sampling_ddpm.py

import torch
from tqdm import tqdm
from diffusers import UNet2DModel

class DDPM:
    def __init__(
        self,
        num_train_timesteps:int = 1000,
        beta_start: float = 0.0001,
        beta_end: float = 0.02,
    ):
        self.num_train_timesteps = num_train_timesteps
        self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
        self.alphas = 1.0 - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
        self.timesteps = torch.arange(num_train_timesteps - 1, -1, -1)

    @torch.no_grad()
    def sample(
        self,
        unet: UNet2DModel,
        batch_size: int,
        in_channels: int,
        sample_size: int,
    ):
        betas = self.betas.to(unet.device)
        alphas = self.alphas.to(unet.device)
        alphas_cumprod = self.alphas_cumprod.to(unet.device)
        timesteps = self.timesteps.to(unet.device)
        images = torch.randn((batch_size, in_channels, sample_size, sample_size), device=unet.device)
        for timestep in tqdm(timesteps, desc='Sampling'):
            pred_noise: torch.Tensor = unet(images, timestep).sample

            # mean of q(x_{t-1}|x_t)
            alpha_t = alphas[timestep]
            alpha_cumprod_t = alphas_cumprod[timestep]
            sqrt_alpha_t = alpha_t ** 0.5
            one_minus_alpha_t = 1.0 - alpha_t
            sqrt_one_minus_alpha_cumprod_t = (1 - alpha_cumprod_t) ** 0.5
            mean = (images - one_minus_alpha_t / sqrt_one_minus_alpha_cumprod_t * pred_noise) / sqrt_alpha_t
            
            # variance of q(x_{t-1}|x_t)
            if timestep > 0:
                beta_t = betas[timestep]
                one_minus_alpha_cumprod_t_minus_one = 1.0 - alphas_cumprod[timestep - 1]
                one_divided_by_sigma_square = alpha_t / beta_t + 1.0 / one_minus_alpha_cumprod_t_minus_one
                variance = (1.0 / one_divided_by_sigma_square) ** 0.5
            else:
                variance = torch.zeros_like(timestep)
            
            epsilon = torch.randn_like(images)
            images = mean + variance * epsilon
        images = (images / 2.0 + 0.5).clamp(0, 1).cpu().permute(0, 2, 3, 1).numpy()
        return images

model = UNet2DModel.from_pretrained('ddpm-animefaces-64').cuda()
ddpm = DDPM()
images = ddpm.sample(model, 32, 3, 64)

from diffusers.utils import make_image_grid, numpy_to_pil
image_grid = make_image_grid(numpy_to_pil(images), rows=4, cols=8)
image_grid.save('ddpm-sample-results.png')