Apply InstantID Advanced

Documentation

• Class name: ApplyInstantIDAdvanced
• Category: InstantID
• Output node: False

The ApplyInstantIDAdvanced node is designed to enhance image generation with specific identity features by applying advanced conditioning techniques. It utilizes a combination of InstantID, insight face analysis, control net adjustments, and additional parameters to fine-tune the identity preservation and control in the generated images.

Input types

Required

• instantid
  • The InstantID parameter is crucial for providing the identity-specific features to be preserved or emphasized in the generated image.
  • Comfy dtype: INSTANTID
  • Python dtype: dict
• insightface
  • Insightface parameter is used for face analysis, providing critical facial feature information that aids in the identity preservation process.
  • Comfy dtype: FACEANALYSIS
  • Python dtype: dict
• control_net
  • Control net parameter allows for fine-tuning and control over the generation process, ensuring the identity features are accurately applied.
  • Comfy dtype: CONTROL_NET
  • Python dtype: dict
• image
  • The image parameter serves as the base for identity application, where the identity features are to be preserved or emphasized.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• model
  • Model parameter specifies the generative model to be used in conjunction with identity features for image generation.
  • Comfy dtype: MODEL
  • Python dtype: dict
• positive
  • Positive conditioning to emphasize certain identity features in the generated image.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• negative
  • Negative conditioning to de-emphasize certain identity features in the generated image.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• ip_weight
  • IP weight parameter adjusts the influence of identity preservation in the generation process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• cn_strength
  • CN strength parameter controls the strength of the control net adjustments in the identity application process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• start_at
  • Start at parameter defines the beginning point of identity feature application in the generation process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_at
  • End at parameter defines the end point of identity feature application in the generation process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• noise
  • Noise parameter introduces variability in the identity features application, enhancing the naturalness of the generated image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• combine_embeds
  • Combine embeds parameter determines the method of combining multiple identity embeddings for application in the image generation.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

• image_kps
  • Image keypoints parameter provides additional facial feature information for enhanced identity preservation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• mask
  • Mask parameter allows for selective application of identity features in the generated image, enhancing control over the preservation process.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• MODEL
  • Comfy dtype: MODEL
  • The output model after applying InstantIDAdvanced, incorporating the identity features and adjustments made through the input parameters.
  • Python dtype: dict
• positive
  • Comfy dtype: CONDITIONING
  • The enhanced positive conditioning output, reflecting the emphasized identity features in the generated image.
  • Python dtype: str
• negative
  • Comfy dtype: CONDITIONING
  • The enhanced negative conditioning output, reflecting the de-emphasized identity features in the generated image.
  • Python dtype: str

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ApplyInstantIDAdvanced(ApplyInstantID):
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "instantid": ("INSTANTID", ),
                "insightface": ("FACEANALYSIS", ),
                "control_net": ("CONTROL_NET", ),
                "image": ("IMAGE", ),
                "model": ("MODEL", ),
                "positive": ("CONDITIONING", ),
                "negative": ("CONDITIONING", ),
                "ip_weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 3.0, "step": 0.01, }),
                "cn_strength": ("FLOAT", {"default": .8, "min": 0.0, "max": 10.0, "step": 0.01, }),
                "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, }),
                "noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1, }),
                "combine_embeds": (['average', 'norm average', 'concat'], {"default": 'average'}),
            },
            "optional": {
                "image_kps": ("IMAGE",),
                "mask": ("MASK",),
            }
        }