Update pipeline_stable_diffusion_inpaint_legacy.py resize to integer multiple...

Update pipeline_stable_diffusion_inpaint_legacy.py resize to integer multiple of 8 instead of 32 for init image and mask (#2350)

Update pipeline_stable_diffusion_inpaint_legacy.py

Change resize to integer multiple of 8 instead of 32

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -42,7 +42,7 @@ logger = logging.get_logger(__name__)
 
 def preprocess_image(image):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
@@ -54,7 +54,7 @@ def preprocess_mask(mask, scale_factor=8):
     if not isinstance(mask, torch.FloatTensor):
         mask = mask.convert("L")
         w, h = mask.size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
         mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
         mask = np.array(mask).astype(np.float32) / 255.0
         mask = np.tile(mask, (4, 1, 1))
@@ -76,7 +76,7 @@ def preprocess_mask(mask, scale_factor=8):
         # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape
         mask = mask.mean(dim=1, keepdim=True)
         h, w = mask.shape[-2:]
-        h, w = map(lambda x: x - x % 32, (h, w))  # resize to integer multiple of 32
+        h, w = map(lambda x: x - x % 8, (h, w))  # resize to integer multiple of 8
         mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))
         return mask