Add Hunyuan 3D 2.1 Support (Comfy-Org#8714)

Author: yousef-rafat

comfy/clip_vision.py

@@ -17,10 +17,227 @@ def __getitem__(self, key):
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+
+def cubic_kernel(x, a: float = -0.75):
+    absx = x.abs()
+    absx2 = absx ** 2
+    absx3 = absx ** 3
+
+    w = (a + 2) * absx3 - (a + 3) * absx2 + 1
+    w2 = a * absx3 - 5*a * absx2 + 8*a * absx - 4*a
+
+    return torch.where(absx <= 1, w, torch.where(absx < 2, w2, torch.zeros_like(x)))
+
+def get_indices_weights(in_size, out_size, scale):
+    # OpenCV-style half-pixel mapping
+    x = torch.arange(out_size, dtype=torch.float32)
+    x = (x + 0.5) / scale - 0.5
+
+    x0 = x.floor().long()
+    dx = x.unsqueeze(1) - (x0.unsqueeze(1) + torch.arange(-1, 3))
+
+    weights = cubic_kernel(dx)
+    weights = weights / weights.sum(dim=1, keepdim=True)
+
+    indices = x0.unsqueeze(1) + torch.arange(-1, 3)
+    indices = indices.clamp(0, in_size - 1)
+
+    return indices, weights
+
+def resize_cubic_1d(x, out_size, dim):
+    b, c, h, w = x.shape
+    in_size = h if dim == 2 else w
+    scale = out_size / in_size
+
+    indices, weights = get_indices_weights(in_size, out_size, scale)
+
+    if dim == 2:
+        x = x.permute(0, 1, 3, 2)
+        x = x.reshape(-1, h)
+    else:
+        x = x.reshape(-1, w)
+
+    gathered = x[:, indices]
+    out = (gathered * weights.unsqueeze(0)).sum(dim=2)
+
+    if dim == 2:
+        out = out.reshape(b, c, w, out_size).permute(0, 1, 3, 2)
+    else:
+        out = out.reshape(b, c, h, out_size)
+
+    return out
+
+def resize_cubic(img: torch.Tensor, size: tuple) -> torch.Tensor:
+    """
+    Resize image using OpenCV-equivalent INTER_CUBIC interpolation.
+    Implemented in pure PyTorch
+    """
+
+    if img.ndim == 3:
+        img = img.unsqueeze(0)
+
+    img = img.permute(0, 3, 1, 2)
+
+    out_h, out_w = size
+    img = resize_cubic_1d(img, out_h, dim=2)
+    img = resize_cubic_1d(img, out_w, dim=3)
+    return img
+
+def resize_area(img: torch.Tensor, size: tuple) -> torch.Tensor:
+    # vectorized implementation for OpenCV's INTER_AREA using pure PyTorch
+    original_shape = img.shape
+    is_hwc = False
+
+    if img.ndim == 3:
+        if img.shape[0] <= 4:
+            img = img.unsqueeze(0)
+        else:
+            is_hwc = True
+            img = img.permute(2, 0, 1).unsqueeze(0)
+    elif img.ndim == 4:
+        pass
+    else:
+        raise ValueError("Expected image with 3 or 4 dims.")
+
+    B, C, H, W = img.shape
+    out_h, out_w = size
+    scale_y = H / out_h
+    scale_x = W / out_w
+
+    device = img.device
+
+    # compute the grid boundries
+    y_start = torch.arange(out_h, device=device).float() * scale_y
+    y_end = y_start + scale_y
+    x_start = torch.arange(out_w, device=device).float() * scale_x
+    x_end = x_start + scale_x
+
+    # for each output pixel, we will compute the range for it
+    y_start_int = torch.floor(y_start).long()
+    y_end_int = torch.ceil(y_end).long()
+    x_start_int = torch.floor(x_start).long()
+    x_end_int = torch.ceil(x_end).long()
+
+    # We will build the weighted sums by iterating over contributing input pixels once
+    output = torch.zeros((B, C, out_h, out_w), dtype=torch.float32, device=device)
+    area = torch.zeros((out_h, out_w), dtype=torch.float32, device=device)
+
+    max_kernel_h = int(torch.max(y_end_int - y_start_int).item())
+    max_kernel_w = int(torch.max(x_end_int - x_start_int).item())
+
+    for dy in range(max_kernel_h):
+        for dx in range(max_kernel_w):
+            # compute the weights for this offset for all output pixels
+
+            y_idx = y_start_int.unsqueeze(1) + dy
+            x_idx = x_start_int.unsqueeze(0) + dx
+
+            # clamp indices to image boundaries
+            y_idx_clamped = torch.clamp(y_idx, 0, H - 1)
+            x_idx_clamped = torch.clamp(x_idx, 0, W - 1)
+
+            # compute weights by broadcasting
+            y_weight = (torch.min(y_end.unsqueeze(1), y_idx_clamped.float() + 1.0) - torch.max(y_start.unsqueeze(1), y_idx_clamped.float())).clamp(min=0)
+            x_weight = (torch.min(x_end.unsqueeze(0), x_idx_clamped.float() + 1.0) - torch.max(x_start.unsqueeze(0), x_idx_clamped.float())).clamp(min=0)
+
+            weight = (y_weight * x_weight)
+
+            y_expand = y_idx_clamped.expand(out_h, out_w)
+            x_expand = x_idx_clamped.expand(out_h, out_w)
+
+
+            pixels = img[:, :, y_expand, x_expand]
+
+            # unsqueeze to broadcast
+            w = weight.unsqueeze(0).unsqueeze(0)
+
+            output += pixels * w
+            area += weight
+
+    # Normalize by area
+    output /= area.unsqueeze(0).unsqueeze(0)
+
+    if is_hwc:
+        return output[0].permute(1, 2, 0)
+    elif img.shape[0] == 1 and original_shape[0] <= 4:
+        return output[0]
+    else:
+        return output
+
+def recenter(image, border_ratio: float = 0.2):
+
+    if image.shape[-1] == 4:
+        mask = image[..., 3]
+    else:
+        mask = torch.ones_like(image[..., 0:1]) * 255
+        image = torch.concatenate([image, mask], axis=-1)
+        mask = mask[..., 0]
+
+    H, W, C = image.shape
+
+    size = max(H, W)
+    result = torch.zeros((size, size, C), dtype = torch.uint8)
+
+    # as_tuple to match numpy behaviour
+    x_coords, y_coords = torch.nonzero(mask, as_tuple=True)
+
+    y_min, y_max = y_coords.min(), y_coords.max()
+    x_min, x_max = x_coords.min(), x_coords.max()
+
+    h = x_max - x_min
+    w = y_max - y_min
+
+    if h == 0 or w == 0:
+        raise ValueError('input image is empty')
+
+    desired_size = int(size * (1 - border_ratio))
+    scale = desired_size / max(h, w)
+
+    h2 = int(h * scale)
+    w2 = int(w * scale)
+
+    x2_min = (size - h2) // 2
+    x2_max = x2_min + h2
+
+    y2_min = (size - w2) // 2
+    y2_max = y2_min + w2
+
+    # note: opencv takes columns first (opposite to pytorch and numpy that take the row first)
+    result[x2_min:x2_max, y2_min:y2_max] = resize_area(image[x_min:x_max, y_min:y_max], (h2, w2))
+
+    bg = torch.ones((result.shape[0], result.shape[1], 3), dtype = torch.uint8) * 255
+
+    mask = result[..., 3:].to(torch.float32) / 255
+    result = result[..., :3] * mask + bg * (1 - mask)
+
+    mask = mask * 255
+    result = result.clip(0, 255).to(torch.uint8)
+    mask = mask.clip(0, 255).to(torch.uint8)
+
+    return result
+
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711],
+                    crop=True, value_range = (-1, 1), border_ratio: float = None, recenter_size: int = 512):
+
+    if border_ratio is not None:
+
+        image = (image * 255).clamp(0, 255).to(torch.uint8)
+        image = [recenter(img, border_ratio = border_ratio) for img in image]
+
+        image = torch.stack(image, dim = 0)
+        image = resize_cubic(image, size = (recenter_size, recenter_size))
+
+        image = image / 255 * 2 - 1
+        low, high = value_range
+
+        image = (image - low) / (high - low)
+        image = image.permute(0, 2, 3, 1)
+
     image = image[:, :, :, :3] if image.shape[3] > 3 else image
+
     mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
     std = torch.tensor(std, device=image.device, dtype=image.dtype)
+
     image = image.movedim(-1, 1)
     if not (image.shape[2] == size and image.shape[3] == size):
         if crop:
@@ -29,7 +246,7 @@ def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], s
         else:
             scale_size = (size, size)
 
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bilinear" if border_ratio is not None else "bicubic", antialias=True)
         h = (image.shape[2] - size)//2
         w = (image.shape[3] - size)//2
         image = image[:,:,h:h+size,w:w+size]
@@ -71,9 +288,9 @@ def load_sd(self, sd):
     def get_sd(self):
         return self.model.state_dict()
 
-    def encode_image(self, image, crop=True):
+    def encode_image(self, image, crop=True, border_ratio: float = None):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop, border_ratio=border_ratio).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()
@@ -136,8 +353,12 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
         else:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
-    elif "embeddings.patch_embeddings.projection.weight" in sd:
+
+    # Dinov2
+    elif 'encoder.layer.39.layer_scale2.lambda1' in sd:
         json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
+    elif 'encoder.layer.23.layer_scale2.lambda1' in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json")
     else:
         return None
 

comfy/image_encoders/dino2.py

@@ -31,6 +31,20 @@ def __init__(self, dim, dtype, device, operations):
     def forward(self, x):
         return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype)
 
+class Dinov2MLP(torch.nn.Module):
+    def __init__(self, hidden_size: int, dtype, device, operations):
+        super().__init__()
+
+        mlp_ratio = 4
+        hidden_features = int(hidden_size * mlp_ratio)
+        self.fc1 = operations.Linear(hidden_size, hidden_features, bias = True, device=device, dtype=dtype)
+        self.fc2 = operations.Linear(hidden_features, hidden_size, bias = True, device=device, dtype=dtype)
+
+    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        hidden_state = self.fc1(hidden_state)
+        hidden_state = torch.nn.functional.gelu(hidden_state)
+        hidden_state = self.fc2(hidden_state)
+        return hidden_state
 
 class SwiGLUFFN(torch.nn.Module):
     def __init__(self, dim, dtype, device, operations):
@@ -50,12 +64,15 @@ def forward(self, x):
 
 
 class Dino2Block(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
         super().__init__()
         self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
         self.layer_scale1 = LayerScale(dim, dtype, device, operations)
         self.layer_scale2 = LayerScale(dim, dtype, device, operations)
-        self.mlp = SwiGLUFFN(dim, dtype, device, operations)
+        if use_swiglu_ffn:
+            self.mlp = SwiGLUFFN(dim, dtype, device, operations)
+        else:
+            self.mlp = Dinov2MLP(dim, dtype, device, operations)
         self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
         self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
@@ -66,9 +83,10 @@ def forward(self, x, optimized_attention):
 
 
 class Dino2Encoder(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
         super().__init__()
-        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)])
+        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
+                                          for _ in range(num_layers)])
 
     def forward(self, x, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
@@ -78,8 +96,8 @@ def forward(self, x, intermediate_output=None):
                 intermediate_output = len(self.layer) + intermediate_output
 
         intermediate = None
-        for i, l in enumerate(self.layer):
-            x = l(x, optimized_attention)
+        for i, layer in enumerate(self.layer):
+            x = layer(x, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
         return x, intermediate
@@ -128,9 +146,10 @@ def __init__(self, config_dict, dtype, device, operations):
         dim = config_dict["hidden_size"]
         heads = config_dict["num_attention_heads"]
         layer_norm_eps = config_dict["layer_norm_eps"]
+        use_swiglu_ffn = config_dict["use_swiglu_ffn"]
 
         self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
-        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations)
+        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
         self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
     def forward(self, pixel_values, attention_mask=None, intermediate_output=None):

comfy/image_encoders/dino2_large.json

@@ -0,0 +1,22 @@
+{
+  "hidden_size": 1024,
+  "use_mask_token": true,
+  "patch_size": 14,
+  "image_size": 518,
+  "num_channels": 3,
+  "num_attention_heads": 16,
+  "initializer_range": 0.02,
+  "attention_probs_dropout_prob": 0.0,
+  "hidden_dropout_prob": 0.0,
+  "hidden_act": "gelu",
+  "mlp_ratio": 4,
+  "model_type": "dinov2",
+  "num_hidden_layers": 24,
+  "layer_norm_eps": 1e-6,
+  "qkv_bias": true,
+  "use_swiglu_ffn": false,
+  "layerscale_value": 1.0,
+  "drop_path_rate": 0.0,
+  "image_mean": [0.485, 0.456, 0.406],
+  "image_std": [0.229, 0.224, 0.225]
+}

comfy/latent_formats.py

@@ -538,6 +538,11 @@ class Hunyuan3Dv2(LatentFormat):
     latent_dimensions = 1
     scale_factor = 0.9990943042622529
 
+class Hunyuan3Dv2_1(LatentFormat):
+    scale_factor = 1.0039506158752403
+    latent_channels = 64
+    latent_dimensions = 1
+
 class Hunyuan3Dv2mini(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1