add clip to ddim

models/vision/ddim/example.py

+#!/usr/bin/env python3
+import os
+import pathlib
+from modeling_ddim import DDIM
+import PIL.Image
+import numpy as np
+
+model_ids = ["ddim-celeba-hq", "ddim-lsun-church", "ddim-lsun-bedroom"]
+
+for model_id in model_ids:
+    path = os.path.join("/home/patrick/images/hf", model_id)
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    ddpm = DDIM.from_pretrained("fusing/" + model_id)
+    image = ddpm(batch_size=4)
+
+    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)
+
+    for i in range(image_processed.shape[0]):
+        image_pil = PIL.Image.fromarray(image_processed[i])
+        image_pil.save(os.path.join(path, f"image_{i}.png"))

models/vision/ddim/modeling_ddim.py

@@ -59,6 +59,7 @@ class DDIM(DiffusionPipeline):
 
             # predict mean of prev image
             pred_mean = alpha_prod_t_rsqrt * (image - beta_prod_t_sqrt * noise_residual)
+            pred_mean = torch.clamp(pred_mean, -1, 1)
             pred_mean = (1 / alpha_prod_t_prev_rsqrt) * pred_mean + coeff_2 * noise_residual
 
             # if eta > 0.0 add noise. Note eta = 1.0 essentially corresponds to DDPM