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Add rescaling_scale from STG to SkipLayerGuidanceDiT.
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@ -16,7 +16,8 @@ class SkipLayerGuidanceDiT:
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"single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
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"scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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"start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
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"end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
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"end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}),
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"rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "skip_guidance"
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@ -26,7 +27,7 @@ class SkipLayerGuidanceDiT:
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CATEGORY = "advanced/guidance"
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def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers=""):
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def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0):
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# check if layer is comma separated integers
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def skip(args, extra_args):
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return args
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@ -65,6 +66,11 @@ class SkipLayerGuidanceDiT:
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if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
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(slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
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cfg_result = cfg_result + (cond_pred - slg) * scale
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if rescaling_scale != 0:
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factor = cond_pred.std() / cfg_result.std()
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factor = rescaling_scale * factor + (1 - rescaling_scale)
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cfg_result *= factor
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return cfg_result
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m = model.clone()
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