diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 8ec52c9ff7..16f740066e 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -10,9 +10,5 @@ from .models.unet_glide import GLIDESuperResUNetModel, GLIDETextToImageUNetModel from .models.unet_ldm import UNetLDMModel from .pipeline_utils import DiffusionPipeline from .pipelines import DDIM, DDPM, GLIDE, LatentDiffusion - -from .schedulers import SchedulerMixin, DDIMScheduler, DDPMScheduler - +from .schedulers import DDIMScheduler, DDPMScheduler, SchedulerMixin from .schedulers.classifier_free_guidance import ClassifierFreeGuidanceScheduler -from .schedulers.ddim import DDIMScheduler -from .schedulers.gaussian_ddpm import GaussianDDPMScheduler diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index eb8664dbc6..3b1cf92c77 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -16,12 +16,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from .classifier_free_guidance import ClassifierFreeGuidanceScheduler from .scheduling_ddim import DDIMScheduler from .scheduling_ddpm import DDPMScheduler from .scheduling_utils import SchedulerMixin - -from .classifier_free_guidance import ClassifierFreeGuidanceScheduler -from .ddim import DDIMScheduler -from .gaussian_ddpm import GaussianDDPMScheduler -from .glide_ddim import GlideDDIMScheduler -from .schedulers_utils import SchedulerMixin diff --git a/src/diffusers/schedulers/scheduling_plms.py b/src/diffusers/schedulers/scheduling_plms.py index 119747fa1d..fd9809e5b5 100644 --- a/src/diffusers/schedulers/scheduling_plms.py +++ b/src/diffusers/schedulers/scheduling_plms.py @@ -15,6 +15,7 @@ import math import numpy as np import torch + from tqdm import tqdm from ..configuration_utils import ConfigMixin @@ -28,11 +29,11 @@ def noise_like(shape, device, repeat=False): def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): - if ddim_discr_method == 'uniform': + if ddim_discr_method == "uniform": c = num_ddpm_timesteps // num_ddim_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) - elif ddim_discr_method == 'quad': - ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + elif ddim_discr_method == "quad": + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') @@ -40,7 +41,7 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep # add one to get the final alpha values right (the ones from first scale to data during sampling) steps_out = ddim_timesteps + 1 if verbose: - print(f'Selected timesteps for ddim sampler: {steps_out}') + print(f"Selected timesteps for ddim sampler: {steps_out}") return steps_out @@ -52,9 +53,11 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): # according the the formula provided in https://arxiv.org/abs/2010.02502 sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: - print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') - print(f'For the chosen value of eta, which is {eta}, ' - f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + print(f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}") + print( + f"For the chosen value of eta, which is {eta}, " + f"this results in the following sigma_t schedule for ddim sampler {sigmas}" + ) return sigmas, alphas, alphas_prev @@ -71,64 +74,71 @@ class PLMSSampler(object): attr = attr.to(torch.device("cuda")) setattr(self, name, attr) - def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True): if ddim_eta != 0: - raise ValueError('ddim_eta must be 0 for PLMS') - self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, - num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + raise ValueError("ddim_eta must be 0 for PLMS") + self.ddim_timesteps = make_ddim_timesteps( + ddim_discr_method=ddim_discretize, + num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps, + verbose=verbose, + ) alphas_cumprod = self.model.alphas_cumprod - assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, "alphas have to be defined for each timestep" to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) - self.register_buffer('betas', to_torch(self.model.betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + self.register_buffer("betas", to_torch(self.model.betas)) + self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) + self.register_buffer("alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer("sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod.cpu()))) + self.register_buffer("log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))) + self.register_buffer("sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))) + self.register_buffer("sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1))) # ddim sampling parameters - ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), - ddim_timesteps=self.ddim_timesteps, - eta=ddim_eta,verbose=verbose) - self.register_buffer('ddim_sigmas', ddim_sigmas) - self.register_buffer('ddim_alphas', ddim_alphas) - self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) - self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( + alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta, verbose=verbose + ) + self.register_buffer("ddim_sigmas", ddim_sigmas) + self.register_buffer("ddim_alphas", ddim_alphas) + self.register_buffer("ddim_alphas_prev", ddim_alphas_prev) + self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( - (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( - 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) - self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + (1 - self.alphas_cumprod_prev) + / (1 - self.alphas_cumprod) + * (1 - self.alphas_cumprod / self.alphas_cumprod_prev) + ) + self.register_buffer("ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps) @torch.no_grad() - def sample(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): + def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0.0, + mask=None, + x0=None, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs, + ): if conditioning is not None: if isinstance(conditioning, dict): cbs = conditioning[list(conditioning.keys())[0]].shape[0] @@ -142,32 +152,49 @@ class PLMSSampler(object): # sampling C, H, W = shape size = (batch_size, C, H, W) - print(f'Data shape for PLMS sampling is {size}') + print(f"Data shape for PLMS sampling is {size}") - samples, intermediates = self.plms_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - ) + samples, intermediates = self.plms_sampling( + conditioning, + size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, + x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) return samples, intermediates @torch.no_grad() - def plms_sampling(self, cond, shape, - x_T=None, ddim_use_original_steps=False, - callback=None, timesteps=None, quantize_denoised=False, - mask=None, x0=None, img_callback=None, log_every_t=100, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None,): + def plms_sampling( + self, + cond, + shape, + x_T=None, + ddim_use_original_steps=False, + callback=None, + timesteps=None, + quantize_denoised=False, + mask=None, + x0=None, + img_callback=None, + log_every_t=100, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + ): device = self.model.betas.device b = shape[0] if x_T is None: @@ -181,12 +208,12 @@ class PLMSSampler(object): subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] - intermediates = {'x_inter': [img], 'pred_x0': [img]} - time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) + intermediates = {"x_inter": [img], "pred_x0": [img]} + time_range = list(reversed(range(0, timesteps))) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running PLMS Sampling with {total_steps} timesteps") - iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) + iterator = tqdm(time_range, desc="PLMS Sampler", total=total_steps) old_eps = [] for i, step in enumerate(iterator): @@ -197,36 +224,62 @@ class PLMSSampler(object): if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? - img = img_orig * mask + (1. - mask) * img + img = img_orig * mask + (1.0 - mask) * img - outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, - quantize_denoised=quantize_denoised, temperature=temperature, - noise_dropout=noise_dropout, score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - old_eps=old_eps, t_next=ts_next) + outs = self.p_sample_plms( + img, + cond, + ts, + index=index, + use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, + temperature=temperature, + noise_dropout=noise_dropout, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, + t_next=ts_next, + ) img, pred_x0, e_t = outs old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) - if callback: callback(i) - if img_callback: img_callback(pred_x0, i) + if callback: + callback(i) + if img_callback: + img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) + intermediates["x_inter"].append(img) + intermediates["pred_x0"].append(pred_x0) return img, intermediates @torch.no_grad() - def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): + def p_sample_plms( + self, + x, + c, + t, + index, + repeat_noise=False, + use_original_steps=False, + quantize_denoised=False, + temperature=1.0, + noise_dropout=0.0, + score_corrector=None, + corrector_kwargs=None, + unconditional_guidance_scale=1.0, + unconditional_conditioning=None, + old_eps=None, + t_next=None, + ): b, *_, device = *x.shape, x.device def get_model_output(x, t): - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + if unconditional_conditioning is None or unconditional_guidance_scale == 1.0: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) @@ -243,7 +296,9 @@ class PLMSSampler(object): alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sqrt_one_minus_alphas = ( + self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + ) sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas def get_x_prev_and_pred_x0(e_t, index): @@ -251,16 +306,16 @@ class PLMSSampler(object): a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: + if noise_dropout > 0.0: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0