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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 .utils import make_ddim_timesteps, make_ddim_sampling_parameters, noise_like |
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from loguru import logger |
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class DDIMSampler(object): |
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def __init__(self, model, schedule="linear"): |
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super().__init__() |
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self.model = model |
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self.ddpm_num_timesteps = model.num_timesteps |
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self.schedule = schedule |
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def register_buffer(self, name, attr): |
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setattr(self, name, attr) |
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def make_schedule( |
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self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True |
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): |
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self.ddim_timesteps = make_ddim_timesteps( |
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ddim_discr_method=ddim_discretize, |
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num_ddim_timesteps=ddim_num_steps, |
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num_ddpm_timesteps=self.ddpm_num_timesteps, |
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verbose=verbose, |
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) |
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alphas_cumprod = self.model.alphas_cumprod |
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assert ( |
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alphas_cumprod.shape[0] == self.ddpm_num_timesteps |
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), "alphas have to be defined for each timestep" |
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) |
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self.register_buffer("betas", to_torch(self.model.betas)) |
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self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod)) |
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self.register_buffer( |
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"alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev) |
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) |
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self.register_buffer( |
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"sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu())) |
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) |
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self.register_buffer( |
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"sqrt_one_minus_alphas_cumprod", |
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to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())), |
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) |
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self.register_buffer( |
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu())) |
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) |
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self.register_buffer( |
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())) |
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) |
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self.register_buffer( |
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"sqrt_recipm1_alphas_cumprod", |
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to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)), |
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) |
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ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters( |
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alphacums=alphas_cumprod.cpu(), |
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ddim_timesteps=self.ddim_timesteps, |
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eta=ddim_eta, |
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verbose=verbose, |
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) |
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self.register_buffer("ddim_sigmas", ddim_sigmas) |
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self.register_buffer("ddim_alphas", ddim_alphas) |
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self.register_buffer("ddim_alphas_prev", ddim_alphas_prev) |
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self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas)) |
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sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( |
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(1 - self.alphas_cumprod_prev) |
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/ (1 - self.alphas_cumprod) |
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* (1 - self.alphas_cumprod / self.alphas_cumprod_prev) |
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) |
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self.register_buffer( |
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"ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps |
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) |
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@torch.no_grad() |
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def sample(self, steps, conditioning, batch_size, shape): |
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self.make_schedule(ddim_num_steps=steps, ddim_eta=0, verbose=False) |
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C, H, W = shape |
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size = (batch_size, C, H, W) |
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return self.ddim_sampling( |
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conditioning, |
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size, |
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quantize_denoised=False, |
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ddim_use_original_steps=False, |
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noise_dropout=0, |
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temperature=1.0, |
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) |
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@torch.no_grad() |
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def ddim_sampling( |
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self, |
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cond, |
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shape, |
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ddim_use_original_steps=False, |
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quantize_denoised=False, |
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temperature=1.0, |
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noise_dropout=0.0, |
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): |
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device = self.model.betas.device |
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b = shape[0] |
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img = torch.randn(shape, device=device, dtype=cond.dtype) |
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timesteps = ( |
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self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps |
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) |
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time_range = ( |
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reversed(range(0, timesteps)) |
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if ddim_use_original_steps |
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else np.flip(timesteps) |
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) |
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total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] |
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logger.info(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((b,), step, device=device, dtype=torch.long) |
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outs = self.p_sample_ddim( |
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img, |
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cond, |
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ts, |
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index=index, |
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use_original_steps=ddim_use_original_steps, |
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quantize_denoised=quantize_denoised, |
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temperature=temperature, |
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noise_dropout=noise_dropout, |
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) |
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img, _ = outs |
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return img |
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@torch.no_grad() |
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def p_sample_ddim( |
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self, |
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x, |
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c, |
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t, |
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index, |
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repeat_noise=False, |
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use_original_steps=False, |
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quantize_denoised=False, |
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temperature=1.0, |
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noise_dropout=0.0, |
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): |
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b, *_, device = *x.shape, x.device |
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e_t = self.model.apply_model(x, t, c) |
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas |
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alphas_prev = ( |
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self.model.alphas_cumprod_prev |
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if use_original_steps |
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else self.ddim_alphas_prev |
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) |
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sqrt_one_minus_alphas = ( |
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self.model.sqrt_one_minus_alphas_cumprod |
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if use_original_steps |
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else self.ddim_sqrt_one_minus_alphas |
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) |
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sigmas = ( |
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self.model.ddim_sigmas_for_original_num_steps |
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if use_original_steps |
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else self.ddim_sigmas |
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) |
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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( |
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(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device |
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) |
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() |
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if quantize_denoised: |
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) |
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dir_xt = (1.0 - a_prev - sigma_t ** 2).sqrt() * e_t |
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noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature |
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if noise_dropout > 0.0: |
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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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