Spaces:
Runtime error
Runtime error
| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from functools import partial | |
| from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like | |
| from .ddim import DDIMSampler | |
| class DDIMSampler_DualContext(DDIMSampler): | |
| def sample_text(self, *args, **kwargs): | |
| self.cond_type = 'prompt' | |
| return self.sample(*args, **kwargs) | |
| def sample_vision(self, *args, **kwargs): | |
| self.cond_type = 'vision' | |
| return self.sample(*args, **kwargs) | |
| def sample_mixed(self, *args, **kwargs): | |
| self.cond_type = kwargs.pop('cond_mixed_p') | |
| return self.sample(*args, **kwargs) | |
| def sample(self, | |
| steps, | |
| shape, | |
| xt=None, | |
| conditioning=None, | |
| eta=0., | |
| temperature=1., | |
| noise_dropout=0., | |
| verbose=True, | |
| log_every_t=100, | |
| unconditional_guidance_scale=1., | |
| unconditional_conditioning=None,): | |
| self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose) | |
| # sampling | |
| print(f'Data shape for DDIM sampling is {shape}, eta {eta}') | |
| samples, intermediates = self.ddim_sampling( | |
| conditioning, | |
| shape, | |
| xt=xt, | |
| ddim_use_original_steps=False, | |
| noise_dropout=noise_dropout, | |
| temperature=temperature, | |
| log_every_t=log_every_t, | |
| unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=unconditional_conditioning,) | |
| return samples, intermediates | |
| def ddim_sampling(self, | |
| conditioning, | |
| shape, | |
| xt=None, | |
| ddim_use_original_steps=False, | |
| timesteps=None, | |
| log_every_t=100, | |
| temperature=1., | |
| noise_dropout=0., | |
| unconditional_guidance_scale=1., | |
| unconditional_conditioning=None,): | |
| device = self.model.betas.device | |
| bs = shape[0] | |
| if xt is None: | |
| img = torch.randn(shape, device=device) | |
| else: | |
| img = xt | |
| 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] | |
| intermediates = {'x_inter': [img], 'pred_x0': [img]} | |
| 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") | |
| iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) | |
| for i, step in enumerate(iterator): | |
| index = total_steps - i - 1 | |
| ts = torch.full((bs,), step, device=device, dtype=torch.long) | |
| outs = self.p_sample_ddim(img, conditioning, ts, index=index, use_original_steps=ddim_use_original_steps, | |
| temperature=temperature, | |
| noise_dropout=noise_dropout, | |
| unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=unconditional_conditioning) | |
| img, pred_x0 = outs | |
| 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, conditioning, t, index, repeat_noise=False, use_original_steps=False, | |
| temperature=1., noise_dropout=0., | |
| unconditional_guidance_scale=1., unconditional_conditioning=None): | |
| b, *_, device = *x.shape, x.device | |
| if unconditional_conditioning is None or unconditional_guidance_scale == 1.: | |
| e_t = self.model.apply_model(x, t, conditioning, cond_type=self.cond_type) | |
| else: | |
| x_in = torch.cat([x] * 2) | |
| t_in = torch.cat([t] * 2) | |
| # c_in = torch.cat([unconditional_conditioning, conditioning]) | |
| # Added for vd-dc dual guidance | |
| if isinstance(unconditional_conditioning, list): | |
| c_in = [torch.cat([ui, ci]) for ui, ci in zip(unconditional_conditioning, conditioning)] | |
| else: | |
| c_in = torch.cat([unconditional_conditioning, conditioning]) | |
| e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, cond_type=self.cond_type).chunk(2) | |
| e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) | |
| 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() | |
| 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 | |