Apply Style Model

Documentation

• Class name: StyleModelApply
• Category: conditioning/style_model
• Output node: False

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.

Input types

Required

• conditioning
  • 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.
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[torch.Tensor, Dict]]
• style_model
  • 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.
  • Comfy dtype: STYLE_MODEL
  • Python dtype: StyleModel
• clip_vision_output
  • 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.
  • Comfy dtype: CLIP_VISION_OUTPUT
  • Python dtype: torch.Tensor

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • The enhanced or altered conditioning, incorporating the style model's output. It represents the final, styled conditioning ready for further processing or generation.
  • Python dtype: List[Tuple[torch.Tensor, Dict]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class StyleModelApply:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                             "style_model": ("STYLE_MODEL", ),
                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "apply_stylemodel"

    CATEGORY = "conditioning/style_model"

    def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
        c = []
        for t in conditioning:
            n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
            c.append(n)
        return (c, )