74 lines
3.0 KiB
Markdown
74 lines
3.0 KiB
Markdown
# Scale Ref Image and VAE Encode 🎭🅐🅓②
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## Documentation
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- Class name: ADE_UpscaleAndVAEEncode
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- Category: Animate Diff 🎭🅐🅓/② Gen2 nodes ②/AnimateLCM-I2V
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- Output node: False
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- Repo Ref: https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved.git
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ADE_UpscaleAndVAEEncode节点旨在通过首先将图像放大到更高分辨率，然后使用变分自编码器（VAE）将其编码为潜在表示来处理图像。此节点是AnimateDiff套件的一部分，专门用于在应用进一步的生成或转换过程之前增强图像质量。
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## Input types
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### Required
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- image
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- 表示要放大和编码的输入图像的参数。它在确定最终潜在表示的质量和分辨率方面起着关键作用。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- vae
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- 指定用于将放大的图像编码为其潜在表示的变分自编码器模型。它影响编码效率和生成的潜在空间的质量。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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- latent_size
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- 表示要生成的潜在表示的大小。它决定输出潜在空间的维度。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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- scale_method
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- 定义用于放大图像的方法。它影响放大图像的质量。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- crop
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- 指定放大后应用的裁剪方法，影响最终图像的构图。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Output types
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- latent
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- Comfy dtype: LATENT
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- 输出是输入图像的潜在表示，由VAE在放大后编码。它以压缩形式捕捉图像的基本特征，适合进一步的生成任务。
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- Python dtype: Dict[str, torch.Tensor]
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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 UpscaleAndVaeEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"vae": ("VAE",),
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"latent_size": ("LATENT",),
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"scale_method": (ScaleMethods._LIST_IMAGE,),
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"crop": (CropMethods._LIST, {"default": CropMethods.CENTER},),
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}
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}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "preprocess_images"
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CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②/AnimateLCM-I2V"
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def preprocess_images(self, image: torch.Tensor, vae: VAE, latent_size: torch.Tensor, scale_method: str, crop: str):
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b, c, h, w = latent_size["samples"].size()
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image = image.movedim(-1,1)
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image = comfy.utils.common_upscale(samples=image, width=w*8, height=h*8, upscale_method=scale_method, crop=crop)
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image = image.movedim(1,-1)
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# now that images are the expected size, VAEEncode them
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try: # account for old ComfyUI versions (TODO: remove this when other changes require ComfyUI update)
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if not hasattr(vae, "vae_encode_crop_pixels"):
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image = VAEEncode.vae_encode_crop_pixels(image)
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except Exception:
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pass
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return ({"samples": vae.encode(image[:,:,:,:3])},) |