| import torch, math |
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| class EnhancedDDIMScheduler(): |
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| def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"): |
| self.num_train_timesteps = num_train_timesteps |
| if beta_schedule == "scaled_linear": |
| betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32)) |
| elif beta_schedule == "linear": |
| betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) |
| else: |
| raise NotImplementedError(f"{beta_schedule} is not implemented") |
| self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0).tolist() |
| self.set_timesteps(10) |
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| def set_timesteps(self, num_inference_steps, denoising_strength=1.0): |
| |
| |
| max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0) |
| num_inference_steps = min(num_inference_steps, max_timestep + 1) |
| if num_inference_steps == 1: |
| self.timesteps = [max_timestep] |
| else: |
| step_length = max_timestep / (num_inference_steps - 1) |
| self.timesteps = [round(max_timestep - i*step_length) for i in range(num_inference_steps)] |
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| def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev): |
| weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t) |
| weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t) |
| |
| prev_sample = sample * weight_x + model_output * weight_e |
| |
| weight_e = -math.sqrt((1 - alpha_prod_t) / alpha_prod_t) |
| weight_x = math.sqrt(1 / alpha_prod_t) |
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| return prev_sample |
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| def step(self, model_output, timestep, sample, to_final=False): |
| alpha_prod_t = self.alphas_cumprod[timestep] |
| timestep_id = self.timesteps.index(timestep) |
| if to_final or timestep_id + 1 >= len(self.timesteps): |
| alpha_prod_t_prev = 1.0 |
| else: |
| timestep_prev = self.timesteps[timestep_id + 1] |
| alpha_prod_t_prev = self.alphas_cumprod[timestep_prev] |
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| return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev) |
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| def return_to_timestep(self, timestep, sample, sample_stablized): |
| alpha_prod_t = self.alphas_cumprod[timestep] |
| noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t) |
| return noise_pred |
| |
| |
| def add_noise(self, original_samples, noise, timestep): |
| sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep]) |
| sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep]) |
| noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise |
| return noisy_samples |
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