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import torch |
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import numpy as np |
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from tqdm import tqdm |
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from functools import partial |
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from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like |
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from .ddim import DDIMSampler |
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class DDIMSampler_DualContext(DDIMSampler): |
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@torch.no_grad() |
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def sample_text(self, *args, **kwargs): |
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self.cond_type = 'prompt' |
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return self.sample(*args, **kwargs) |
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@torch.no_grad() |
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def sample_vision(self, *args, **kwargs): |
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self.cond_type = 'vision' |
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return self.sample(*args, **kwargs) |
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@torch.no_grad() |
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def sample_mixed(self, *args, **kwargs): |
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self.cond_type = kwargs.pop('cond_mixed_p') |
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return self.sample(*args, **kwargs) |
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@torch.no_grad() |
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def sample(self, |
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steps, |
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shape, |
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xt=None, |
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conditioning=None, |
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eta=0., |
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temperature=1., |
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noise_dropout=0., |
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verbose=True, |
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log_every_t=100, |
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unconditional_guidance_scale=1., |
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unconditional_conditioning=None,): |
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self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose) |
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print(f'Data shape for DDIM sampling is {shape}, eta {eta}') |
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samples, intermediates = self.ddim_sampling( |
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conditioning, |
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shape, |
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xt=xt, |
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ddim_use_original_steps=False, |
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noise_dropout=noise_dropout, |
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temperature=temperature, |
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log_every_t=log_every_t, |
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unconditional_guidance_scale=unconditional_guidance_scale, |
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unconditional_conditioning=unconditional_conditioning,) |
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return samples, intermediates |
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@torch.no_grad() |
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def ddim_sampling(self, |
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conditioning, |
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shape, |
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xt=None, |
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ddim_use_original_steps=False, |
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timesteps=None, |
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log_every_t=100, |
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temperature=1., |
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noise_dropout=0., |
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unconditional_guidance_scale=1., |
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unconditional_conditioning=None,): |
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device = self.model.betas.device |
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bs = shape[0] |
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if xt is None: |
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img = torch.randn(shape, device=device) |
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else: |
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img = xt |
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if timesteps is None: |
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timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps |
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elif timesteps is not None and not ddim_use_original_steps: |
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subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 |
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timesteps = self.ddim_timesteps[:subset_end] |
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intermediates = {'x_inter': [img], 'pred_x0': [img]} |
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time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) |
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total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] |
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print(f"Running DDIM Sampling with {total_steps} timesteps") |
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iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) |
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for i, step in enumerate(iterator): |
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index = total_steps - i - 1 |
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ts = torch.full((bs,), step, device=device, dtype=torch.long) |
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outs = self.p_sample_ddim(img, conditioning, ts, index=index, use_original_steps=ddim_use_original_steps, |
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temperature=temperature, |
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noise_dropout=noise_dropout, |
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unconditional_guidance_scale=unconditional_guidance_scale, |
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unconditional_conditioning=unconditional_conditioning) |
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img, pred_x0 = outs |
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if index % log_every_t == 0 or index == total_steps - 1: |
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intermediates['x_inter'].append(img) |
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intermediates['pred_x0'].append(pred_x0) |
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return img, intermediates |
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@torch.no_grad() |
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def p_sample_ddim(self, x, conditioning, t, index, repeat_noise=False, use_original_steps=False, |
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temperature=1., noise_dropout=0., |
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unconditional_guidance_scale=1., unconditional_conditioning=None): |
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b, *_, device = *x.shape, x.device |
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if unconditional_conditioning is None or unconditional_guidance_scale == 1.: |
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e_t = self.model.apply_model(x, t, conditioning, cond_type=self.cond_type) |
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else: |
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x_in = torch.cat([x] * 2) |
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t_in = torch.cat([t] * 2) |
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if isinstance(unconditional_conditioning, list): |
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c_in = [torch.cat([ui, ci]) for ui, ci in zip(unconditional_conditioning, conditioning)] |
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else: |
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c_in = torch.cat([unconditional_conditioning, conditioning]) |
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e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, cond_type=self.cond_type).chunk(2) |
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e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) |
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas |
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alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev |
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas |
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sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas |
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) |
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) |
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) |
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sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) |
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() |
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dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t |
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noise = sigma_t * noise_like(x, repeat_noise) * temperature |
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if noise_dropout > 0.: |
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noise = torch.nn.functional.dropout(noise, p=noise_dropout) |
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise |
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return x_prev, pred_x0 |
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