122 lines
5.3 KiB
Markdown
122 lines
5.3 KiB
Markdown
---
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tags:
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- IdentityImage
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---
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# Apply InstantID Advanced
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## Documentation
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- Class name: `ApplyInstantIDAdvanced`
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- Category: `InstantID`
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- Output node: `False`
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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.
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## Input types
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### Required
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- **`instantid`**
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- The InstantID parameter is crucial for providing the identity-specific features to be preserved or emphasized in the generated image.
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- Comfy dtype: `INSTANTID`
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- Python dtype: `dict`
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- **`insightface`**
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- Insightface parameter is used for face analysis, providing critical facial feature information that aids in the identity preservation process.
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- Comfy dtype: `FACEANALYSIS`
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- Python dtype: `dict`
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- **`control_net`**
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- Control net parameter allows for fine-tuning and control over the generation process, ensuring the identity features are accurately applied.
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- Comfy dtype: `CONTROL_NET`
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- Python dtype: `dict`
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- **`image`**
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- The image parameter serves as the base for identity application, where the identity features are to be preserved or emphasized.
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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- **`model`**
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- Model parameter specifies the generative model to be used in conjunction with identity features for image generation.
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- Comfy dtype: `MODEL`
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- Python dtype: `dict`
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- **`positive`**
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- Positive conditioning to emphasize certain identity features in the generated image.
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- Comfy dtype: `CONDITIONING`
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- Python dtype: `str`
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- **`negative`**
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- Negative conditioning to de-emphasize certain identity features in the generated image.
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- Comfy dtype: `CONDITIONING`
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- Python dtype: `str`
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- **`ip_weight`**
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- IP weight parameter adjusts the influence of identity preservation in the generation process.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`cn_strength`**
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- CN strength parameter controls the strength of the control net adjustments in the identity application process.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`start_at`**
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- Start at parameter defines the beginning point of identity feature application in the generation process.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`end_at`**
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- End at parameter defines the end point of identity feature application in the generation process.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`noise`**
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- Noise parameter introduces variability in the identity features application, enhancing the naturalness of the generated image.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`combine_embeds`**
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- Combine embeds parameter determines the method of combining multiple identity embeddings for application in the image generation.
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- Comfy dtype: `COMBO[STRING]`
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- Python dtype: `str`
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### Optional
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- **`image_kps`**
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- Image keypoints parameter provides additional facial feature information for enhanced identity preservation.
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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- **`mask`**
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- Mask parameter allows for selective application of identity features in the generated image, enhancing control over the preservation process.
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- Comfy dtype: `MASK`
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- Python dtype: `torch.Tensor`
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## Output types
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- **`MODEL`**
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- Comfy dtype: `MODEL`
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- The output model after applying InstantIDAdvanced, incorporating the identity features and adjustments made through the input parameters.
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- Python dtype: `dict`
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- **`positive`**
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- Comfy dtype: `CONDITIONING`
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- The enhanced positive conditioning output, reflecting the emphasized identity features in the generated image.
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- Python dtype: `str`
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- **`negative`**
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- Comfy dtype: `CONDITIONING`
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- The enhanced negative conditioning output, reflecting the de-emphasized identity features in the generated image.
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- Python dtype: `str`
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class ApplyInstantIDAdvanced(ApplyInstantID):
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"instantid": ("INSTANTID", ),
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"insightface": ("FACEANALYSIS", ),
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"control_net": ("CONTROL_NET", ),
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"image": ("IMAGE", ),
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"model": ("MODEL", ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"ip_weight": ("FLOAT", {"default": .8, "min": 0.0, "max": 3.0, "step": 0.01, }),
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"cn_strength": ("FLOAT", {"default": .8, "min": 0.0, "max": 10.0, "step": 0.01, }),
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"start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
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"end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001, }),
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"noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.1, }),
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"combine_embeds": (['average', 'norm average', 'concat'], {"default": 'average'}),
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},
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"optional": {
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"image_kps": ("IMAGE",),
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"mask": ("MASK",),
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}
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}
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```
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