MagCache support for WAN 2.1 models

README.md

@@ -17,6 +17,8 @@
 [![Unit Tests](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml/badge.svg)](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml)
 
 # What's new?
+- **`2026/03/25`**: Wan2.1 and Wan2.2 Magcache inference is now supported
+- **`2026/03/25`**: LTX-2 Video Inference is now supported
 - **`2026/01/29`**: Wan LoRA for inference is now supported
 - **`2026/01/15`**: Wan2.1 and Wan2.2 Img2vid generation is now supported
 - **`2025/11/11`**: Wan2.2 txt2vid generation is now supported
@@ -49,6 +51,7 @@ MaxDiffusion supports
 * ControlNet inference (Stable Diffusion 1.4 & SDXL).
 * Dreambooth training support for Stable Diffusion 1.x,2.x.
 * LTX-Video text2vid, img2vid (inference).
+* LTX-2 Video text2vid (inference).
 * Wan2.1 text2vid (training and inference).
 * Wan2.2 text2vid (inference).
 
@@ -73,6 +76,7 @@ MaxDiffusion supports
   - [Inference](#inference)
     - [Wan](#wan-models)
     - [LTX-Video](#ltx-video)
+    - [LTX-2 Video](#ltx-2-video)
     - [Flux](#flux)
       - [Fused Attention for GPU](#fused-attention-for-gpu)
     - [SDXL](#stable-diffusion-xl)
@@ -497,6 +501,33 @@ To generate images, run the following command:
 
   Add conditioning image path as conditioning_media_paths in the form of ["IMAGE_PATH"] along with other generation parameters in the ltx_video.yml file. Then follow same instruction as above.
 
+  ## LTX-2 Video
+
+  Although not required, attaching an external disk is recommended as weights take up a lot of disk space. [Follow these instructions if you would like to attach an external disk](https://cloud.google.com/tpu/docs/attach-durable-block-storage).
+
+  The following command will run LTX-2 T2V:
+
+   ```bash
+  HF_HUB_CACHE=/mnt/disks/external_disk/maxdiffusion_hf_cache/ \
+  LIBTPU_INIT_ARGS="--xla_tpu_enable_async_collective_fusion=true \
+  --xla_tpu_enable_async_collective_fusion_fuse_all_reduce=true \
+  --xla_tpu_enable_async_collective_fusion_multiple_steps=true \
+  --xla_tpu_overlap_compute_collective_tc=true \
+  --xla_enable_async_all_reduce=true" \
+  HF_HUB_ENABLE_HF_TRANSFER=1 \
+  python src/maxdiffusion/generate_ltx2.py \
+  src/maxdiffusion/configs/ltx2_video.yml \
+  attention="flash" \
+  num_inference_steps=40 \
+  num_frames=121 \
+  width=768 \
+  height=512 \
+  per_device_batch_size=.125 \
+  ici_data_parallelism=2 \
+  ici_context_parallelism=4 \
+  run_name=ltx2-inference
+  ```
+
   ## Wan Models
 
   Although not required, attaching an external disk is recommended as weights take up a lot of disk space. [Follow these instructions if you would like to attach an external disk](https://cloud.google.com/tpu/docs/attach-durable-block-storage).

src/maxdiffusion/configs/base_wan_14b.yml

@@ -328,6 +328,11 @@ flow_shift: 3.0
 # Skips the unconditional forward pass on ~35% of steps via residual compensation.
 # See: FasterCache (Lv et al. 2024), WAN 2.1 paper §4.4.2
 use_cfg_cache: False
+use_magcache: False
+magcache_thresh: 0.12
+magcache_K: 2
+retention_ratio: 0.2
+mag_ratios_base: [1.0, 1.0, 1.02504, 1.03017, 1.00025, 1.00251, 0.9985, 0.99962, 0.99779, 0.99771, 0.9966, 0.99658, 0.99482, 0.99476, 0.99467, 0.99451, 0.99664, 0.99656, 0.99434, 0.99431, 0.99533, 0.99545, 0.99468, 0.99465, 0.99438, 0.99434, 0.99516, 0.99517, 0.99384, 0.9938, 0.99404, 0.99401, 0.99517, 0.99516, 0.99409, 0.99408, 0.99428, 0.99426, 0.99347, 0.99343, 0.99418, 0.99416, 0.99271, 0.99269, 0.99313, 0.99311, 0.99215, 0.99215, 0.99218, 0.99215, 0.99216, 0.99217, 0.99163, 0.99161, 0.99138, 0.99135, 0.98982, 0.9898, 0.98996, 0.98995, 0.9887, 0.98866, 0.98772, 0.9877, 0.98767, 0.98765, 0.98573, 0.9857, 0.98501, 0.98498, 0.9838, 0.98376, 0.98177, 0.98173, 0.98037, 0.98035, 0.97678, 0.97677, 0.97546, 0.97543, 0.97184, 0.97183, 0.96711, 0.96708, 0.96349, 0.96345, 0.95629, 0.95625, 0.94926, 0.94929, 0.93964, 0.93961, 0.92511, 0.92504, 0.90693, 0.90678, 0.8796, 0.87945, 0.86111, 0.86189]
 
 # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
 guidance_rescale: 0.0

src/maxdiffusion/configs/base_wan_i2v_14b.yml

@@ -288,6 +288,12 @@ flow_shift: 5.0
 
 # Diffusion CFG cache (FasterCache-style, WAN 2.1 T2V only)
 use_cfg_cache: False
+use_magcache: False
+magcache_thresh: 0.12
+magcache_K: 2
+retention_ratio: 0.2
+mag_ratios_base_720p: [1.0, 1.0, 0.99428, 0.99498, 0.98588, 0.98621, 0.98273, 0.98281, 0.99018, 0.99023, 0.98911, 0.98917, 0.98646, 0.98652, 0.99454, 0.99456, 0.9891, 0.98909, 0.99124, 0.99127, 0.99102, 0.99103, 0.99215, 0.99212, 0.99515, 0.99515, 0.99576, 0.99572, 0.99068, 0.99072, 0.99097, 0.99097, 0.99166, 0.99169, 0.99041, 0.99042, 0.99201, 0.99198, 0.99101, 0.99101, 0.98599, 0.98603, 0.98845, 0.98844, 0.98848, 0.98851, 0.98862, 0.98857, 0.98718, 0.98719, 0.98497, 0.98497, 0.98264, 0.98263, 0.98389, 0.98393, 0.97938, 0.9794, 0.97535, 0.97536, 0.97498, 0.97499, 0.973, 0.97301, 0.96827, 0.96828, 0.96261, 0.96263, 0.95335, 0.9534, 0.94649, 0.94655, 0.93397, 0.93414, 0.91636, 0.9165, 0.89088, 0.89109, 0.8679, 0.86768]
+mag_ratios_base_480p: [1.0, 1.0, 0.98783, 0.98993, 0.97559, 0.97593, 0.98311, 0.98319, 0.98202, 0.98225, 0.9888, 0.98878, 0.98762, 0.98759, 0.98957, 0.98971, 0.99052, 0.99043, 0.99383, 0.99384, 0.98857, 0.9886, 0.99065, 0.99068, 0.98845, 0.98847, 0.99057, 0.99057, 0.98957, 0.98961, 0.98601, 0.9861, 0.98823, 0.98823, 0.98756, 0.98759, 0.98808, 0.98814, 0.98721, 0.98724, 0.98571, 0.98572, 0.98543, 0.98544, 0.98157, 0.98165, 0.98411, 0.98413, 0.97952, 0.97953, 0.98149, 0.9815, 0.9774, 0.97742, 0.97825, 0.97826, 0.97355, 0.97361, 0.97085, 0.97087, 0.97056, 0.97055, 0.96588, 0.96587, 0.96113, 0.96124, 0.9567, 0.95681, 0.94961, 0.94969, 0.93973, 0.93988, 0.93217, 0.93224, 0.91878, 0.91896, 0.90955, 0.90954, 0.92617, 0.92616]
 
 # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
 guidance_rescale: 0.0

src/maxdiffusion/configs/ltx2_video.yml

@@ -80,7 +80,7 @@ dataset_name: ''
 train_split: 'train'
 dataset_type: 'tfrecord'
 cache_latents_text_encoder_outputs: True
-per_device_batch_size: 0.125
+per_device_batch_size: 1
 compile_topology_num_slices: -1 
 quantization_local_shard_count: -1
 use_qwix_quantization: False

src/maxdiffusion/generate_wan.py

@@ -100,6 +100,10 @@ def call_pipeline(config, pipeline, prompt, negative_prompt):
           num_frames=config.num_frames,
           num_inference_steps=config.num_inference_steps,
           guidance_scale=config.guidance_scale,
+          use_magcache=config.use_magcache,
+          magcache_thresh=config.magcache_thresh,
+          magcache_K=config.magcache_K,
+          retention_ratio=config.retention_ratio,
       )
     elif model_key == WAN2_2:
       return pipeline(
@@ -127,6 +131,10 @@ def call_pipeline(config, pipeline, prompt, negative_prompt):
           num_inference_steps=config.num_inference_steps,
           guidance_scale=config.guidance_scale,
           use_cfg_cache=config.use_cfg_cache,
+          use_magcache=config.use_magcache,
+          magcache_thresh=config.magcache_thresh,
+          magcache_K=config.magcache_K,
+          retention_ratio=config.retention_ratio,
       )
     elif model_key == WAN2_2:
       return pipeline(

src/maxdiffusion/models/wan/transformers/transformer_wan.py

@@ -593,8 +593,11 @@ def __call__(
       return_dict: bool = True,
       attention_kwargs: Optional[Dict[str, Any]] = None,
       deterministic: bool = True,
-      rngs: nnx.Rngs = None,
-  ) -> Union[jax.Array, Dict[str, jax.Array]]:
+      rngs: Optional[nnx.Rngs] = None,
+      skip_blocks: Optional[jax.Array] = None,
+      cached_residual: Optional[jax.Array] = None,
+      return_residual: bool = False,
+  ) -> Union[jax.Array, Tuple[jax.Array, jax.Array], Dict[str, jax.Array]]:
     hidden_states = nn.with_logical_constraint(hidden_states, ("batch", None, None, None, None))
     batch_size, _, num_frames, height, width = hidden_states.shape
     p_t, p_h, p_w = self.config.patch_size
@@ -628,52 +631,69 @@ def __call__(
         encoder_attention_mask = jnp.concatenate([encoder_attention_mask, text_mask], axis=1)
       encoder_hidden_states = encoder_hidden_states.astype(hidden_states.dtype)
 
-    if self.scan_layers:
-
-      def scan_fn(carry, block):
-        hidden_states_carry, rngs_carry = carry
-        hidden_states = block(
-            hidden_states_carry,
-            encoder_hidden_states,
-            timestep_proj,
-            rotary_emb,
-            deterministic,
-            rngs_carry,
-            encoder_attention_mask,
-        )
-        new_carry = (hidden_states, rngs_carry)
-        return new_carry, None
-
-      rematted_block_forward = self.gradient_checkpoint.apply(
-          scan_fn, self.names_which_can_be_saved, self.names_which_can_be_offloaded, prevent_cse=not self.scan_layers
-      )
-      initial_carry = (hidden_states, rngs)
-      final_carry, _ = nnx.scan(
-          rematted_block_forward,
-          length=self.num_layers,
-          in_axes=(nnx.Carry, 0),
-          out_axes=(nnx.Carry, 0),
-      )(initial_carry, self.blocks)
-
-      hidden_states, _ = final_carry
-    else:
-      for block in self.blocks:
+    def _run_all_blocks(h):
+      if self.scan_layers:
 
-        def layer_forward(hidden_states):
-          return block(
-              hidden_states,
+        def scan_fn(carry, block):
+          hidden_states_carry, rngs_carry = carry
+          hidden_states = block(
+              hidden_states_carry,
               encoder_hidden_states,
               timestep_proj,
               rotary_emb,
               deterministic,
-              rngs,
-              encoder_attention_mask=encoder_attention_mask,
+              rngs_carry,
+              encoder_attention_mask,
           )
+          new_carry = (hidden_states, rngs_carry)
+          return new_carry, None
 
-        rematted_layer_forward = self.gradient_checkpoint.apply(
-            layer_forward, self.names_which_can_be_saved, self.names_which_can_be_offloaded, prevent_cse=not self.scan_layers
+        rematted_block_forward = self.gradient_checkpoint.apply(
+            scan_fn, self.names_which_can_be_saved, self.names_which_can_be_offloaded, prevent_cse=not self.scan_layers
         )
-        hidden_states = rematted_layer_forward(hidden_states)
+        initial_carry = (h, rngs)
+        final_carry, _ = nnx.scan(
+            rematted_block_forward,
+            length=self.num_layers,
+            in_axes=(nnx.Carry, 0),
+            out_axes=(nnx.Carry, 0),
+        )(initial_carry, self.blocks)
+
+        h_out, _ = final_carry
+      else:
+        h_out = h
+        for block in self.blocks:
+
+          def layer_forward(hidden_states):
+            return block(
+                hidden_states,
+                encoder_hidden_states,
+                timestep_proj,
+                rotary_emb,
+                deterministic,
+                rngs,
+                encoder_attention_mask=encoder_attention_mask,
+            )
+
+          rematted_layer_forward = self.gradient_checkpoint.apply(
+              layer_forward,
+              self.names_which_can_be_saved,
+              self.names_which_can_be_offloaded,
+              prevent_cse=not self.scan_layers,
+          )
+          h_out = rematted_layer_forward(h_out)
+      return h_out
+
+    hidden_states_before_blocks = hidden_states
+
+    if skip_blocks:
+      if cached_residual is None:
+        raise ValueError("cached_residual must be provided when skip_blocks is True")
+      hidden_states = hidden_states + cached_residual
+    else:
+      hidden_states = _run_all_blocks(hidden_states)
+
+    residual_x = hidden_states - hidden_states_before_blocks
 
     shift, scale = jnp.split(self.scale_shift_table + jnp.expand_dims(temb, axis=1), 2, axis=1)
     hidden_states = (self.norm_out(hidden_states.astype(jnp.float32)) * (1 + scale) + shift).astype(hidden_states.dtype)
@@ -685,4 +705,7 @@ def layer_forward(hidden_states):
     )
     hidden_states = jnp.transpose(hidden_states, (0, 7, 1, 4, 2, 5, 3, 6))
     hidden_states = hidden_states.reshape(batch_size, -1, num_frames, height, width)
+
+    if return_residual:
+      return hidden_states, residual_x
     return hidden_states