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import nodes
from comfy.k_diffusion import sampling as k_diffusion_sampling
from comfy import samplers
from comfy_extras import nodes_custom_sampler
import latent_preview
import comfy
import torch
import math
import comfy.model_management as mm
try:
from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise
import node_helpers
except:
print(f"\n#############################################\n[Impact Pack] ComfyUI is an outdated version.\n#############################################\n")
raise Exception("[Impact Pack] ComfyUI is an outdated version.")
def calculate_sigmas(model, sampler, scheduler, steps):
discard_penultimate_sigma = False
if sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
steps += 1
discard_penultimate_sigma = True
if scheduler.startswith('AYS'):
sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
elif scheduler.startswith('GITS[coeff='):
sigmas = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']().get_sigmas(float(scheduler[11:-1]), steps, denoise=1.0)[0]
else:
sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
if discard_penultimate_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
return sigmas
def get_noise_sampler(x, cpu, total_sigmas, **kwargs):
if 'extra_args' in kwargs and 'seed' in kwargs['extra_args']:
sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max()
seed = kwargs['extra_args'].get("seed", None)
return k_diffusion_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=cpu)
return None
def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
if sampler_name == "dpmpp_sde":
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs['noise_sampler'] = noise_sampler
return k_diffusion_sampling.sample_dpmpp_sde(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
elif sampler_name == "dpmpp_sde_gpu":
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs['noise_sampler'] = noise_sampler
return k_diffusion_sampling.sample_dpmpp_sde_gpu(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
elif sampler_name == "dpmpp_2m_sde":
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs['noise_sampler'] = noise_sampler
return k_diffusion_sampling.sample_dpmpp_2m_sde(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
elif sampler_name == "dpmpp_2m_sde_gpu":
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs['noise_sampler'] = noise_sampler
return k_diffusion_sampling.sample_dpmpp_2m_sde_gpu(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
elif sampler_name == "dpmpp_3m_sde":
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs['noise_sampler'] = noise_sampler
return k_diffusion_sampling.sample_dpmpp_3m_sde(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
elif sampler_name == "dpmpp_3m_sde_gpu":
def sample_dpmpp_sde(model, x, sigmas, **kwargs):
noise_sampler = get_noise_sampler(x, False, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs['noise_sampler'] = noise_sampler
return k_diffusion_sampling.sample_dpmpp_3m_sde_gpu(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
else:
return comfy.samplers.sampler_object(sampler_name)
return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options)
# modified version of SamplerCustom.sample
def sample_with_custom_noise(model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image, noise=None, callback=None):
latent = latent_image
latent_image = latent["samples"]
if hasattr(comfy.sample, 'fix_empty_latent_channels'):
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
out = latent.copy()
out['samples'] = latent_image
if noise is None:
if not add_noise:
noise = Noise_EmptyNoise().generate_noise(out)
else:
noise = Noise_RandomNoise(noise_seed).generate_noise(out)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
x0_output = {}
preview_callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
if callback is not None:
def touched_callback(step, x0, x, total_steps):
callback(step, x0, x, total_steps)
preview_callback(step, x0, x, total_steps)
else:
touched_callback = preview_callback
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
device = mm.get_torch_device()
noise = noise.to(device)
latent_image = latent_image.to(device)
if noise_mask is not None:
noise_mask = noise_mask.to(device)
if negative != 'NegativePlaceholder':
# This way is incompatible with Advanced ControlNet, yet.
# guider = comfy.samplers.CFGGuider(model)
# guider.set_conds(positive, negative)
# guider.set_cfg(cfg)
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image,
noise_mask=noise_mask, callback=touched_callback,
disable_pbar=disable_pbar, seed=noise_seed)
else:
guider = nodes_custom_sampler.Guider_Basic(model)
positive = node_helpers.conditioning_set_values(positive, {"guidance": cfg})
guider.set_conds(positive)
samples = guider.sample(noise, latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=touched_callback, disable_pbar=disable_pbar, seed=noise_seed)
samples = samples.to(comfy.model_management.intermediate_device())
out["samples"] = samples
if "x0" in x0_output:
out_denoised = latent.copy()
out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
else:
out_denoised = out
return out, out_denoised
# When sampling one step at a time, it mitigates the problem. (especially for _sde series samplers)
def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative,
latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None, noise=None, callback=None, scheduler_func=None):
if scheduler_func is not None:
total_sigmas = scheduler_func(model, sampler_name, steps)
else:
if sampler_opt is None:
total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
else:
total_sigmas = calculate_sigmas(model, "", scheduler, steps)
sigmas = total_sigmas
if end_at_step is not None and end_at_step < (len(total_sigmas) - 1):
sigmas = total_sigmas[:end_at_step + 1]
if not return_with_leftover_noise:
sigmas[-1] = 0
if start_at_step is not None:
if start_at_step < (len(sigmas) - 1):
sigmas = sigmas[start_at_step:] * sigma_ratio
else:
if latent_image is not None:
return latent_image
else:
return {'samples': torch.zeros_like(noise)}
if sampler_opt is None:
impact_sampler = ksampler(sampler_name, total_sigmas)
else:
impact_sampler = sampler_opt
if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0):
return latent_image
res = sample_with_custom_noise(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image, noise=noise, callback=callback)
if return_with_leftover_noise:
return res[0]
else:
return res[1]
def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None, scheduler_func=None):
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
end_at_step = start_at_step + steps
return separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
start_at_step, end_at_step, False, scheduler_func=scheduler_func)
def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0, noise=None, scheduler_func=None):
if refiner_ratio is None or refiner_model is None or refiner_clip is None or refiner_positive is None or refiner_negative is None:
# Use separated_sample instead of KSampler for `AYS scheduler`
# refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0]
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
end_at_step = start_at_step + steps
refined_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step, False,
sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
else:
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
end_at_step = start_at_step + math.floor(steps * (1.0 - refiner_ratio))
# print(f"pre: {start_at_step} .. {end_at_step} / {advanced_steps}")
temp_latent = separated_sample(model, True, seed, advanced_steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step, True,
sigma_ratio=sigma_factor, noise=noise, scheduler_func=scheduler_func)
if 'noise_mask' in latent_image:
# noise_latent = \
# impact_sampling.separated_sample(refiner_model, "enable", seed, advanced_steps, cfg, sampler_name,
# scheduler, refiner_positive, refiner_negative, latent_image, end_at_step,
# end_at_step, "enable")
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
temp_latent = latent_compositor.composite(latent_image, temp_latent, 0, 0, False, latent_image['noise_mask'])[0]
# print(f"post: {end_at_step} .. {advanced_steps + 1} / {advanced_steps}")
refined_latent = separated_sample(refiner_model, False, seed, advanced_steps, cfg, sampler_name, scheduler,
refiner_positive, refiner_negative, temp_latent, end_at_step, advanced_steps + 1, False,
sigma_ratio=sigma_factor, scheduler_func=scheduler_func)
return refined_latent
class KSamplerAdvancedWrapper:
params = None
def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0, scheduler_func=None):
self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor
self.sampler_opt = sampler_opt
self.scheduler_func = scheduler_func
def clone_with_conditionings(self, positive, negative):
model, cfg, sampler_name, scheduler, _, _, _ = self.params
return KSamplerAdvancedWrapper(model, cfg, sampler_name, scheduler, positive, negative, self.sampler_opt)
def sample_advanced(self, add_noise, seed, steps, latent_image, start_at_step, end_at_step, return_with_leftover_noise, hook=None,
recovery_mode="ratio additional", recovery_sampler="AUTO", recovery_sigma_ratio=1.0, noise=None):
model, cfg, sampler_name, scheduler, positive, negative, sigma_factor = self.params
# steps, start_at_step, end_at_step = self.compensate_denoise(steps, start_at_step, end_at_step)
if hook is not None:
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent = hook.pre_ksample_advanced(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise)
if recovery_mode != 'DISABLE' and sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']:
base_image = latent_image.copy()
if recovery_mode == "ratio between":
sigma_ratio = 1.0 - recovery_sigma_ratio
else:
sigma_ratio = 1.0
else:
base_image = None
sigma_ratio = 1.0
try:
if sigma_ratio > 0:
latent_image = separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler,
positive, negative, latent_image, start_at_step, end_at_step,
return_with_leftover_noise, sigma_ratio=sigma_ratio * sigma_factor,
sampler_opt=self.sampler_opt, noise=noise, scheduler_func=self.scheduler_func)
except ValueError as e:
if str(e) == 'sigma_min and sigma_max must not be 0':
print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
return latent_image
if (recovery_sigma_ratio > 0 and recovery_mode != 'DISABLE' and
sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu']):
compensate = 0 if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu'] else 2
if recovery_sampler == "AUTO":
recovery_sampler = 'dpm_fast' if sampler_name in ['uni_pc', 'uni_pc_bh2', 'dpmpp_sde', 'dpmpp_sde_gpu'] else 'dpmpp_2m'
latent_compositor = nodes.NODE_CLASS_MAPPINGS['LatentCompositeMasked']()
noise_mask = latent_image['noise_mask']
if len(noise_mask.shape) == 4:
noise_mask = noise_mask.squeeze(0).squeeze(0)
latent_image = latent_compositor.composite(base_image, latent_image, 0, 0, False, noise_mask)[0]
try:
latent_image = separated_sample(model, add_noise, seed, steps, cfg, recovery_sampler, scheduler,
positive, negative, latent_image, start_at_step-compensate, end_at_step, return_with_leftover_noise,
sigma_ratio=recovery_sigma_ratio * sigma_factor, sampler_opt=self.sampler_opt, scheduler_func=self.scheduler_func)
except ValueError as e:
if str(e) == 'sigma_min and sigma_max must not be 0':
print(f"\nWARN: sampling skipped - sigma_min and sigma_max are 0")
return latent_image
class KSamplerWrapper:
params = None
def __init__(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, scheduler_func=None):
self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
self.scheduler_func = scheduler_func
def sample(self, latent_image, hook=None):
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise = self.params
if hook is not None:
model, seed, steps, cfg, sampler_name, scheduler, positive, negative, upscaled_latent, denoise = \
hook.pre_ksample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
return impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, scheduler_func=self.scheduler_func)
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