run.py

#!/usr/bin/env python3
import torch
from diffusers import UNetModel, DDIMScheduler
import PIL
import numpy as np
import tqdm

generator = torch.manual_seed(0)
torch_device = "cuda" if torch.cuda.is_available() else "cpu"

# 1. Load models
noise_scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
unet = UNetModel.from_pretrained("fusing/ddpm-celeba-hq").to(torch_device)

# 2. Sample gaussian noise
image = torch.randn(
   (1, unet.in_channels, unet.resolution, unet.resolution),
   generator=generator,
)
image = image.to(torch_device)

# 3. Denoise
num_inference_steps = 50
eta = 0.0  # <- deterministic sampling

for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
    # 1. predict noise residual
    orig_t = len(noise_scheduler) // num_inference_steps * t

    with torch.no_grad():
        residual = unet(image, orig_t)

    # 2. predict previous mean of image x_t-1
    pred_prev_image = noise_scheduler.step(residual, image, t, num_inference_steps, eta)

    # 3. optionally sample variance
    variance = 0
    if eta > 0:
        noise = torch.randn(image.shape, generator=generator).to(image.device)
        variance = noise_scheduler.get_variance(t).sqrt() * eta * noise

    # 4. set current image to prev_image: x_t -> x_t-1
    image = pred_prev_image + variance

# 5. process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# 6. save image
image_pil.save("test.png")