102 lines
3.6 KiB
Markdown
102 lines
3.6 KiB
Markdown
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# Documentation
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- Class name: Inference_Core_LayeredDiffusionJointApply
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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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Inference_Core_LayeredDiffusionJointApply节点旨在将分层扩散过程应用于联合模型，整合多个扩散模型或层以协调一致的方式生成或操作图像。它利用每个单独模型层的优势来产生增强的、高质量的输出。
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# Input types
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## Required
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- model
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- model参数代表要应用的扩散模型。它对于定义特定的扩散过程及其配置至关重要，直接影响输出的质量和特征。
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- Comfy dtype: MODEL
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- Python dtype: ModelPatcher
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- config
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- config定义了分层扩散过程的配置设置，包括模型细节和操作参数，对于根据所需结果定制扩散过程至关重要。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Optional
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- fg_cond
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- 前景条件指定扩散过程前景的条件输入，指导输出在前景区域的生成或转换。
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- Comfy dtype: CONDITIONING
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- Python dtype: Optional[List[List[torch.Tensor]]]
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- bg_cond
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- 背景条件指定扩散过程背景的条件输入，影响输出在背景区域的生成或转换。
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- Comfy dtype: CONDITIONING
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- Python dtype: Optional[List[List[torch.Tensor]]]
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- blended_cond
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- 混合条件将前景和背景条件结合，为扩散过程提供跨两个区域的统一条件输入。
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- Comfy dtype: CONDITIONING
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- Python dtype: Optional[List[List[torch.Tensor]]]
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# Output types
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- model
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- 输出是输入模型的修改版本，通过分层扩散过程得到增强，以根据提供的条件和配置生成或转换图像。
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- Comfy dtype: MODEL
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- Python dtype: ModelPatcher
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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 LayeredDiffusionJoint:
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"""Generate FG + BG + Blended in one inference batch. Batch size = 3N."""
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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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"model": ("MODEL",),
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"config": ([c.config_string for c in s.MODELS],),
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},
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"optional": {
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"fg_cond": ("CONDITIONING",),
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"bg_cond": ("CONDITIONING",),
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"blended_cond": ("CONDITIONING",),
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},
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}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "apply_layered_diffusion"
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CATEGORY = "layer_diffuse"
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MODELS = (
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LayeredDiffusionBase(
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model_file_name="layer_sd15_joint.safetensors",
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model_url="https://huggingface.co/LayerDiffusion/layerdiffusion-v1/resolve/main/layer_sd15_joint.safetensors",
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sd_version=StableDiffusionVersion.SD1x,
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attn_sharing=True,
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frames=3,
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),
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)
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def apply_layered_diffusion(
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self,
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model: ModelPatcher,
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config: str,
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fg_cond: Optional[List[List[torch.TensorType]]] = None,
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bg_cond: Optional[List[List[torch.TensorType]]] = None,
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blended_cond: Optional[List[List[torch.TensorType]]] = None,
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):
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ld_model = [m for m in self.MODELS if m.config_string == config][0]
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assert get_model_sd_version(model) == ld_model.sd_version
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assert ld_model.attn_sharing
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work_model = ld_model.apply_layered_diffusion_attn_sharing(model)[0]
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work_model.model_options.setdefault("transformer_options", {})
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work_model.model_options["transformer_options"]["cond_overwrite"] = [
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cond[0][0] if cond is not None else None
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for cond in (
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fg_cond,
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bg_cond,
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blended_cond,
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)
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]
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return (work_model,)
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```
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