297 lines
16 KiB
Python
297 lines
16 KiB
Python
from copy import deepcopy
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import comfy.samplers
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import torch
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import math
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original_sampling_function = deepcopy(comfy.samplers.sampling_function)
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minimum_sigma_to_disable_uncond = 0
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maximum_sigma_to_enable_uncond = 1000000
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global_skip_uncond = False
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def sampling_function_patched(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
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if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False or ((timestep[0] < minimum_sigma_to_disable_uncond or timestep[0] > maximum_sigma_to_enable_uncond) and global_skip_uncond):
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uncond_ = None
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else:
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uncond_ = uncond
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conds = [cond, uncond_]
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out = comfy.samplers.calc_cond_batch(model, conds, x, timestep, model_options)
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cond_pred = out[0]
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uncond_pred = out[1]
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if "sampler_cfg_function" in model_options:
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args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
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"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
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cfg_result = x - model_options["sampler_cfg_function"](args)
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else:
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cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
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for fn in model_options.get("sampler_post_cfg_function", []):
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args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
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"sigma": timestep, "model_options": model_options, "input": x}
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cfg_result = fn(args)
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return cfg_result
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def map_sigma(sigma, sigmax, sigmin):
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return 1 + ((sigma - sigmax) * (0 - 1)) / (sigmin - sigmax)
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def center_latent_mean_values(latent, per_channel, mult):
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for b in range(len(latent)):
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if per_channel:
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for c in range(len(latent[b])):
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latent[b][c] -= latent[b][c].mean() * mult
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else:
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latent[b] -= latent[b].mean() * mult
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return latent
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def get_denoised_ranges(latent, measure="hard", top_k=0.25):
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chans = []
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for x in range(len(latent)):
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max_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=True).values
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min_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=False).values
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max_val = torch.mean(max_values).item()
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min_val = torch.mean(torch.abs(min_values)).item() if (measure == "hard" or measure == "range") else abs(torch.mean(min_values).item())
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denoised_range = (max_val + min_val) / 2
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chans.append(denoised_range)
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return chans
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def get_sigmin_sigmax(model):
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model_sampling = model.model.model_sampling
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sigmin = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_min))
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sigmax = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_max))
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return sigmin, sigmax
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def get_sigmas_start_end(sigmin, sigmax, start_percentage, end_percentage):
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high_sigma_threshold = (sigmax - sigmin) / 100 * start_percentage
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low_sigma_threshold = (sigmax - sigmin) / 100 * end_percentage
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return high_sigma_threshold, low_sigma_threshold
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def check_skip(sigma, high_sigma_threshold, low_sigma_threshold):
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return sigma > high_sigma_threshold or sigma < low_sigma_threshold
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class advancedDynamicCFG:
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def __init__(self):
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self.last_cfg_ht_one = 8
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"automatic_cfg" : (["None","soft","hard","range"], {"default": "hard"},),
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"skip_uncond" : ("BOOLEAN", {"default": True}),
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"uncond_sigma_start": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
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"uncond_sigma_end": ("FLOAT", {"default": 1, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
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"lerp_uncond" : ("BOOLEAN", {"default": False}),
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"lerp_uncond_strength": ("FLOAT", {"default": 2, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.1}),
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"lerp_uncond_sigma_start": ("FLOAT", {"default": 15, "min": 0.0, "max": 10000.0, "step": 0.01, "round": 0.01}),
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"lerp_uncond_sigma_end": ("FLOAT", {"default": 1, "min": 0.0, "max": 10000.0, "step": 0.01, "round": 0.01}),
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"subtract_latent_mean" : ("BOOLEAN", {"default": False}),
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"subtract_latent_mean_sigma_start": ("FLOAT", {"default": 15, "min": 0.0, "max": 10000.0, "step": 0.01, "round": 0.01}),
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"subtract_latent_mean_sigma_end": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 10000.0, "step": 0.01, "round": 0.01}),
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"latent_intensity_rescale" : ("BOOLEAN", {"default": True}),
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"latent_intensity_rescale_method" : (["soft","hard","range"], {"default": "hard"},),
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"latent_intensity_rescale_cfg" : ("FLOAT", {"default": 7.6, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.1}),
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"latent_intensity_rescale_sigma_start": ("FLOAT", {"default": 15, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
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"latent_intensity_rescale_sigma_end": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches/automatic_cfg"
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def patch(self, model, automatic_cfg = "None",
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skip_uncond = False, uncond_sigma_start = 15, uncond_sigma_end = 0,
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lerp_uncond = False, lerp_uncond_strength = 1, lerp_uncond_sigma_start = 15, lerp_uncond_sigma_end = 1,
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subtract_latent_mean = False, subtract_latent_mean_sigma_start = 15, subtract_latent_mean_sigma_end = 1,
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latent_intensity_rescale = False, latent_intensity_rescale_sigma_start = 15, latent_intensity_rescale_sigma_end = 1,
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latent_intensity_rescale_cfg = 8, latent_intensity_rescale_method = "hard",
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ignore_pre_cfg_func = False):
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global minimum_sigma_to_disable_uncond, maximum_sigma_to_enable_uncond, global_skip_uncond
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sigmin, sigmax = get_sigmin_sigmax(model)
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lerp_start, lerp_end = lerp_uncond_sigma_start, lerp_uncond_sigma_end
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subtract_start, subtract_end = subtract_latent_mean_sigma_start, subtract_latent_mean_sigma_end
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rescale_start, rescale_end = latent_intensity_rescale_sigma_start, latent_intensity_rescale_sigma_end
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print(f"Model maximum sigma: {sigmax} / Model minimum sigma: {sigmin}")
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if skip_uncond:
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global_skip_uncond = skip_uncond
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comfy.samplers.sampling_function = sampling_function_patched
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maximum_sigma_to_enable_uncond, minimum_sigma_to_disable_uncond = uncond_sigma_start, uncond_sigma_end
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print(f"Sampling function patched. Uncond enabled from {round(maximum_sigma_to_enable_uncond,2)} to {round(minimum_sigma_to_disable_uncond,2)}")
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elif not ignore_pre_cfg_func:
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global_skip_uncond = skip_uncond # just in case of mixup with another node
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comfy.samplers.sampling_function = original_sampling_function
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maximum_sigma_to_enable_uncond, minimum_sigma_to_disable_uncond = 1000000, 0
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print(f"Sampling function unpatched.")
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top_k = 0.25
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reference_cfg = 8
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def automatic_cfg(args):
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cond_scale = args["cond_scale"]
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input_x = args["input"]
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cond_pred = args["cond_denoised"]
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uncond_pred = args["uncond_denoised"]
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sigma = args["sigma"][0]
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if sigma >= sigmax or cond_scale > 1:
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self.last_cfg_ht_one = cond_scale
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target_intensity = self.last_cfg_ht_one / 10
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if (check_skip(sigma, maximum_sigma_to_enable_uncond, minimum_sigma_to_disable_uncond) and skip_uncond) or cond_scale == 1:
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return input_x - cond_pred
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if lerp_uncond and not check_skip(sigma, lerp_start, lerp_end) and lerp_uncond_strength != 1:
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uncond_pred = torch.lerp(cond_pred, uncond_pred, lerp_uncond_strength)
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cond = input_x - cond_pred
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uncond = input_x - uncond_pred
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if automatic_cfg == "None":
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return uncond + cond_scale * (cond - uncond)
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denoised_tmp = input_x - (uncond + reference_cfg * (cond - uncond))
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for b in range(len(denoised_tmp)):
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denoised_ranges = get_denoised_ranges(denoised_tmp[b], automatic_cfg, top_k)
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for c in range(len(denoised_tmp[b])):
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fixeds_scale = reference_cfg * target_intensity / denoised_ranges[c]
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denoised_tmp[b][c] = uncond[b][c] + fixeds_scale * (cond[b][c] - uncond[b][c])
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return denoised_tmp
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def center_mean_latent_post_cfg(args):
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denoised = args["denoised"]
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sigma = args["sigma"][0]
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if check_skip(sigma, subtract_start, subtract_end):
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return denoised
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denoised = center_latent_mean_values(denoised, False, 1)
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return denoised
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def rescale_post_cfg(args):
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denoised = args["denoised"]
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sigma = args["sigma"][0]
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if check_skip(sigma, rescale_start, rescale_end):
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return denoised
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target_intensity = latent_intensity_rescale_cfg / 10
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for b in range(len(denoised)):
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denoised_ranges = get_denoised_ranges(denoised[b], latent_intensity_rescale_method)
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for c in range(len(denoised[b])):
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scale_correction = target_intensity / denoised_ranges[c]
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denoised[b][c] = denoised[b][c] * scale_correction
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return denoised
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m = model.clone()
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if not ignore_pre_cfg_func:
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m.set_model_sampler_cfg_function(automatic_cfg, disable_cfg1_optimization = False)
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if subtract_latent_mean:
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m.set_model_sampler_post_cfg_function(center_mean_latent_post_cfg)
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if latent_intensity_rescale:
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m.set_model_sampler_post_cfg_function(rescale_post_cfg)
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return (m, )
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class simpleDynamicCFG:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"boost" : ("BOOLEAN", {"default": True}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches/automatic_cfg/presets"
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def patch(self, model, boost):
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advcfg = advancedDynamicCFG()
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m = advcfg.patch(model,
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skip_uncond = boost,
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uncond_sigma_start = 15, uncond_sigma_end = 1,
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automatic_cfg = "hard" if boost else "soft"
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)[0]
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return (m, )
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class simpleDynamicCFGlerpUncond:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"boost" : ("BOOLEAN", {"default": True}),
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"negative_strength": ("FLOAT", {"default": 1, "min": 0.0, "max": 5.0, "step": 0.1, "round": 0.1}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches/automatic_cfg/presets"
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def patch(self, model, boost, negative_strength):
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advcfg = advancedDynamicCFG()
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m = advcfg.patch(model=model,
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automatic_cfg="hard", skip_uncond=boost,
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uncond_sigma_start = 15, uncond_sigma_end = 1,
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lerp_uncond=negative_strength != 1, lerp_uncond_strength=negative_strength,
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lerp_uncond_sigma_start = 15, lerp_uncond_sigma_end = 1
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)[0]
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return (m, )
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class postCFGrescaleOnly:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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"subtract_latent_mean" : ("BOOLEAN", {"default": True}),
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"subtract_latent_mean_sigma_start": ("FLOAT", {"default": 15, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
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"subtract_latent_mean_sigma_end": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
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"latent_intensity_rescale" : ("BOOLEAN", {"default": True}),
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"latent_intensity_rescale_method" : (["soft","hard","range"], {"default": "hard"},),
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"latent_intensity_rescale_cfg" : ("FLOAT", {"default": 7.6, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.1}),
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"latent_intensity_rescale_sigma_start": ("FLOAT", {"default": 15, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
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"latent_intensity_rescale_sigma_end": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches/automatic_cfg/presets"
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def patch(self, model,
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subtract_latent_mean, subtract_latent_mean_sigma_start, subtract_latent_mean_sigma_end,
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latent_intensity_rescale, latent_intensity_rescale_method, latent_intensity_rescale_cfg, latent_intensity_rescale_sigma_start, latent_intensity_rescale_sigma_end
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):
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advcfg = advancedDynamicCFG()
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m = advcfg.patch(model=model,
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subtract_latent_mean = subtract_latent_mean,
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subtract_latent_mean_sigma_start = subtract_latent_mean_sigma_start, subtract_latent_mean_sigma_end = subtract_latent_mean_sigma_end,
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latent_intensity_rescale = latent_intensity_rescale, latent_intensity_rescale_cfg = latent_intensity_rescale_cfg, latent_intensity_rescale_method = latent_intensity_rescale_method,
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latent_intensity_rescale_sigma_start = latent_intensity_rescale_sigma_start, latent_intensity_rescale_sigma_end = latent_intensity_rescale_sigma_end,
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ignore_pre_cfg_func = True
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)[0]
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return (m, )
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class simpleDynamicCFGHighSpeed:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ("MODEL",),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "model_patches/automatic_cfg/presets"
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def patch(self, model):
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advcfg = advancedDynamicCFG()
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m = advcfg.patch(model=model, automatic_cfg = "hard",
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skip_uncond = True, uncond_sigma_start = 7.5, uncond_sigma_end = 1,
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latent_intensity_rescale = False, latent_intensity_rescale_cfg = 7.6,
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latent_intensity_rescale_sigma_start = 15, latent_intensity_rescale_sigma_end = 7.5,
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latent_intensity_rescale_method = "hard")[0]
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return (m, )
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