""" Partially ported from https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py """ from typing import Dict, Union import torch from omegaconf import ListConfig, OmegaConf from tqdm import tqdm from ...modules.diffusionmodules.sampling_utils import ( get_ancestral_step, linear_multistep_coeff, to_d, to_neg_log_sigma, to_sigma, ) from ...util import append_dims, default, instantiate_from_config from ...util import SeededNoise from .guiders import DynamicCFG DEFAULT_GUIDER = {"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"} class BaseDiffusionSampler: def __init__( self, discretization_config: Union[Dict, ListConfig, OmegaConf], num_steps: Union[int, None] = None, guider_config: Union[Dict, ListConfig, OmegaConf, None] = None, verbose: bool = False, device: str = "cuda", ): self.num_steps = num_steps self.discretization = instantiate_from_config(discretization_config) self.guider = instantiate_from_config( default( guider_config, DEFAULT_GUIDER, ) ) self.verbose = verbose self.device = device def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None): sigmas = self.discretization(self.num_steps if num_steps is None else num_steps, device=self.device) uc = default(uc, cond) x *= torch.sqrt(1.0 + sigmas[0] ** 2.0) num_sigmas = len(sigmas) s_in = x.new_ones([x.shape[0]]).float() return x, s_in, sigmas, num_sigmas, cond, uc def denoise(self, x, denoiser, sigma, cond, uc): denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc)) denoised = self.guider(denoised, sigma) return denoised def get_sigma_gen(self, num_sigmas): sigma_generator = range(num_sigmas - 1) if self.verbose: print("#" * 30, " Sampling setting ", "#" * 30) print(f"Sampler: {self.__class__.__name__}") print(f"Discretization: {self.discretization.__class__.__name__}") print(f"Guider: {self.guider.__class__.__name__}") sigma_generator = tqdm( sigma_generator, total=num_sigmas, desc=f"Sampling with {self.__class__.__name__} for {num_sigmas} steps", ) return sigma_generator class SingleStepDiffusionSampler(BaseDiffusionSampler): def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc, *args, **kwargs): raise NotImplementedError def euler_step(self, x, d, dt): return x + dt * d class EDMSampler(SingleStepDiffusionSampler): def __init__(self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, *args, **kwargs): super().__init__(*args, **kwargs) self.s_churn = s_churn self.s_tmin = s_tmin self.s_tmax = s_tmax self.s_noise = s_noise def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0): sigma_hat = sigma * (gamma + 1.0) if gamma > 0: eps = torch.randn_like(x) * self.s_noise x = x + eps * append_dims(sigma_hat**2 - sigma**2, x.ndim) ** 0.5 denoised = self.denoise(x, denoiser, sigma_hat, cond, uc) d = to_d(x, sigma_hat, denoised) dt = append_dims(next_sigma - sigma_hat, x.ndim) euler_step = self.euler_step(x, d, dt) x = self.possible_correction_step(euler_step, x, d, dt, next_sigma, denoiser, cond, uc) return x def __call__(self, denoiser, x, cond, uc=None, num_steps=None): x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) for i in self.get_sigma_gen(num_sigmas): gamma = ( min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1) if self.s_tmin <= sigmas[i] <= self.s_tmax else 0.0 ) x = self.sampler_step( s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc, gamma, ) return x class DDIMSampler(SingleStepDiffusionSampler): def __init__(self, s_noise=0.1, *args, **kwargs): super().__init__(*args, **kwargs) self.s_noise = s_noise def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, s_noise=0.0): denoised = self.denoise(x, denoiser, sigma, cond, uc) d = to_d(x, sigma, denoised) dt = append_dims(next_sigma * (1 - s_noise**2) ** 0.5 - sigma, x.ndim) euler_step = x + dt * d + s_noise * append_dims(next_sigma, x.ndim) * torch.randn_like(x) x = self.possible_correction_step(euler_step, x, d, dt, next_sigma, denoiser, cond, uc) return x def __call__(self, denoiser, x, cond, uc=None, num_steps=None): x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) for i in self.get_sigma_gen(num_sigmas): x = self.sampler_step( s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc, self.s_noise, ) return x class AncestralSampler(SingleStepDiffusionSampler): def __init__(self, eta=1.0, s_noise=1.0, *args, **kwargs): super().__init__(*args, **kwargs) self.eta = eta self.s_noise = s_noise self.noise_sampler = lambda x: torch.randn_like(x) def ancestral_euler_step(self, x, denoised, sigma, sigma_down): d = to_d(x, sigma, denoised) dt = append_dims(sigma_down - sigma, x.ndim) return self.euler_step(x, d, dt) def ancestral_step(self, x, sigma, next_sigma, sigma_up): x = torch.where( append_dims(next_sigma, x.ndim) > 0.0, x + self.noise_sampler(x) * self.s_noise * append_dims(sigma_up, x.ndim), x, ) return x def __call__(self, denoiser, x, cond, uc=None, num_steps=None): x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) for i in self.get_sigma_gen(num_sigmas): x = self.sampler_step( s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc, ) return x class LinearMultistepSampler(BaseDiffusionSampler): def __init__( self, order=4, *args, **kwargs, ): super().__init__(*args, **kwargs) self.order = order def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs): x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) ds = [] sigmas_cpu = sigmas.detach().cpu().numpy() for i in self.get_sigma_gen(num_sigmas): sigma = s_in * sigmas[i] denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc), **kwargs) denoised = self.guider(denoised, sigma) d = to_d(x, sigma, denoised) ds.append(d) if len(ds) > self.order: ds.pop(0) cur_order = min(i + 1, self.order) coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) return x class EulerEDMSampler(EDMSampler): def possible_correction_step(self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc): return euler_step class HeunEDMSampler(EDMSampler): def possible_correction_step(self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc): if torch.sum(next_sigma) < 1e-14: # Save a network evaluation if all noise levels are 0 return euler_step else: denoised = self.denoise(euler_step, denoiser, next_sigma, cond, uc) d_new = to_d(euler_step, next_sigma, denoised) d_prime = (d + d_new) / 2.0 # apply correction if noise level is not 0 x = torch.where(append_dims(next_sigma, x.ndim) > 0.0, x + d_prime * dt, euler_step) return x class EulerAncestralSampler(AncestralSampler): def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc): sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta) denoised = self.denoise(x, denoiser, sigma, cond, uc) x = self.ancestral_euler_step(x, denoised, sigma, sigma_down) x = self.ancestral_step(x, sigma, next_sigma, sigma_up) return x class DPMPP2SAncestralSampler(AncestralSampler): def get_variables(self, sigma, sigma_down): t, t_next = [to_neg_log_sigma(s) for s in (sigma, sigma_down)] h = t_next - t s = t + 0.5 * h return h, s, t, t_next def get_mult(self, h, s, t, t_next): mult1 = to_sigma(s) / to_sigma(t) mult2 = (-0.5 * h).expm1() mult3 = to_sigma(t_next) / to_sigma(t) mult4 = (-h).expm1() return mult1, mult2, mult3, mult4 def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, **kwargs): sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta) denoised = self.denoise(x, denoiser, sigma, cond, uc) x_euler = self.ancestral_euler_step(x, denoised, sigma, sigma_down) if torch.sum(sigma_down) < 1e-14: # Save a network evaluation if all noise levels are 0 x = x_euler else: h, s, t, t_next = self.get_variables(sigma, sigma_down) mult = [append_dims(mult, x.ndim) for mult in self.get_mult(h, s, t, t_next)] x2 = mult[0] * x - mult[1] * denoised denoised2 = self.denoise(x2, denoiser, to_sigma(s), cond, uc) x_dpmpp2s = mult[2] * x - mult[3] * denoised2 # apply correction if noise level is not 0 x = torch.where(append_dims(sigma_down, x.ndim) > 0.0, x_dpmpp2s, x_euler) x = self.ancestral_step(x, sigma, next_sigma, sigma_up) return x class DPMPP2MSampler(BaseDiffusionSampler): def get_variables(self, sigma, next_sigma, previous_sigma=None): t, t_next = [to_neg_log_sigma(s) for s in (sigma, next_sigma)] h = t_next - t if previous_sigma is not None: h_last = t - to_neg_log_sigma(previous_sigma) r = h_last / h return h, r, t, t_next else: return h, None, t, t_next def get_mult(self, h, r, t, t_next, previous_sigma): mult1 = to_sigma(t_next) / to_sigma(t) mult2 = (-h).expm1() if previous_sigma is not None: mult3 = 1 + 1 / (2 * r) mult4 = 1 / (2 * r) return mult1, mult2, mult3, mult4 else: return mult1, mult2 def sampler_step( self, old_denoised, previous_sigma, sigma, next_sigma, denoiser, x, cond, uc=None, ): denoised = self.denoise(x, denoiser, sigma, cond, uc) h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma) mult = [append_dims(mult, x.ndim) for mult in self.get_mult(h, r, t, t_next, previous_sigma)] x_standard = mult[0] * x - mult[1] * denoised if old_denoised is None or torch.sum(next_sigma) < 1e-14: # Save a network evaluation if all noise levels are 0 or on the first step return x_standard, denoised else: denoised_d = mult[2] * denoised - mult[3] * old_denoised x_advanced = mult[0] * x - mult[1] * denoised_d # apply correction if noise level is not 0 and not first step x = torch.where(append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard) return x, denoised def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs): x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) old_denoised = None for i in self.get_sigma_gen(num_sigmas): x, old_denoised = self.sampler_step( old_denoised, None if i == 0 else s_in * sigmas[i - 1], s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc=uc, ) return x class SDEDPMPP2MSampler(BaseDiffusionSampler): def get_variables(self, sigma, next_sigma, previous_sigma=None): t, t_next = [to_neg_log_sigma(s) for s in (sigma, next_sigma)] h = t_next - t if previous_sigma is not None: h_last = t - to_neg_log_sigma(previous_sigma) r = h_last / h return h, r, t, t_next else: return h, None, t, t_next def get_mult(self, h, r, t, t_next, previous_sigma): mult1 = to_sigma(t_next) / to_sigma(t) * (-h).exp() mult2 = (-2 * h).expm1() if previous_sigma is not None: mult3 = 1 + 1 / (2 * r) mult4 = 1 / (2 * r) return mult1, mult2, mult3, mult4 else: return mult1, mult2 def sampler_step( self, old_denoised, previous_sigma, sigma, next_sigma, denoiser, x, cond, uc=None, ): denoised = self.denoise(x, denoiser, sigma, cond, uc) h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma) mult = [append_dims(mult, x.ndim) for mult in self.get_mult(h, r, t, t_next, previous_sigma)] mult_noise = append_dims(next_sigma * (1 - (-2 * h).exp()) ** 0.5, x.ndim) x_standard = mult[0] * x - mult[1] * denoised + mult_noise * torch.randn_like(x) if old_denoised is None or torch.sum(next_sigma) < 1e-14: # Save a network evaluation if all noise levels are 0 or on the first step return x_standard, denoised else: denoised_d = mult[2] * denoised - mult[3] * old_denoised x_advanced = mult[0] * x - mult[1] * denoised_d + mult_noise * torch.randn_like(x) # apply correction if noise level is not 0 and not first step x = torch.where(append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard) return x, denoised def __call__(self, denoiser, x, cond, uc=None, num_steps=None, scale=None, **kwargs): x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(x, cond, uc, num_steps) old_denoised = None for i in self.get_sigma_gen(num_sigmas): x, old_denoised = self.sampler_step( old_denoised, None if i == 0 else s_in * sigmas[i - 1], s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc=uc, ) return x class SdeditEDMSampler(EulerEDMSampler): def __init__(self, edit_ratio=0.5, *args, **kwargs): super().__init__(*args, **kwargs) self.edit_ratio = edit_ratio def __call__(self, denoiser, image, randn, cond, uc=None, num_steps=None, edit_ratio=None): randn_unit = randn.clone() randn, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(randn, cond, uc, num_steps) if num_steps is None: num_steps = self.num_steps if edit_ratio is None: edit_ratio = self.edit_ratio x = None for i in self.get_sigma_gen(num_sigmas): if i / num_steps < edit_ratio: continue if x is None: x = image + randn_unit * append_dims(s_in * sigmas[i], len(randn_unit.shape)) gamma = ( min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1) if self.s_tmin <= sigmas[i] <= self.s_tmax else 0.0 ) x = self.sampler_step( s_in * sigmas[i], s_in * sigmas[i + 1], denoiser, x, cond, uc, gamma, ) return x class VideoDDIMSampler(BaseDiffusionSampler): def __init__(self, fixed_frames=0, sdedit=False, **kwargs): super().__init__(**kwargs) self.fixed_frames = fixed_frames self.sdedit = sdedit def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None): alpha_cumprod_sqrt, timesteps = self.discretization( self.num_steps if num_steps is None else num_steps, device=self.device, return_idx=True, do_append_zero=False, ) alpha_cumprod_sqrt = torch.cat([alpha_cumprod_sqrt, alpha_cumprod_sqrt.new_ones([1])]) timesteps = torch.cat([torch.tensor(list(timesteps)).new_zeros([1]) - 1, torch.tensor(list(timesteps))]) uc = default(uc, cond) num_sigmas = len(alpha_cumprod_sqrt) s_in = x.new_ones([x.shape[0]]) return x, s_in, alpha_cumprod_sqrt, num_sigmas, cond, uc, timesteps def denoise(self, x, denoiser, alpha_cumprod_sqrt, cond, uc, timestep=None, idx=None, scale=None, scale_emb=None): additional_model_inputs = {} if isinstance(scale, torch.Tensor) == False and scale == 1: additional_model_inputs["idx"] = x.new_ones([x.shape[0]]) * timestep if scale_emb is not None: additional_model_inputs["scale_emb"] = scale_emb denoised = denoiser(x, alpha_cumprod_sqrt, cond, **additional_model_inputs).to(torch.float32) else: additional_model_inputs["idx"] = torch.cat([x.new_ones([x.shape[0]]) * timestep] * 2) denoised = denoiser( *self.guider.prepare_inputs(x, alpha_cumprod_sqrt, cond, uc), **additional_model_inputs ).to(torch.float32) if isinstance(self.guider, DynamicCFG): denoised = self.guider( denoised, (1 - alpha_cumprod_sqrt**2) ** 0.5, step_index=self.num_steps - timestep, scale=scale ) else: denoised = self.guider(denoised, (1 - alpha_cumprod_sqrt**2) ** 0.5, scale=scale) return denoised def sampler_step( self, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, denoiser, x, cond, uc=None, idx=None, timestep=None, scale=None, scale_emb=None, ): denoised = self.denoise( x, denoiser, alpha_cumprod_sqrt, cond, uc, timestep, idx, scale=scale, scale_emb=scale_emb ).to(torch.float32) a_t = ((1 - next_alpha_cumprod_sqrt**2) / (1 - alpha_cumprod_sqrt**2)) ** 0.5 b_t = next_alpha_cumprod_sqrt - alpha_cumprod_sqrt * a_t x = append_dims(a_t, x.ndim) * x + append_dims(b_t, x.ndim) * denoised return x def __call__(self, denoiser, x, cond, uc=None, num_steps=None, scale=None, scale_emb=None): x, s_in, alpha_cumprod_sqrt, num_sigmas, cond, uc, timesteps = self.prepare_sampling_loop( x, cond, uc, num_steps ) for i in self.get_sigma_gen(num_sigmas): x = self.sampler_step( s_in * alpha_cumprod_sqrt[i], s_in * alpha_cumprod_sqrt[i + 1], denoiser, x, cond, uc, idx=self.num_steps - i, timestep=timesteps[-(i + 1)], scale=scale, scale_emb=scale_emb, ) return x class VPSDEDPMPP2MSampler(VideoDDIMSampler): def get_variables(self, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt=None): alpha_cumprod = alpha_cumprod_sqrt**2 lamb = ((alpha_cumprod / (1 - alpha_cumprod)) ** 0.5).log() next_alpha_cumprod = next_alpha_cumprod_sqrt**2 lamb_next = ((next_alpha_cumprod / (1 - next_alpha_cumprod)) ** 0.5).log() h = lamb_next - lamb if previous_alpha_cumprod_sqrt is not None: previous_alpha_cumprod = previous_alpha_cumprod_sqrt**2 lamb_previous = ((previous_alpha_cumprod / (1 - previous_alpha_cumprod)) ** 0.5).log() h_last = lamb - lamb_previous r = h_last / h return h, r, lamb, lamb_next else: return h, None, lamb, lamb_next def get_mult(self, h, r, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt): mult1 = ((1 - next_alpha_cumprod_sqrt**2) / (1 - alpha_cumprod_sqrt**2)) ** 0.5 * (-h).exp() mult2 = (-2 * h).expm1() * next_alpha_cumprod_sqrt if previous_alpha_cumprod_sqrt is not None: mult3 = 1 + 1 / (2 * r) mult4 = 1 / (2 * r) return mult1, mult2, mult3, mult4 else: return mult1, mult2 def sampler_step( self, old_denoised, previous_alpha_cumprod_sqrt, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, denoiser, x, cond, uc=None, idx=None, timestep=None, scale=None, scale_emb=None, ): denoised = self.denoise( x, denoiser, alpha_cumprod_sqrt, cond, uc, timestep, idx, scale=scale, scale_emb=scale_emb ).to(torch.float32) if idx == 1: return denoised, denoised h, r, lamb, lamb_next = self.get_variables( alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt ) mult = [ append_dims(mult, x.ndim) for mult in self.get_mult(h, r, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt) ] mult_noise = append_dims((1 - next_alpha_cumprod_sqrt**2) ** 0.5 * (1 - (-2 * h).exp()) ** 0.5, x.ndim) x_standard = mult[0] * x - mult[1] * denoised + mult_noise * torch.randn_like(x) if old_denoised is None or torch.sum(next_alpha_cumprod_sqrt) < 1e-14: # Save a network evaluation if all noise levels are 0 or on the first step return x_standard, denoised else: denoised_d = mult[2] * denoised - mult[3] * old_denoised x_advanced = mult[0] * x - mult[1] * denoised_d + mult_noise * torch.randn_like(x) x = x_advanced return x, denoised def __call__(self, denoiser, x, cond, uc=None, num_steps=None, scale=None, scale_emb=None): x, s_in, alpha_cumprod_sqrt, num_sigmas, cond, uc, timesteps = self.prepare_sampling_loop( x, cond, uc, num_steps ) if self.fixed_frames > 0: prefix_frames = x[:, : self.fixed_frames] old_denoised = None for i in self.get_sigma_gen(num_sigmas): if self.fixed_frames > 0: if self.sdedit: rd = torch.randn_like(prefix_frames) noised_prefix_frames = alpha_cumprod_sqrt[i] * prefix_frames + rd * append_dims( s_in * (1 - alpha_cumprod_sqrt[i] ** 2) ** 0.5, len(prefix_frames.shape) ) x = torch.cat([noised_prefix_frames, x[:, self.fixed_frames :]], dim=1) else: x = torch.cat([prefix_frames, x[:, self.fixed_frames :]], dim=1) x, old_denoised = self.sampler_step( old_denoised, None if i == 0 else s_in * alpha_cumprod_sqrt[i - 1], s_in * alpha_cumprod_sqrt[i], s_in * alpha_cumprod_sqrt[i + 1], denoiser, x, cond, uc=uc, idx=self.num_steps - i, timestep=timesteps[-(i + 1)], scale=scale, scale_emb=scale_emb, ) if self.fixed_frames > 0: x = torch.cat([prefix_frames, x[:, self.fixed_frames :]], dim=1) return x class VPODEDPMPP2MSampler(VideoDDIMSampler): def get_variables(self, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt=None): alpha_cumprod = alpha_cumprod_sqrt**2 lamb = ((alpha_cumprod / (1 - alpha_cumprod)) ** 0.5).log() next_alpha_cumprod = next_alpha_cumprod_sqrt**2 lamb_next = ((next_alpha_cumprod / (1 - next_alpha_cumprod)) ** 0.5).log() h = lamb_next - lamb if previous_alpha_cumprod_sqrt is not None: previous_alpha_cumprod = previous_alpha_cumprod_sqrt**2 lamb_previous = ((previous_alpha_cumprod / (1 - previous_alpha_cumprod)) ** 0.5).log() h_last = lamb - lamb_previous r = h_last / h return h, r, lamb, lamb_next else: return h, None, lamb, lamb_next def get_mult(self, h, r, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt): mult1 = ((1 - next_alpha_cumprod_sqrt**2) / (1 - alpha_cumprod_sqrt**2)) ** 0.5 mult2 = (-h).expm1() * next_alpha_cumprod_sqrt if previous_alpha_cumprod_sqrt is not None: mult3 = 1 + 1 / (2 * r) mult4 = 1 / (2 * r) return mult1, mult2, mult3, mult4 else: return mult1, mult2 def sampler_step( self, old_denoised, previous_alpha_cumprod_sqrt, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, denoiser, x, cond, uc=None, idx=None, timestep=None, ): denoised = self.denoise(x, denoiser, alpha_cumprod_sqrt, cond, uc, timestep, idx).to(torch.float32) if idx == 1: return denoised, denoised h, r, lamb, lamb_next = self.get_variables( alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt ) mult = [ append_dims(mult, x.ndim) for mult in self.get_mult(h, r, alpha_cumprod_sqrt, next_alpha_cumprod_sqrt, previous_alpha_cumprod_sqrt) ] x_standard = mult[0] * x - mult[1] * denoised if old_denoised is None or torch.sum(next_alpha_cumprod_sqrt) < 1e-14: # Save a network evaluation if all noise levels are 0 or on the first step return x_standard, denoised else: denoised_d = mult[2] * denoised - mult[3] * old_denoised x_advanced = mult[0] * x - mult[1] * denoised_d x = x_advanced return x, denoised def __call__(self, denoiser, x, cond, uc=None, num_steps=None, scale=None, **kwargs): x, s_in, alpha_cumprod_sqrt, num_sigmas, cond, uc, timesteps = self.prepare_sampling_loop( x, cond, uc, num_steps ) old_denoised = None for i in self.get_sigma_gen(num_sigmas): x, old_denoised = self.sampler_step( old_denoised, None if i == 0 else s_in * alpha_cumprod_sqrt[i - 1], s_in * alpha_cumprod_sqrt[i], s_in * alpha_cumprod_sqrt[i + 1], denoiser, x, cond, uc=uc, idx=self.num_steps - i, timestep=timesteps[-(i + 1)], ) return x