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https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-10 06:10:50 +08:00
remove cfg cutoff node
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a4e9d071e8
commit
5db5da790f
@ -949,19 +949,8 @@ class CFGGuider:
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for k in conds:
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self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
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def __call__(self, *args, **kwargs):
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def __call__(self, *args, **kwargs):
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return self.outer_predict_noise(*args, **kwargs)
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def handle_dynamic_cfg(self, timestep, model_options):
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if hasattr(self.model_patcher.model.diffusion_model, "stop_cfg_index"):
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stop_index = self.model_patcher.model.diffusion_model.stop_cfg_index
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transformer_options = model_options.get("transformer_options", {})
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sigmas = transformer_options.get("sample_sigmas", None)
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if sigmas is not None or self.cfg != 1.0:
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dist = torch.abs(sigmas - timestep)
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i = torch.argmin(dist).item()
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if stop_index == i or (stop_index == -1 and i == len(sigmas) - 2):
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self.set_cfg(1.0)
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def outer_predict_noise(self, x, timestep, model_options={}, seed=None):
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return comfy.patcher_extension.WrapperExecutor.new_class_executor(
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@ -971,7 +960,6 @@ class CFGGuider:
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).execute(x, timestep, model_options, seed)
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def predict_noise(self, x, timestep, model_options={}, seed=None):
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self.handle_dynamic_cfg(timestep, model_options)
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return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
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def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None):
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@ -996,9 +984,6 @@ class CFGGuider:
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self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
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device = self.model_patcher.load_device
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if denoise_mask is not None:
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denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
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noise = noise.to(device)
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latent_image = latent_image.to(device)
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sigmas = sigmas.to(device)
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@ -5,22 +5,6 @@ import nodes
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import torch
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import node_helpers
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class CFGCutoff:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model": ("MODEL",), "cfg_stop_index": ("INT", {"default": -1, "min": -1, })}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "advanced/model"
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def patch(self, model, cfg_stop_index):
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diff_model = model.model.diffusion_model
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if hasattr(diff_model, "set_cfg_stop_index"):
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diff_model.set_cfg_stop_index(cfg_stop_index)
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return (model,)
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class LCM(comfy.model_sampling.EPS):
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def calculate_denoised(self, sigma, model_output, model_input):
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@ -342,5 +326,4 @@ NODE_CLASS_MAPPINGS = {
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"ModelSamplingFlux": ModelSamplingFlux,
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"RescaleCFG": RescaleCFG,
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"ModelComputeDtype": ModelComputeDtype,
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"CFGCutoff": CFGCutoff
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}
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