| import torch
|
| import inspect
|
| import k_diffusion.sampling
|
| from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers
|
| from modules.sd_samplers_cfg_denoiser import CFGDenoiser
|
| from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
|
|
|
| from modules.shared import opts
|
| import modules.shared as shared
|
|
|
| samplers_k_diffusion = [
|
| ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {'scheduler': 'karras'}),
|
| ('DPM++ 2M Karras Sharp v1', 'sample_dpmpp_2m_v1', ['k_dpmpp_2m_ka_v1'], {'scheduler': 'karras'}),
|
| ('DPM++ 2M Test', 'sample_dpmpp_2m_test', ['k_dpmpp_2m'], {}),
|
| ('DPM++ 2M Karras Test', 'sample_dpmpp_2m_test', ['k_dpmpp_2m_ka'], {'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}),
|
| ]
|
|
|
|
|
| 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)
|
| ]
|
|
|
| @torch.no_grad()
|
| def sample_dpmpp_2m_alt(model, x, sigmas, extra_args=None, callback=None, disable=None):
|
| """DPM-Solver++(2M)."""
|
| extra_args = {} if extra_args is None else extra_args
|
| s_in = x.new_ones([x.shape[0]])
|
| sigma_fn = lambda t: t.neg().exp()
|
| t_fn = lambda sigma: sigma.log().neg()
|
| old_denoised = None
|
|
|
| for i in trange(len(sigmas) - 1, disable=disable):
|
| denoised = model(x, sigmas[i] * s_in, **extra_args)
|
| if callback is not None:
|
| callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
| t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
|
| h = t_next - t
|
| if old_denoised is None or sigmas[i + 1] == 0:
|
| x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
|
| else:
|
| h_last = t - t_fn(sigmas[i - 1])
|
| r = h_last / h
|
| denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
|
| x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
|
| sigma_progress = i / len(sigmas)
|
| adjustment_factor = 1 + (0.15 * (sigma_progress * sigma_progress))
|
| old_denoised = denoised * adjustment_factor
|
| return x
|
|
|
| k_diffusion.sampling.sample_dpmpp_2m_alt = sample_dpmpp_2m_alt
|
|
|
| samplers_data_k_diffusion.insert(9, sd_samplers_common.SamplerData('DPM++ 2M alt', lambda model: KDiffusionSampler('sample_dpmpp_2m_alt', model), ['k_dpmpp_2m_alt'], {}))
|
| samplers_data_k_diffusion.insert(10, sd_samplers_common.SamplerData('DPM++ 2M alt Karras', lambda model: KDiffusionSampler('sample_dpmpp_2m_alt', model), ['k_dpmpp_2m_alt_ka'], {'scheduler': 'karras'}))
|
|
|
| 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:
|
| denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
|
| self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
|
|
|
| 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
|
|
|
| sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
|
|
|
| if discard_next_to_last_sigma:
|
| sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
|
|
|
| return sigmas
|
|
|
| def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
| steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
|
|
|
| sigmas = self.get_sigmas(p, steps)
|
| sigma_sched = sigmas[steps - t_enc - 1:]
|
|
|
| xi = x + noise * sigma_sched[0]
|
|
|
| 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:
|
|
|
| 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)
|
|
|
| return samples
|
|
|
| def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
| steps = steps or p.steps
|
|
|
| sigmas = self.get_sigmas(p, steps)
|
|
|
| if opts.sgm_noise_multiplier:
|
| p.extra_generation_params["SGM noise multiplier"] = True
|
| x = x * torch.sqrt(1.0 + sigmas[0] ** 2.0)
|
| else:
|
| x = x * sigmas[0]
|
|
|
| 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)
|
|
|
| return samples
|
|
|
|
|
|
|
