5.3 KiB
5.3 KiB
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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
- Comfy dtype:
positive- Comfy dtype:
CONDITIONING - The enhanced positive conditioning output, reflecting the emphasized identity features in the generated image.
- Python dtype:
str
- Comfy dtype:
negative- Comfy dtype:
CONDITIONING - The enhanced negative conditioning output, reflecting the de-emphasized identity features in the generated image.
- Python dtype:
str
- Comfy dtype:
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",),
}
}