62 lines
2.1 KiB
Markdown
62 lines
2.1 KiB
Markdown
---
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tags:
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- Sampling
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---
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# ModelSamplingStableCascade
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## Documentation
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- Class name: `ModelSamplingStableCascade`
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- Category: `advanced/model`
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- Output node: `False`
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This node is designed to enhance the sampling process of models by applying a stable cascade patch. It clones the input model and integrates advanced sampling techniques, thereby potentially improving the model's performance or altering its behavior in a specified manner.
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## Input types
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### Required
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- **`model`**
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- The model to which the stable cascade sampling patch will be applied. This parameter is crucial as it determines the base model that will undergo modification.
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- Comfy dtype: `MODEL`
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- Python dtype: `comfy.model_base.BaseModel`
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- **`shift`**
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- A floating-point value that specifies the degree of shift to be applied during the sampling process. This parameter influences how the model's behavior is altered by the patch.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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## Output types
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- **`model`**
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- Comfy dtype: `MODEL`
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- The modified model with the stable cascade sampling patch applied. This output reflects the enhanced or altered version of the input model.
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- Python dtype: `comfy.model_base.BaseModel`
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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 ModelSamplingStableCascade:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, shift):
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m = model.clone()
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sampling_base = comfy.model_sampling.StableCascadeSampling
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sampling_type = comfy.model_sampling.EPS
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class ModelSamplingAdvanced(sampling_base, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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model_sampling.set_parameters(shift)
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m.add_object_patch("model_sampling", model_sampling)
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return (m, )
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```
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