoencoderKLLTXVideo: encode/decode/forward + slicing + tiling + temporal tiling (API как в python) +// +// Примечание: gradient_checkpointing флаги сохранены как поля, но без checkpoint логики. + +#![allow(clippy::too_many_arguments)] +#![allow(clippy::type_complexity)] + +use super::t2v_pipeline::{VaeConfig, VaeLtxVideo}; +use candle_core::{DType, IndexOp, Module, Result, Tensor}; +use candle_nn::{ + Activation, Conv2d, Conv2dConfig, LayerNorm, LayerNormConfig, Linear, RmsNorm, VarBuilder, ops, +}; + +use serde::{Deserialize, Serialize}; + + +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(default)] +pub struct AutoencoderKLLtxVideoConfig { + pub in_channels: usize, + pub out_channels: usize, + pub latent_channels: usize, + pub block_out_channels: Vec, + pub decoder_block_out_channels: Vec, + #[serde(alias = "spatio_temporal_scaling")] + pub spatiotemporal_scaling: Vec, + #[serde(alias = "decoder_spatio_temporal_scaling")] + pub decoder_spatiotemporal_scaling: Vec, + pub layers_per_block: Vec, + pub decoder_layers_per_block: Vec, + pub patch_size: usize, + pub patch_size_t: usize, + #[serde(alias = "resnet_norm_eps")] + pub resnet_eps: f64, + pub scaling_factor: f64, + pub spatial_compression_ratio: usize, + pub temporal_compression_ratio: usize, + pub decoder_inject_noise: Vec, + #[serde(alias = "upsample_residual")] + pub decoder_upsample_residual: Vec, + #[serde(alias = "upsample_factor")] + pub decoder_upsample_factor: Vec, + pub timestep_conditioning: bool, + /// Per-block upsampler strides (t, h, w). If empty, derived from decoder_spatiotemporal_scaling. + #[serde(default)] + pub decoder_strides: Vec<(usize, usize, usize)>, + #[serde(default)] + pub latents_mean: Vec, + #[serde(default)] + pub latents_std: Vec, + #[serde(alias = "downsample_type")] + pub downsample_types: Vec, + #[serde(alias = "encoder_causal")] + pub is_causal: bool, + pub decoder_causal: bool, +} + +impl Default for AutoencoderKLLtxVideoConfig { + fn default() -> Self { + // Values from official LTX-Video 0.9.5 VAE config.json + Self { + in_channels: 3, + out_channels: 3, + latent_channels: 128, + block_out_channels: vec![128, 256, 512, 1024, 2048], + decoder_block_out_channels: vec![256, 512, 1024], + spatiotemporal_scaling: vec![true, true, true, true], + decoder_spatiotemporal_scaling: vec![true, true, true], + layers_per_block: vec![4, 6, 6, 2, 2], + decoder_layers_per_block: vec![5, 5, 5, 5], + patch_size: 4, + patch_size_t: 1, + resnet_eps: 1e-6, + scaling_factor: 1.0, + spatial_compression_ratio: 32, + temporal_compression_ratio: 8, + decoder_inject_noise: vec![false, false, false, false], + decoder_upsample_residual: vec![true, true, true], + decoder_upsample_factor: vec![2, 2, 2], + timestep_conditioning: true, + decoder_strides: vec![], + latents_mean: vec![0.0; 128], + latents_std: vec![1.0; 128], + downsample_types: vec![ + "spatial".into(), + "temporal".into(), + "spatiotemporal".into(), + "spatiotemporal".into(), + ], + is_causal: true, + decoder_causal: false, + } + } +} + +#[derive(Clone, Debug)] +pub struct DecoderOutput { + pub sample: Tensor, +} + +#[derive(Clone, Debug)] +pub struct AutoencoderKLOutput { + pub latent_dist: DiagonalGaussianDistribution, +} + +/// Аналог diffusers.models.vae.DiagonalGaussianDistribution. +#[derive(Clone, Debug)] +pub struct DiagonalGaussianDistribution { + pub mean: Tensor, + pub logvar: Tensor, +} + +impl DiagonalGaussianDistribution { + pub fn new(moments: &Tensor) -> Result { + // moments: (B, 2*C, T, H, W) -> mean/logvar split по каналу. + let (_b, ch2, _t, _h, _w) = moments.dims5()?; + if ch2 % 2 != 0 { + candle_core::bail!("moments channels must be even, got {}", ch2) + } + let ch = ch2 / 2; + let mean = moments.i((.., 0..ch, .., .., ..))?; + let logvar = moments.i((.., ch..(2 * ch), .., .., ..))?; + Ok(Self { mean, logvar }) + } + + pub fn mode(&self) -> Result { + Ok(self.mean.clone()) + } + + pub fn sample(&self) -> Result { + // eps ~ N(0,1), z = mean + exp(0.5*logvar)*eps + let eps = Tensor::randn(0f32, 1f32, self.mean.shape(), self.mean.device())? + .to_dtype(self.mean.dtype())?; + let std = (self.logvar.affine(0.5, 0.)?).exp()?; + self.mean.add(&std.mul(&eps)?) // mean + std*eps + } +} + +fn rmsnorm_channels_first(norm: &RmsNorm, x: &Tensor) -> Result { + // (B,C,T,H,W) -> (B,T,H,W,C) -> norm -> back + x.permute((0, 2, 3, 4, 1))? + .apply(norm)? + .permute((0, 4, 1, 2, 3)) +} + +fn layernorm_channels_first(norm: &LayerNorm, x: &Tensor) -> Result { + x.permute((0, 2, 3, 4, 1))? + .apply(norm)? + .permute((0, 4, 1, 2, 3)) +} + +fn silu(x: &Tensor) -> Result { + ops::silu(x) +} + +fn cat_dim(xs: &[Tensor], dim: usize) -> Result { + let refs: Vec<&Tensor> = xs.iter().collect(); + Tensor::cat(&refs, dim) +} + +/// Sinusoidal timestep embeddings (like Timesteps in diffusers) +/// Parameters match PixArtAlphaCombinedTimestepSizeEmbeddings: flip_sin_to_cos=True, downscale_freq_shift=0 +fn get_timestep_embedding(timesteps: &Tensor, embedding_dim: usize) -> Result { + let half_dim = embedding_dim / 2; + let device = timesteps.device(); + let dtype = timesteps.dtype(); + + // Python: exponent = -math.log(max_period) * torch.arange(0, half_dim) / (half_dim - downscale_freq_shift) + // With downscale_freq_shift=0: exponent / half_dim (not half_dim - 1!) + let max_period = 10000f64; + let downscale_freq_shift = 0.0; // PixArtAlphaCombinedTimestepSizeEmbeddings uses 0 + + let exponent_coef = -(max_period.ln()) / (half_dim as f64 - downscale_freq_shift); + let emb = (Tensor::arange(0u32, half_dim as u32, device)? + .to_dtype(DType::F32)? + .affine(exponent_coef, 0.0))? + .exp()?; + + // timesteps: (B,) -> (B, 1) * emb -> (B, half_dim) + let timesteps_f = timesteps.to_dtype(DType::F32)?.unsqueeze(1)?; + let emb = timesteps_f.broadcast_mul(&emb.unsqueeze(0)?)?; + + // Python: [sin, cos] then flip -> [cos, sin] if flip_sin_to_cos=True + // PixArtAlphaCombinedTimestepSizeEmbeddings uses flip_sin_to_cos=True + let sin_emb = emb.sin()?; + let cos_emb = emb.cos()?; + // flip_sin_to_cos=True means [cos, sin] order + Tensor::cat(&[&cos_emb, &sin_emb], 1)?.to_dtype(dtype) +} + +/// TimestepEmbedder: MLP that embeds timesteps (like TimestepEmbedding in diffusers) +#[derive(Debug, Clone)] +pub struct TimestepEmbedder { + linear_1: Linear, + linear_2: Linear, +} + +impl TimestepEmbedder { + pub fn new(in_channels: usize, time_embed_dim: usize, vb: VarBuilder) -> Result { + let linear_1 = candle_nn::linear(in_channels, time_embed_dim, vb.pp("linear_1"))?; + let linear_2 = candle_nn::linear(time_embed_dim, time_embed_dim, vb.pp("linear_2"))?; + Ok(Self { linear_1, linear_2 }) + } + + pub fn forward(&self, t: &Tensor) -> Result { + // Debug: print weight info + let h = self.linear_1.forward(t)?; + let h = silu(&h)?; + self.linear_2.forward(&h) + } +} + +/// Combined timestep embedder (like PixArtAlphaCombinedTimestepSizeEmbeddings) +#[derive(Debug, Clone)] +pub struct CombinedTimestepEmbedder { + timestep_embedder: TimestepEmbedder, +} + +impl CombinedTimestepEmbedder { + pub fn new(embedding_dim: usize, vb: VarBuilder) -> Result { + let timestep_embedder = + TimestepEmbedder::new(256, embedding_dim, vb.pp("timestep_embedder"))?; + Ok(Self { timestep_embedder }) + } + + pub fn forward(&self, timestep: &Tensor, hidden_dtype: DType) -> Result { + // timestep -> sinusoidal -> MLP + let timesteps_proj = get_timestep_embedding(timestep, 256)?; + + + self.timestep_embedder + .forward(×teps_proj.to_dtype(hidden_dtype)?) + } +} + +#[derive(Clone, Copy, Debug)] +pub struct Conv3dLikeConfig { + pub stride_t: usize, + pub stride_h: usize, + pub stride_w: usize, + pub dil_t: usize, + pub dil_h: usize, + pub dil_w: usize, + pub groups: usize, + pub padding_mode_zeros: bool, + pub is_causal: bool, +} + +impl Default for Conv3dLikeConfig { + fn default() -> Self { + Self { + stride_t: 1, + stride_h: 1, + stride_w: 1, + dil_t: 1, + dil_h: 1, + dil_w: 1, + groups: 1, + padding_mode_zeros: true, + is_causal: true, + } + } +} + +/// Эквивалент LTXVideoCausalConv3d из python, но реализованный через kt Conv2d по времени. +#[derive(Debug, Clone)] +pub struct LtxVideoCausalConv3d { + kt: usize, + pub _kh: usize, + pub _kw: usize, + cfg: Conv3dLikeConfig, + conv2d_slices: Vec, // длина kt + bias: Option, // (out_channels) +} + +impl LtxVideoCausalConv3d { + pub fn new( + in_channels: usize, + out_channels: usize, + kernel: (usize, usize, usize), + stride: (usize, usize, usize), + dilation: (usize, usize, usize), + groups: usize, + is_causal: bool, + vb: VarBuilder, + ) -> Result { + let (kt, kh, kw) = kernel; + let (st, sh, sw) = stride; + let (dt, dh, dw) = dilation; + + // In diffusers, LtxVideoCausalConv3d has an inner `conv` module + let conv_vb = vb.pp("conv"); + // вес как у conv3d: (out, in/groups, kt, kh, kw) + let w = conv_vb.get((out_channels, in_channels / groups, kt, kh, kw), "weight")?; + + // Wait. Python Conv3d default bias=True. + // Are there cases where bias is disabled? + // LTX uses LayerNorm/RMSNorm. Sometimes Conv bias is removed. + // But Diffusers code initialized Conv3d with defaults (bias=True). + // Exceptions? + // Code snippet 4772 line 66: padding_mode passed. bias NOT passed (so True). + // So ALL CausalConv3d layers MUST have bias. + // So I should remove .ok(). + let b = conv_vb.get(out_channels, "bias")?; + + let hpad = kh / 2; + let _wpad = kw / 2; + + let mut conv2d_slices = Vec::with_capacity(kt); + for ti in 0..kt { + let w2 = w.i((.., .., ti, .., ..))?.contiguous()?; + let c2cfg = Conv2dConfig { + padding: hpad, + stride: sh, + dilation: dh, + groups, + ..Default::default() + }; + // bias добавим один раз после суммы. + conv2d_slices.push(Conv2d::new(w2, None, c2cfg)); + } + + Ok(Self { + kt, + _kh: kh, + _kw: kw, + cfg: Conv3dLikeConfig { + stride_t: st, + stride_h: sh, + stride_w: sw, + dil_t: dt, + dil_h: dh, + dil_w: dw, + groups, + padding_mode_zeros: true, + is_causal, + }, + conv2d_slices, + bias: Some(b), + }) + } + + fn pad_time_replicate(&self, x: &Tensor) -> Result { + // x: (B,C,T,H,W) + let (_, _, t, _, _) = x.dims5()?; + let kt = self.kt; + + if kt <= 1 { + return Ok(x.clone()); + } + + if self.cfg.is_causal { + let left = kt - 1; + let first = x.i((.., .., 0, .., ..))?.unsqueeze(2)?; + let pad_left = first.repeat((1, 1, left, 1, 1))?; + cat_dim(&[pad_left, x.clone()], 2) + } else { + let left = (kt - 1) / 2; + let right = (kt - 1) / 2; + + let first = x.i((.., .., 0, .., ..))?.unsqueeze(2)?; + let last = x.i((.., .., t - 1, .., ..))?.unsqueeze(2)?; + + let pad_left = if left == 0 { + None + } else { + Some(first.repeat((1, 1, left, 1, 1))?) + }; + let pad_right = if right == 0 { + None + } else { + Some(last.repeat((1, 1, right, 1, 1))?) + }; + + match (pad_left, pad_right) { + (None, None) => Ok(x.clone()), + (Some(pl), None) => cat_dim(&[pl, x.clone()], 2), + (None, Some(pr)) => cat_dim(&[x.clone(), pr], 2), + (Some(pl), Some(pr)) => cat_dim(&[pl, x.clone(), pr], 2), + } + } + } + + pub fn forward(&self, x: &Tensor) -> Result { + // Реализация свертки по времени: y[t_out] = sum_k Conv2d(x[t_in + k*dil_t]) + // с temporal padding replicate и temporal stride. + let x = self.pad_time_replicate(x)?; + let (_b, _c, t_pad, _h, _w) = x.dims5()?; + + let kt = self.kt; + let dt = self.cfg.dil_t; + let st = self.cfg.stride_t; + + // t_out: сколько можно сдвигов без выхода за границу. + let needed = (kt - 1) * dt + 1; + if t_pad < needed { + candle_core::bail!( + "time dim too small after padding: t_pad={}, needed={}", + t_pad, + needed + ) + } + let t_out = (t_pad - needed) / st + 1; + + let mut ys: Vec = Vec::with_capacity(t_out); + + for to in 0..t_out { + let base_t = to * st; + + let mut acc: Option = None; + for ki in 0..kt { + let ti = base_t + ki * dt; + let xt = x.i((.., .., ti, .., ..))?; // (B,C,H,W) + let yt = xt.apply(&self.conv2d_slices[ki])?; // (B,Out,H',W') + acc = Some(match acc { + None => yt, + Some(prev) => prev.add(&yt)?, + }); + } + + let yt = acc.expect("kt>=1 so acc is Some"); + ys.push(yt.unsqueeze(2)?); // (B,Out,1,H',W') + } + + let y = cat_dim(&ys, 2)?; // (B,Out,T_out,H',W') + + if let Some(bias) = &self.bias { + let bias = bias.reshape((1, bias.dims1()?, 1, 1, 1))?; + y.broadcast_add(&bias) + } else { + Ok(y) + } + } +} + +/// Downsample type for LTX-Video 0.9.5 VAE +#[derive(Debug, Clone, Copy, PartialEq)] +pub enum DownsampleType { + Conv, // stride (2,2,2) direct conv + Spatial, // stride (1,2,2) pixel unshuffle + Temporal, // stride (2,1,1) pixel unshuffle + Spatiotemporal, // stride (2,2,2) pixel unshuffle +} + +impl DownsampleType { + pub fn parse(s: &str) -> Self { + match s.to_lowercase().as_str() { + "spatial" => Self::Spatial, + "temporal" => Self::Temporal, + "spatiotemporal" => Self::Spatiotemporal, + _ => Self::Conv, + } + } + + pub fn stride(&self) -> (usize, usize, usize) { + match self { + Self::Conv => (2, 2, 2), + Self::Spatial => (1, 2, 2), + Self::Temporal => (2, 1, 1), + Self::Spatiotemporal => (2, 2, 2), + } + } +} + +/// Pixel unshuffle downsampler for LTX-Video 0.9.5 +#[derive(Debug, Clone)] +pub struct LtxVideoDownsampler3d { + stride: (usize, usize, usize), + group_size: usize, + conv: LtxVideoCausalConv3d, +} + +impl LtxVideoDownsampler3d { + pub fn new( + in_channels: usize, + out_channels: usize, + stride: (usize, usize, usize), + is_causal: bool, + vb: VarBuilder, + ) -> Result { + let (st, sh, sw) = stride; + let group_size = (in_channels * st * sh * sw) / out_channels; + let conv_out_channels = out_channels / (st * sh * sw); + + let conv = LtxVideoCausalConv3d::new( + in_channels, + conv_out_channels, + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv"), + )?; + + Ok(Self { + stride, + group_size, + conv, + }) + } + + pub fn forward(&self, x: &Tensor) -> Result { + let (st, sh, sw) = self.stride; + let (b, c, _t, _h, _w) = x.dims5()?; + + // Pad temporal dimension: cat(x[:,:,:st-1], x, dim=2) + let padded = if st > 1 { + let pad_slice = x.i((.., .., ..(st - 1), .., ..))?; + Tensor::cat(&[&pad_slice, x], 2)? + } else { + x.clone() + }; + let (_, _, t_pad, h_pad, w_pad) = padded.dims5()?; + + // Compute new dimensions after pixel unshuffle + let t_new = t_pad / st; + let h_new = h_pad / sh; + let w_new = w_pad / sw; + + // === Residual path: pixel unshuffle + mean === + // Shape: (B, C, T, H, W) -> (B, C, T', st, H', sh, W', sw) + let residual = padded + .reshape(&[b, c, t_new, st, h_new, sh, w_new, sw])? + .permute(vec![0, 1, 3, 5, 7, 2, 4, 6])? // (B, C, st, sh, sw, T', H', W') + .reshape((b, c * st * sh * sw, t_new, h_new, w_new))?; + + // Group and average: unflatten(1, (-1, group_size)).mean(dim=2) + let residual = residual + .reshape(&[ + b, + c * st * sh * sw / self.group_size, + self.group_size, + t_new, + h_new, + w_new, + ])? + .mean(2)?; + + // === Conv path: same pixel unshuffle === + let conv_out = self.conv.forward(&padded)?; + let (_, c_conv, _, _, _) = conv_out.dims5()?; + + let hidden = conv_out + .reshape(&[b, c_conv, t_new, st, h_new, sh, w_new, sw])? + .permute(vec![0, 1, 3, 5, 7, 2, 4, 6])? + .reshape((b, c_conv * st * sh * sw, t_new, h_new, w_new))?; + + hidden.add(&residual) + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoResnetBlock3d { + norm1: Option, + conv1: LtxVideoCausalConv3d, + norm2: Option, + _dropout: f64, + conv2: LtxVideoCausalConv3d, + + // shortcut при смене каналов + norm3: Option, + conv_shortcut: Option, + + // noise injection + per_channel_scale1: Option, // (C,1,1) + per_channel_scale2: Option, + + // timestep conditioning + scale_shift_table: Option, // (4, C) +} + +impl LtxVideoResnetBlock3d { + pub fn new( + in_channels: usize, + out_channels: usize, + dropout: f64, + eps: f64, + elementwise_affine: bool, + is_causal: bool, + inject_noise: bool, + timestep_conditioning: bool, + vb: VarBuilder, + ) -> Result { + // LTX-Video resnet blocks may not have norm layers - make them optional + // Helper to load RmsNorm or fallback to default (ones) if affine is false or loading fails + let load_norm = |name: &str, size: usize| -> Result { + if elementwise_affine { + let norm_res = candle_nn::rms_norm(size, 1e-8, vb.pp(name)); + if let Ok(norm) = norm_res { + return Ok(norm); + } + } + // Fallback: create RmsNorm with ones (representing no affine scaling) + let ones = Tensor::ones((size,), vb.dtype(), vb.device())?; + Ok(RmsNorm::new(ones, 1e-8)) + }; + + let norm1 = Some(load_norm("norm1", in_channels)?); + let conv1 = LtxVideoCausalConv3d::new( + in_channels, + out_channels, + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv1"), + )?; + + let norm2 = Some(load_norm("norm2", out_channels)?); + let conv2 = LtxVideoCausalConv3d::new( + out_channels, + out_channels, + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv2"), + )?; + + let (norm3, conv_shortcut) = if in_channels != out_channels { + let lncfg = LayerNormConfig { + eps, + affine: elementwise_affine, + ..Default::default() + }; + let norm3 = candle_nn::layer_norm(in_channels, lncfg, vb.pp("norm3")).ok(); + let conv_shortcut = LtxVideoCausalConv3d::new( + in_channels, + out_channels, + (1, 1, 1), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv_shortcut"), + )?; + (norm3, Some(conv_shortcut)) + } else { + (None, None) + }; + + let per_channel_scale1 = if inject_noise { + vb.pp("per_channel_scale1") + .get((in_channels, 1, 1), "weight") + .ok() + } else { + None + }; + let per_channel_scale2 = if inject_noise { + vb.pp("per_channel_scale2") + .get((in_channels, 1, 1), "weight") + .ok() + } else { + None + }; + + let scale_shift_table = if timestep_conditioning { + vb.get((4, in_channels), "scale_shift_table").ok() + } else { + None + }; + + Ok(Self { + norm1, + conv1, + norm2, + _dropout: dropout, + conv2, + norm3, + conv_shortcut, + per_channel_scale1, + per_channel_scale2, + scale_shift_table, + }) + } + + fn maybe_apply_scale_shift( + &self, + x: Tensor, + temb: Option<&Tensor>, + stage: usize, // 0: (shift1,scale1), 1: (shift2,scale2) + ) -> Result { + let Some(tbl) = &self.scale_shift_table else { + return Ok(x); + }; + let Some(temb) = temb else { + return Ok(x); + }; + + // temb: (B, 4*C, 1, 1, 1) -> unflatten dim1 to (B, 4, C, 1, 1, 1) + let (b, temb_dim, _, _, _) = temb.dims5()?; + let c = tbl.dims2()?.1; + if temb_dim != 4 * c { + candle_core::bail!("temb dim mismatch: got {}, expected {}", temb_dim, 4 * c) + } + let temb = temb + .reshape((b, 4, c, 1, 1, 1))? + .broadcast_add(&tbl.unsqueeze(0)?.unsqueeze(3)?.unsqueeze(4)?.unsqueeze(5)?)?; + + let shift = temb.i((.., stage * 2, .., .., .., ..))?; // (B,C,1,1,1) + let scale = temb.i((.., stage * 2 + 1, .., .., .., ..))?; + // x * (1 + scale) + shift + x.broadcast_mul(&scale.affine(1.0, 1.0)?)? + .broadcast_add(&shift) + } + + fn maybe_inject_noise(&self, x: Tensor, pcs: &Option) -> Result { + let Some(scale) = pcs else { + return Ok(x); + }; + // spatialshape = (H,W) как в python-коде. + let (_b, _c, _t, h, w) = x.dims5()?; + let noise = Tensor::randn(0f32, 1f32, (h, w), x.device())?.to_dtype(x.dtype())?; + // (H,W) -> (1,1,1,H,W) + let noise = noise.unsqueeze(0)?.unsqueeze(0)?.unsqueeze(0)?; + // scale: (C,1,1) -> (1,C,1,1,1) + let scale = scale.unsqueeze(0)?.unsqueeze(2)?; + x.add(&(noise.broadcast_mul(&scale)?)) + } + + pub fn forward(&self, inputs: &Tensor, temb: Option<&Tensor>, _train: bool) -> Result { + let mut h = inputs.clone(); + + // Only apply norm if it exists + if let Some(ref norm1) = self.norm1 { + h = rmsnorm_channels_first(norm1, &h)?; + } + + h = self.maybe_apply_scale_shift(h, temb, 0)?; + + h = silu(&h)?; + + h = self.conv1.forward(&h)?; + + h = self.maybe_inject_noise(h, &self.per_channel_scale1)?; + + if let Some(ref norm2) = self.norm2 { + h = rmsnorm_channels_first(norm2, &h)?; + } + h = self.maybe_apply_scale_shift(h, temb, 1)?; + h = silu(&h)?; + + // dropout unused in inference + + h = self.conv2.forward(&h)?; + + // if let Ok(vals) = h.flatten_all()?.to_vec1::() { + // println!("[DEBUG] Resnet output conv2 mean: {:.4}", vals.iter().sum::() / vals.len() as f32); + // } + + h = self.maybe_inject_noise(h, &self.per_channel_scale2)?; + + let mut x = inputs.clone(); + if let Some(n3) = &self.norm3 { + x = layernorm_channels_first(n3, &x)?; + } + if let Some(cs) = &self.conv_shortcut { + x = cs.forward(&x)?; + } + let result = h.add(&x)?; + + Ok(result) + } +} + +/// Wrapper for different downsampler types +#[derive(Debug, Clone)] +pub enum Downsampler { + Conv(LtxVideoCausalConv3d), + PixelUnshuffle(LtxVideoDownsampler3d), +} + +impl Downsampler { + pub fn forward(&self, x: &Tensor) -> Result { + match self { + Downsampler::Conv(c) => c.forward(x), + Downsampler::PixelUnshuffle(p) => p.forward(x), + } + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoDownBlock3d { + resnets: Vec, + downsamplers: Option>, + conv_out: Option, + pub gradient_checkpointing: bool, +} + +impl LtxVideoDownBlock3d { + pub fn new( + in_channels: usize, + out_channels: usize, + num_layers: usize, + dropout: f64, + resnet_eps: f64, + spatiotemporal_scale: bool, + is_causal: bool, + downsample_type: DownsampleType, + vb: VarBuilder, + ) -> Result { + let mut resnets = Vec::with_capacity(num_layers); + for i in 0..num_layers { + resnets.push(LtxVideoResnetBlock3d::new( + in_channels, + in_channels, + dropout, + resnet_eps, + false, + is_causal, + false, + false, + vb.pp(format!("resnets.{i}")), + )?); + } + + let downsamplers = if spatiotemporal_scale { + let ds = match downsample_type { + DownsampleType::Conv => { + // Direct stride conv + Downsampler::Conv(LtxVideoCausalConv3d::new( + in_channels, + in_channels, + (3, 3, 3), + (2, 2, 2), + (1, 1, 1), + 1, + is_causal, + vb.pp("downsamplers.0").pp("conv"), + )?) + } + _ => { + // Pixel unshuffle types (spatial/temporal/spatiotemporal) + let stride = downsample_type.stride(); + Downsampler::PixelUnshuffle(LtxVideoDownsampler3d::new( + in_channels, + out_channels, + stride, + is_causal, + vb.pp("downsamplers.0"), + )?) + } + }; + Some(vec![ds]) + } else { + None + }; + + // conv_out only needed for channel change in some configs + let conv_out = if in_channels != out_channels && downsample_type == DownsampleType::Conv { + LtxVideoResnetBlock3d::new( + in_channels, + out_channels, + dropout, + resnet_eps, + true, + is_causal, + false, + false, + vb.pp("conv_out"), + ) + .ok() + } else { + None + }; + + Ok(Self { + resnets, + downsamplers, + conv_out, + gradient_checkpointing: false, + }) + } + + pub fn forward(&self, x: &Tensor, temb: Option<&Tensor>, train: bool) -> Result { + let mut h = x.clone(); + for r in self.resnets.iter() { + h = r.forward(&h, temb, train)?; + } + if let Some(ds) = &self.downsamplers { + for d in ds.iter() { + h = d.forward(&h)?; + } + } + if let Some(co) = &self.conv_out { + h = co.forward(&h, temb, train)?; + } + Ok(h) + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoMidBlock3d { + resnets: Vec, + pub gradient_checkpointing: bool, + time_embedder: Option, +} + +impl LtxVideoMidBlock3d { + pub fn new( + in_channels: usize, + num_layers: usize, + dropout: f64, + resnet_eps: f64, + is_causal: bool, + inject_noise: bool, + timestep_conditioning: bool, + vb: VarBuilder, + ) -> Result { + let mut resnets = Vec::with_capacity(num_layers); + for i in 0..num_layers { + resnets.push(LtxVideoResnetBlock3d::new( + in_channels, + in_channels, + dropout, + resnet_eps, + false, + is_causal, + inject_noise, + timestep_conditioning, + vb.pp(format!("resnets.{i}")), + )?); + } + + let time_embedder = if timestep_conditioning { + // Block channels * 4 for scale_shift_table compatibility + let emb_dim = in_channels * 4; + CombinedTimestepEmbedder::new(emb_dim, vb.pp("time_embedder")).ok() + } else { + None + }; + + Ok(Self { + resnets, + time_embedder, + gradient_checkpointing: false, + }) + } + + pub fn forward(&self, x: &Tensor, temb: Option<&Tensor>, train: bool) -> Result { + let mut h = x.clone(); + + // Apply time embedding if present + let temb_proj = if let (Some(te), Some(t)) = (&self.time_embedder, temb) { + // Python: temb = temb.view(hidden_states.size(0), -1, 1, 1, 1) + let emb = te.forward(t, h.dtype())?; + let batch_size = h.dims5()?.0; + let emb_dim = emb.dims2()?.1; + Some(emb.reshape((batch_size, emb_dim, 1, 1, 1))?) + } else { + None + }; + + for r in self.resnets.iter() { + h = r.forward(&h, temb_proj.as_ref(), train)?; + } + Ok(h) + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoUpsampler3d { + stride_t: usize, + stride_h: usize, + stride_w: usize, + residual: bool, + + channel_repeats: usize, + conv: LtxVideoCausalConv3d, +} + +impl LtxVideoUpsampler3d { + pub fn new( + in_channels: usize, + out_channels: usize, + stride: (usize, usize, usize), + is_causal: bool, + residual: bool, + _upscale_factor: usize, + vb: VarBuilder, + ) -> Result { + let (st, sh, sw) = stride; + let stride_product = st * sh * sw; + // Conv output needs to be such that after shuffle we get out_channels. + // Shuffle reduces channels by stride_product. + // So conv_out = out_channels * stride_product. + let conv_out_channels = out_channels * stride_product; + // For residual: must match main path channels after shuffle + // main_channels = conv_out_channels / stride_product = out_channels + // residual_channels = in_channels / stride_product + // channel_repeats = main_channels / residual_channels = (out_channels * stride_product / in_channels) + let channel_repeats = conv_out_channels / in_channels; + + let conv = LtxVideoCausalConv3d::new( + in_channels, + conv_out_channels, + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv"), + )?; + Ok(Self { + stride_t: st, + stride_h: sh, + stride_w: sw, + residual, + channel_repeats, + conv, + }) + } + + pub fn forward(&self, x: &Tensor) -> Result { + // Python: permute(0, 1, 5, 2, 6, 3, 7, 4).flatten(6, 7).flatten(4, 5).flatten(2, 3) + let (b, _c, t, h, w) = x.dims5()?; + let st = self.stride_t; + let sh = self.stride_h; + let sw = self.stride_w; + + + let residual = if self.residual { + let cprime = x.dims5()?.1; + let c_out = cprime / (st * sh * sw); + + // reshape to [B, C', st, sh, sw, T, H, W] + let x2 = x.reshape(&[b, c_out, st, sh, sw, t, h, w])?; + // permute(0, 1, 5, 2, 6, 3, 7, 4) -> [B, C', T, st, H, sh, W, sw] + let x2 = x2.permute(vec![0, 1, 5, 2, 6, 3, 7, 4])?.contiguous()?; + // flatten(6, 7) -> [B, C', T, st, H, sh, W*sw] + let x2 = x2.reshape(&[b, c_out, t, st, h, sh, w * sw])?; + // flatten(4, 5) -> [B, C', T, st, H*sh, W*sw] + let x2 = x2.reshape(&[b, c_out, t, st, h * sh, w * sw])?; + // flatten(2, 3) -> [B, C', T*st, H*sh, W*sw] + let x2 = x2.reshape(&[b, c_out, t * st, h * sh, w * sw])?; + + // repeat channels if needed + let x2 = if self.channel_repeats > 1 { + x2.repeat((1, self.channel_repeats, 1, 1, 1))? + } else { + x2 + }; + // slice: [:, :, st-1:] + let x2 = x2.i((.., .., (st - 1).., .., ..))?; + Some(x2) + } else { + None + }; + + let h0 = self.conv.forward(x)?; + let h0 = h0.contiguous()?; + + let (_b2, c2, t2, h2, w2) = h0.dims5()?; + let c_out = c2 / (st * sh * sw); + + // reshape to [B, C', st, sh, sw, T, H, W] + let h1 = h0.reshape(&[b, c_out, st, sh, sw, t2, h2, w2])?; + + // permute(0, 1, 5, 2, 6, 3, 7, 4) -> [B, C', T, st, H, sh, W, sw] + let h1 = h1.permute(vec![0, 1, 5, 2, 6, 3, 7, 4])?.contiguous()?; + + // flatten(6, 7) -> [B, C', T, st, H, sh, W*sw] + let h1 = h1.reshape(&[b, c_out, t2, st, h2, sh, w2 * sw])?; + // flatten(4, 5) -> [B, C', T, st, H*sh, W*sw] + let h1 = h1.reshape(&[b, c_out, t2, st, h2 * sh, w2 * sw])?; + // flatten(2, 3) -> [B, C', T*st, H*sh, W*sw] + let h1 = h1.reshape(&[b, c_out, t2 * st, h2 * sh, w2 * sw])?; + + // slice: [:, :, st-1:] + let h1 = h1.i((.., .., (st - 1).., .., ..))?; + + let h1 = if let Some(r) = residual { + h1.add(&r)? + } else { + h1 + }; + Ok(h1) + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoUpBlock3d { + conv_in: Option, + upsamplers: Option>, + resnets: Vec, + time_embedder: Option, + pub gradient_checkpointing: bool, +} + +impl LtxVideoUpBlock3d { + pub fn new( + in_channels: usize, + out_channels: usize, + num_layers: usize, + dropout: f64, + resnet_eps: f64, + spatiotemporal_scale: bool, + is_causal: bool, + inject_noise: bool, + timestep_conditioning: bool, + upsampler_residual: bool, + up_scale_factor: usize, + vb: VarBuilder, + ) -> Result { + let stride = if spatiotemporal_scale { + (2, 2, 2) + } else { + (1, 2, 2) + }; + Self::new_with_stride( + in_channels, out_channels, num_layers, dropout, resnet_eps, + stride, is_causal, inject_noise, timestep_conditioning, + upsampler_residual, up_scale_factor, vb, + ) + } + + /// Create an up block with an explicit upsampler stride (t, h, w). + pub fn new_with_stride( + in_channels: usize, + out_channels: usize, + num_layers: usize, + dropout: f64, + resnet_eps: f64, + stride: (usize, usize, usize), + is_causal: bool, + inject_noise: bool, + timestep_conditioning: bool, + upsampler_residual: bool, + up_scale_factor: usize, + vb: VarBuilder, + ) -> Result { + // conv_in may not exist in some VAE configs (e.g. official 0.9.5) + let conv_in = if in_channels != out_channels { + Some(LtxVideoResnetBlock3d::new( + in_channels, + out_channels, + dropout, + resnet_eps, + false, + is_causal, + inject_noise, + timestep_conditioning, + vb.pp("conv_in"), + )?) + } else { + None + }; + + let upsamplers = Some(vec![LtxVideoUpsampler3d::new( + out_channels * up_scale_factor, + out_channels, + stride, + is_causal, + upsampler_residual, + up_scale_factor, + vb.pp("upsamplers.0"), + )?]); + + let mut resnets = Vec::with_capacity(num_layers); + for i in 0..num_layers { + // If upsampler exists (which is always true in default config/logic above), it changes channels to out_channels. + // If explicit None case handled later, logic changes. + // Assuming upsamplers always exist for now (based on diffusers 'if i > 0' and reversed decoder logic starting at block 2). + // But wait, what if `spatiotemporal_scale` logic creates None? NO, I removed the `else { None }` block above and added spatial fallback. + // So upsampler ALWAYS outputs `out_channels`. + let in_c = out_channels; + resnets.push(LtxVideoResnetBlock3d::new( + in_c, + out_channels, + dropout, + resnet_eps, + false, + is_causal, + inject_noise, + timestep_conditioning, + vb.pp(format!("resnets.{i}")), + )?); + } + + let time_embedder = if timestep_conditioning { + // Block channels * 4 for scale_shift_table compatibility + let emb_dim = out_channels * 4; + CombinedTimestepEmbedder::new(emb_dim, vb.pp("time_embedder")).ok() + } else { + None + }; + + Ok(Self { + conv_in, + upsamplers, + resnets, + time_embedder, + gradient_checkpointing: false, + }) + } + + pub fn forward(&self, x: &Tensor, temb: Option<&Tensor>, train: bool) -> Result { + let mut h = x.clone(); + + // Python order: + // 1. conv_in with RAW temb (if exists) + // 2. time_embedder to get temb_proj + // 3. upsamplers + // 4. resnets with temb_proj + + // 1. conv_in uses RAW temb (before time_embedder transformation) + // Note: conv_in's internal scale_shift_table expects 4*C dimensional temb + // But the raw temb passed from decoder is just a scalar, so conv_in won't apply scale_shift + if let Some(ci) = &self.conv_in { + h = ci.forward(&h, None, train)?; // conv_in doesn't use temb in 0.9.5 + } + + // 2. Apply time_embedder AFTER conv_in (matches Python order) + let temb_proj = if let (Some(te), Some(t)) = (&self.time_embedder, temb) { + let emb = te.forward(t, h.dtype())?; + let batch_size = h.dims5()?.0; + let emb_dim = emb.dims2()?.1; + Some(emb.reshape((batch_size, emb_dim, 1, 1, 1))?) + } else { + None + }; + + // 3. upsamplers + if let Some(us) = &self.upsamplers { + for u in us.iter() { + h = u.forward(&h)?; + } + } + + // 4. resnets use the transformed temb_proj + for r in self.resnets.iter() { + h = r.forward(&h, temb_proj.as_ref(), train)?; + } + Ok(h) + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoEncoder3d { + patch_size: usize, + patch_size_t: usize, + conv_in: LtxVideoCausalConv3d, + down_blocks: Vec, + mid_block: LtxVideoMidBlock3d, + norm_out: Option, + conv_act: Activation, + conv_out: LtxVideoCausalConv3d, + pub gradient_checkpointing: bool, +} + +impl LtxVideoEncoder3d { + pub fn new( + in_channels: usize, + out_channels: usize, + block_out_channels: &[usize], + spatiotemporal_scaling: &[bool], + layers_per_block: &[usize], + downsample_types: &[DownsampleType], + patch_size: usize, + patch_size_t: usize, + resnet_eps: f64, + is_causal: bool, + vb: VarBuilder, + ) -> Result { + let in_channels_patched = in_channels * patch_size * patch_size * patch_size_t; + let conv_in = LtxVideoCausalConv3d::new( + in_channels_patched, + block_out_channels[0], + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv_in"), + )?; + + let mut down_blocks = Vec::new(); + let n = block_out_channels.len() - 1; + let mut current = block_out_channels[0]; + + for i in 0..n { + // For pixel unshuffle downsamplers, out_channels is the NEXT block's channels + let outc = block_out_channels[i + 1]; + + // Use downsample_type from config, default to Conv if not provided + let ds_type = downsample_types + .get(i) + .copied() + .unwrap_or(DownsampleType::Conv); + + let db = LtxVideoDownBlock3d::new( + current, + outc, + layers_per_block[i], + 0.0, + resnet_eps, + spatiotemporal_scaling[i], + is_causal, + ds_type, + vb.pp(format!("down_blocks.{i}")), + )?; + down_blocks.push(db); + current = outc; + } + + let mid_layers = *layers_per_block.last().unwrap_or(&1); + let mid_block = LtxVideoMidBlock3d::new( + current, + mid_layers.saturating_sub(1), + 0.0, + resnet_eps, + is_causal, + false, + false, + vb.pp("mid_block"), + )?; + + let norm_out = if let Ok(norm) = candle_nn::rms_norm(current, 1e-8, vb.pp("norm_out")) { + Some(norm) + } else { + let ones = Tensor::ones((current,), vb.dtype(), vb.device())?; + Some(RmsNorm::new(ones, 1e-8)) + }; + let conv_act = Activation::Silu; + let conv_out = LtxVideoCausalConv3d::new( + current, + out_channels + 1, + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv_out"), + )?; + + Ok(Self { + patch_size, + patch_size_t, + conv_in, + down_blocks, + mid_block, + norm_out, + conv_act, + conv_out, + gradient_checkpointing: false, + }) + } + + fn patchify(&self, x: &Tensor) -> Result { + // (B,C,F,H,W) -> (B, C*pt*p*p, F/pt, H/p, W/p) с тем же порядком, что в python. + let p = self.patch_size; + let pt = self.patch_size_t; + let (b, c, f, h, w) = x.dims5()?; + if f % pt != 0 || h % p != 0 || w % p != 0 { + candle_core::bail!("input not divisible by patch sizes") + } + let post_f = f / pt; + let post_h = h / p; + let post_w = w / p; + + let x = x.reshape(&[b, c, post_f, pt, post_h, p, post_w, p])?; + let x = x + .permute(vec![0, 1, 3, 7, 5, 2, 4, 6])? + .contiguous()? + .reshape((b, c * pt * p * p, post_f, post_h, post_w))?; + Ok(x) + } + + pub fn forward(&self, x: &Tensor, train: bool) -> Result { + let mut h = self.patchify(x)?; + h = self.conv_in.forward(&h)?; + for db in self.down_blocks.iter() { + h = db.forward(&h, None, train)?; + } + h = self.mid_block.forward(&h, None, train)?; + + // Apply norm_out only if it exists + if let Some(ref norm) = self.norm_out { + h = rmsnorm_channels_first(norm, &h)?; + } + + h = h.apply(&self.conv_act)?; + h = self.conv_out.forward(&h)?; + // println!("[DEBUG] conv_out final min/max: {:.4}/{:.4}", h.flatten_all()?.to_vec1::()?.iter().cloned().fold(f32::INFINITY, f32::min), h.flatten_all()?.to_vec1::()?.iter().cloned().fold(f32::NEG_INFINITY, f32::max)); + + // last channel replication trick (как в python) + let (_b, ch, _t, _h, _w) = h.dims5()?; + let last = h.i((.., (ch - 1), .., .., ..))?.unsqueeze(1)?; // (B,1,T,H,W) + let rep = last.repeat((1, ch.saturating_sub(2), 1, 1, 1))?; + cat_dim(&[h, rep], 1) + } +} + +#[derive(Debug, Clone)] +pub struct LtxVideoDecoder3d { + patch_size: usize, + patch_size_t: usize, + pub conv_in: LtxVideoCausalConv3d, + pub mid_block: LtxVideoMidBlock3d, + pub up_blocks: Vec, + pub norm_out: Option, + pub conv_act: Activation, + pub conv_out: LtxVideoCausalConv3d, + // Timestep conditioning + pub time_embedder: Option, + pub scale_shift_table: Option, + pub timestep_scale_multiplier: Option, + pub gradient_checkpointing: bool, +} + +impl LtxVideoDecoder3d { + #[allow(clippy::too_many_arguments)] + pub fn new( + in_channels: usize, + out_channels: usize, + block_out_channels: &[usize], + spatiotemporal_scaling: &[bool], + layers_per_block: &[usize], + patch_size: usize, + patch_size_t: usize, + resnet_eps: f64, + is_causal: bool, + inject_noise: &[bool], + timestep_conditioning: bool, + upsampler_residual: &[bool], + upsample_factor: &[usize], + vb: VarBuilder, + ) -> Result { + // Derive strides from spatiotemporal_scaling + let strides: Vec<(usize, usize, usize)> = spatiotemporal_scaling + .iter() + .map(|&s| if s { (2, 2, 2) } else { (1, 2, 2) }) + .collect(); + Self::new_with_strides( + in_channels, out_channels, block_out_channels, &strides, + layers_per_block, patch_size, patch_size_t, resnet_eps, + is_causal, inject_noise, timestep_conditioning, + upsampler_residual, upsample_factor, vb, + ) + } + + /// Create a decoder with explicit per-block upsampler strides. + #[allow(clippy::too_many_arguments)] + pub fn new_with_strides( + in_channels: usize, + out_channels: usize, + block_out_channels: &[usize], + strides: &[(usize, usize, usize)], + layers_per_block: &[usize], + patch_size: usize, + patch_size_t: usize, + resnet_eps: f64, + is_causal: bool, + inject_noise: &[bool], + timestep_conditioning: bool, + upsampler_residual: &[bool], + upsample_factor: &[usize], + vb: VarBuilder, + ) -> Result { + // decoder uses reversed lists + let mut boc = block_out_channels.to_vec(); + boc.reverse(); + let mut strides_rev = strides.to_vec(); + strides_rev.reverse(); + let mut lpb = layers_per_block.to_vec(); + lpb.reverse(); + + let mut inj = inject_noise.to_vec(); + inj.reverse(); + let mut upr = upsampler_residual.to_vec(); + upr.reverse(); + let mut upf = upsample_factor.to_vec(); + upf.reverse(); + + let conv_in = LtxVideoCausalConv3d::new( + in_channels, + boc[0], + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv_in"), + )?; + + let mid_block = LtxVideoMidBlock3d::new( + boc[0], + lpb[0], + 0.0, + resnet_eps, + is_causal, + inj[0], + timestep_conditioning, + vb.pp("mid_block"), + )?; + + let mut up_blocks = Vec::new(); + let n = boc.len(); + let mut current_channels = boc[0]; + + for i in 0..n { + let output_channel = boc[i] / upf[i]; + let input_channel = output_channel; + + let ub = LtxVideoUpBlock3d::new_with_stride( + input_channel, + output_channel, + lpb[i + 1], + 0.0, + resnet_eps, + strides_rev[i], + is_causal, + inj[i + 1], + timestep_conditioning, + upr[i], + upf[i], + vb.pp(format!("up_blocks.{i}")), + )?; + up_blocks.push(ub); + current_channels = output_channel; + } + + // norm_out has elementwise_affine=False in Python, so no weights in safetensors. + // We must create a default LayerNorm (ones weight, zeros bias) if loading fails. + // Note: Python uses eps=1e-6. We use resnet_eps (which is 1e-6 in config). + // Python hardcodes eps=1e-8 for norm_out and elementwise_affine=False + // We use RmsNorm with fallback to ones (no weights in safetensors) + let norm_out = + if let Ok(norm) = candle_nn::rms_norm(current_channels, 1e-8, vb.pp("norm_out")) { + Some(norm) + } else { + let ones = Tensor::ones((current_channels,), vb.dtype(), vb.device())?; + Some(RmsNorm::new(ones, 1e-8)) + }; + let conv_act = Activation::Silu; + + let conv_out_channels = out_channels * patch_size * patch_size; + let conv_out = LtxVideoCausalConv3d::new( + current_channels, + conv_out_channels, + (3, 3, 3), + (1, 1, 1), + (1, 1, 1), + 1, + is_causal, + vb.pp("conv_out"), + )?; + + // Timestep conditioning (0.9.5 has timestep_conditioning=true) + // Global decoder-level time embedder and scale_shift_table + let (time_embedder, scale_shift_table, timestep_scale_multiplier) = if timestep_conditioning + { + // time_embedder output is 256 = 2 * 128 (for shift and scale) + let emb_dim = current_channels * 2; // 128 * 2 = 256 + let te = CombinedTimestepEmbedder::new(emb_dim, vb.pp("time_embedder")).ok(); + // scale_shift_table: [2, 128] + let sst = vb.get((2, current_channels), "scale_shift_table").ok(); + let tsm = vb.get((), "timestep_scale_multiplier").ok(); + (te, sst, tsm) + } else { + (None, None, None) + }; + + Ok(Self { + patch_size, + patch_size_t, + conv_in, + mid_block, + up_blocks, + norm_out, + conv_act, + conv_out, + time_embedder, + scale_shift_table, + timestep_scale_multiplier, + gradient_checkpointing: false, + }) + } + + pub fn unpatchify(&self, x: &Tensor) -> Result { + // Python: reshape(batch, -1, p_t, p, p, num_frames, height, width) + // permute(0, 1, 5, 2, 6, 4, 7, 3) + // flatten(6, 7).flatten(4, 5).flatten(2, 3) + let (b, c, f, h, w) = x.dims5()?; + let p = self.patch_size; // 4 + let pt = self.patch_size_t; // 1 + let out_c = c / (pt * p * p); // 48 / 16 = 3 + let x = x.reshape(&[b, out_c, pt, p, p, f, h, w])?; + + // permute(0, 1, 5, 2, 6, 4, 7, 3) -> [B, C, F, pt, H, p_h, W, p_w] + let x = x.permute(vec![0, 1, 5, 2, 6, 4, 7, 3])?; + let x = x.contiguous()?; + + // After permute shape: [B, C, F, pt, H, p, W, p] + // Python flattens: flatten(6, 7).flatten(4, 5).flatten(2, 3) + // We must do this step-by-step to match Python's memory layout + + // flatten(6, 7): merge dimensions 6 and 7 -> [B, C, F, pt, H, p, W*p] + let x = x.reshape(&[b, out_c, f, pt, h, p, w * p])?; + + // flatten(4, 5): merge dimensions 4 and 5 -> [B, C, F, pt, H*p, W*p] + let x = x.reshape(&[b, out_c, f, pt, h * p, w * p])?; + + // flatten(2, 3): merge dimensions 2 and 3 -> [B, C, F*pt, H*p, W*p] + let x = x.reshape(&[b, out_c, f * pt, h * p, w * p])?; + + Ok(x) + } + + pub fn forward(&self, z: &Tensor, temb: Option<&Tensor>, train: bool) -> Result { + let model_dtype = self.conv_in.conv2d_slices[0].weight().dtype(); + let z = z.to_dtype(model_dtype)?; + let temb = match temb { + Some(t) => Some(t.to_dtype(model_dtype)?), + None => None, + }; + + let mut h = self.conv_in.forward(&z)?; + + // CRITICAL: Python applies timestep_scale_multiplier at the START of decoder.forward(), + // BEFORE passing to mid_block and up_blocks. Each block's internal time_embedder + // then receives the SCALED temb value. + let temb_scaled = + if let (Some(tsm), Some(t)) = (&self.timestep_scale_multiplier, temb.as_ref()) { + let t_flat = t.flatten_all()?; + Some(t_flat.broadcast_mul(tsm)?) + } else if let Some(t) = temb.as_ref() { + Some(t.flatten_all()?) + } else { + None + }; + let temb_for_blocks_ref = temb_scaled.as_ref(); + + h = self.mid_block.forward(&h, temb_for_blocks_ref, train)?; + + for ub in self.up_blocks.iter() { + h = ub.forward(&h, temb_for_blocks_ref, train)?; + } + + // Apply norm_out only if it exists + if let Some(ref norm) = self.norm_out { + h = rmsnorm_channels_first(norm, &h)?; + } + + // Apply global time_embedder + scale_shift_table if present + // NOTE: temb_scaled already has timestep_scale_multiplier applied from earlier + if let (Some(te), Some(sst), Some(temb_s)) = + (&self.time_embedder, &self.scale_shift_table, &temb_scaled) + { + let temb_proj = te.forward(temb_s, h.dtype())?; + + // temb_proj: (B, 256) = (B, 2*128) + // reshape to (B, 2, 128) and add scale_shift_table (2, 128) + let batch_size = h.dims5()?.0; + let c = sst.dims2()?.1; // 128 + let temb_shaped = temb_proj + .reshape((batch_size, 2, c))? + .broadcast_add(&sst.unsqueeze(0)?)? // (B, 2, C) + .unsqueeze(3)? // (B, 2, C, 1) + .unsqueeze(4)? // (B, 2, C, 1, 1) + .unsqueeze(5)?; // (B, 2, C, 1, 1, 1) + + // shift = temb_shaped[:, 0], scale = temb_shaped[:, 1] + let shift = temb_shaped.i((.., 0, .., .., .., ..))?.squeeze(1)?; + let scale = temb_shaped.i((.., 1, .., .., .., ..))?.squeeze(1)?; + + // Python: h = h * (1 + scale) + shift + let h_shape = h.shape(); + let scale_b = scale.broadcast_as(h_shape)?; + let shift_b = shift.broadcast_as(h_shape)?; + + h = h + .broadcast_mul(&scale_b.affine(1.0, 1.0)?)? + .broadcast_add(&shift_b)?; + } + + h = h.apply(&self.conv_act)?; + h = self.conv_out.forward(&h)?; + self.unpatchify(&h) + } +} + +#[derive(Debug, Clone)] +pub struct AutoencoderKLLtxVideo { + pub encoder: LtxVideoEncoder3d, + pub decoder: LtxVideoDecoder3d, + pub quant_conv: Option, + pub post_quant_conv: Option, + + pub latents_mean: Tensor, // (C,) + pub latents_std: Tensor, // (C,) + + pub scaling_factor: f64, + + pub spatial_compression_ratio: usize, + pub temporal_compression_ratio: usize, + + pub use_slicing: bool, + pub use_tiling: bool, + pub use_framewise_encoding: bool, + pub use_framewise_decoding: bool, + + pub num_sample_frames_batch_size: usize, + pub num_latent_frames_batch_size: usize, + + pub tile_sample_min_height: usize, + pub tile_sample_min_width: usize, + pub tile_sample_min_num_frames: usize, + + pub tile_sample_stride_height: usize, + pub tile_sample_stride_width: usize, + pub tile_sample_stride_num_frames: usize, + + pub config: AutoencoderKLLtxVideoConfig, + pub vae_config: VaeConfig, +} + +impl AutoencoderKLLtxVideo { + pub fn new(config: AutoencoderKLLtxVideoConfig, vb: VarBuilder) -> Result { + let ds_types: Vec = config + .downsample_types + .iter() + .map(|s| DownsampleType::parse(s)) + .collect(); + + let encoder = LtxVideoEncoder3d::new( + config.in_channels, + config.latent_channels, + &config.block_out_channels, + &config.spatiotemporal_scaling, + &config.layers_per_block, + &ds_types, + config.patch_size, + config.patch_size_t, + config.resnet_eps, + config.is_causal, + vb.pp("encoder"), + )?; + + let quant_conv = LtxVideoCausalConv3d::new( + config.latent_channels * 2, + config.latent_channels * 2, + (1, 1, 1), + (1, 1, 1), + (1, 1, 1), + 1, + config.is_causal, + vb.pp("quant_conv"), + ) + .ok(); + + let post_quant_conv = LtxVideoCausalConv3d::new( + config.latent_channels, + config.latent_channels, + (1, 1, 1), + (1, 1, 1), + (1, 1, 1), + 1, + config.is_causal, + vb.pp("post_quant_conv"), + ) + .ok(); + + let decoder = LtxVideoDecoder3d::new( + config.latent_channels, + config.out_channels, + &config.decoder_block_out_channels, + &config.decoder_spatiotemporal_scaling, + &config.decoder_layers_per_block, + config.patch_size, + config.patch_size_t, + config.resnet_eps, + config.decoder_causal, + &config.decoder_inject_noise, + config.timestep_conditioning, + &config.decoder_upsample_residual, + &config.decoder_upsample_factor, + vb.pp("decoder"), + )?; + + let latents_mean = if vb.contains_tensor("latents_mean") { + println!("Loading latents_mean from weights"); + vb.get(config.latent_channels, "latents_mean")? + } else { + Tensor::new(config.latents_mean.as_slice(), vb.device())?.to_dtype(vb.dtype())? + }; + let latents_std = if vb.contains_tensor("latents_std") { + println!("Loading latents_std from weights"); + vb.get(config.latent_channels, "latents_std")? + } else { + Tensor::new(config.latents_std.as_slice(), vb.device())?.to_dtype(vb.dtype())? + }; + let vae_config = VaeConfig { + scaling_factor: config.scaling_factor as f32, + timestep_conditioning: config.timestep_conditioning, + }; + + Ok(Self { + encoder, + decoder, + quant_conv, + post_quant_conv, + tile_sample_min_height: 512, + tile_sample_min_width: 512, + tile_sample_min_num_frames: 16, + tile_sample_stride_height: 384, + tile_sample_stride_width: 384, + tile_sample_stride_num_frames: 8, + scaling_factor: config.scaling_factor, + spatial_compression_ratio: config.spatial_compression_ratio, + temporal_compression_ratio: config.temporal_compression_ratio, + use_slicing: false, + use_tiling: true, + use_framewise_encoding: false, + use_framewise_decoding: true, + num_sample_frames_batch_size: 1, + num_latent_frames_batch_size: 1, + config, + latents_mean, + latents_std, + vae_config, + }) + } + + pub fn enable_tiling( + &mut self, + tile_sample_min_height: Option, + tile_sample_min_width: Option, + tile_sample_min_num_frames: Option, + tile_sample_stride_height: Option, + tile_sample_stride_width: Option, + tile_sample_stride_num_frames: Option, + ) { + self.use_tiling = true; + if let Some(h) = tile_sample_min_height { + self.tile_sample_min_height = h; + } + if let Some(w) = tile_sample_min_width { + self.tile_sample_min_width = w; + } + if let Some(f) = tile_sample_min_num_frames { + self.tile_sample_min_num_frames = f; + } + if let Some(h) = tile_sample_stride_height { + self.tile_sample_stride_height = h; + } + if let Some(w) = tile_sample_stride_width { + self.tile_sample_stride_width = w; + } + if let Some(f) = tile_sample_stride_num_frames { + self.tile_sample_stride_num_frames = f; + } + } + + pub fn latents_mean(&self) -> &Tensor { + &self.latents_mean + } + + pub fn latents_std(&self) -> &Tensor { + &self.latents_std + } + + pub fn config(&self) -> &AutoencoderKLLtxVideoConfig { + &self.config + } + + pub fn vae_config(&self) -> &VaeConfig { + &self.vae_config + } + + fn split_batch_5d(x: &Tensor) -> Result> { + let b = x.dims5()?.0; + let mut out = Vec::with_capacity(b); + for i in 0..b { + out.push(x.i((i..(i + 1), .., .., .., ..))?); + } + Ok(out) + } + + /// Blend по ширине W (dim=4): b[..., :blend] = lerp(a[..., -blend:], b[..., :blend], w) + fn blend_h(&self, a: &Tensor, b: &Tensor, blend_extent: usize) -> Result { + // python: for x in range(blend): b[..., x] = a[..., -blend+x]*(1-x/blend) + b[..., x]*(x/blend) [file:1] + let blend = blend_extent.min(a.dims5()?.4).min(b.dims5()?.4); + if blend == 0 { + return Ok(b.clone()); + } + + // w: (blend,) from 0..blend-1 divided by blend + let w = Tensor::arange(0u32, blend as u32, b.device())? + .to_dtype(DType::F32)? + .affine(1.0 / (blend as f64), 0.0)?; + let w = w.reshape((1, 1, 1, 1, blend))?.to_dtype(b.dtype())?; + let one_minus = w.neg()?.affine(1.0, 1.0)?; + + // b_head: первые blend столбцов, b_tail: остаток + let b_head = b.i((.., .., .., .., 0..blend))?; + let b_tail = b.i((.., .., .., .., blend..))?; + + // a_tail: последние blend столбцов + let aw = a.dims5()?.4; + let a_tail = a.i((.., .., .., .., (aw - blend)..aw))?; + + // mixed = a_tail*(1-w) + b_head*w + let mixed = a_tail + .broadcast_mul(&one_minus)? + .add(&b_head.broadcast_mul(&w)?)?; + Tensor::cat(&[&mixed, &b_tail], 4) + } + + /// Blend по высоте H (dim=3) + fn blend_v(&self, a: &Tensor, b: &Tensor, blend_extent: usize) -> Result { + // python: for y in range(blend): b[..., y, :] = a[..., -blend+y, :]*(1-y/blend) + b[..., y, :]*(y/blend) [file:1] + let blend = blend_extent.min(a.dims5()?.3).min(b.dims5()?.3); + if blend == 0 { + return Ok(b.clone()); + } + + let w = Tensor::arange(0u32, blend as u32, b.device())? + .to_dtype(DType::F32)? + .affine(1.0 / (blend as f64), 0.0)?; + let w = w.reshape((1, 1, 1, blend, 1))?.to_dtype(b.dtype())?; + let one_minus = w.neg()?.affine(1.0, 1.0)?; + + let b_head = b.i((.., .., .., 0..blend, ..))?; + let b_tail = b.i((.., .., .., blend.., ..))?; + + let ah = a.dims5()?.3; + let a_tail = a.i((.., .., .., (ah - blend)..ah, ..))?; + + let mixed = a_tail + .broadcast_mul(&one_minus)? + .add(&b_head.broadcast_mul(&w)?)?; + Tensor::cat(&[&mixed, &b_tail], 3) + } + + /// Blend по времени T (dim=2) + fn blend_t(&self, a: &Tensor, b: &Tensor, blend_extent: usize) -> Result { + // python: for x in range(blend): b[..., x, :, :] = a[..., -blend+x, :, :]*(1-x/blend) + b[..., x, :, :]*(x/blend) [file:1] + let blend = blend_extent.min(a.dims5()?.2).min(b.dims5()?.2); + if blend == 0 { + return Ok(b.clone()); + } + + let w = Tensor::arange(0u32, blend as u32, b.device())? + .to_dtype(DType::F32)? + .affine(1.0 / (blend as f64), 0.0)?; + let w = w.reshape((1, 1, blend, 1, 1))?.to_dtype(b.dtype())?; + let one_minus = w.neg()?.affine(1.0, 1.0)?; + + let b_head = b.i((.., .., 0..blend, .., ..))?; + let b_tail = b.i((.., .., blend.., .., ..))?; + + let at = a.dims5()?.2; + let a_tail = a.i((.., .., (at - blend)..at, .., ..))?; + + let mixed = a_tail + .broadcast_mul(&one_minus)? + .add(&b_head.broadcast_mul(&w)?)?; + Tensor::cat(&[&mixed, &b_tail], 2) + } + + fn split_batch_2d(x: &Tensor) -> Result> { + let (b, _d) = x.dims2()?; + let mut out = Vec::with_capacity(b); + for i in 0..b { + out.push(x.i((i..(i + 1), ..))?); + } + Ok(out) + } + + fn encode_z(&self, x: &Tensor, train: bool) -> Result { + let tile_sample_min_num_frames = self.tile_sample_min_num_frames; + if self.use_framewise_encoding && x.dims5()?.2 > tile_sample_min_num_frames { + return self.temporal_tiled_encode(x, train); + } + + if self.use_tiling + && (x.dims5()?.3 > self.tile_sample_min_height + || x.dims5()?.4 > self.tile_sample_min_width) + { + return self.tiled_encode(x, train); + } + + let mut h = self.encoder.forward(x, train)?; + if let Some(ref qc) = self.quant_conv { + h = qc.forward(&h)?; + } + Ok(h) + } + + fn decode_z(&self, z: &Tensor, temb: Option<&Tensor>, train: bool) -> Result { + // Python LTX VAE _decode does NOT use post_quant_conv or latents_mean/std + // It directly calls decoder(z, temb) + + // Convert inputs to model dtype (BF16) if needed + let model_dtype = self.decoder.conv_in.conv2d_slices[0].weight().dtype(); + let z = z.to_dtype(model_dtype)?; + let temb_converted = match temb { + Some(t) => Some(t.to_dtype(model_dtype)?), + None => None, + }; + + let (_b, _c, t, h, w) = z.dims5()?; + + let tile_latent_min_h = self.tile_sample_min_height / self.spatial_compression_ratio; + let tile_latent_min_w = self.tile_sample_min_width / self.spatial_compression_ratio; + let tile_latent_min_t = self.tile_sample_min_num_frames / self.temporal_compression_ratio; + + if self.use_framewise_decoding && t > tile_latent_min_t { + let out = self.temporal_tiled_decode(&z, temb_converted.as_ref(), train)?; + return Ok(out); + } + + if self.use_tiling && (w > tile_latent_min_w || h > tile_latent_min_h) { + let out = self.tiled_decode(&z, temb_converted.as_ref(), train)?; + return Ok(out); + } + + self.decoder.forward(&z, temb_converted.as_ref(), train) + } + + // ===== public API ===== + + pub fn encode( + &self, + x: &Tensor, + return_dict: bool, + train: bool, + ) -> Result<(Option, DiagonalGaussianDistribution)> { + // python: if useslicing and batch>1: encode each slice then cat [file:1] + let h = if self.use_slicing && x.dims5()?.0 > 1 { + let xs = Self::split_batch_5d(x)?; + let mut encs = Vec::with_capacity(xs.len()); + for xs_i in xs.iter() { + encs.push(self.encode_z(xs_i, train)?); + } + cat_dim(&encs, 0)? + } else { + self.encode_z(x, train)? + }; + + let posterior = DiagonalGaussianDistribution::new(&h)?; + if return_dict { + Ok(( + Some(AutoencoderKLOutput { + latent_dist: posterior.clone(), + }), + posterior, + )) + } else { + Ok((None, posterior)) + } + } + + pub fn decode( + &self, + z: &Tensor, + temb: Option<&Tensor>, + return_dict: bool, + train: bool, + ) -> Result<(Option, Tensor)> { + // python: if useslicing and batch>1: decode each slice then cat [file:1] + let decoded = if self.use_slicing && z.dims5()?.0 > 1 { + let zs = Self::split_batch_5d(z)?; + let ts = match temb { + None => None, + Some(t) => Some(Self::split_batch_2d(t)?), + }; + + let mut outs = Vec::with_capacity(zs.len()); + for (idx, z_i) in zs.iter().enumerate() { + let t_i = ts.as_ref().map(|v| v[idx].as_ref()); + outs.push(self.decode_z(z_i, t_i, train)?); + } + cat_dim(&outs, 0)? + } else { + self.decode_z(z, temb, train)? + }; + + if return_dict { + Ok(( + Some(DecoderOutput { + sample: decoded.clone(), + }), + decoded, + )) + } else { + Ok((None, decoded)) + } + } + + /// python forward(sample, temb=None, sample_posterior=False, return_dict=True) [file:1] + pub fn forward( + &self, + sample: &Tensor, + temb: Option<&Tensor>, + sample_posterior: bool, + return_dict: bool, + train: bool, + ) -> Result<(Option, Tensor)> { + let (_out, posterior) = self.encode(sample, true, train)?; + let z = if sample_posterior { + posterior.sample()? + } else { + posterior.mode()? + }; + self.decode(&z, temb, return_dict, train) + } + + // ===== spatial tiling ===== + + fn tiled_encode(&self, x: &Tensor, train: bool) -> Result { + // python tiled_encode: loops in sample-space, blends in latent-space [file:1] + let (_b, _c, _t, height, width) = x.dims5()?; + + let latent_height = height / self.spatial_compression_ratio; + let latent_width = width / self.spatial_compression_ratio; + + let tile_latent_min_h = self.tile_sample_min_height / self.spatial_compression_ratio; + let tile_latent_min_w = self.tile_sample_min_width / self.spatial_compression_ratio; + + let tile_latent_stride_h = self.tile_sample_stride_height / self.spatial_compression_ratio; + let tile_latent_stride_w = self.tile_sample_stride_width / self.spatial_compression_ratio; + + let blend_h = tile_latent_min_h.saturating_sub(tile_latent_stride_h); + let blend_w = tile_latent_min_w.saturating_sub(tile_latent_stride_w); + + // rows[i][j] = encoder(tile) + let mut rows: Vec> = Vec::new(); + for i in (0..height).step_by(self.tile_sample_stride_height) { + let mut row: Vec = Vec::new(); + for j in (0..width).step_by(self.tile_sample_stride_width) { + let h_end = (i + self.tile_sample_min_height).min(height); + let w_end = (j + self.tile_sample_min_width).min(width); + let tile = x.i((.., .., .., i..h_end, j..w_end))?; + let mut enc = self.encoder.forward(&tile, train)?; + if let Some(ref qc) = self.quant_conv { + enc = qc.forward(&enc)?; + } + row.push(enc); + } + rows.push(row); + } + + let mut prev_row_blended: Vec = Vec::new(); + let mut result_rows: Vec = Vec::with_capacity(rows.len()); + for (ri, row) in rows.iter().enumerate() { + let mut result_row: Vec = Vec::with_capacity(row.len()); + let mut curr_row_blended: Vec = Vec::with_capacity(row.len()); + for (cj, tile) in row.iter().enumerate() { + let mut tile = tile.clone(); + + if ri > 0 { + let above = &prev_row_blended[cj]; + tile = self.blend_v(above, &tile, blend_h)?; + } + if cj > 0 { + let left = &curr_row_blended[cj - 1]; + tile = self.blend_h(left, &tile, blend_w)?; + } + + // Store fully blended tile for future neighbors + curr_row_blended.push(tile.clone()); + + // Keep only the non-overlapping part for concatenation + let h_slice = tile_latent_stride_h.min(tile.dim(3)?); + let w_slice = tile_latent_stride_w.min(tile.dim(4)?); + let sliced_tile = tile.i((.., .., .., 0..h_slice, 0..w_slice))?; + result_row.push(sliced_tile); + } + result_rows.push(cat_dim(&result_row, 4)?); + prev_row_blended = curr_row_blended; + } + + let enc = cat_dim(&result_rows, 3)?; + enc.i((.., .., .., 0..latent_height, 0..latent_width)) + } + + fn tiled_decode(&self, z: &Tensor, temb: Option<&Tensor>, train: bool) -> Result { + // python tiled_decode: loops in latent-space, blends/crops in sample-space [file:1] + let (_b, _c, _t, height, width) = z.dims5()?; + + let sample_height = height * self.spatial_compression_ratio; + let sample_width = width * self.spatial_compression_ratio; + + let tile_latent_min_h = self.tile_sample_min_height / self.spatial_compression_ratio; + let tile_latent_min_w = self.tile_sample_min_width / self.spatial_compression_ratio; + + let tile_latent_stride_h = self.tile_sample_stride_height / self.spatial_compression_ratio; + let tile_latent_stride_w = self.tile_sample_stride_width / self.spatial_compression_ratio; + + let blend_h = self + .tile_sample_min_height + .saturating_sub(self.tile_sample_stride_height); + let blend_w = self + .tile_sample_min_width + .saturating_sub(self.tile_sample_stride_width); + + // rows[i][j] = decoder(tile) + let mut rows: Vec> = Vec::new(); + for i in (0..height).step_by(tile_latent_stride_h) { + let mut row: Vec = Vec::new(); + for j in (0..width).step_by(tile_latent_stride_w) { + let h_end = (i + tile_latent_min_h).min(height); + let w_end = (j + tile_latent_min_w).min(width); + let tile = z.i((.., .., .., i..h_end, j..w_end))?; + let dec = self.decoder.forward(&tile, temb, train)?; + row.push(dec); + } + rows.push(row); + } + + let mut prev_row_blended: Vec = Vec::new(); + let mut result_rows: Vec = Vec::with_capacity(rows.len()); + for (ri, row) in rows.iter().enumerate() { + let mut result_row: Vec = Vec::with_capacity(row.len()); + let mut curr_row_blended: Vec = Vec::with_capacity(row.len()); + for (cj, tile) in row.iter().enumerate() { + let mut tile = tile.clone(); + + if ri > 0 { + let above = &prev_row_blended[cj]; + tile = self.blend_v(above, &tile, blend_h)?; + } + if cj > 0 { + let left = &curr_row_blended[cj - 1]; + tile = self.blend_h(left, &tile, blend_w)?; + } + + // Store fully blended tile for future neighbors + curr_row_blended.push(tile.clone()); + + let h_slice = self.tile_sample_stride_height.min(tile.dim(3)?); + let w_slice = self.tile_sample_stride_width.min(tile.dim(4)?); + let sliced_tile = tile.i((.., .., .., 0..h_slice, 0..w_slice))?; + result_row.push(sliced_tile); + } + result_rows.push(cat_dim(&result_row, 4)?); + prev_row_blended = curr_row_blended; + } + + let dec = cat_dim(&result_rows, 3)?; + dec.i((.., .., .., 0..sample_height, 0..sample_width)) + } + + // ===== temporal tiling ===== + + fn temporal_tiled_encode(&self, x: &Tensor, train: bool) -> Result { + // python temporal_tiled_encode (stride in sample frames), blends in latent time [file:1] + let (_b, _c, num_frames, _h, _w) = x.dims5()?; + + let latent_num_frames = (num_frames - 1) / self.temporal_compression_ratio + 1; // python formula [file:1] + + let tile_latent_min_t = self.tile_sample_min_num_frames / self.temporal_compression_ratio; + let tile_latent_stride_t = + self.tile_sample_stride_num_frames / self.temporal_compression_ratio; + let blend_t = tile_latent_min_t.saturating_sub(tile_latent_stride_t); + + let mut row: Vec = Vec::new(); + for i in (0..num_frames).step_by(self.tile_sample_stride_num_frames) { + let t_end = (i + self.tile_sample_min_num_frames + 1).min(num_frames); + let tile = x.i((.., .., i..t_end, .., ..))?; + + let tile = if self.use_tiling + && (tile.dims5()?.3 > self.tile_sample_min_height + || tile.dims5()?.4 > self.tile_sample_min_width) + { + self.tiled_encode(&tile, train)? + } else { + let mut h = self.encoder.forward(&tile, train)?; + if let Some(ref qc) = self.quant_conv { + h = qc.forward(&h)?; + } + h + }; + + // python: if i == 0: tile = tile[:, :, 1:] [file:1] + let tile = if i == 0 { + tile.i((.., .., 1.., .., ..))? + } else { + tile + }; + row.push(tile); + } + + // Python logic: + // for i, tile in enumerate(row): + // if i > 0: + // tile = self.blend_t(row[i - 1], tile, blend_num_frames) + // result_row.append(tile[:, :, :stride, :, :]) # Take FIRST stride frames + // else: + // result_row.append(tile[:, :, :stride+1, :, :]) # First tile: stride+1 frames + let mut result_row: Vec = Vec::with_capacity(row.len()); + for (idx, tile) in row.iter().enumerate() { + let tile = if idx > 0 { + let blended = self.blend_t(&row[idx - 1], tile, blend_t)?; + // Take FIRST stride frames (not last!) + let end = tile_latent_stride_t.min(blended.dim(2)?); + blended.i((.., .., 0..end, .., ..))? + } else { + // First tile: keep stride + 1 frames + let end = (tile_latent_stride_t + 1).min(tile.dim(2)?); + tile.i((.., .., 0..end, .., ..))? + }; + result_row.push(tile); + } + + let enc = cat_dim(&result_row, 2)?; + enc.i((.., .., 0..latent_num_frames, .., ..)) + } + + fn temporal_tiled_decode( + &self, + z: &Tensor, + temb: Option<&Tensor>, + train: bool, + ) -> Result { + // python temporal_tiled_decode: stride in latent time, blends in sample time [file:1] + let (_b, _c, num_frames, _h, _w) = z.dims5()?; + + let num_sample_frames = (num_frames - 1) * self.temporal_compression_ratio + 1; // python formula [file:1] + + let tile_latent_min_h = self.tile_sample_min_height / self.spatial_compression_ratio; + let tile_latent_min_w = self.tile_sample_min_width / self.spatial_compression_ratio; + + let tile_latent_min_t = self.tile_sample_min_num_frames / self.temporal_compression_ratio; + let tile_latent_stride_t = + self.tile_sample_stride_num_frames / self.temporal_compression_ratio; + + // Python: blend_num_frames = tile_sample_min - tile_sample_stride = 16 - 8 = 8 + let blend_t_sample = self + .tile_sample_min_num_frames + .saturating_sub(self.tile_sample_stride_num_frames); + + let mut row: Vec = Vec::new(); + for (loop_idx, i) in (0..num_frames).step_by(tile_latent_stride_t).enumerate() { + let t_end = (i + tile_latent_min_t + 1).min(num_frames); + let tile = z.i((.., .., i..t_end, .., ..))?; + + let decoded = if self.use_tiling + && (tile.dims5()?.3 > tile_latent_min_h || tile.dims5()?.4 > tile_latent_min_w) + { + self.tiled_decode(&tile, temb, train)? + } else { + self.decoder.forward(&tile, temb, train)? + }; + + // Python: if i > 0: decoded = decoded[:, :, :-1, :, :] + // Remove last sample frame from all tiles except first + let decoded = if loop_idx > 0 { + let t = decoded.dim(2)?; + if t > 1 { + decoded.i((.., .., 0..(t - 1), .., ..))? + } else { + decoded + } + } else { + decoded + }; + + row.push(decoded); + } + + // Python logic: + // for i, tile in enumerate(row): + // if i > 0: + // tile = self.blend_t(row[i - 1], tile, blend_num_frames) + // tile = tile[:, :, :stride, :, :] # Take FIRST stride frames + // else: + // tile = tile[:, :, :stride+1, :, :] # First tile: stride+1 frames + let mut result_row: Vec = Vec::with_capacity(row.len()); + for (idx, tile) in row.iter().enumerate() { + let tile = if idx > 0 { + let blended = self.blend_t(&row[idx - 1], tile, blend_t_sample)?; + // Take FIRST stride frames (not last!) + let end = self.tile_sample_stride_num_frames.min(blended.dim(2)?); + blended.i((.., .., 0..end, .., ..))? + } else { + // First tile: keep stride + 1 frames + let end = (self.tile_sample_stride_num_frames + 1).min(tile.dim(2)?); + tile.i((.., .., 0..end, .., ..))? + }; + result_row.push(tile); + } + + let dec = cat_dim(&result_row, 2)?; + dec.i((.., .., 0..num_sample_frames, .., ..)) + } +} + +impl VaeLtxVideo for AutoencoderKLLtxVideo { + fn dtype(&self) -> DType { + // Return actual weight dtype (e.g., DType::BF16) + self.decoder.conv_in.conv2d_slices[0].weight().dtype() + } + fn spatial_compression_ratio(&self) -> usize { + self.config.spatial_compression_ratio + } + fn temporal_compression_ratio(&self) -> usize { + self.config.temporal_compression_ratio + } + fn config(&self) -> &VaeConfig { + &self.vae_config + } + + fn latents_mean(&self) -> &Tensor { + &self.latents_mean + } + fn latents_std(&self) -> &Tensor { + &self.latents_std + } + + fn decode(&self, latents: &Tensor, timestep: Option<&Tensor>) -> Result { + let (_, decoded) = self.decode(latents, timestep, false, false)?; + Ok(decoded) + } +} diff --git a/cake-core/src/models/ltx_video/vendored/weight_format.rs b/cake-core/src/models/ltx_video/vendored/weight_format.rs new file mode 100644 index 00000000..f1848afd --- /dev/null +++ b/cake-core/src/models/ltx_video/vendored/weight_format.rs @@ -0,0 +1,269 @@ +//! Weight format detection and key remapping for LTX-Video models. +//! +//! Supports two formats: +//! - Diffusers: separate files in transformer/, vae/, text_encoder/ directories +//! - Official: single unified safetensors file (e.g., ltx-video-2b-v0.9.5.safetensors) +//! +//! Key mapping based on diffusers/scripts/convert_ltx_to_diffusers.py + +use regex::Regex; +use std::path::Path; + +/// Weight format detection +#[derive(Debug, Clone, Copy, PartialEq)] +pub enum WeightFormat { + /// Diffusers format: separate files in subdirectories + Diffusers, + /// Official LTX-Video format: single unified safetensors file + Official, +} + +/// Detect weight format from path +pub fn detect_format(path: &Path) -> WeightFormat { + if path.is_file() { + WeightFormat::Official + } else { + // Both directory and non-existent paths default to Diffusers format + WeightFormat::Diffusers + } +} + +/// Key remapping from Official (native LTX-Video) format to Diffusers format. +/// Based on diffusers/scripts/convert_ltx_to_diffusers.py VAE_095_RENAME_DICT +#[derive(Debug, Clone)] +pub struct KeyRemapper { + encoder_block_re: Regex, + decoder_block_re: Regex, +} + +impl Default for KeyRemapper { + fn default() -> Self { + Self::new() + } +} + +impl KeyRemapper { + pub fn new() -> Self { + Self { + encoder_block_re: Regex::new(r"encoder\.down_blocks\.(\d+)").unwrap(), + decoder_block_re: Regex::new(r"decoder\.up_blocks\.(\d+)").unwrap(), + } + } + + /// Remap a key from Official (native) format to Diffusers format + /// Uses VAE_095_RENAME_DICT mapping from convert_ltx_to_diffusers.py + pub fn remap_key(&self, key: &str) -> String { + let mut result = key.to_string(); + + // 1. Transformer mappings (simple replacements) + result = result.replace("patchify_proj", "proj_in"); + result = result.replace("adaln_single", "time_embed"); + result = result.replace("q_norm", "norm_q"); + result = result.replace("k_norm", "norm_k"); + + // 2. VAE: Replace res_blocks -> resnets + result = result.replace("res_blocks", "resnets"); + + // 3. VAE: Remap encoder block indices (0.9.5+ format) + result = self.remap_encoder_blocks_095(&result); + + // 4. VAE: Remap decoder block indices (0.9.5+ format) + result = self.remap_decoder_blocks_095(&result); + + // 5. Other VAE mappings from VAE_095_RENAME_DICT + result = result.replace("last_time_embedder", "time_embedder"); + result = result.replace("last_scale_shift_table", "scale_shift_table"); + result = result.replace("norm3.norm", "norm3"); + result = result.replace("per_channel_statistics.mean-of-means", "latents_mean"); + result = result.replace("per_channel_statistics.std-of-means", "latents_std"); + + result + } + + /// Remap encoder block indices from native flat format to Diffusers hierarchical format + /// Based on VAE_095_RENAME_DICT from convert_ltx_to_diffusers.py: + /// Native 0 -> Diffusers down_blocks.0 + /// Native 1 -> Diffusers down_blocks.0.downsamplers.0 + /// Native 2 -> Diffusers down_blocks.1 + /// Native 3 -> Diffusers down_blocks.1.downsamplers.0 + /// Native 4 -> Diffusers down_blocks.2 + /// Native 5 -> Diffusers down_blocks.2.downsamplers.0 + /// Native 6 -> Diffusers down_blocks.3 + /// Native 7 -> Diffusers down_blocks.3.downsamplers.0 + /// Native 8 -> Diffusers mid_block + fn remap_encoder_blocks_095(&self, key: &str) -> String { + self.encoder_block_re + .replace_all(key, |caps: ®ex::Captures| { + let native_idx: usize = caps[1].parse().unwrap_or(0); + match native_idx { + 0 => "encoder.down_blocks.0".to_string(), + 1 => "encoder.down_blocks.0.downsamplers.0".to_string(), + 2 => "encoder.down_blocks.1".to_string(), + 3 => "encoder.down_blocks.1.downsamplers.0".to_string(), + 4 => "encoder.down_blocks.2".to_string(), + 5 => "encoder.down_blocks.2.downsamplers.0".to_string(), + 6 => "encoder.down_blocks.3".to_string(), + 7 => "encoder.down_blocks.3.downsamplers.0".to_string(), + 8 => "encoder.mid_block".to_string(), + _ => format!("encoder.down_blocks.{}", native_idx), + } + }) + .to_string() + } + + /// Remap decoder block indices from native flat format to Diffusers hierarchical format + /// Based on VAE_095_RENAME_DICT from convert_ltx_to_diffusers.py: + /// Native 0 -> Diffusers mid_block + /// Native 1 -> Diffusers up_blocks.0.upsamplers.0 + /// Native 2 -> Diffusers up_blocks.0 + /// Native 3 -> Diffusers up_blocks.1.upsamplers.0 + /// Native 4 -> Diffusers up_blocks.1 + /// Native 5 -> Diffusers up_blocks.2.upsamplers.0 + /// Native 6 -> Diffusers up_blocks.2 + /// Native 7 -> Diffusers up_blocks.3.upsamplers.0 + /// Native 8 -> Diffusers up_blocks.3 + fn remap_decoder_blocks_095(&self, key: &str) -> String { + self.decoder_block_re + .replace_all(key, |caps: ®ex::Captures| { + let native_idx: usize = caps[1].parse().unwrap_or(0); + match native_idx { + 0 => "decoder.mid_block".to_string(), + 1 => "decoder.up_blocks.0.upsamplers.0".to_string(), + 2 => "decoder.up_blocks.0".to_string(), + 3 => "decoder.up_blocks.1.upsamplers.0".to_string(), + 4 => "decoder.up_blocks.1".to_string(), + 5 => "decoder.up_blocks.2.upsamplers.0".to_string(), + 6 => "decoder.up_blocks.2".to_string(), + 7 => "decoder.up_blocks.3.upsamplers.0".to_string(), + 8 => "decoder.up_blocks.3".to_string(), + _ => format!("decoder.up_blocks.{}", native_idx), + } + }) + .to_string() + } + + /// Check if a key belongs to the transformer + pub fn is_transformer_key(key: &str) -> bool { + key.starts_with("transformer.") + || key.starts_with("model.diffusion_model.") // Native format prefix + || key.contains("transformer_blocks") + || key.contains("patchify_proj") + || key.contains("proj_in") + || key.contains("adaln_single") + || key.contains("time_embed") + } + + /// Check if a key belongs to the VAE + pub fn is_vae_key(key: &str) -> bool { + key.starts_with("vae.") + || key.starts_with("encoder.") + || key.starts_with("decoder.") + || key.contains("per_channel_statistics") + || key.contains("latents_mean") + || key.contains("latents_std") + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_remap_transformer_key() { + let remapper = KeyRemapper::new(); + assert_eq!( + remapper.remap_key("transformer.patchify_proj.weight"), + "transformer.proj_in.weight" + ); + assert_eq!( + remapper.remap_key("transformer.adaln_single.linear.weight"), + "transformer.time_embed.linear.weight" + ); + } + + #[test] + fn test_remap_encoder_blocks_095() { + let remapper = KeyRemapper::new(); + + // Native block 0 -> Diffusers block 0 + assert_eq!( + remapper.remap_key("encoder.down_blocks.0.res_blocks.0.conv1.weight"), + "encoder.down_blocks.0.resnets.0.conv1.weight" + ); + + // Native block 1 -> Diffusers downsamplers + assert_eq!( + remapper.remap_key("encoder.down_blocks.1.conv.weight"), + "encoder.down_blocks.0.downsamplers.0.conv.weight" + ); + + // Native block 2 -> Diffusers block 1 (NOT conv_out for 0.9.5+) + assert_eq!( + remapper.remap_key("encoder.down_blocks.2.res_blocks.0.conv1.weight"), + "encoder.down_blocks.1.resnets.0.conv1.weight" + ); + + // Native block 6 -> Diffusers block 3 + assert_eq!( + remapper.remap_key("encoder.down_blocks.6.res_blocks.0.weight"), + "encoder.down_blocks.3.resnets.0.weight" + ); + + // Native block 8 -> mid_block + assert_eq!( + remapper.remap_key("encoder.down_blocks.8.res_blocks.0.weight"), + "encoder.mid_block.resnets.0.weight" + ); + } + + #[test] + fn test_remap_decoder_blocks_095() { + let remapper = KeyRemapper::new(); + + // Native block 0 -> mid_block + assert_eq!( + remapper.remap_key("decoder.up_blocks.0.res_blocks.0.weight"), + "decoder.mid_block.resnets.0.weight" + ); + + // Native block 1 -> upsamplers + assert_eq!( + remapper.remap_key("decoder.up_blocks.1.conv.weight"), + "decoder.up_blocks.0.upsamplers.0.conv.weight" + ); + + // Native block 2 -> Diffusers block 0 + assert_eq!( + remapper.remap_key("decoder.up_blocks.2.res_blocks.0.weight"), + "decoder.up_blocks.0.resnets.0.weight" + ); + + // Native block 8 -> Diffusers block 3 + assert_eq!( + remapper.remap_key("decoder.up_blocks.8.res_blocks.0.weight"), + "decoder.up_blocks.3.resnets.0.weight" + ); + } + + #[test] + fn test_remap_time_embedder() { + let remapper = KeyRemapper::new(); + assert_eq!( + remapper.remap_key("decoder.last_time_embedder.weight"), + "decoder.time_embedder.weight" + ); + } + + #[test] + fn test_remap_latents_stats() { + let remapper = KeyRemapper::new(); + assert_eq!( + remapper.remap_key("per_channel_statistics.mean-of-means"), + "latents_mean" + ); + assert_eq!( + remapper.remap_key("per_channel_statistics.std-of-means"), + "latents_std" + ); + } +} diff --git a/cake-core/src/models/mod.rs b/cake-core/src/models/mod.rs index 9ed05305..55e9aa69 100644 --- a/cake-core/src/models/mod.rs +++ b/cake-core/src/models/mod.rs @@ -5,6 +5,7 @@ use image::{ImageBuffer, Rgb}; use chat::Message; use crate::cake::{Context, Forwarder}; +use crate::video::VideoOutput; use crate::ImageGenerationArgs; pub mod chat; @@ -35,7 +36,10 @@ pub mod qwen3_5; pub mod qwen3_5_moe; #[cfg(feature = "flux")] pub mod flux; +pub mod ltx_video; +pub mod ltx2; pub mod sd; +pub mod speculative; /// A token. pub struct Token { @@ -99,3 +103,15 @@ pub trait ImageGenerator: Generator { where F: FnMut(Vec, Vec>>) + Send + 'static; } + +/// A model that generates video (sequence of frames with temporal metadata). +#[async_trait] +pub trait VideoGenerator: Generator { + /// Generate a video from the given arguments. + /// Returns a `VideoOutput` containing all frames, fps, and dimensions. + async fn generate_video( + &mut self, + args: &ImageGenerationArgs, + ) -> Result; +} + diff --git a/cake-core/src/models/qwen2/qwen.rs b/cake-core/src/models/qwen2/qwen.rs index a93de23b..668891d2 100644 --- a/cake-core/src/models/qwen2/qwen.rs +++ b/cake-core/src/models/qwen2/qwen.rs @@ -26,7 +26,12 @@ impl Generator for Qwen2 { /// Load this model from the context. async fn load(ctx: &mut Context) -> Result>> { - let base = TextModelBase::load::(ctx, DEFAULT_EOS_TOKEN).await?; + let mut base = TextModelBase::load::(ctx, DEFAULT_EOS_TOKEN).await?; + + if let Some(ref draft_model) = ctx.args.draft_model.clone() { + base.load_draft::(draft_model, DEFAULT_EOS_TOKEN).await?; + } + let history = QwenHistory::new(); Ok(Some(Box::new(Self { base, history }))) } diff --git a/cake-core/src/models/qwen3_5/full_attention.rs b/cake-core/src/models/qwen3_5/full_attention.rs index 9b6fbfad..d7764ac9 100644 --- a/cake-core/src/models/qwen3_5/full_attention.rs +++ b/cake-core/src/models/qwen3_5/full_attention.rs @@ -161,7 +161,7 @@ impl Qwen3_5FullAttention { let (q, k, v) = if seq_len == 1 { (q.squeeze(1)?.unsqueeze(2)?, k.squeeze(1)?.unsqueeze(2)?, v.squeeze(1)?.unsqueeze(2)?) } else { - (q.transpose(1, 2)?.contiguous()?, k.transpose(1, 2)?.contiguous()?, v.transpose(1, 2)?) + (q.transpose(1, 2)?.contiguous()?, k.transpose(1, 2)?.contiguous()?, v.transpose(1, 2)?.contiguous()?) }; // Apply partial RoPE @@ -175,6 +175,7 @@ impl Qwen3_5FullAttention { .map_err(|e| anyhow!("process_kv: {e}"))?; // Attention + let in_dtype = q.dtype(); #[allow(unused_labels)] let y = 'attn: { // Flash Attention on CUDA — fused kernel, native GQA (no repeat_kv needed) @@ -210,7 +211,7 @@ impl Qwen3_5FullAttention { .map_err(|e| anyhow!("masked_fill: {e}"))? }; let att = candle_nn::ops::softmax_last_dim(&att)?; - att.matmul(&v.contiguous()?)? + att.matmul(&v)? }; // Reshape: (batch, heads, seq, head_dim) -> (batch, seq, hidden_size) diff --git a/cake-core/src/models/qwen3_5/model.rs b/cake-core/src/models/qwen3_5/model.rs index 75ebfbb8..01ddbd65 100644 --- a/cake-core/src/models/qwen3_5/model.rs +++ b/cake-core/src/models/qwen3_5/model.rs @@ -29,6 +29,12 @@ impl Generator for Qwen3_5 { /// Load this model from the context. async fn load(ctx: &mut Context) -> Result>> { let mut base = TextModelBase::load::(ctx, DEFAULT_EOS_TOKEN).await?; + + if let Some(ref draft_model) = ctx.args.draft_model.clone() { + // Draft model uses standard Transformer blocks (dense model) + base.load_draft::(draft_model, DEFAULT_EOS_TOKEN).await?; + } + // Qwen3.5 config sets eos_token_id=<|endoftext|> but ChatML uses <|im_end|> as turn terminator. // Add <|im_end|> to the EOS set so generation stops at turn boundaries. if let Some(im_end_id) = base.tokenizer.token_to_id("<|im_end|>") { @@ -41,6 +47,7 @@ impl Generator for Qwen3_5 { None => EosTokenId::Single(im_end_id), }); } + let history = ChatMLHistory::new(); Ok(Some(Box::new(Self { base, history }))) } diff --git a/cake-core/src/models/speculative.rs b/cake-core/src/models/speculative.rs new file mode 100644 index 00000000..28fd25fc --- /dev/null +++ b/cake-core/src/models/speculative.rs @@ -0,0 +1,316 @@ +//! Speculative decoding for distributed inference. +//! +//! A small "draft" model generates K tokens locally, then the large +//! distributed "full" model verifies them in a single batched forward pass. +//! Accepted tokens skip K-1 expensive distributed round-trips. +//! +//! When tokens are rejected, the full model's KV cache is reset and +//! re-prefilled on the next call (lazy rollback). This is acceptable +//! because rejection should be infrequent with a well-matched draft model. + +use std::collections::VecDeque; + +use anyhow::Result; +use candle_core::{IndexOp, Tensor}; +use candle_nn::Module; + +use super::common::text_model::TextModelBase; + +/// Speculative decoding state, embedded in a TextGenerator implementation. +pub struct SpeculativeState { + /// Buffered tokens that have been verified but not yet returned. + pub accepted_buffer: VecDeque<(u32, Option, bool)>, + /// Number of speculative tokens to draft per round. + pub spec_tokens: usize, + /// Running stats: total accepted / total drafted. + pub total_accepted: usize, + pub total_drafted: usize, +} + +impl SpeculativeState { + pub fn new(spec_tokens: usize) -> Self { + Self { + accepted_buffer: VecDeque::new(), + spec_tokens, + total_accepted: 0, + total_drafted: 0, + } + } + + pub fn acceptance_rate(&self) -> f64 { + if self.total_drafted == 0 { + 0.0 + } else { + self.total_accepted as f64 / self.total_drafted as f64 + } + } +} + +/// Run one round of speculative decoding. +/// +/// 1. Draft `K` tokens using `draft` model (local-only, fast) +/// 2. Verify all K tokens with `full` model in one batched forward pass +/// 3. Accept matching prefix, use full model's prediction at first mismatch +/// +/// Returns the list of accepted (token_id, text) pairs. +/// The full model's state (tokens, index_pos, KV cache) is updated to reflect +/// only the accepted tokens. +pub async fn speculate_and_verify( + full: &mut TextModelBase, + draft: &mut TextModelBase, + state: &mut SpeculativeState, +) -> Result, bool)>> { + let k = state.spec_tokens; + + // Save full model state before speculation + let saved_index_pos = full.index_pos; + let saved_tokens_len = full.tokens.len(); + let saved_generated = full.generated; + + // Phase 1: Draft K tokens with the local draft model + let mut draft_token_ids: Vec = Vec::with_capacity(k); + for _ in 0..k { + let token = draft_next_token(draft).await?; + if token.2 { + // EOS from draft — just verify what we have + draft_token_ids.push(token.0); + break; + } + draft_token_ids.push(token.0); + } + + let num_drafted = draft_token_ids.len(); + if num_drafted == 0 { + return Ok(vec![]); + } + + state.total_drafted += num_drafted; + + // Phase 2: Verify all draft tokens with full model in one forward pass + let all_logits = forward_verify(full, &draft_token_ids).await?; + + // Phase 3: Compare predictions + // all_logits shape: [num_drafted, vocab_size] + // logits[i] predicts the token AFTER draft_token_ids[i] + // + // But we also need to verify draft_token_ids[0] itself. + // draft_token_ids[0] should match what the full model would predict + // given the context before speculation. We check this by looking at + // the full model's logits from the position before d[0]. + // + // For simplicity in this first version: + // - We trust d[0] (the draft and full model saw the same context) + // - We verify d[1..K] using all_logits[0..K-1] + + let mut accepted = Vec::new(); + let mut num_accepted = 0; + + // Accept d[0] (first draft token) — same context seen by both models + accepted.push(draft_token_ids[0]); + num_accepted += 1; + + // Verify d[1..K] using full model logits + for i in 0..num_drafted - 1 { + let logits_i = all_logits.i(i)?; + let predicted = logits_i + .argmax(candle_core::D::Minus1)? + .to_scalar::()?; + + if predicted == draft_token_ids[i + 1] { + accepted.push(draft_token_ids[i + 1]); + num_accepted += 1; + } else { + // Mismatch: use full model's prediction instead + accepted.push(predicted); + num_accepted += 1; + break; + } + } + + // If all K tokens matched, also sample the bonus token from logits[K-1] + if num_accepted == num_drafted { + let last_logits = all_logits.i(num_drafted - 1)?; + let bonus = full + .logits_processor + .sample(&last_logits) + .map_err(|e| anyhow!("bonus sample: {e}"))?; + accepted.push(bonus); + num_accepted += 1; + } + + state.total_accepted += num_accepted; + + // Phase 4: Update full model state to reflect accepted tokens + // Reset to saved state first + full.index_pos = 0; // Must be 0 so next forward_verify does full re-prefill + full.tokens.truncate(saved_tokens_len); + full.generated = saved_generated; + + // Clear KV cache — will re-prefill lazily on next forward + full.ctx.cache.as_mut().expect("No cache").clear(); + + // Add accepted tokens to full model + let mut results = Vec::with_capacity(accepted.len()); + for &token_id in &accepted { + full.tokens.push(token_id); + full.generated += 1; + + let is_eos = full + .eos_token_id + .as_ref() + .map_or(false, |eos| eos.is_eos(token_id)); + + let text = full.tokenizer.decode(&[token_id], false).ok(); + results.push((token_id, text, is_eos)); + + if is_eos { + break; + } + } + + // Sync draft model to accepted state + draft.tokens.truncate(saved_tokens_len); + draft.generated = saved_generated; + draft.index_pos = saved_index_pos; + draft.ctx.cache.as_mut().expect("No cache").clear(); + for &(token_id, _, _) in &results { + draft.tokens.push(token_id); + draft.generated += 1; + } + + log::debug!( + "speculative: drafted={} accepted={} rate={:.0}%", + num_drafted, + results.len(), + state.acceptance_rate() * 100.0, + ); + + Ok(results) +} + +/// Generate one token from the draft model. +/// Returns (token_id, text, is_eos). +async fn draft_next_token( + draft: &mut TextModelBase, +) -> Result<(u32, Option, bool)> { + let num_tokens = draft.tokens.len(); + let (context_size, context_index) = if draft + .ctx + .cache + .as_ref() + .expect("No cache") + .with_kv_cache() + && draft.generated > 0 + { + (1, draft.index_pos) + } else { + (num_tokens, 0) + }; + + let context_offset = num_tokens.saturating_sub(context_size); + let context_tokens: Vec = draft.tokens[context_offset..].to_vec(); + let num_context = context_tokens.len(); + + let input = Tensor::new(context_tokens.as_slice(), &draft.ctx.device)?.unsqueeze(0)?; + let logits = draft.forward(&input, context_index).await?; + let logits = logits.squeeze(0)?; + + draft.index_pos += num_context; + + let next_token = draft + .logits_processor + .sample(&logits) + .map_err(|e| anyhow!("draft sample: {e}"))?; + + draft.generated += 1; + draft.tokens.push(next_token); + + let is_eos = draft + .eos_token_id + .as_ref() + .map_or(false, |eos| eos.is_eos(next_token)); + + let text = draft.tokenizer.decode(&[next_token], false).ok(); + Ok((next_token, text, is_eos)) +} + +/// Forward K tokens through the full model and return logits at ALL positions. +/// +/// Unlike `TextModelBase::forward()` which returns only the last-position logits, +/// this returns shape `[K, vocab_size]` for verification. +async fn forward_verify( + full: &mut TextModelBase, + draft_tokens: &[u32], +) -> Result { + let seq_len = draft_tokens.len(); + + // Build the context: if KV cache is populated, just the draft tokens. + // If KV cache was reset, include ALL tokens for re-prefill. + let (context_tokens, context_index) = if full + .ctx + .cache + .as_ref() + .expect("No cache") + .with_kv_cache() + && full.index_pos > 0 + { + (draft_tokens.to_vec(), full.index_pos) + } else { + // Need full re-prefill: all tokens + draft tokens + let mut all = full.tokens.clone(); + all.extend_from_slice(draft_tokens); + (all, 0) + }; + + let input = Tensor::new(context_tokens.as_slice(), &full.ctx.device)?.unsqueeze(0)?; + let (_batch_size, input_len) = input.dims2()?; + + // Run through all blocks (same as forward() but without truncating to last position) + let mut x = full.embedding.forward(&input)?; + + let num_blocks = full.blocks.len(); + let mut block_idx = 0; + + while block_idx < num_blocks { + if full.blocks[block_idx].ident() == "local" { + x = full.blocks[block_idx] + .forward_mut(&x, context_index, block_idx, &mut full.ctx) + .await?; + block_idx += 1; + } else { + let mut batch = vec![]; + let first = block_idx; + let curr_block_id = full.blocks[block_idx].ident().to_owned(); + while block_idx < num_blocks && full.blocks[block_idx].ident() == curr_block_id { + batch.push(( + full.blocks[block_idx].layer_name().to_string(), + context_index, + block_idx, + )); + block_idx += 1; + } + x = full.blocks[first] + .forward_batch(&x, batch, &mut full.ctx) + .await?; + } + } + + let x = full.ln_f.forward(&x)?; + + // Take only the last `seq_len` positions (the draft tokens) + // If we did a full re-prefill, the context is longer than draft_tokens + let x = if input_len > seq_len { + x.narrow(1, input_len - seq_len, seq_len)? + } else { + x + }; + + // Apply lm_head to ALL positions (not just last) + let logits = full.lm_head.forward(&x)?; + let logits = logits.squeeze(0)?; // [seq_len, vocab_size] + + // Update index_pos to reflect the full forward + full.index_pos = context_index + input_len; + + Ok(logits) +} diff --git a/cake-core/src/utils/gguf.rs b/cake-core/src/utils/gguf.rs new file mode 100644 index 00000000..0e0b2b25 --- /dev/null +++ b/cake-core/src/utils/gguf.rs @@ -0,0 +1,231 @@ +//! GGUF model loading support. +//! +//! Loads quantized GGUF files, dequantizes tensors to the target dtype, +//! and remaps GGUF tensor names to HuggingFace-style names so that +//! existing model code (LLaMA, Qwen2, etc.) works unchanged. + +use std::collections::HashMap; +use std::path::Path; + +use anyhow::{bail, Result}; +use candle_core::{DType, Device, Tensor}; +use candle_nn::VarBuilder; + +/// Remap a GGUF tensor name to HuggingFace-style name. +/// +/// GGUF (llama.cpp) uses names like `blk.0.attn_q.weight`, +/// HuggingFace uses `model.layers.0.self_attn.q_proj.weight`. +fn remap_gguf_name(name: &str, prefix: &str) -> String { + // Non-layer tensors + if name == "token_embd.weight" { + return format!("{prefix}.embed_tokens.weight"); + } + if name == "output_norm.weight" { + return format!("{prefix}.norm.weight"); + } + if name == "output.weight" { + return "lm_head.weight".to_string(); + } + + // Block-level tensors: blk.{i}.{component}.weight + if let Some(rest) = name.strip_prefix("blk.") { + if let Some(dot_pos) = rest.find('.') { + let layer_idx = &rest[..dot_pos]; + let component = &rest[dot_pos + 1..]; + + let hf_component = match component { + // Attention + "attn_q.weight" => "self_attn.q_proj.weight", + "attn_k.weight" => "self_attn.k_proj.weight", + "attn_v.weight" => "self_attn.v_proj.weight", + "attn_output.weight" => "self_attn.o_proj.weight", + // MLP + "ffn_gate.weight" => "mlp.gate_proj.weight", + "ffn_down.weight" => "mlp.down_proj.weight", + "ffn_up.weight" => "mlp.up_proj.weight", + // Norms + "attn_norm.weight" => "input_layernorm.weight", + "ffn_norm.weight" => "post_attention_layernorm.weight", + // Qwen-specific (QKV bias) + "attn_q.bias" => "self_attn.q_proj.bias", + "attn_k.bias" => "self_attn.k_proj.bias", + "attn_v.bias" => "self_attn.v_proj.bias", + // Pass through unknown components + other => return format!("{prefix}.layers.{layer_idx}.{other}"), + }; + + return format!("{prefix}.layers.{layer_idx}.{hf_component}"); + } + } + + // Unknown: pass through unchanged + name.to_string() +} + +/// Load a GGUF file and return a standard VarBuilder with dequantized tensors. +/// +/// All quantized tensors are dequantized to `dtype` and placed on `device`. +/// Tensor names are remapped from GGUF conventions to HuggingFace conventions +/// using the given `model_prefix` (e.g., "model" for LLaMA, "model.language_model" +/// for Qwen3.5). +pub fn load_var_builder_from_gguf<'a>( + gguf_path: &Path, + dtype: DType, + device: Device, + model_prefix: &str, +) -> Result> { + log::info!("loading GGUF model from {} ...", gguf_path.display()); + + let mut file = std::fs::File::open(gguf_path) + .map_err(|e| anyhow!("can't open GGUF file {}: {e}", gguf_path.display()))?; + + let content = candle_core::quantized::gguf_file::Content::read(&mut file) + .map_err(|e| anyhow!("can't parse GGUF file {}: {e}", gguf_path.display()))?; + + log::info!( + "GGUF: {} tensors, {} metadata entries", + content.tensor_infos.len(), + content.metadata.len(), + ); + + // Log useful metadata + for key in ["general.architecture", "general.name", "general.quantization_version"] { + if let Some(val) = content.metadata.get(key) { + log::info!(" {}: {:?}", key, val); + } + } + + let mut tensors: HashMap = HashMap::new(); + let start = std::time::Instant::now(); + + for tensor_name in content.tensor_infos.keys() { + let qtensor = content + .tensor(&mut file, tensor_name, &device) + .map_err(|e| anyhow!("can't load GGUF tensor '{}': {e}", tensor_name))?; + + // Dequantize to target dtype + let tensor = if dtype == DType::F16 { + qtensor + .dequantize_f16(&device) + .map_err(|e| anyhow!("can't dequantize_f16 '{}': {e}", tensor_name))? + } else { + qtensor + .dequantize(&device) + .map_err(|e| anyhow!("can't dequantize '{}': {e}", tensor_name))? + .to_dtype(dtype) + .map_err(|e| anyhow!("can't cast '{}' to {:?}: {e}", tensor_name, dtype))? + }; + + let hf_name = remap_gguf_name(tensor_name, model_prefix); + log::debug!(" {} → {} {:?}", tensor_name, hf_name, tensor.shape()); + tensors.insert(hf_name, tensor); + } + + log::info!( + "GGUF: loaded and dequantized {} tensors in {:.1}s", + tensors.len(), + start.elapsed().as_secs_f64(), + ); + + Ok(VarBuilder::from_tensors(tensors, dtype, &device)) +} + +/// Detect GGUF file(s) in a model directory. +/// Returns the path to the first `.gguf` file found, or None. +pub fn detect_gguf_file(model_dir: &Path) -> Option { + if model_dir.is_file() && model_dir.extension().map_or(false, |ext| ext == "gguf") { + return Some(model_dir.to_path_buf()); + } + + if model_dir.is_dir() { + if let Ok(entries) = std::fs::read_dir(model_dir) { + for entry in entries.flatten() { + let path = entry.path(); + if path.extension().map_or(false, |ext| ext == "gguf") { + return Some(path); + } + } + } + } + + None +} + +/// Extract the model architecture string from GGUF metadata. +/// Returns e.g. "llama", "qwen2", etc. +pub fn detect_architecture_from_gguf(gguf_path: &Path) -> Result { + let mut file = std::fs::File::open(gguf_path) + .map_err(|e| anyhow!("can't open GGUF file: {e}"))?; + + let content = candle_core::quantized::gguf_file::Content::read(&mut file) + .map_err(|e| anyhow!("can't parse GGUF file: {e}"))?; + + if let Some(val) = content.metadata.get("general.architecture") { + Ok(format!("{:?}", val).trim_matches('"').to_string()) + } else { + bail!("GGUF file missing general.architecture metadata") + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_remap_gguf_names_llama() { + let prefix = "model"; + + assert_eq!( + remap_gguf_name("token_embd.weight", prefix), + "model.embed_tokens.weight" + ); + assert_eq!( + remap_gguf_name("output_norm.weight", prefix), + "model.norm.weight" + ); + assert_eq!(remap_gguf_name("output.weight", prefix), "lm_head.weight"); + + assert_eq!( + remap_gguf_name("blk.0.attn_q.weight", prefix), + "model.layers.0.self_attn.q_proj.weight" + ); + assert_eq!( + remap_gguf_name("blk.15.attn_output.weight", prefix), + "model.layers.15.self_attn.o_proj.weight" + ); + assert_eq!( + remap_gguf_name("blk.3.ffn_gate.weight", prefix), + "model.layers.3.mlp.gate_proj.weight" + ); + assert_eq!( + remap_gguf_name("blk.7.attn_norm.weight", prefix), + "model.layers.7.input_layernorm.weight" + ); + assert_eq!( + remap_gguf_name("blk.7.ffn_norm.weight", prefix), + "model.layers.7.post_attention_layernorm.weight" + ); + } + + #[test] + fn test_remap_gguf_names_qwen3_5() { + let prefix = "model.language_model"; + + assert_eq!( + remap_gguf_name("token_embd.weight", prefix), + "model.language_model.embed_tokens.weight" + ); + assert_eq!( + remap_gguf_name("blk.0.attn_q.weight", prefix), + "model.language_model.layers.0.self_attn.q_proj.weight" + ); + } + + #[test] + fn test_remap_unknown_passthrough() { + assert_eq!( + remap_gguf_name("some.unknown.tensor", "model"), + "some.unknown.tensor" + ); + } +} diff --git a/cake-core/src/utils/mod.rs b/cake-core/src/utils/mod.rs index a4160b0d..2ebc6d80 100644 --- a/cake-core/src/utils/mod.rs +++ b/cake-core/src/utils/mod.rs @@ -1,6 +1,7 @@ //! Utility functions and abstractions. pub mod fp8; +pub mod gguf; #[cfg(feature = "cuda")] pub mod flash_attn; pub mod gptq; diff --git a/cake-core/src/video/avi.rs b/cake-core/src/video/avi.rs new file mode 100644 index 00000000..af2febca --- /dev/null +++ b/cake-core/src/video/avi.rs @@ -0,0 +1,247 @@ +//! Pure-Rust uncompressed AVI writer (RIFF/AVI 1.0). +//! +//! Writes an AVI file containing a single video stream with uncompressed +//! RGB24 (DIB) frames. The resulting file is playable by VLC, ffmpeg, +//! QuickTime, Windows Media Player, and most other video software. +//! +//! AVI 1.0 with uncompressed frames has a theoretical 2 GB RIFF size limit. +//! For the frame counts and resolutions used in LTX-Video generation this +//! is more than sufficient (41 frames @ 512x704 ≈ 44 MB). + +use image::{ImageBuffer, Rgb}; +use std::io::Write; + +/// Write an uncompressed AVI to any `Write` sink. +/// +/// Frames must all be the same dimensions. Each frame is stored as a +/// bottom-up DIB (the AVI/BMP convention), with row order flipped. +pub fn write_avi( + w: &mut W, + frames: &[ImageBuffer, Vec>], + fps: usize, + width: u32, + height: u32, +) -> anyhow::Result<()> { + if frames.is_empty() { + anyhow::bail!("cannot write AVI with zero frames"); + } + if fps == 0 { + anyhow::bail!("fps must be > 0"); + } + + let num_frames = frames.len() as u32; + // Each row is padded to 4-byte boundary (RGB24 = 3 bytes per pixel) + let row_bytes = width * 3; + let row_stride = (row_bytes + 3) & !3; // pad to 4-byte boundary + let frame_size = row_stride * height; // raw DIB frame size + let usec_per_frame = 1_000_000u32 / fps as u32; + + // movi list: each frame is a "00dc" chunk (4 byte tag + 4 byte size + data) + let movi_payload_size: u32 = num_frames * (8 + frame_size); + let movi_list_size: u32 = 4 + movi_payload_size; // "movi" + chunks + + // hdrl list size + let avih_chunk_size: u32 = 8 + 56; // "avih" + size_u32 + 56 bytes payload + let strh_chunk_size: u32 = 8 + 56; // "strh" + size_u32 + 56 bytes payload + let strf_chunk_size: u32 = 8 + 40; // "strf" + size_u32 + BITMAPINFOHEADER(40) + let strl_list_size: u32 = 4 + strh_chunk_size + strf_chunk_size; // "strl" + chunks + let hdrl_list_size: u32 = 4 + avih_chunk_size + 8 + strl_list_size; // "hdrl" + avih + LIST(strl) + + // idx1 chunk: 8 byte header + 16 bytes per frame + let idx1_chunk_size: u32 = 8 + num_frames * 16; + + // Total RIFF size: "AVI " + LIST(hdrl) + LIST(movi) + idx1 + let riff_size: u32 = 4 + (8 + hdrl_list_size) + (8 + movi_list_size) + idx1_chunk_size; + + // ── RIFF header ────────────────────────────────────────────── + w.write_all(b"RIFF")?; + w.write_all(&riff_size.to_le_bytes())?; + w.write_all(b"AVI ")?; + + // ── hdrl LIST ──────────────────────────────────────────────── + w.write_all(b"LIST")?; + w.write_all(&hdrl_list_size.to_le_bytes())?; + w.write_all(b"hdrl")?; + + // ── avih (main AVI header) ─────────────────────────────────── + w.write_all(b"avih")?; + w.write_all(&56u32.to_le_bytes())?; // size of avih data + w.write_all(&usec_per_frame.to_le_bytes())?; // dwMicroSecPerFrame + w.write_all(&(frame_size * fps as u32).to_le_bytes())?; // dwMaxBytesPerSec + w.write_all(&0u32.to_le_bytes())?; // dwPaddingGranularity + w.write_all(&0x10u32.to_le_bytes())?; // dwFlags: AVIF_HASINDEX (0x10) + w.write_all(&num_frames.to_le_bytes())?; // dwTotalFrames + w.write_all(&0u32.to_le_bytes())?; // dwInitialFrames + w.write_all(&1u32.to_le_bytes())?; // dwStreams + w.write_all(&frame_size.to_le_bytes())?; // dwSuggestedBufferSize + w.write_all(&width.to_le_bytes())?; // dwWidth + w.write_all(&height.to_le_bytes())?; // dwHeight + w.write_all(&[0u8; 16])?; // dwReserved[4] + + // ── strl LIST (stream list) ────────────────────────────────── + w.write_all(b"LIST")?; + w.write_all(&strl_list_size.to_le_bytes())?; + w.write_all(b"strl")?; + + // ── strh (stream header) ───────────────────────────────────── + w.write_all(b"strh")?; + w.write_all(&56u32.to_le_bytes())?; // size of strh data + w.write_all(b"vids")?; // fccType: video stream + w.write_all(&0u32.to_le_bytes())?; // fccHandler: 0 = uncompressed DIB + w.write_all(&0u32.to_le_bytes())?; // dwFlags + w.write_all(&0u16.to_le_bytes())?; // wPriority + w.write_all(&0u16.to_le_bytes())?; // wLanguage + w.write_all(&0u32.to_le_bytes())?; // dwInitialFrames + w.write_all(&1u32.to_le_bytes())?; // dwScale + w.write_all(&(fps as u32).to_le_bytes())?; // dwRate + w.write_all(&0u32.to_le_bytes())?; // dwStart + w.write_all(&num_frames.to_le_bytes())?; // dwLength + w.write_all(&frame_size.to_le_bytes())?; // dwSuggestedBufferSize + w.write_all(&0xFFFFFFFFu32.to_le_bytes())?; // dwQuality (-1 = default) + w.write_all(&0u32.to_le_bytes())?; // dwSampleSize + w.write_all(&0u16.to_le_bytes())?; // rcFrame.left + w.write_all(&0u16.to_le_bytes())?; // rcFrame.top + w.write_all(&(width as u16).to_le_bytes())?; // rcFrame.right + w.write_all(&(height as u16).to_le_bytes())?; // rcFrame.bottom + + // ── strf (stream format = BITMAPINFOHEADER) ────────────────── + w.write_all(b"strf")?; + w.write_all(&40u32.to_le_bytes())?; // size of BITMAPINFOHEADER + w.write_all(&40u32.to_le_bytes())?; // biSize + w.write_all(&width.to_le_bytes())?; // biWidth + w.write_all(&height.to_le_bytes())?; // biHeight (positive = bottom-up) + w.write_all(&1u16.to_le_bytes())?; // biPlanes + w.write_all(&24u16.to_le_bytes())?; // biBitCount (RGB24) + w.write_all(&0u32.to_le_bytes())?; // biCompression (BI_RGB = 0) + w.write_all(&frame_size.to_le_bytes())?; // biSizeImage + w.write_all(&0u32.to_le_bytes())?; // biXPelsPerMeter + w.write_all(&0u32.to_le_bytes())?; // biYPelsPerMeter + w.write_all(&0u32.to_le_bytes())?; // biClrUsed + w.write_all(&0u32.to_le_bytes())?; // biClrImportant + + // ── movi LIST ──────────────────────────────────────────────── + w.write_all(b"LIST")?; + w.write_all(&movi_list_size.to_le_bytes())?; + w.write_all(b"movi")?; + + // Row buffer for bottom-up flip + RGB→BGR + row padding + let mut row_buf = vec![0u8; row_stride as usize]; + + for frame in frames { + w.write_all(b"00dc")?; // chunk ID: stream 0, compressed (dc) + w.write_all(&frame_size.to_le_bytes())?; + + // AVI DIB frames are bottom-up: write rows in reverse order + // Also convert RGB to BGR (BMP/AVI convention) + for y in (0..height).rev() { + let row_start = (y * width * 3) as usize; + let row_end = row_start + (width * 3) as usize; + let src = &frame.as_raw()[row_start..row_end]; + + // Convert RGB -> BGR + for x in 0..width as usize { + row_buf[x * 3] = src[x * 3 + 2]; // B + row_buf[x * 3 + 1] = src[x * 3 + 1]; // G + row_buf[x * 3 + 2] = src[x * 3]; // R + } + // Padding bytes are already zeroed from vec initialization + w.write_all(&row_buf)?; + } + } + + // ── idx1 (AVI 1.0 index) ───────────────────────────────────── + let idx1_size = num_frames * 16; // 16 bytes per entry + w.write_all(b"idx1")?; + w.write_all(&idx1_size.to_le_bytes())?; + + let mut offset: u32 = 4; // offset from start of movi data (after "movi" tag) + for _ in 0..num_frames { + w.write_all(b"00dc")?; // ckid + w.write_all(&0x10u32.to_le_bytes())?; // dwFlags: AVIIF_KEYFRAME + w.write_all(&offset.to_le_bytes())?; // dwOffset + w.write_all(&frame_size.to_le_bytes())?; // dwSize + offset += 8 + frame_size; // skip chunk header (tag + size) + data + } + + w.flush()?; + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn make_test_frame(width: u32, height: u32, color: [u8; 3]) -> ImageBuffer, Vec> { + ImageBuffer::from_fn(width, height, |_, _| Rgb(color)) + } + + #[test] + fn test_write_avi_basic() { + let frames = vec![ + make_test_frame(8, 6, [255, 0, 0]), + make_test_frame(8, 6, [0, 255, 0]), + make_test_frame(8, 6, [0, 0, 255]), + ]; + let mut buf = Vec::new(); + write_avi(&mut buf, &frames, 24, 8, 6).unwrap(); + + // Check RIFF header + assert_eq!(&buf[0..4], b"RIFF"); + assert_eq!(&buf[8..12], b"AVI "); + + // Verify total size matches RIFF size field + 8 + let riff_size = u32::from_le_bytes([buf[4], buf[5], buf[6], buf[7]]); + assert_eq!(buf.len() as u32, riff_size + 8); + } + + #[test] + fn test_write_avi_empty_fails() { + let frames: Vec, Vec>> = vec![]; + let mut buf = Vec::new(); + assert!(write_avi(&mut buf, &frames, 24, 8, 6).is_err()); + } + + #[test] + fn test_write_avi_zero_fps_fails() { + let frames = vec![make_test_frame(8, 6, [0, 0, 0])]; + let mut buf = Vec::new(); + assert!(write_avi(&mut buf, &frames, 0, 8, 6).is_err()); + } + + #[test] + fn test_write_avi_single_frame() { + let frames = vec![make_test_frame(4, 4, [128, 64, 32])]; + let mut buf = Vec::new(); + write_avi(&mut buf, &frames, 1, 4, 4).unwrap(); + + let riff_size = u32::from_le_bytes([buf[4], buf[5], buf[6], buf[7]]); + assert_eq!(buf.len() as u32, riff_size + 8); + } + + #[test] + fn test_write_avi_odd_width_padding() { + // Width=5, RGB24: 5*3=15 bytes/row, padded to 16 (next multiple of 4) + let frames = vec![make_test_frame(5, 3, [255, 128, 0])]; + let mut buf = Vec::new(); + write_avi(&mut buf, &frames, 30, 5, 3).unwrap(); + + let riff_size = u32::from_le_bytes([buf[4], buf[5], buf[6], buf[7]]); + assert_eq!(buf.len() as u32, riff_size + 8); + } + + #[test] + fn test_video_output_roundtrip() { + use crate::video::VideoOutput; + + let frames = vec![ + make_test_frame(8, 6, [255, 0, 0]), + make_test_frame(8, 6, [0, 255, 0]), + ]; + let output = VideoOutput::new(frames, 24, 8, 6); + assert_eq!(output.num_frames(), 2); + assert!((output.duration_secs() - 2.0 / 24.0).abs() < 0.001); + + let avi_bytes = output.to_avi().unwrap(); + assert_eq!(&avi_bytes[0..4], b"RIFF"); + } +} diff --git a/cake-core/src/video/mod.rs b/cake-core/src/video/mod.rs new file mode 100644 index 00000000..81185740 --- /dev/null +++ b/cake-core/src/video/mod.rs @@ -0,0 +1,82 @@ +//! Video output types and pure-Rust AVI muxer. +//! +//! No third-party codec dependencies — writes uncompressed RGB24 AVI +//! that any video player, ffmpeg, or browser can read. Users can +//! transcode to H.264/H.265 externally if compression is needed. + +mod avi; + +use image::{ImageBuffer, Rgb}; + +pub use avi::write_avi; + +/// Complete video output from a generation pipeline. +pub struct VideoOutput { + /// Individual frames in RGB8 format, ordered chronologically. + pub frames: Vec, Vec>>, + /// Frames per second. + pub fps: usize, + /// Frame width in pixels. + pub width: u32, + /// Frame height in pixels. + pub height: u32, +} + +impl VideoOutput { + /// Create a VideoOutput from frames and metadata. + pub fn new( + frames: Vec, Vec>>, + fps: usize, + width: u32, + height: u32, + ) -> Self { + Self { + frames, + fps, + width, + height, + } + } + + /// Encode this video as an uncompressed AVI file in memory. + pub fn to_avi(&self) -> anyhow::Result> { + let mut buf = Vec::new(); + write_avi(&mut buf, &self.frames, self.fps, self.width, self.height)?; + Ok(buf) + } + + /// Write this video as an AVI file to the given path. + pub fn save_avi(&self, path: &std::path::Path) -> anyhow::Result<()> { + let mut file = std::fs::File::create(path)?; + write_avi( + &mut file, + &self.frames, + self.fps, + self.width, + self.height, + ) + } + + /// Save individual frames as numbered PNG files in the given directory. + pub fn save_frames(&self, dir: &std::path::Path, prefix: &str) -> anyhow::Result<()> { + std::fs::create_dir_all(dir)?; + for (i, frame) in self.frames.iter().enumerate() { + let path = dir.join(format!("{}_{:04}.png", prefix, i)); + frame.save(&path)?; + } + Ok(()) + } + + /// Total number of frames. + pub fn num_frames(&self) -> usize { + self.frames.len() + } + + /// Duration in seconds. + pub fn duration_secs(&self) -> f64 { + if self.fps == 0 { + return 0.0; + } + self.frames.len() as f64 / self.fps as f64 + } +} diff --git a/cake-core/tests/integration.rs b/cake-core/tests/integration.rs index 7ab575b3..51dc072e 100644 --- a/cake-core/tests/integration.rs +++ b/cake-core/tests/integration.rs @@ -3,8 +3,10 @@ //! These tests validate that a model integration works correctly end-to-end: //! loading, token generation, chat coherence, state management, and API compatibility. //! -//! LLaMA tests require CAKE_TEST_MODEL (default: ./cake-data/Llama-3.2-1B-Instruct/). -//! Qwen2 tests require CAKE_TEST_QWEN2_MODEL env var (skipped if not set). +//! All model tests require model files on disk. They skip gracefully when not available. +//! Set env vars to enable: +//! CAKE_TEST_MODEL=./path/to/Llama-3.2-1B-Instruct/ +//! CAKE_TEST_QWEN2_MODEL=./path/to/Qwen2-0.5B/ //! //! Run with: cargo test --test integration -- --test-threads=1 @@ -65,8 +67,17 @@ where response } +/// Returns the model path from `env_var`, or None if not set / path doesn't exist. +fn resolve_model_path(env_var: &str, default_path: &str) -> Option { + let path = env::var(env_var).unwrap_or_else(|_| default_path.into()); + if path.is_empty() || !std::path::Path::new(&path).exists() { + None + } else { + Some(path) + } +} + /// Macro to generate the full test suite for a given model type. -/// This avoids duplicating all test bodies between LLaMA and Qwen2. macro_rules! model_test_suite { ( module_name: $mod_name:ident, @@ -76,7 +87,6 @@ macro_rules! model_test_suite { env_var: $env_var:expr, default_path: $default_path:expr, arch: $arch:expr, - skip_if_missing: $skip:expr, ) => { mod $mod_name { use super::*; @@ -87,11 +97,7 @@ macro_rules! model_test_suite { static MODEL: OnceCell>> = OnceCell::const_new(); fn get_model_path() -> Option { - if $skip { - env::var($env_var).ok() - } else { - Some(env::var($env_var).unwrap_or_else(|_| $default_path.into())) - } + resolve_model_path($env_var, $default_path) } async fn get_or_load_model() -> Option>> { @@ -597,13 +603,12 @@ model_test_suite! { image_model: cake_core::models::sd::SD, model_name_const: "llama3", env_var: "CAKE_TEST_MODEL", - default_path: "./cake-data/Llama-3.2-1B-Instruct/", + default_path: "", arch: TextModelArch::Llama, - skip_if_missing: false, } // ============================================================================= -// Qwen2 test suite (same tests, different model) +// Qwen2 test suite // ============================================================================= #[cfg(feature = "qwen2")] @@ -615,5 +620,4 @@ model_test_suite! { env_var: "CAKE_TEST_QWEN2_MODEL", default_path: "", arch: TextModelArch::Qwen2, - skip_if_missing: true, } diff --git a/scripts/convert_ltx23.py b/scripts/convert_ltx23.py new file mode 100644 index 00000000..26389ff6 --- /dev/null +++ b/scripts/convert_ltx23.py @@ -0,0 +1,271 @@ +#!/usr/bin/env python3 +"""Convert LTX-2.3 monolithic safetensors to diffusers directory format. + +Usage: + python scripts/convert_ltx23.py \ + --input /path/to/ltx-2.3-22b-dev.safetensors \ + --output /path/to/LTX-2.3-diffusers/ + +This creates the directory structure expected by Cake's Rust loader: + output/ + transformer/ + config.json + diffusion_pytorch_model.safetensors + connectors/ + diffusion_pytorch_model.safetensors + vae/ + diffusion_pytorch_model.safetensors + vocoder/ + diffusion_pytorch_model.safetensors +""" + +import argparse +import json +import os +from collections import defaultdict + +import torch +from safetensors.torch import load_file, save_file + + +# Key rename mappings: monolithic key substring -> diffusers key substring +TRANSFORMER_RENAMES = [ + # Must be ordered: longer/more specific matches first + ("patchify_proj.", "proj_in."), + ("adaln_single.", "time_embed."), + ("q_norm.", "norm_q."), + ("k_norm.", "norm_k."), +] + +CONNECTOR_RENAMES = [ + # In monolithic: model.diffusion_model.video_embeddings_connector.transformer_1d_blocks.N.attn1.q_norm + # In diffusers: video_connector.transformer_blocks.N.attn1.norm_q + ("transformer_1d_blocks.", "transformer_blocks."), + ("q_norm.", "norm_q."), + ("k_norm.", "norm_k."), +] + +VAE_RENAMES = [ + ("res_blocks.", "resnets."), + ("per_channel_statistics.mean-of-means", "latents_mean"), + ("per_channel_statistics.std-of-means", "latents_std"), +] + +# VAE block index remapping (monolithic -> diffusers) +# Monolithic: up_blocks.0 = mid_block, up_blocks.1 = up_blocks.0.upsamplers.0, etc. +VAE_DECODER_BLOCK_REMAP = [ + ("up_blocks.0.", "mid_block."), + ("up_blocks.1.", "up_blocks.0.upsamplers.0."), + ("up_blocks.2.", "up_blocks.0."), + ("up_blocks.3.", "up_blocks.1.upsamplers.0."), + ("up_blocks.4.", "up_blocks.1."), + ("up_blocks.5.", "up_blocks.2.upsamplers.0."), + ("up_blocks.6.", "up_blocks.2."), + # LTX-2.3 has 4 up_blocks (vs 3 for LTX-2): + ("up_blocks.7.", "up_blocks.3.upsamplers.0."), + ("up_blocks.8.", "up_blocks.3."), +] + +VAE_ENCODER_BLOCK_REMAP = [ + ("down_blocks.0.", "down_blocks.0."), + ("down_blocks.1.", "down_blocks.0.downsamplers.0."), + ("down_blocks.2.", "down_blocks.1."), + ("down_blocks.3.", "down_blocks.1.downsamplers.0."), + ("down_blocks.4.", "down_blocks.2."), + ("down_blocks.5.", "down_blocks.2.downsamplers.0."), + ("down_blocks.6.", "down_blocks.3."), + ("down_blocks.7.", "down_blocks.3.downsamplers.0."), + ("down_blocks.8.", "mid_block."), +] + + +def apply_renames(key: str, renames: list[tuple[str, str]]) -> str: + for old, new in renames: + key = key.replace(old, new) + return key + + +def apply_block_remap(key: str, remaps: list[tuple[str, str]]) -> str: + """Apply block index remapping (must match longest prefix first).""" + for old, new in sorted(remaps, key=lambda x: -len(x[0])): + if old in key: + return key.replace(old, new, 1) + return key + + +def convert(input_path: str, output_dir: str, skip_audio: bool = True): + print(f"Loading {input_path}...") + checkpoint = load_file(input_path) + print(f"Loaded {len(checkpoint)} tensors") + + # Categorize keys by component + components = defaultdict(dict) + feature_extractor = {} + skipped = [] + + for key, tensor in checkpoint.items(): + if key.startswith("model.diffusion_model."): + stripped = key[len("model.diffusion_model."):] + + if stripped.startswith("video_embeddings_connector."): + # Connector (video) + conn_key = stripped[len("video_embeddings_connector."):] + conn_key = apply_renames(conn_key, CONNECTOR_RENAMES) + components["connectors"]["video_connector." + conn_key] = tensor + + elif stripped.startswith("audio_embeddings_connector."): + if skip_audio: + skipped.append(key) + continue + conn_key = stripped[len("audio_embeddings_connector."):] + conn_key = apply_renames(conn_key, CONNECTOR_RENAMES) + components["connectors"]["audio_connector." + conn_key] = tensor + + elif stripped.startswith("audio_"): + if skip_audio: + skipped.append(key) + continue + # Audio transformer components + trans_key = apply_renames(stripped, TRANSFORMER_RENAMES) + components["transformer"][trans_key] = tensor + else: + # Video transformer + trans_key = apply_renames(stripped, TRANSFORMER_RENAMES) + components["transformer"][trans_key] = tensor + + elif key.startswith("text_embedding_projection."): + # Feature extractor — goes into connectors + feat_key = key[len("text_embedding_projection."):] + feature_extractor[feat_key] = tensor + + elif key.startswith("vae."): + vae_key = key[len("vae."):] + + if vae_key.startswith("decoder."): + inner = vae_key[len("decoder."):] + inner = apply_block_remap(inner, VAE_DECODER_BLOCK_REMAP) + inner = apply_renames(inner, VAE_RENAMES) + components["vae"]["decoder." + inner] = tensor + elif vae_key.startswith("encoder."): + inner = vae_key[len("encoder."):] + inner = apply_block_remap(inner, VAE_ENCODER_BLOCK_REMAP) + inner = apply_renames(inner, VAE_RENAMES) + components["vae"]["encoder." + inner] = tensor + elif "per_channel_statistics" in vae_key: + renamed = apply_renames(vae_key, VAE_RENAMES) + components["vae"][renamed] = tensor + else: + components["vae"][vae_key] = tensor + + elif key.startswith("audio_vae."): + if skip_audio: + skipped.append(key) + continue + components["audio_vae"][key[len("audio_vae."):]] = tensor + + elif key.startswith("vocoder."): + components["vocoder"][key[len("vocoder."):]] = tensor + + else: + print(f" WARNING: Unknown key prefix: {key}") + skipped.append(key) + + # Add feature extractor to connectors + if feature_extractor: + # In LTX-2.3, text_embedding_projection replaces the connector's text_proj_in + # Store as a separate component within connectors + for feat_key, tensor in feature_extractor.items(): + components["connectors"]["feature_extractor." + feat_key] = tensor + + print(f"\nComponent summary:") + for comp, tensors in sorted(components.items()): + total_params = sum(t.numel() for t in tensors.values()) + total_bytes = sum(t.numel() * t.element_size() for t in tensors.values()) + print(f" {comp}: {len(tensors)} tensors, {total_params:,} params, {total_bytes / 1e9:.2f} GB") + if skipped: + print(f" skipped: {len(skipped)} tensors (audio)") + + # Save each component + for comp_name, tensors in components.items(): + comp_dir = os.path.join(output_dir, comp_name) + os.makedirs(comp_dir, exist_ok=True) + out_path = os.path.join(comp_dir, "diffusion_pytorch_model.safetensors") + print(f"\nSaving {comp_name} ({len(tensors)} tensors) -> {out_path}") + save_file(tensors, out_path) + + # Write transformer config + transformer_config = { + "_class_name": "LTX2VideoTransformer3DModel", + "num_attention_heads": 32, + "attention_head_dim": 128, + "in_channels": 128, + "out_channels": 128, + "cross_attention_dim": 4096, + "num_layers": 48, + "norm_eps": 1e-6, + "activation_fn": "gelu-approximate", + "attention_bias": True, + "timestep_scale_multiplier": 1000.0, + "positional_embedding_theta": 10000.0, + "positional_embedding_max_pos": [20, 2048, 2048], + "caption_channels": 3840, + "cross_attention_adaln": True, + # LTX-2.3 specific + "gated_attention": True, + "prompt_modulation": True, + } + config_path = os.path.join(output_dir, "transformer", "config.json") + with open(config_path, "w") as f: + json.dump(transformer_config, f, indent=2) + print(f"Saved transformer config -> {config_path}") + + # Write connector config + connector_config = { + "caption_channels": 3840, + "video_connector_num_layers": 8, + "video_connector_num_attention_heads": 32, + "video_connector_attention_head_dim": 128, + "video_connector_num_learnable_registers": 128, + "audio_connector_num_layers": 8, + "audio_connector_num_attention_heads": 32, + "audio_connector_attention_head_dim": 128, + "audio_connector_num_learnable_registers": 128, + "text_proj_in_factor": 49, + "rope_theta": 10000.0, + "connector_rope_base_seq_len": 4096, + "has_feature_extractor": True, + "feature_extractor_out_dim": 4096, + } + config_path = os.path.join(output_dir, "connectors", "config.json") + with open(config_path, "w") as f: + json.dump(connector_config, f, indent=2) + print(f"Saved connector config -> {config_path}") + + # Write VAE config + vae_config = { + "latent_channels": 128, + "block_out_channels": [256, 512, 1024, 2048], + "decoder_block_out_channels": [128, 256, 512, 1024], + "layers_per_block": [4, 6, 6, 2, 2], + "decoder_layers_per_block": [4, 6, 4, 2, 2], + "patch_size": 4, + "patch_size_t": 1, + "timestep_conditioning": False, + } + config_path = os.path.join(output_dir, "vae", "config.json") + with open(config_path, "w") as f: + json.dump(vae_config, f, indent=2) + print(f"Saved VAE config -> {config_path}") + + print(f"\nDone! Output directory: {output_dir}") + print(f"Use with: cake master --model {output_dir} --ltx-version 2.3 ...") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Convert LTX-2.3 monolithic checkpoint to diffusers format") + parser.add_argument("--input", "-i", required=True, help="Path to ltx-2.3-*.safetensors") + parser.add_argument("--output", "-o", required=True, help="Output directory") + parser.add_argument("--include-audio", action="store_true", help="Include audio components") + args = parser.parse_args() + + convert(args.input, args.output, skip_audio=not args.include_audio) diff --git a/topology-ltx2.yml b/topology-ltx2.yml new file mode 100644 index 00000000..fb485709 --- /dev/null +++ b/topology-ltx2.yml @@ -0,0 +1,3 @@ +worker1: + host: "192.168.1.158:10128" + layers: ["ltx2-transformer"] diff --git a/videos/frames/frame_0000.png b/videos/frames/frame_0000.png new file mode 100644 index 00000000..6be777bd Binary files /dev/null and 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Cake LTX-2 Distributed Video Generation

+

768x512, 41 frames @ 24fps | 2 GPUs (4090 master + 5090 worker)

+ +

Benchmark Videos (GPU Gemma Q4_K_M, ~55-70s each)

+
+
+

"A golden retriever puppy running through a field of autumn leaves"

+ +
+
+

"Waves crashing on rocks at sunset, ocean spray, golden light"

+ +
+
+

"City skyline timelapse at dusk, lights turning on"

+ +
+
+ +

CFG / Step Comparison (CPU Gemma, earlier runs)

+
+
+

CFG=1.0, 30 steps (~60s)

+ +
+
+

CFG=2.0, 30 steps (~120s)

+ +
+
+

CFG=3.0, 20 steps (~55s)

+ +
+
+ + + diff --git a/videos/latents_pre_vae.json b/videos/latents_pre_vae.json new file mode 100644 index 00000000..47f1de72 --- /dev/null +++ b/videos/latents_pre_vae.json @@ -0,0 +1 @@ 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