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https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-13 15:07:13 +08:00
* Simplify and align with comfy style
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@ -3,7 +3,7 @@ import comfy.sd
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import comfy.model_management
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import comfy.model_management
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import nodes
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import nodes
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import torch
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import torch
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import re
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class TripleCLIPLoader:
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class TripleCLIPLoader:
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@classmethod
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@classmethod
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def INPUT_TYPES(s):
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def INPUT_TYPES(s):
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@ -106,8 +106,8 @@ class SkipLayerGuidanceSD3:
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return {"required": {"model": ("MODEL", ),
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return {"required": {"model": ("MODEL", ),
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"layers": ("STRING", {"default": "7,8,9", "multiline": False}),
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"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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"scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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"start_percent": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 1.0, "step": 0.001}),
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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": 0.5, "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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}}
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}}
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RETURN_TYPES = ("MODEL",)
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "skip_guidance"
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FUNCTION = "skip_guidance"
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@ -122,31 +122,35 @@ class SkipLayerGuidanceSD3:
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assert layers.replace(",", "").isdigit(), "Layers must be comma separated integers"
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assert layers.replace(",", "").isdigit(), "Layers must be comma separated integers"
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def skip(args, extra_args):
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def skip(args, extra_args):
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return args
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return args
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model_sampling = model.get_model_object("model_sampling")
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def post_cfg_function(args):
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def post_cfg_function(args):
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model = args["model"]
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cond_pred = args["cond_denoised"]
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cond_pred = args["cond_denoised"]
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cond = args["cond"]
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cond = args["cond"]
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cfg_result = args["denoised"]
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cfg_result = args["denoised"]
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sigma = args["sigma"]
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sigma = args["sigma"]
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sigma = args["sigma"]
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x = args["input"]
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x = args["input"]
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model_options = args["model_options"].copy()
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percentage = 1 - (1000 ** sigma[0].item())/1000
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if scale > 0 and percentage > start_percent and percentage < end_percent:
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for layer in layers:
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(slg,) = comfy.samplers.calc_cond_batch(m_slg.model, [cond], x, sigma, m_slg.model_options)
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model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
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model_sampling.percent_to_sigma(start_percent)
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sigma_start = model_sampling.percent_to_sigma(start_percent)
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sigma_end = model_sampling.percent_to_sigma(end_percent)
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sigma_ = sigma[0].item()
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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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cfg_result = cfg_result + (cond_pred - slg) * scale
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return cfg_result
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return cfg_result
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layers = [int(x) for x in layers.split(",")]
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m_post_cfg = model.clone()
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m_slg = model.clone()
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for layer in layers:
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layers = re.findall(r'\d+', layers)
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m_slg.set_model_patch_replace(skip, "dit", "double_block", layer)
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layers = [int(i) for i in layers]
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m = model.clone()
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m.set_model_sampler_post_cfg_function(post_cfg_function)
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m_post_cfg.set_model_sampler_post_cfg_function(post_cfg_function)
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return (m, )
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return (m_post_cfg, )
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NODE_CLASS_MAPPINGS = {
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NODE_CLASS_MAPPINGS = {
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