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Bacruru Sakaguchi
2025-10-21 23:06:12 +07:00
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modules/sd_samplers_kdiffusion.py Executable file
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import torch
import inspect
import k_diffusion.sampling
import k_diffusion.external
from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers, devices
from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
from modules.shared import opts
import modules.shared as shared
from backend.sampling.sampling_function import sampling_prepare, sampling_cleanup
samplers_k_diffusion = [
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {'scheduler': 'karras'}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde'], {'scheduler': 'exponential', "brownian_noise": True}),
('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
('Euler', 'sample_euler', ['k_euler'], {}),
('LMS', 'sample_lms', ['k_lms'], {}),
('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "second_order": True}),
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
('HeunPP2', 'sample_heunpp2', ['heunpp2'], {}),
('IPNDM', 'sample_ipndm', ['ipndm'], {}),
('IPNDM_V', 'sample_ipndm_v', ['ipndm_v'], {}),
('DEIS', 'sample_deis', ['deis'], {}),
]
samplers_data_k_diffusion = [
sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
for label, funcname, aliases, options in samplers_k_diffusion
if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
]
sampler_extra_params = {
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
'sample_dpm_fast': ['s_noise'],
'sample_dpm_2_ancestral': ['s_noise'],
'sample_dpmpp_2s_ancestral': ['s_noise'],
'sample_dpmpp_sde': ['s_noise'],
'sample_dpmpp_2m_sde': ['s_noise'],
'sample_dpmpp_3m_sde': ['s_noise'],
}
k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
k_diffusion_scheduler = {x.name: x.function for x in sd_schedulers.schedulers}
class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
@property
def inner_model(self):
if self.model_wrap is None:
self.model_wrap = k_diffusion.external.ForgeScheduleLinker(shared.sd_model.forge_objects.unet.model.predictor)
self.model_wrap.inner_model = shared.sd_model
return self.model_wrap
class KDiffusionSampler(sd_samplers_common.Sampler):
def __init__(self, funcname, sd_model, options=None):
super().__init__(funcname)
self.extra_params = sampler_extra_params.get(funcname, [])
self.options = options or {}
self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
self.model_wrap = self.model_wrap_cfg.inner_model
def get_sigmas(self, p, steps):
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
discard_next_to_last_sigma = True
p.extra_generation_params["Discard penultimate sigma"] = True
steps += 1 if discard_next_to_last_sigma else 0
scheduler_name = (p.hr_scheduler if p.is_hr_pass else p.scheduler) or 'Automatic'
if scheduler_name == 'Automatic':
scheduler_name = self.config.options.get('scheduler', None)
scheduler = sd_schedulers.schedulers_map.get(scheduler_name)
m_sigma_min, m_sigma_max = self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
elif scheduler is None or scheduler.function is None:
sigmas = self.model_wrap.get_sigmas(steps)
else:
sigmas_kwargs = {'sigma_min': sigma_min, 'sigma_max': sigma_max}
if scheduler.label != 'Automatic' and not p.is_hr_pass:
p.extra_generation_params["Schedule type"] = scheduler.label
elif scheduler.label != p.extra_generation_params.get("Schedule type"):
p.extra_generation_params["Hires schedule type"] = scheduler.label
if opts.sigma_min != 0 and opts.sigma_min != m_sigma_min:
sigmas_kwargs['sigma_min'] = opts.sigma_min
p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
if opts.sigma_max != 0 and opts.sigma_max != m_sigma_max:
sigmas_kwargs['sigma_max'] = opts.sigma_max
p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
if scheduler.default_rho != -1 and opts.rho != 0 and opts.rho != scheduler.default_rho:
sigmas_kwargs['rho'] = opts.rho
p.extra_generation_params["Schedule rho"] = opts.rho
if scheduler.need_inner_model:
sigmas_kwargs['inner_model'] = self.model_wrap
if scheduler.label == 'Beta':
p.extra_generation_params["Beta schedule alpha"] = opts.beta_dist_alpha
p.extra_generation_params["Beta schedule beta"] = opts.beta_dist_beta
sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=devices.cpu)
if discard_next_to_last_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
return sigmas.cpu()
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
unet_patcher = self.model_wrap.inner_model.forge_objects.unet
sampling_prepare(self.model_wrap.inner_model.forge_objects.unet, x=x)
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
sigmas = self.get_sigmas(p, steps).to(x.device)
sigma_sched = sigmas[steps - t_enc - 1:]
x = x.to(noise)
xi = self.model_wrap.predictor.noise_scaling(sigma_sched[0], noise, x, max_denoise=False)
if opts.img2img_extra_noise > 0:
p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
extra_noise_params = ExtraNoiseParams(noise, x, xi)
extra_noise_callback(extra_noise_params)
noise = extra_noise_params.noise
xi += noise * opts.img2img_extra_noise
extra_params_kwargs = self.initialize(p)
parameters = inspect.signature(self.func).parameters
if 'sigma_min' in parameters:
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
if 'sigma_max' in parameters:
extra_params_kwargs['sigma_max'] = sigma_sched[0]
if 'n' in parameters:
extra_params_kwargs['n'] = len(sigma_sched) - 1
if 'sigma_sched' in parameters:
extra_params_kwargs['sigma_sched'] = sigma_sched
if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigma_sched
if self.config.options.get('brownian_noise', False):
noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
if self.config.options.get('solver_type', None) == 'heun':
extra_params_kwargs['solver_type'] = 'heun'
self.model_wrap_cfg.init_latent = x
self.last_latent = x
self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
self.add_infotext(p)
sampling_cleanup(unet_patcher)
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
unet_patcher = self.model_wrap.inner_model.forge_objects.unet
sampling_prepare(self.model_wrap.inner_model.forge_objects.unet, x=x)
steps = steps or p.steps
sigmas = self.get_sigmas(p, steps).to(x.device)
if opts.sgm_noise_multiplier:
p.extra_generation_params["SGM noise multiplier"] = True
x = self.model_wrap.predictor.noise_scaling(sigmas[0], x, torch.zeros_like(x), max_denoise=opts.sgm_noise_multiplier)
extra_params_kwargs = self.initialize(p)
parameters = inspect.signature(self.func).parameters
if 'n' in parameters:
extra_params_kwargs['n'] = steps
if 'sigma_min' in parameters:
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigmas
if self.config.options.get('brownian_noise', False):
noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
if self.config.options.get('solver_type', None) == 'heun':
extra_params_kwargs['solver_type'] = 'heun'
self.last_latent = x
self.sampler_extra_args = {
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
self.add_infotext(p)
sampling_cleanup(unet_patcher)
return samples