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9a0a5d32ee
This one should work for skipping the single layers of models like Flux and Auraflow. If you want to see how these models work and how many double/single layers they have see the "ModelMerge*" nodes for the specific model.
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
import comfy.model_patcher
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import comfy.samplers
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import re
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class SkipLayerGuidanceDiT:
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'''
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Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
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Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
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Original experimental implementation for SD3 by Dango233@StabilityAI.
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'''
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model": ("MODEL", ),
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"double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
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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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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "skip_guidance"
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EXPERIMENTAL = True
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DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model."
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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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# 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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model_sampling = model.get_model_object("model_sampling")
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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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double_layers = re.findall(r'\d+', double_layers)
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double_layers = [int(i) for i in double_layers]
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single_layers = re.findall(r'\d+', single_layers)
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single_layers = [int(i) for i in single_layers]
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if len(double_layers) == 0 and len(single_layers) == 0:
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return (model, )
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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 = args["cond"]
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cfg_result = args["denoised"]
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sigma = args["sigma"]
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x = args["input"]
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model_options = args["model_options"].copy()
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for layer in double_layers:
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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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for layer in single_layers:
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model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "single_block", layer)
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model_sampling.percent_to_sigma(start_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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return cfg_result
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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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return (m, )
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NODE_CLASS_MAPPINGS = {
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"SkipLayerGuidanceDiT": SkipLayerGuidanceDiT,
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}
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