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| import os | |
| import torch | |
| from einops import repeat | |
| from omegaconf import ListConfig | |
| import ldm.models.diffusion.ddpm | |
| import ldm.models.diffusion.ddim | |
| import ldm.models.diffusion.plms | |
| from ldm.models.diffusion.ddpm import LatentDiffusion | |
| from ldm.models.diffusion.plms import PLMSSampler | |
| from ldm.models.diffusion.ddim import DDIMSampler, noise_like | |
| from ldm.models.diffusion.sampling_util import norm_thresholding | |
| 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, dynamic_threshold=None): | |
| b, *_, device = *x.shape, x.device | |
| def get_model_output(x, t): | |
| if unconditional_conditioning is None or unconditional_guidance_scale == 1.: | |
| e_t = self.model.apply_model(x, t, c) | |
| else: | |
| x_in = torch.cat([x] * 2) | |
| t_in = torch.cat([t] * 2) | |
| if isinstance(c, dict): | |
| assert isinstance(unconditional_conditioning, dict) | |
| c_in = dict() | |
| for k in c: | |
| if isinstance(c[k], list): | |
| c_in[k] = [ | |
| torch.cat([unconditional_conditioning[k][i], c[k][i]]) | |
| for i in range(len(c[k])) | |
| ] | |
| else: | |
| c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) | |
| else: | |
| c_in = torch.cat([unconditional_conditioning, c]) | |
| e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) | |
| e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) | |
| if score_corrector is not None: | |
| assert self.model.parameterization == "eps" | |
| e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) | |
| return e_t | |
| 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 | |
| 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): | |
| # select parameters corresponding to the currently considered timestep | |
| 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) | |
| # 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) | |
| if dynamic_threshold is not None: | |
| pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) | |
| # direction pointing to x_t | |
| dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t | |
| noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature | |
| if noise_dropout > 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 | |
| e_t = get_model_output(x, t) | |
| if len(old_eps) == 0: | |
| # Pseudo Improved Euler (2nd order) | |
| x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) | |
| e_t_next = get_model_output(x_prev, t_next) | |
| 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 | |
| def do_inpainting_hijack(): | |
| # p_sample_plms is needed because PLMS can't work with dicts as conditionings | |
| ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms | |