Merge branch 'main' of https://github.com/huggingface/diffusers into main

README.md

@@ -67,10 +67,13 @@ for t in reversed(range(len(scheduler))):
     sampled_prev_image = prev_image + prev_variance
     image = sampled_prev_image
 
+# 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])
+
+# save image
 image_pil.save("test.png")
 ```
 
@@ -80,28 +83,24 @@ image_pil.save("test.png")
 Example:
 
 ```python
-from diffusers import UNetModel, GaussianDDPMScheduler
 from modeling_ddpm import DDPM
-import tempfile
+import PIL.Image
+import numpy as np
 
-unet = UNetModel.from_pretrained("fusing/ddpm_dummy")
-sampler = GaussianDDPMScheduler.from_config("fusing/ddpm_dummy")
+# load model and scheduler
+ddpm = DDPM.from_pretrained("fusing/ddpm-lsun-bedroom-pipe")
 
-# compose Diffusion Pipeline
-ddpm = DDPM(unet, sampler)
-# generate / sample
+# run pipeline in inference (sample random noise and denoise)
 image = ddpm()
-print(image)
 
+# 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])
 
-# save and load with 0 extra code (handled by general `DiffusionPipeline` class)
-# will also be possible to do so from the Hub
-with tempfile.TemporaryDirectory() as tmpdirname:
-    ddpm.save_pretrained(tmpdirname)
-    print("Model saved")
-    ddpm_new = DDPM.from_pretrained(tmpdirname)
-    print("Model loaded")
-    print(ddpm_new)
+# save image
+image_pil.save("test.png")
 ```
 
 ## Library structure: