Update scheduling_repaint.py (huggingface#1582)

* Update scheduling_repaint.py

* update the expected image

Co-authored-by: anton- <[email protected]>

Author: Randolph-zeng

src/diffusers/schedulers/scheduling_repaint.py

@@ -287,7 +287,7 @@ def step(
         prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
 
         # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
-        prev_known_part = (alpha_prod_t**0.5) * original_image + ((1 - alpha_prod_t) ** 0.5) * noise
+        prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
 
         # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
         pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part

tests/pipelines/repaint/test_repaint.py

@@ -19,7 +19,7 @@
 import torch
 
 from diffusers import RePaintPipeline, RePaintScheduler, UNet2DModel
-from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
+from diffusers.utils.testing_utils import load_image, load_numpy, require_torch_gpu, slow, torch_device
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -36,11 +36,10 @@ def test_celebahq(self):
         mask_image = load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
         )
-        expected_image = load_image(
+        expected_image = load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
-            "repaint/celeba_hq_256_result.png"
+            "repaint/celeba_hq_256_result.npy"
         )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
         model_id = "google/ddpm-ema-celebahq-256"
         unet = UNet2DModel.from_pretrained(model_id)