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| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from functools import partial | |
| from copy import deepcopy | |
| from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like | |
| class PLMSSampler(object): | |
| def __init__(self, diffusion, model, schedule="linear", alpha_generator_func=None, set_alpha_scale=None): | |
| super().__init__() | |
| self.diffusion = diffusion | |
| self.model = model | |
| self.device = diffusion.betas.device | |
| self.ddpm_num_timesteps = diffusion.num_timesteps | |
| self.schedule = schedule | |
| self.alpha_generator_func = alpha_generator_func | |
| self.set_alpha_scale = set_alpha_scale | |
| def register_buffer(self, name, attr): | |
| if type(attr) == torch.Tensor: | |
| attr = attr.to(self.device) | |
| setattr(self, name, attr) | |
| def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=False): | |
| 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) | |
| alphas_cumprod = self.diffusion.alphas_cumprod | |
| 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.device) | |
| self.register_buffer('betas', to_torch(self.diffusion.betas)) | |
| self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) | |
| self.register_buffer('alphas_cumprod_prev', to_torch(self.diffusion.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))) | |
| # 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)) | |
| 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) | |
| def sample(self, S, shape, input, uc=None, guidance_scale=1, mask=None, x0=None): | |
| self.make_schedule(ddim_num_steps=S) | |
| return self.plms_sampling(shape, input, uc, guidance_scale, mask=mask, x0=x0) | |
| def plms_sampling(self, shape, input, uc=None, guidance_scale=1, mask=None, x0=None): | |
| b = shape[0] | |
| img = input["x"] | |
| if img == None: | |
| img = torch.randn(shape, device=self.device) | |
| input["x"] = img | |
| time_range = np.flip(self.ddim_timesteps) | |
| total_steps = self.ddim_timesteps.shape[0] | |
| old_eps = [] | |
| if self.alpha_generator_func != None: | |
| alphas = self.alpha_generator_func(len(time_range)) | |
| for i, step in enumerate(time_range): | |
| # set alpha | |
| if self.alpha_generator_func != None: | |
| self.set_alpha_scale(self.model, alphas[i]) | |
| # run | |
| index = total_steps - i - 1 | |
| ts = torch.full((b,), step, device=self.device, dtype=torch.long) | |
| ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=self.device, dtype=torch.long) | |
| if mask is not None: | |
| assert x0 is not None | |
| img_orig = self.diffusion.q_sample(x0, ts) | |
| img = img_orig * mask + (1. - mask) * img | |
| input["x"] = img | |
| img, pred_x0, e_t = self.p_sample_plms(input, ts, index=index, uc=uc, guidance_scale=guidance_scale, old_eps=old_eps, t_next=ts_next) | |
| input["x"] = img | |
| old_eps.append(e_t) | |
| if len(old_eps) >= 4: | |
| old_eps.pop(0) | |
| return img | |
| def p_sample_plms(self, input, t, index, guidance_scale=1., uc=None, old_eps=None, t_next=None): | |
| x = deepcopy(input["x"]) | |
| b = x.shape[0] | |
| def get_model_output(input): | |
| e_t = self.model(input) | |
| if uc is not None and guidance_scale != 1: | |
| unconditional_input = dict(x=input["x"], timesteps=input["timesteps"], context=uc) | |
| if "inpainting_extra_input" in input: | |
| unconditional_input["inpainting_extra_input"] = input["inpainting_extra_input"] | |
| e_t_uncond = self.model( unconditional_input ) | |
| e_t = e_t_uncond + guidance_scale * (e_t - e_t_uncond) | |
| return e_t | |
| def get_x_prev_and_pred_x0(e_t, index): | |
| # select parameters corresponding to the currently considered timestep | |
| a_t = torch.full((b, 1, 1, 1), self.ddim_alphas[index], device=self.device) | |
| a_prev = torch.full((b, 1, 1, 1), self.ddim_alphas_prev[index], device=self.device) | |
| sigma_t = torch.full((b, 1, 1, 1), self.ddim_sigmas[index], device=self.device) | |
| sqrt_one_minus_at = torch.full((b, 1, 1, 1), self.ddim_sqrt_one_minus_alphas[index],device=self.device) | |
| # current prediction for x_0 | |
| pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() | |
| # direction pointing to x_t | |
| dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t | |
| noise = sigma_t * torch.randn_like(x) | |
| x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise | |
| return x_prev, pred_x0 | |
| input["timesteps"] = t | |
| e_t = get_model_output(input) | |
| if len(old_eps) == 0: | |
| # Pseudo Improved Euler (2nd order) | |
| x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) | |
| input["x"] = x_prev | |
| input["timesteps"] = t_next | |
| e_t_next = get_model_output(input) | |
| e_t_prime = (e_t + e_t_next) / 2 | |
| elif len(old_eps) == 1: | |
| # 2nd order Pseudo Linear Multistep (Adams-Bashforth) | |
| e_t_prime = (3 * e_t - old_eps[-1]) / 2 | |
| elif len(old_eps) == 2: | |
| # 3nd order Pseudo Linear Multistep (Adams-Bashforth) | |
| e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 | |
| elif len(old_eps) >= 3: | |
| # 4nd order Pseudo Linear Multistep (Adams-Bashforth) | |
| e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 | |
| x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) | |
| return x_prev, pred_x0, e_t | |