InstantID Apply ControlNet

Documentation

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

This node is designed to apply a control network to modify facial embeddings based on specified conditions, leveraging insights from facial analysis and control network parameters to achieve targeted modifications in facial representations.

Input types

Required

• face_embeds
  • Facial embeddings that represent the key features of a face, serving as the input for targeted modifications.
  • Comfy dtype: FACE_EMBEDS
  • Python dtype: torch.Tensor
• control_net
  • The control network model used to apply modifications to the facial embeddings.
  • Comfy dtype: CONTROL_NET
  • Python dtype: torch.nn.Module
• image_kps
  • Key points from the image that are used to guide the modifications applied by the control network.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• positive
  • Positive conditioning phrases or keywords that guide the direction of the modifications.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• negative
  • Negative conditioning phrases or keywords that specify undesired attributes to be avoided in the modifications.
  • Comfy dtype: CONDITIONING
  • Python dtype: str
• strength
  • The intensity of the modifications applied by the control network.
  • Comfy dtype: FLOAT
  • Python dtype: float
• start_at
  • The starting point in the process where modifications begin to be applied.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_at
  • The ending point in the process where modifications cease to be applied.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• mask
  • An optional mask that can be used to localize the modifications applied by the control network.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• positive
  • Comfy dtype: CONDITIONING
  • The modified positive conditioning after applying the control network, reflecting the targeted modifications.
  • Python dtype: List[Tuple[str, Dict]]
• negative
  • Comfy dtype: CONDITIONING
  • The modified negative conditioning after applying the control network, reflecting the avoidance of specified undesired attributes.
  • Python dtype: List[Tuple[str, Dict]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ApplyInstantIDControlNet:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "face_embeds": ("FACE_EMBEDS", ),
                "control_net": ("CONTROL_NET", ),
                "image_kps": ("IMAGE", ),
                "positive": ("CONDITIONING", ),
                "negative": ("CONDITIONING", ),
                "strength": ("FLOAT", {"default": 1.0, "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, }),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }

    RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
    RETURN_NAMES = ("positive", "negative", )
    FUNCTION = "apply_controlnet"
    CATEGORY = "InstantID"

    def apply_controlnet(self, face_embeds, control_net, image_kps, positive, negative, strength, start_at, end_at, mask=None):
        self.device = comfy.model_management.get_torch_device()

        if strength == 0:
            return (positive, negative)

        if mask is not None:
            mask = mask.to(self.device)

        if mask is not None and len(mask.shape) < 3:
            mask = mask.unsqueeze(0)

        image_prompt_embeds = face_embeds['cond']
        uncond_image_prompt_embeds = face_embeds['uncond']

        cnets = {}
        cond_uncond = []
        control_hint = image_kps.movedim(-1,1)

        is_cond = True
        for conditioning in [positive, negative]:
            c = []
            for t in conditioning:
                d = t[1].copy()

                prev_cnet = d.get('control', None)
                if prev_cnet in cnets:
                    c_net = cnets[prev_cnet]
                else:
                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_at, end_at))
                    c_net.set_previous_controlnet(prev_cnet)
                    cnets[prev_cnet] = c_net

                d['control'] = c_net
                d['control_apply_to_uncond'] = False
                d['cross_attn_controlnet'] = image_prompt_embeds.to(comfy.model_management.intermediate_device()) if is_cond else uncond_image_prompt_embeds.to(comfy.model_management.intermediate_device())

                if mask is not None and is_cond:
                    d['mask'] = mask
                    d['set_area_to_bounds'] = False

                n = [t[0], d]
                c.append(n)
            cond_uncond.append(c)
            is_cond = False

        return(cond_uncond[0], cond_uncond[1])