additional minor cleaning
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+6
-6
@@ -29,17 +29,17 @@ class DynamicThresholdingComfyNode:
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dynamic_thresh = DynThresh(mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, 0, 999, separate_feature_channels == "enable", scaling_startpoint, variability_measure, interpolate_phi)
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def sampler_dyn_thrash(args):
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x_out = args["cond"]
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def sampler_dyn_thresh(args):
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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time_step = args["timestep"]
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dynamic_thresh.step = 999 - time_step[0]
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return dynamic_thresh.dynthresh(x_out, uncond, cond_scale, None)
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return dynamic_thresh.dynthresh(cond, uncond, cond_scale, None)
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m = model.clone()
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m.set_model_sampler_cfg_function(sampler_dyn_thrash)
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m.set_model_sampler_cfg_function(sampler_dyn_thresh)
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return (m, )
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class DynamicThresholdingSimpleComfyNode:
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@@ -62,7 +62,7 @@ class DynamicThresholdingSimpleComfyNode:
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dynamic_thresh = DynThresh(mimic_scale, threshold_percentile, "CONSTANT", 0, "CONSTANT", 0, 0, 0, 999, False, "MEAN", "AD", 1)
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def sampler_dyn_thrash(args):
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def sampler_dyn_thresh(args):
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cond = args["cond"]
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uncond = args["uncond"]
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cond_scale = args["cond_scale"]
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@@ -72,5 +72,5 @@ class DynamicThresholdingSimpleComfyNode:
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return dynamic_thresh.dynthresh(cond, uncond, cond_scale, None)
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m = model.clone()
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m.set_model_sampler_cfg_function(sampler_dyn_thrash)
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m.set_model_sampler_cfg_function(sampler_dyn_thresh)
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return (m, )
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@@ -212,7 +212,7 @@ class CustomCFGDenoiser(cfgdenoisekdiff):
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weights = torch.tensor(conds_list, device=uncond.device).select(2, 1)
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weights = weights.reshape(*weights.shape, 1, 1, 1)
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self.main_class.step = self.step
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if self.main_class.experiment_mode >= 4 and self.main_class.experiment_mode <= 5:
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# https://arxiv.org/pdf/2305.08891.pdf "Rescale CFG". It's not good, but if you want to test it, just set experiment_mode = 4 + phi.
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denoised = torch.clone(denoised_uncond)
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