66 lines
2.6 KiB
Markdown
66 lines
2.6 KiB
Markdown
---
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tags:
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- ControlNet
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- Weight
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---
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# T2IAdapter Soft Weights 🛂🅐🅒🅝
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## Documentation
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- Class name: `SoftT2IAdapterWeights`
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- Category: `Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter`
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- Output node: `False`
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The SoftT2IAdapterWeights node is designed to adjust the influence of control weights within a text-to-image adaptation process, allowing for a more nuanced and customizable image generation based on the specified weights and the option to flip these weights.
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## Input types
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### Required
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- **`weight_i`**
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- Specifies a control weight at index 'i', influencing the adaptation process at various stages. The index 'i' represents a sequence of control weights, allowing for detailed customization of the image generation process.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`flip_weights`**
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- A boolean flag that, when true, reverses the order of control weights, potentially altering the adaptation process.
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- Comfy dtype: `BOOLEAN`
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- Python dtype: `bool`
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## Output types
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- **`CN_WEIGHTS`**
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- Comfy dtype: `CONTROL_NET_WEIGHTS`
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- The adjusted control weights after processing through the SoftT2IAdapterWeights node.
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- Python dtype: `list`
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- **`TK_SHORTCUT`**
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- Comfy dtype: `TIMESTEP_KEYFRAME`
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- A keyframe group indicating specific timesteps where the control weights have significant influence.
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- Python dtype: `TimestepKeyframeGroup`
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class SoftT2IAdapterWeights:
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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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"weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = WEIGHTS_RETURN_NAMES
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FUNCTION = "load_weights"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
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weights = [weight_00, weight_01, weight_02, weight_03]
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weights = get_properly_arranged_t2i_weights(weights)
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weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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```
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