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 Custom Weights 🛂🅐🅒🅝
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## Documentation
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- Class name: `CustomT2IAdapterWeights`
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- Category: `Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter`
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- Output node: `False`
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This node is designed to load and configure weights for a custom Text-to-Image (T2I) Adapter within the Advanced ControlNet framework. It allows for the dynamic adjustment of weight parameters to fine-tune the control over the image generation process, incorporating options to flip weights for varied effects.
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## Input types
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### Required
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- **`weight_i`**
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- unknown
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- Comfy dtype: `FLOAT`
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- Python dtype: `unknown`
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- **`flip_weights`**
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- A boolean parameter that, when enabled, reverses the order of weights, potentially altering the image generation outcome for creative variations.
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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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- Outputs the configured weights as a set, ready for application within the T2I Adapter for controlling image generation.
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- Python dtype: `list[float]`
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- **`TK_SHORTCUT`**
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- Comfy dtype: `TIMESTEP_KEYFRAME`
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- Generates a keyframe group based on the configured weights, facilitating precise control over the image generation timeline.
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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 CustomT2IAdapterWeights:
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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": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_02": ("FLOAT", {"default": 1.0, "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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