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Add rescaling_scale from STG to SkipLayerGuidanceDiT.

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comfyanonymous 2024-12-04 19:25:50 -05:00
parent 3bed56bb13
commit 9a616b81c1
1 changed files with 8 additions and 2 deletions
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10
comfy_extras/nodes_slg.py
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@ -16,7 +16,8 @@ class SkipLayerGuidanceDiT:
"single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
"scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}) "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}),
"rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}} }}
RETURN_TYPES = ("MODEL",) RETURN_TYPES = ("MODEL",)
FUNCTION = "skip_guidance" FUNCTION = "skip_guidance"
@ -26,7 +27,7 @@ class SkipLayerGuidanceDiT:
CATEGORY = "advanced/guidance" CATEGORY = "advanced/guidance"
def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers=""): def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0):
# check if layer is comma separated integers # check if layer is comma separated integers
def skip(args, extra_args): def skip(args, extra_args):
return args return args
@ -65,6 +66,11 @@ class SkipLayerGuidanceDiT:
if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start: if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
(slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
cfg_result = cfg_result + (cond_pred - slg) * scale cfg_result = cfg_result + (cond_pred - slg) * scale
if rescaling_scale != 0:
factor = cond_pred.std() / cfg_result.std()
factor = rescaling_scale * factor + (1 - rescaling_scale)
cfg_result *= factor
return cfg_result return cfg_result
m = model.clone() m = model.clone()