Spaces:
Paused
Paused
| import torch | |
| import numpy as np | |
| class DDPMSampler: | |
| def __init__(self, generator: torch.Generator, num_training_steps=1000, beta_start: float = 0.00085, beta_end: float = 0.0120): | |
| self.betas = torch.linspace(beta_start ** 0.5, beta_end ** 0.5, num_training_steps, dtype=torch.float32) ** 2 | |
| self.alphas = 1.0 - self.betas | |
| self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) | |
| self.one = torch.tensor(1.0) | |
| self.generator = generator | |
| self.num_train_timesteps = num_training_steps | |
| self.timesteps = torch.from_numpy(np.arange(0, num_training_steps)[::-1].copy()) | |
| def set_inference_timesteps(self, num_inference_steps=50): | |
| self.num_inference_steps = num_inference_steps | |
| step_ratio = self.num_train_timesteps // self.num_inference_steps | |
| inference_timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64) | |
| self.timesteps = torch.from_numpy(inference_timesteps) | |
| def _get_previous_timestep(self, timestep: int) -> int: | |
| return timestep - self.num_train_timesteps // self.num_inference_steps | |
| def _get_variance(self, timestep: int) -> torch.Tensor: | |
| prev_timestep = self._get_previous_timestep(timestep) | |
| alpha_prod_t = self.alphas_cumprod[timestep] | |
| alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one | |
| current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev | |
| variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t | |
| return torch.clamp(variance, min=1e-20) | |
| def set_strength(self, strength=1): | |
| start_step = self.num_inference_steps - int(self.num_inference_steps * strength) | |
| self.timesteps = self.timesteps[start_step:] | |
| self.start_step = start_step | |
| def step(self, timestep: int, latents: torch.Tensor, model_output: torch.Tensor): | |
| prev_timestep = self._get_previous_timestep(timestep) | |
| alpha_prod_t = self.alphas_cumprod[timestep] | |
| alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one | |
| beta_prod_t = 1 - alpha_prod_t | |
| beta_prod_t_prev = 1 - alpha_prod_t_prev | |
| current_alpha_t = alpha_prod_t / alpha_prod_t_prev | |
| current_beta_t = 1 - current_alpha_t | |
| pred_original_sample = (latents - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 | |
| pred_original_sample_coeff = (alpha_prod_t_prev ** 0.5 * current_beta_t) / beta_prod_t | |
| current_sample_coeff = current_alpha_t ** 0.5 * beta_prod_t_prev / beta_prod_t | |
| pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * latents | |
| variance = 0 | |
| if timestep > 0: | |
| device = model_output.device | |
| noise = torch.randn(model_output.shape, generator=self.generator, device=device, dtype=model_output.dtype) | |
| variance = (self._get_variance(timestep) ** 0.5) * noise | |
| return pred_prev_sample + variance | |
| def add_noise(self, original_samples: torch.FloatTensor, timesteps: torch.IntTensor) -> torch.FloatTensor: | |
| alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) | |
| timesteps = timesteps.to(original_samples.device) | |
| sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 | |
| sqrt_alpha_prod = sqrt_alpha_prod.flatten() | |
| while len(sqrt_alpha_prod.shape) < len(original_samples.shape): | |
| sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) | |
| sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 | |
| sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() | |
| while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): | |
| sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) | |
| noise = torch.randn(original_samples.shape, generator=self.generator, device=original_samples.device, dtype=original_samples.dtype) | |
| return sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise | |