66 lines
2.0 KiB
Markdown
66 lines
2.0 KiB
Markdown
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# Documentation
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- Class name: Inference_Core_RescaleClassifierFreeGuidanceTest
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- Category: custom_node_experiments
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- Output node: False
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该节点将自定义补丁应用于给定模型，通过重新调整无分类器引导过程来增强其推理能力。它通过指定的乘数调整有条件生成和无条件生成之间的平衡，旨在提高模型的输出质量。
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# Input types
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## Required
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- model
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- 要进行修补的模型，其无分类器引导过程将被重新调整以提高推理性能。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- multiplier
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- 一个标量值，用于调整有条件生成和无条件生成之间的平衡，影响模型的最终输出质量。
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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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- 经过修补的模型，其无分类器引导过程已调整，以增强推理性能。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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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 RescaleClassifierFreeGuidance:
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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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"multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "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 = "custom_node_experiments"
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def patch(self, model, multiplier):
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def rescale_cfg(args):
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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x_cfg = uncond + cond_scale * (cond - uncond)
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ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
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ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
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x_rescaled = x_cfg * (ro_pos / ro_cfg)
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x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
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return x_final
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m = model.clone()
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m.set_model_sampler_cfg_function(rescale_cfg)
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return (m, )
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```
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