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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 torch
import math
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
sigmas = samplers.calculate_sigmas_scheduler(model.model, 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_2m_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_2m_sde_gpu(model, x, sigmas, **kwargs)
sampler_function = sample_dpmpp_sde
else:
return samplers.ksampler(sampler_name, extra_options, inpaint_options)
return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options)
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):
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[start_at_step:end_at_step+1] * sigma_ratio
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 = nodes_custom_sampler.SamplerCustom().sample(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image)
if return_with_leftover_noise:
return res[0]
else:
return res[1]
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):
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:
refined_latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise * sigma_factor)[0]
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)
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)
return refined_latent
class KSamplerAdvancedWrapper:
params = None
def __init__(self, model, cfg, sampler_name, scheduler, positive, negative, sampler_opt=None, sigma_factor=1.0):
self.params = model, cfg, sampler_name, scheduler, positive, negative, sigma_factor
self.sampler_opt = sampler_opt
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):
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)
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)
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):
self.params = model, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
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 nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)[0]
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