⚡️ Speed up method BlipImageProcessor.postprocess by 51%

src/diffusers/pipelines/blip_diffusion/blip_image_processing.py

@@ -299,20 +299,23 @@ def preprocess(
 
     # Follows diffusers.VaeImageProcessor.postprocess
     def postprocess(self, sample: torch.Tensor, output_type: str = "pil"):
-        if output_type not in ["pt", "np", "pil"]:
+        if output_type not in {"pt", "np", "pil"}:
             raise ValueError(
                 f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"
             )
 
         # Equivalent to diffusers.VaeImageProcessor.denormalize
-        sample = (sample / 2 + 0.5).clamp(0, 1)
+        sample = (sample / 2 + 0.5).clamp_(0, 1)
         if output_type == "pt":
             return sample
 
+        # Only move to CPU and numpy if necessary 
+        if sample.device.type != "cpu":
+            sample = sample.cpu()
         # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
-        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
+        sample = sample.permute(0, 2, 3, 1).contiguous().numpy()
         if output_type == "np":
             return sample
+
         # Output_type must be 'pil'
-        sample = numpy_to_pil(sample)
-        return sample
+        return numpy_to_pil(sample)

src/diffusers/utils/pil_utils.py

@@ -38,16 +38,15 @@ def numpy_to_pil(images):
     """
     Convert a numpy image or a batch of images to a PIL image.
     """
+    # If single HWC image, expand dims to NHWC
     if images.ndim == 3:
         images = images[None, ...]
-    images = (images * 255).round().astype("uint8")
+    images = (images * 255).round().astype("uint8", copy=False)
     if images.shape[-1] == 1:
         # special case for grayscale (single channel) images
-        pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
+        return [Image.fromarray(image[..., 0], mode="L") for image in images]
     else:
-        pil_images = [Image.fromarray(image) for image in images]
-
-    return pil_images
+        return [Image.fromarray(image) for image in images]
 
 
 def make_image_grid(images: List[PIL.Image.Image], rows: int, cols: int, resize: int = None) -> PIL.Image.Image: