refactor Image processor for x4 upscaler

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -33,6 +33,11 @@
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.preprocess
 def preprocess(image):
+    warnings.warn(
+        "The preprocess method is deprecated and will be removed in a future version. Please"
+        " use VaeImageProcessor.preprocess instead",
+        FutureWarning,
+    )
     if isinstance(image, torch.Tensor):
         return image
     elif isinstance(image, PIL.Image.Image):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -21,6 +21,7 @@
 import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
+from ...image_processor import VaeImageProcessor
 from ...loaders import TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.attention_processor import AttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
@@ -34,6 +35,11 @@
 
 
 def preprocess(image):
+    warnings.warn(
+        "The preprocess method is deprecated and will be removed in a future version. Please"
+        " use VaeImageProcessor.preprocess instead",
+        FutureWarning,
+    )
     if isinstance(image, torch.Tensor):
         return image
     elif isinstance(image, PIL.Image.Image):
@@ -125,6 +131,8 @@ def __init__(
             watermarker=watermarker,
             feature_extractor=feature_extractor,
         )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, resample="bicubic")
         self.register_to_config(max_noise_level=max_noise_level)
 
     def enable_sequential_cpu_offload(self, gpu_id=0):
@@ -432,14 +440,15 @@ def check_inputs(
         if (
             not isinstance(image, torch.Tensor)
             and not isinstance(image, PIL.Image.Image)
+            and not isinstance(image, np.ndarray)
             and not isinstance(image, list)
         ):
             raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}"
+                f"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}"
             )
 
-        # verify batch size of prompt and image are same if image is a list or tensor
-        if isinstance(image, list) or isinstance(image, torch.Tensor):
+        # verify batch size of prompt and image are same if image is a list or tensor or numpy array
+        if isinstance(image, list) or isinstance(image, torch.Tensor) or isinstance(image, np.ndarray):
             if isinstance(prompt, str):
                 batch_size = 1
             else:
@@ -483,7 +492,14 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
     def __call__(
         self,
         prompt: Union[str, List[str]] = None,
-        image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
+        image: Union[
+            torch.FloatTensor,
+            PIL.Image.Image,
+            np.ndarray,
+            List[torch.FloatTensor],
+            List[PIL.Image.Image],
+            List[np.ndarray],
+        ] = None,
         num_inference_steps: int = 75,
         guidance_scale: float = 9.0,
         noise_level: int = 20,
@@ -506,7 +522,7 @@ def __call__(
             prompt (`str` or `List[str]`, *optional*):
                 The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                 instead.
-            image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
                 `Image`, or tensor representing an image batch which will be upscaled. *
             num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
@@ -627,7 +643,7 @@ def __call__(
         )
 
         # 4. Preprocess image
-        image = preprocess(image)
+        image = self.image_processor.preprocess(image)
         image = image.to(dtype=prompt_embeds.dtype, device=device)
 
         # 5. set timesteps
@@ -723,25 +739,25 @@ def __call__(
         else:
             latents = latents.float()
 
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.decode_latents(latents)
-
+        # post-processing
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
             image, has_nsfw_concept, _ = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-            image = self.numpy_to_pil(image)
-
-            # 11. Apply watermark
-            if self.watermarker is not None:
-                image = self.watermarker.apply_watermark(image)
-        elif output_type == "pt":
-            latents = 1 / self.vae.config.scaling_factor * latents
-            image = self.vae.decode(latents).sample
-            has_nsfw_concept = None
         else:
-            image = self.decode_latents(latents)
+            image = latents
             has_nsfw_concept = None
 
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # 11. Apply watermark
+        if output_type == "pil" and self.watermarker is not None:
+            image = self.watermarker.apply_watermark(image)
+
         # Offload last model to CPU
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.final_offload_hook.offload()