Some changes to the previous hunyuan PR.

Author: comfyanonymous

comfy/clip_vision.py

@@ -17,227 +17,10 @@ def __getitem__(self, key):
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-
-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)
-
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
     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:
@@ -246,7 +29,7 @@ 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="bilinear" if border_ratio is not None else "bicubic", antialias=True)
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
         h = (image.shape[2] - size)//2
         w = (image.shape[3] - size)//2
         image = image[:,:,h:h+size,w:w+size]
@@ -288,9 +71,9 @@ def load_sd(self, sd):
     def get_sd(self):
         return self.model.state_dict()
 
-    def encode_image(self, image, crop=True, border_ratio: float = None):
+    def encode_image(self, image, crop=True):
         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, border_ratio=border_ratio).float()
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()

comfy/sd.py

@@ -1058,27 +1058,6 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     model = None
     model_patcher = None
 
-    if isinstance(sd, dict) and all(k in sd for k in ["model", "vae", "conditioner"]):
-        from collections import OrderedDict
-        import gc
-
-        merged_sd = OrderedDict()
-
-        for k, v in sd["model"].items():
-            merged_sd[f"model.{k}"] = v
-
-        for k, v in sd["vae"].items():
-            merged_sd[f"vae.{k}"] = v
-
-        for key, value in sd["conditioner"].items():
-            merged_sd[f"conditioner.{key}"] = value
-
-        sd = merged_sd
-
-        del merged_sd
-        gc.collect()
-        torch.cuda.empty_cache()
-
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
     parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
     weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)

nodes.py

@@ -998,31 +998,20 @@ def load_clip(self, clip_name):
 class CLIPVisionEncode:
     @classmethod
     def INPUT_TYPES(s):
-        return {
-            "required": {
-                "clip_vision": ("CLIP_VISION",),
-                "image": ("IMAGE",),
-                "crop": (["center", "none", "recenter"],),
-            },
-            "optional": {
-                "border_ratio": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 0.5, "step": 0.01, "visible_if": {"crop": "recenter"},}),
-            }
-        }
-
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "image": ("IMAGE",),
+                              "crop": (["center", "none"],)
+                             }}
     RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
     FUNCTION = "encode"
 
     CATEGORY = "conditioning"
 
-    def encode(self, clip_vision, image, crop, border_ratio):
-        crop_image = crop == "center"
-
-        if crop == "recenter":
-            crop_image = True
-        else:
-            border_ratio = None
-
-        output = clip_vision.encode_image(image, crop=crop_image, border_ratio = border_ratio)
+    def encode(self, clip_vision, image, crop):
+        crop_image = True
+        if crop != "center":
+            crop_image = False
+        output = clip_vision.encode_image(image, crop=crop_image)
         return (output,)
 
 class StyleModelLoader:

requirements.txt

@@ -27,4 +27,4 @@ kornia>=0.7.1
 spandrel
 soundfile
 pydantic~=2.0
-pydantic-settings~=2.0
+pydantic-settings~=2.0