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run.py

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+#!/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")