55 lines
2.4 KiB
Markdown
55 lines
2.4 KiB
Markdown
# Documentation
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- Class name: RescaleClassifierFreeGuidance
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- Category: custom_node_experiments
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI_experiments
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RescaleClassifierFreeGuidance节点的'patch'方法旨在通过引入一个重新缩放操作来修改给定的模型。它调整模型的内部配置，对分类器的引导信号应用动态缩放因子，从而允许对模型的行为进行更精细的控制。这种方法特别适用于微调模型的输出以满足特定的要求或约束。
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# Input types
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## Required
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- model
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- ‘model’参数对于节点至关重要，因为它代表了将由‘patch’方法修改的机器学习模型。它是主要的输入，决定了节点的操作以及修补过程后模型的后续行为。
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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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- ‘multiplier’参数在控制应用于模型引导信号的重新缩放程度中起着关键作用。它是一个浮点数，决定了原始配置和重新缩放配置之间的平衡，直接影响修补后模型的输出。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- modified_model
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- ‘modified_model’输出是将‘patch’方法应用于输入模型的结果。它是原始模型的修改版本，其引导信号的缩放因子已调整，这可能导致基于指定的乘数产生不同的结果。
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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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# Source code
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```
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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',), 'multiplier': ('FLOAT', {'default': 0.7, 'min': 0.0, 'max': 1.0, 'step': 0.01})}}
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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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``` |