IPAdapter FaceID

Documentation

• Class name: IPAdapterFaceID
• Category: ipadapter/faceid
• Output node: False

The IPAdapterFaceID node is designed to enhance image processing capabilities by integrating face identification features. It leverages advanced IPAdapter functionalities to apply face-specific adjustments and embeddings, aiming to improve the quality and relevance of generated images based on facial recognition.

Input types

Required

• model
  • Specifies the model to be used for face identification, central to the node's operation.
  • Comfy dtype: MODEL
  • Python dtype: str
• ipadapter
  • Defines the IPAdapter configuration to be used, crucial for determining how face identification features are integrated.
  • Comfy dtype: IPADAPTER
  • Python dtype: str
• image
  • The input image to be processed, serving as the basis for face identification and subsequent adjustments.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• weight
  • A float value that adjusts the influence of the face identification features on the final image output.
  • Comfy dtype: FLOAT
  • Python dtype: float
• weight_faceidv2
  • A float value specifically for adjusting the influence of FaceID v2 features, offering finer control over the face identification process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• weight_type
  • Determines the method of weighting face identification features, affecting how these features influence the final image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• combine_embeds
  • Specifies the method for combining face identification embeddings, impacting the integration of facial features into the image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: list[str]
• start_at
  • A float value indicating the starting point for applying face identification features within the processing pipeline.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_at
  • A float value indicating the end point for applying face identification features, defining the scope of their influence.
  • Comfy dtype: FLOAT
  • Python dtype: float
• embeds_scaling
  • Describes how face identification embeddings are scaled, affecting their impact on the image processing.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: list[str]

Optional

• image_negative
  • An optional input image to be used in a negative context, providing additional control over the face identification process.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• attn_mask
  • An optional mask to focus or limit the attention mechanism during face identification, enhancing processing accuracy.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor
• clip_vision
  • An optional CLIP vision model to further refine the face identification process through visual context.
  • Comfy dtype: CLIP_VISION
  • Python dtype: torch.Tensor
• insightface
  • An optional InsightFace model required for certain face identification functionalities, enhancing the node's capabilities.
  • Comfy dtype: INSIGHTFACE
  • Python dtype: torch.Tensor

Output types

• MODEL
  • Comfy dtype: MODEL
  • The processed model with integrated face identification features, ready for further image generation tasks.
  • Python dtype: str
• face_image
  • Comfy dtype: IMAGE
  • The output image that has been enhanced with face identification features, showcasing improved facial recognition and integration.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class IPAdapterFaceID(IPAdapterAdvanced):
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL", ),
                "ipadapter": ("IPADAPTER", ),
                "image": ("IMAGE",),
                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
                "weight_faceidv2": ("FLOAT", { "default": 1.0, "min": -1, "max": 5.0, "step": 0.05 }),
                "weight_type": (WEIGHT_TYPES, ),
                "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",),
                "insightface": ("INSIGHTFACE",),
            }
        }

    CATEGORY = "ipadapter/faceid"
    RETURN_TYPES = ("MODEL","IMAGE",)
    RETURN_NAMES = ("MODEL", "face_image", )