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| """SAMPLING ONLY.""" | |
| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from functools import partial | |
| from ldm.modules.diffusionmodules.util import ( | |
| make_ddim_sampling_parameters, | |
| make_ddim_timesteps, | |
| noise_like, | |
| ) | |
| class DDIMSampler(object): | |
| def __init__(self, model, schedule="linear", **kwargs): | |
| super().__init__() | |
| self.model = model | |
| self.ddpm_num_timesteps = model.num_timesteps | |
| self.schedule = schedule | |
| def register_buffer(self, name, attr): | |
| if type(attr) == torch.Tensor: | |
| if attr.device != self.model.device: | |
| attr = attr.to(self.model.device) | |
| setattr(self, name, attr) | |
| def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True): | |
| 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" | |
| 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)) | |
| # 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(1.0 - 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(1.0 - 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 | |
| ) | |
| 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, | |
| org_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, | |
| skip_steps=0, | |
| init_image=None, | |
| percentage_of_pixel_blending=0, | |
| # 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] | |
| if cbs != batch_size: | |
| print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") | |
| else: | |
| if conditioning.shape[0] != batch_size: | |
| print( | |
| f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}" | |
| ) | |
| self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) | |
| # sampling | |
| C, H, W = shape | |
| size = (batch_size, C, H, W) | |
| print(f"Data shape for DDIM sampling is {size}, eta {eta}") | |
| samples, intermediates = self.ddim_sampling( | |
| conditioning, | |
| size, | |
| callback=callback, | |
| img_callback=img_callback, | |
| quantize_denoised=quantize_x0, | |
| mask=mask, | |
| org_mask=org_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, | |
| skip_steps=skip_steps, | |
| init_image=init_image, | |
| percentage_of_pixel_blending=percentage_of_pixel_blending, | |
| ) | |
| return samples, intermediates | |
| def ddim_sampling( | |
| self, | |
| cond, | |
| shape, | |
| x_T=None, | |
| ddim_use_original_steps=False, | |
| callback=None, | |
| timesteps=None, | |
| quantize_denoised=False, | |
| mask=None, | |
| org_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, | |
| skip_steps=0, | |
| init_image=None, | |
| percentage_of_pixel_blending=0, | |
| ): | |
| device = self.model.betas.device | |
| b = shape[0] | |
| if timesteps is None: | |
| timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps | |
| elif timesteps is not None and not ddim_use_original_steps: | |
| subset_end = ( | |
| int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) | |
| - 1 | |
| ) | |
| timesteps = self.ddim_timesteps[:subset_end] | |
| if skip_steps != 0: | |
| timesteps = timesteps[:-skip_steps] | |
| time_range = ( | |
| 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 DDIM Sampling with {total_steps} timesteps") | |
| if init_image is not None: | |
| assert ( | |
| x0 is None and x_T is None | |
| ), "Try to infer x0 and x_t from init_image, but they already provided" | |
| encoder_posterior = self.model.encode_first_stage(init_image) | |
| x0 = self.model.get_first_stage_encoding(encoder_posterior) | |
| last_ts = torch.full((1,), time_range[0], device=device, dtype=torch.long) | |
| x_T = torch.cat([self.model.q_sample(x0, last_ts) for _ in range(b)]) | |
| img = x_T | |
| elif x_T is None: | |
| img = torch.randn(shape, device=device) | |
| else: | |
| img = x_T | |
| intermediates = {"x_inter": [img], "pred_x0": [img]} | |
| iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps) | |
| cutoff_point = int(len(time_range) * (1 - percentage_of_pixel_blending)) | |
| for i, step in enumerate(iterator): | |
| index = total_steps - i - 1 | |
| ts = torch.full((b,), step, device=device, dtype=torch.long) | |
| # Latent-level blending | |
| if mask is not None and i < cutoff_point: | |
| n_masks = mask.shape[0] | |
| masks_interval = len(time_range) // n_masks + 1 | |
| curr_mask = mask[i // masks_interval].unsqueeze(0) | |
| # print(f"Using index {i // masks_interval}") | |
| img_orig = self.model.q_sample(x0, ts) | |
| img = img_orig * (1 - curr_mask) + curr_mask * img | |
| outs = self.p_sample_ddim( | |
| 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, | |
| ) | |
| img, pred_x0 = outs | |
| # Pixel-level blending | |
| if org_mask is not None and i >= cutoff_point: | |
| foreground_pixels = self.model.decode_first_stage(pred_x0) | |
| background_pixels = init_image | |
| pixel_blended = foreground_pixels * org_mask + background_pixels * (1 - org_mask) | |
| img_x0 = self.model.get_first_stage_encoding( | |
| self.model.encode_first_stage(pixel_blended) | |
| ) | |
| img = self.model.q_sample(img_x0, ts) | |
| 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) | |
| return img, intermediates | |
| def p_sample_ddim( | |
| 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, | |
| ): | |
| b, *_, device = *x.shape, x.device | |
| 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) | |
| t_in = torch.cat([t] * 2) | |
| 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) | |
| 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 | |
| ) | |
| # 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) | |
| # direction pointing to x_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 > 0.0: | |
| noise = torch.nn.functional.dropout(noise, p=noise_dropout) | |
| x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise | |
| return x_prev, pred_x0 | |