105 lines
3.4 KiB
Markdown
105 lines
3.4 KiB
Markdown
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# Documentation
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- Class name: Inference_Core_LayeredDiffusionDecodeSplit
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- Category: layer_diffuse
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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这个节点专门用于通过分层扩散过程对图像进行解码，特别适用于处理分割图像数据。它利用修改后的扩散解码方法来分段处理图像，优化了将图像分为帧或层以提高推理性能的场景。
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# Input types
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## Required
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- samples
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- 节点将处理的样本数据集合。这些数据对解码操作至关重要，因为它直接影响解码图像的分割、处理和最终质量。
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- Comfy dtype: LATENT
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- Python dtype: Dict[str, torch.Tensor]
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- images
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- 待解码的图像张量。这个输入是节点功能的核心，因为它会根据提供的样本和帧直接操作这些图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- frames
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- 指定图像被划分的帧数或段数。这个参数对确定图像如何被处理和解码至关重要。
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- Comfy dtype: INT
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- Python dtype: int
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- sd_version
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- 用于解码的Stable Diffusion模型版本。这会影响解码行为和输出图像的质量。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- sub_batch_size
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- 处理的子批次大小，允许在解码过程中优化资源利用。
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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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- 通过分层扩散方法处理的解码图像。这个输出对于理解解码过程的有效性和质量至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: 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 LayeredDiffusionDecodeSplit(LayeredDiffusionDecodeRGBA):
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"""Decode RGBA every N images."""
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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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"samples": ("LATENT",),
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"images": ("IMAGE",),
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# Do RGBA decode every N output images.
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"frames": (
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"INT",
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{"default": 2, "min": 2, "max": s.MAX_FRAMES, "step": 1},
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),
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"sd_version": (
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[
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StableDiffusionVersion.SD1x.value,
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StableDiffusionVersion.SDXL.value,
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],
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{
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"default": StableDiffusionVersion.SDXL.value,
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},
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),
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"sub_batch_size": (
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"INT",
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{"default": 16, "min": 1, "max": 4096, "step": 1},
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),
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},
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}
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MAX_FRAMES = 3
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RETURN_TYPES = ("IMAGE",) * MAX_FRAMES
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def decode(
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self,
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samples,
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images: torch.Tensor,
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frames: int,
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sd_version: str,
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sub_batch_size: int,
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):
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sliced_samples = copy.copy(samples)
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sliced_samples["samples"] = sliced_samples["samples"][::frames]
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return tuple(
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(
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(
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super(LayeredDiffusionDecodeSplit, self).decode(
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sliced_samples, imgs, sd_version, sub_batch_size
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)[0]
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if i == 0
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else imgs
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)
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for i in range(frames)
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for imgs in (images[i::frames],)
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)
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) + (None,) * (self.MAX_FRAMES - frames)
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```
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