IPAdapter Precise Style Transfer

Documentation

• Class name: IPAdapterPreciseStyleTransfer
• Category: ipadapter
• Output node: False

The IPAdapterPreciseStyleTransfer node is designed for executing precise style transfer operations on images within a given model's framework. It leverages advanced image processing techniques to blend and transfer styles between images, offering fine-grained control over the style transfer process through a comprehensive set of parameters.

Input types

Required

• model
  • Specifies the model to be used for the style transfer operation, serving as the foundation for the transformation process.
  • Comfy dtype: MODEL
  • Python dtype: str
• ipadapter
  • Identifies the IPAdapter instance to be utilized, acting as a crucial component in managing and facilitating the style transfer.
  • Comfy dtype: IPADAPTER
  • Python dtype: str
• image
  • The target image to which the style will be transferred, serving as the canvas for the operation.
  • Comfy dtype: IMAGE
  • Python dtype: str
• weight
  • Controls the intensity of the style transfer, allowing for adjustable influence of the style on the target image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• style_boost
  • Amplifies the effect of the style being transferred, providing an additional layer of control over the style's impact.
  • Comfy dtype: FLOAT
  • Python dtype: float
• combine_embeds
  • Determines the method for combining embeddings from different sources, influencing the final outcome of the style transfer.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• start_at
  • Defines the starting point of the style transfer effect within the model's processing layers, offering control over the depth of style application.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_at
  • Sets the endpoint for the style transfer effect, allowing for precision in how deeply the style is integrated into the target image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• embeds_scaling
  • Specifies the approach for scaling embeddings, affecting the balance between key and value components in the style transfer.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

• image_negative
  • An optional negative image to contrast with the target image, potentially guiding the style transfer in a specific direction.
  • Comfy dtype: IMAGE
  • Python dtype: str
• attn_mask
  • An optional attention mask that can be applied to focus or restrict the style transfer to specific regions of the image.
  • Comfy dtype: MASK
  • Python dtype: str
• clip_vision
  • Optionally integrates CLIP vision models to enhance the style transfer with semantic understanding from textual or visual cues.
  • Comfy dtype: CLIP_VISION
  • Python dtype: str

Output types

• model
  • Comfy dtype: MODEL
  • Returns the model after applying the style transfer, reflecting the modifications and enhancements made during the process.
  • Python dtype: str

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class IPAdapterPreciseStyleTransfer(IPAdapterAdvanced):
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL", ),
                "ipadapter": ("IPADAPTER", ),
                "image": ("IMAGE",),
                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 5, "step": 0.05 }),
                "style_boost": ("FLOAT", { "default": 1.0, "min": -5, "max": 5, "step": 0.05 }),
                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
                "start_at": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }),
                "end_at": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }),
                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
            },
            "optional": {
                "image_negative": ("IMAGE",),
                "attn_mask": ("MASK",),
                "clip_vision": ("CLIP_VISION",),
            }
        }