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@@ -470,8 +470,8 @@ class ControlNetExampleForge(scripts.Script):
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# The advanced_sigma_weighting allows you to dynamically compute control
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# weights given diffusion timestep (sigma).
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# For example below code can softly make beginning steps stronger than ending steps.
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sigma_max = unet.model.model_sampling.percent_to_sigma(0.0)
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sigma_min = unet.model.model_sampling.percent_to_sigma(1.0)
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sigma_max = unet.model.model_sampling.sigma_max
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sigma_min = unet.model.model_sampling.sigma_min
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advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
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# But in this simple example we do not use them
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@@ -120,8 +120,8 @@ class ControlNetExampleForge(scripts.Script):
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# The advanced_sigma_weighting allows you to dynamically compute control
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# weights given diffusion timestep (sigma).
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# For example below code can softly make beginning steps stronger than ending steps.
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sigma_max = unet.model.model_sampling.percent_to_sigma(0.0)
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sigma_min = unet.model.model_sampling.percent_to_sigma(1.0)
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sigma_max = unet.model.model_sampling.sigma_max
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sigma_min = unet.model.model_sampling.sigma_min
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advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
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# But in this simple example we do not use them
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@@ -42,8 +42,8 @@ def apply_controlnet_advanced(
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weights given diffusion timestep (sigma).
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For example below code can softly make beginning steps stronger than ending steps.
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sigma_max = unet.model.model_sampling.percent_to_sigma(0.0)
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sigma_min = unet.model.model_sampling.percent_to_sigma(1.0)
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sigma_max = unet.model.model_sampling.sigma_max
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sigma_min = unet.model.model_sampling.sigma_min
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advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
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"""
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