[IF super res] correctly normalize PIL input (#3536)

* [IF super res] correctl normalize PIL input

* 175 -> 127.5

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -759,7 +759,7 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline):
             image = [image]
 
         if isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i).astype(np.float32) / 255.0 for i in image]
+            image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
 
             image = np.stack(image, axis=0)  # to np
             image = torch.from_numpy(image.transpose(0, 3, 1, 2))

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -795,7 +795,7 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline):
             image = [image]
 
         if isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i).astype(np.float32) / 255.0 for i in image]
+            image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
 
             image = np.stack(image, axis=0)  # to np
             image = torch.from_numpy(image.transpose(0, 3, 1, 2))

src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py

@@ -664,7 +664,7 @@ class IFSuperResolutionPipeline(DiffusionPipeline):
             image = [image]
 
         if isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i).astype(np.float32) / 255.0 for i in image]
+            image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
 
             image = np.stack(image, axis=0)  # to np
             image = torch.from_numpy(image.transpose(0, 3, 1, 2))