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@ -58,6 +58,10 @@ class VAEEncode: |
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FUNCTION = "encode" |
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FUNCTION = "encode" |
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def encode(self, vae, pixels): |
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def encode(self, vae, pixels): |
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x = (pixels.shape[1] // 64) * 64 |
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y = (pixels.shape[2] // 64) * 64 |
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if pixels.shape[1] != x or pixels.shape[2] != y: |
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pixels = pixels[:,:x,:y,:] |
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return (vae.encode(pixels), ) |
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return (vae.encode(pixels), ) |
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class CheckpointLoader: |
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class CheckpointLoader: |
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@ -205,6 +209,24 @@ class SaveImage: |
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img.save(f"output/ComfyUI_{self.counter:05}_.png", pnginfo=metadata, optimize=True) |
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img.save(f"output/ComfyUI_{self.counter:05}_.png", pnginfo=metadata, optimize=True) |
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self.counter += 1 |
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self.counter += 1 |
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class LoadImage: |
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input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": |
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{"image": (os.listdir(s.input_dir), )}, |
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} |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "load_image" |
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def load_image(self, image): |
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image_path = os.path.join(self.input_dir, image) |
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image = Image.open(image_path).convert("RGB") |
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image = np.array(image).astype(np.float32) / 255.0 |
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image = torch.from_numpy(image[None])[None,] |
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return image |
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NODE_CLASS_MAPPINGS = { |
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NODE_CLASS_MAPPINGS = { |
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"KSampler": KSampler, |
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"KSampler": KSampler, |
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@ -216,6 +238,7 @@ NODE_CLASS_MAPPINGS = { |
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"EmptyLatentImage": EmptyLatentImage, |
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"EmptyLatentImage": EmptyLatentImage, |
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"LatentUpscale": LatentUpscale, |
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"LatentUpscale": LatentUpscale, |
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"SaveImage": SaveImage, |
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"SaveImage": SaveImage, |
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"LoadImage": LoadImage |
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} |
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} |
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