60 lines
2.3 KiB
Markdown
60 lines
2.3 KiB
Markdown
---
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tags:
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- Style
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---
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# Apply Style Model
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## Documentation
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- Class name: `StyleModelApply`
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- Category: `conditioning/style_model`
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- Output node: `False`
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This node applies a style model to a given conditioning, enhancing or altering its style based on the output of a CLIP vision model. It integrates the style model's conditioning into the existing conditioning, allowing for a seamless blend of styles in the generation process.
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## Input types
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### Required
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- **`conditioning`**
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- The original conditioning data to which the style model's conditioning will be applied. It's crucial for defining the base context or style that will be enhanced or altered.
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- Comfy dtype: `CONDITIONING`
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- Python dtype: `List[Tuple[torch.Tensor, Dict]]`
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- **`style_model`**
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- The style model used to generate new conditioning based on the CLIP vision model's output. It plays a key role in defining the new style to be applied.
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- Comfy dtype: `STYLE_MODEL`
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- Python dtype: `StyleModel`
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- **`clip_vision_output`**
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- The output from a CLIP vision model, which is used by the style model to generate new conditioning. It provides the visual context necessary for style application.
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- Comfy dtype: `CLIP_VISION_OUTPUT`
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- Python dtype: `torch.Tensor`
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## Output types
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- **`conditioning`**
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- Comfy dtype: `CONDITIONING`
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- The enhanced or altered conditioning, incorporating the style model's output. It represents the final, styled conditioning ready for further processing or generation.
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- Python dtype: `List[Tuple[torch.Tensor, Dict]]`
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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 StyleModelApply:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conditioning": ("CONDITIONING", ),
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"style_model": ("STYLE_MODEL", ),
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"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "apply_stylemodel"
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CATEGORY = "conditioning/style_model"
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def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
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cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
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c = []
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for t in conditioning:
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n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
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c.append(n)
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return (c, )
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```
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