New direct-to-PIL code

app.py

@@ -9,11 +9,8 @@ pipeline = LatentDiffusionUncondPipeline.from_pretrained("CompVis/ldm-celebahq-2
 
 def predict(steps=1, seed=42):
     generator = torch.manual_seed(seed)
-    image = pipeline(generator=generator, num_inference_steps=steps)["sample"]
-    image_processed = image.cpu().permute(0, 2, 3, 1)
-    image_processed = (image_processed + 1.0) * 127.5
-    image_processed = image_processed.clamp(0, 255).numpy().astype(np.uint8)
-    return PIL.Image.fromarray(image_processed[0])
+    images = pipeline(generator=generator, num_inference_steps=steps)["sample"]
+    return images[0]
 
 random_seed = random.randint(0, 2147483647)
 gr.Interface(