106 lines
4.5 KiB
Markdown
106 lines
4.5 KiB
Markdown
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# Documentation
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- Class name: FL_ImagePixelator
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- Category: 🏵️Fill Nodes
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- Output node: False
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- Repo Ref: https://github.com/fillmeout/image_manipulators/FL_ImagePixelator
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FL_ImagePixelator节点能够为图像添加像素化效果，支持单张图像和批量图像处理。它可以处理tensor格式或PIL格式的图像，通过应用缩放因子来实现图像像素化，并使用指定的核大小进行额外处理，从而增强像素化效果。
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# Input types
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## Required
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- image
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- 需要进行像素化处理的图像。可以是单张图像或一批图像，格式可以是torch.Tensor或PIL.Image。这是像素化处理的主要输入。
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- Comfy dtype: IMAGE
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- Python dtype: Union[torch.Tensor, PIL.Image.Image]
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- scale_factor
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- 决定像素化效果的强度，通过在放大前先将图像分辨率降低来实现。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- kernel_size
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- 指定像素化后额外处理步骤中使用的核大小，影响最终像素化图像的外观。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 根据指定的缩放因子和核大小处理后的像素化图像。可以是单张图像或一批图像，格式与输入相同。
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- Comfy dtype: IMAGE
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- Python dtype: Union[torch.Tensor, PIL.Image.Image]
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class FL_ImagePixelator:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE", {}),
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"scale_factor": ("FLOAT", {"default": 0.0500, "min": 0.0100, "max": 0.2000, "step": 0.0100}),
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"kernel_size": ("INT", {"default": 3, "max": 10, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "pixelate_image"
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CATEGORY = "🏵️Fill Nodes"
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def pixelate_image(self, image, scale_factor, kernel_size):
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if isinstance(image, torch.Tensor):
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if image.dim() == 4: # Batch dimension is present
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output_images = []
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total_frames = image.shape[0]
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for i, single_image in enumerate(image, start=1):
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single_image = single_image.unsqueeze(0) # Add batch dimension
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single_image = self.apply_pixelation_tensor(single_image, scale_factor)
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single_image = self.process(single_image, kernel_size)
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output_images.append(single_image)
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print(f"Processing frame {i}/{total_frames}")
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image = torch.cat(output_images, dim=0) # Concatenate processed images along batch dimension
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elif image.dim() == 3: # No batch dimension, single image
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image = image.unsqueeze(0) # Add batch dimension
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image = self.apply_pixelation_tensor(image, scale_factor)
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image = self.process(image, kernel_size)
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image = image.squeeze(0) # Remove batch dimension
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print("Processing single image")
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else:
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return (None,)
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elif isinstance(image, Image.Image):
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image = self.apply_pixelation_pil(image, scale_factor)
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image = self.process(image, kernel_size)
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print("Processing single PIL image")
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else:
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return (None,)
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return (image,)
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def apply_pixelation_pil(self, input_image, scale_factor):
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width, height = input_image.size
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new_size = (int(width * scale_factor), int(height * scale_factor))
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resized_image = input_image.resize(new_size, Image.NEAREST)
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pixelated_image = resized_image.resize((width, height), Image.NEAREST)
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return pixelated_image
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def apply_pixelation_tensor(self, input_image, scale_factor):
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_, num_channels, height, width = input_image.shape
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new_height, new_width = max(1, int(height * scale_factor)), max(1, int(width * scale_factor))
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resized_tensor = torch.nn.functional.interpolate(input_image, size=(new_height, new_width), mode='nearest')
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output_tensor = torch.nn.functional.interpolate(resized_tensor, size=(height, width), mode='nearest')
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return output_tensor
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def process(self, image, kernel_size):
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device = comfy.model_management.get_torch_device()
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kernel = torch.ones(kernel_size, kernel_size, device=device)
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image_k = image.to(device).movedim(-1, 1)
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output = gradient(image_k, kernel)
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img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1)
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return img_out
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```
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