import inspect from collections import namedtuple import numpy as np import torch from PIL import Image from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models from modules.shared import opts, state import k_diffusion.sampling SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) class SamplerData(SamplerDataTuple): def total_steps(self, steps): if self.options.get("second_order", False): steps = steps * 2 return steps def setup_img2img_steps(p, steps=None): if opts.img2img_fix_steps or steps is not None: requested_steps = (steps or p.steps) steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 t_enc = requested_steps - 1 else: steps = p.steps t_enc = int(min(p.denoising_strength, 0.999) * steps) return steps, t_enc approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3} def samples_to_images_tensor(sample, approximation=None, model=None): '''latents -> images [-1, 1]''' if approximation is None: approximation = approximation_indexes.get(opts.show_progress_type, 0) if approximation == 2: x_sample = sd_vae_approx.cheap_approximation(sample) elif approximation == 1: x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach() elif approximation == 3: x_sample = sd_vae_taesd.decoder_model()(sample.to(devices.device, devices.dtype)).detach() x_sample = x_sample * 2 - 1 else: if model is None: model = shared.sd_model x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype)) return x_sample def single_sample_to_image(sample, approximation=None): x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5 x_sample = torch.clamp(x_sample, min=0.0, max=1.0) x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) return Image.fromarray(x_sample) def decode_first_stage(model, x): x = x.to(devices.dtype_vae) approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0) return samples_to_images_tensor(x, approx_index, model) def sample_to_image(samples, index=0, approximation=None): return single_sample_to_image(samples[index], approximation) def samples_to_image_grid(samples, approximation=None): return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples]) def images_tensor_to_samples(image, approximation=None, model=None): '''image[0, 1] -> latent''' if approximation is None: approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0) if approximation == 3: image = image.to(devices.device, devices.dtype) x_latent = sd_vae_taesd.encoder_model()(image) else: if model is None: model = shared.sd_model image = image.to(shared.device, dtype=devices.dtype_vae) image = image * 2 - 1 if len(image) > 1: x_latent = torch.stack([ model.get_first_stage_encoding( model.encode_first_stage(torch.unsqueeze(img, 0)) )[0] for img in image ]) else: x_latent = model.get_first_stage_encoding(model.encode_first_stage(image)) return x_latent def store_latent(decoded): state.current_latent = decoded if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: if not shared.parallel_processing_allowed: shared.state.assign_current_image(sample_to_image(decoded)) def is_sampler_using_eta_noise_seed_delta(p): """returns whether sampler from config will use eta noise seed delta for image creation""" sampler_config = sd_samplers.find_sampler_config(p.sampler_name) eta = p.eta if eta is None and p.sampler is not None: eta = p.sampler.eta if eta is None and sampler_config is not None: eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0 if eta == 0: return False return sampler_config.options.get("uses_ensd", False) class InterruptedException(BaseException): pass def replace_torchsde_browinan(): import torchsde._brownian.brownian_interval def torchsde_randn(size, dtype, device, seed): return devices.randn_local(seed, size).to(device=device, dtype=dtype) torchsde._brownian.brownian_interval._randn = torchsde_randn replace_torchsde_browinan() def apply_refiner(cfg_denoiser): completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps refiner_switch_at = cfg_denoiser.p.refiner_switch_at refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info if refiner_switch_at is not None and completed_ratio < refiner_switch_at: return False if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info: return False if getattr(cfg_denoiser.p, "enable_hr", False) and not cfg_denoiser.p.is_hr_pass: return False cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at with sd_models.SkipWritingToConfig(): sd_models.reload_model_weights(info=refiner_checkpoint_info) devices.torch_gc() cfg_denoiser.p.setup_conds() cfg_denoiser.update_inner_model() return True class TorchHijack: """This is here to replace torch.randn_like of k-diffusion. k-diffusion has random_sampler argument for most samplers, but not for all, so this is needed to properly replace every use of torch.randn_like. We need to replace to make images generated in batches to be same as images generated individually.""" def __init__(self, p): self.rng = p.rng def __getattr__(self, item): if item == 'randn_like': return self.randn_like if hasattr(torch, item): return getattr(torch, item) raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'") def randn_like(self, x): return self.rng.next() class Sampler: def __init__(self, funcname): self.funcname = funcname self.func = funcname self.extra_params = [] self.sampler_noises = None self.stop_at = None self.eta = None self.config: SamplerData = None # set by the function calling the constructor self.last_latent = None self.s_min_uncond = None self.s_churn = 0.0 self.s_tmin = 0.0 self.s_tmax = float('inf') self.s_noise = 1.0 self.eta_option_field = 'eta_ancestral' self.eta_infotext_field = 'Eta' self.eta_default = 1.0 self.conditioning_key = shared.sd_model.model.conditioning_key self.p = None self.model_wrap_cfg = None self.sampler_extra_args = None self.options = {} def callback_state(self, d): step = d['i'] if self.stop_at is not None and step > self.stop_at: raise InterruptedException state.sampling_step = step shared.total_tqdm.update() def launch_sampling(self, steps, func): self.model_wrap_cfg.steps = steps self.model_wrap_cfg.total_steps = self.config.total_steps(steps) state.sampling_steps = steps state.sampling_step = 0 try: return func() except RecursionError: print( 'Encountered RecursionError during sampling, returning last latent. ' 'rho >5 with a polyexponential scheduler may cause this error. ' 'You should try to use a smaller rho value instead.' ) return self.last_latent except InterruptedException: return self.last_latent def number_of_needed_noises(self, p): return p.steps def initialize(self, p) -> dict: self.p = p self.model_wrap_cfg.p = p self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None self.model_wrap_cfg.step = 0 self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None) self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0) self.s_min_uncond = getattr(p, 's_min_uncond', 0.0) k_diffusion.sampling.torch = TorchHijack(p) extra_params_kwargs = {} for param_name in self.extra_params: if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters: extra_params_kwargs[param_name] = getattr(p, param_name) if 'eta' in inspect.signature(self.func).parameters: if self.eta != self.eta_default: p.extra_generation_params[self.eta_infotext_field] = self.eta extra_params_kwargs['eta'] = self.eta if len(self.extra_params) > 0: s_churn = getattr(opts, 's_churn', p.s_churn) s_tmin = getattr(opts, 's_tmin', p.s_tmin) s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf s_noise = getattr(opts, 's_noise', p.s_noise) if 's_churn' in extra_params_kwargs and s_churn != self.s_churn: extra_params_kwargs['s_churn'] = s_churn p.s_churn = s_churn p.extra_generation_params['Sigma churn'] = s_churn if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin: extra_params_kwargs['s_tmin'] = s_tmin p.s_tmin = s_tmin p.extra_generation_params['Sigma tmin'] = s_tmin if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax: extra_params_kwargs['s_tmax'] = s_tmax p.s_tmax = s_tmax p.extra_generation_params['Sigma tmax'] = s_tmax if 's_noise' in extra_params_kwargs and s_noise != self.s_noise: extra_params_kwargs['s_noise'] = s_noise p.s_noise = s_noise p.extra_generation_params['Sigma noise'] = s_noise return extra_params_kwargs def create_noise_sampler(self, x, sigmas, p): """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes""" if shared.opts.no_dpmpp_sde_batch_determinism: return None from k_diffusion.sampling import BrownianTreeNoiseSampler sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size] return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds) def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): raise NotImplementedError() def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): raise NotImplementedError()