import torch import inspect import k_diffusion.sampling from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401 from modules.shared import opts import modules.shared as shared samplers_k_diffusion = [ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}), ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}), ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "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'], {'discard_next_to_last_sigma': True}), ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}), ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}), ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}), ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}), ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {"brownian_noise": True}), ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}), ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}), ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}), ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}), ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}), ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}), ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}), ] 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'], } k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion} k_diffusion_scheduler = { 'Automatic': None, 'karras': k_diffusion.sampling.get_sigmas_karras, 'exponential': k_diffusion.sampling.get_sigmas_exponential, 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential } 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.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 if p.sampler_noise_scheduler_override: sigmas = p.sampler_noise_scheduler_override(steps) elif opts.k_sched_type != "Automatic": 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) sigmas_kwargs = { 'sigma_min': sigma_min, 'sigma_max': sigma_max, } sigmas_func = k_diffusion_scheduler[opts.k_sched_type] p.extra_generation_params["Schedule type"] = opts.k_sched_type if opts.sigma_min != m_sigma_min and opts.sigma_min != 0: sigmas_kwargs['sigma_min'] = opts.sigma_min p.extra_generation_params["Schedule min sigma"] = opts.sigma_min if opts.sigma_max != m_sigma_max and opts.sigma_max != 0: sigmas_kwargs['sigma_max'] = opts.sigma_max p.extra_generation_params["Schedule max sigma"] = opts.sigma_max default_rho = 1. if opts.k_sched_type == "polyexponential" else 7. if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho: sigmas_kwargs['rho'] = opts.rho p.extra_generation_params["Schedule rho"] = opts.rho sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device) elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device) elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential': m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device) else: sigmas = self.model_wrap.get_sigmas(steps) 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] 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 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)) if self.model_wrap_cfg.padded_cond_uncond: p.extra_generation_params["Pad conds"] = True 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) 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 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)) if self.model_wrap_cfg.padded_cond_uncond: p.extra_generation_params["Pad conds"] = True return samples