Add image_processor (#2617)

* add image_processor

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

src/diffusers/image_processor.py

+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import warnings
+from typing import Union
+
+import numpy as np
+import PIL
+import torch
+from PIL import Image
+
+from .configuration_utils import ConfigMixin, register_to_config
+from .utils import CONFIG_NAME, PIL_INTERPOLATION
+
+
+class VaeImageProcessor(ConfigMixin):
+    """
+    Image Processor for VAE
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
+        vae_scale_factor (`int`, *optional*, defaults to `8`):
+            VAE scale factor. If `do_resize` is True, the image will be automatically resized to multiples of this
+            factor.
+        resample (`str`, *optional*, defaults to `lanczos`):
+            Resampling filter to use when resizing the image.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image to [-1,1]
+    """
+
+    config_name = CONFIG_NAME
+
+    @register_to_config
+    def __init__(
+        self,
+        do_resize: bool = True,
+        vae_scale_factor: int = 8,
+        resample: str = "lanczos",
+        do_normalize: bool = True,
+    ):
+        super().__init__()
+
+    @staticmethod
+    def numpy_to_pil(images):
+        """
+        Convert a numpy image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images = (images * 255).round().astype("uint8")
+        if images.shape[-1] == 1:
+            # special case for grayscale (single channel) images
+            pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
+        else:
+            pil_images = [Image.fromarray(image) for image in images]
+
+        return pil_images
+
+    @staticmethod
+    def numpy_to_pt(images):
+        """
+        Convert a numpy image to a pytorch tensor
+        """
+        if images.ndim == 3:
+            images = images[..., None]
+
+        images = torch.from_numpy(images.transpose(0, 3, 1, 2))
+        return images
+
+    @staticmethod
+    def pt_to_numpy(images):
+        """
+        Convert a numpy image to a pytorch tensor
+        """
+        images = images.cpu().permute(0, 2, 3, 1).float().numpy()
+        return images
+
+    @staticmethod
+    def normalize(images):
+        """
+        Normalize an image array to [-1,1]
+        """
+        return 2.0 * images - 1.0
+
+    def resize(self, images: PIL.Image.Image) -> PIL.Image.Image:
+        """
+        Resize a PIL image. Both height and width will be downscaled to the next integer multiple of `vae_scale_factor`
+        """
+        w, h = images.size
+        w, h = map(lambda x: x - x % self.vae_scale_factor, (w, h))  # resize to integer multiple of vae_scale_factor
+        images = images.resize((w, h), resample=PIL_INTERPOLATION[self.resample])
+        return images
+
+    def preprocess(
+        self,
+        image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
+    ) -> torch.Tensor:
+        """
+        Preprocess the image input, accepted formats are PIL images, numpy arrays or pytorch tensors"
+        """
+        supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
+        if isinstance(image, supported_formats):
+            image = [image]
+        elif not (isinstance(image, list) and all(isinstance(i, supported_formats) for i in image)):
+            raise ValueError(
+                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support {', '.join(supported_formats)}"
+            )
+
+        if isinstance(image[0], PIL.Image.Image):
+            if self.do_resize:
+                image = [self.resize(i) for i in image]
+            image = [np.array(i).astype(np.float32) / 255.0 for i in image]
+            image = np.stack(image, axis=0)  # to np
+            image = self.numpy_to_pt(image)  # to pt
+
+        elif isinstance(image[0], np.ndarray):
+            image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
+            image = self.numpy_to_pt(image)
+            _, _, height, width = image.shape
+            if self.do_resize and (height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0):
+                raise ValueError(
+                    f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.vae_scale_factor}"
+                    f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
+                )
+
+        elif isinstance(image[0], torch.Tensor):
+            image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
+            _, _, height, width = image.shape
+            if self.do_resize and (height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0):
+                raise ValueError(
+                    f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.vae_scale_factor}"
+                    f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
+                )
+
+        # expected range [0,1], normalize to [-1,1]
+        do_normalize = self.do_normalize
+        if image.min() < 0:
+            warnings.warn(
+                "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
+                f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]",
+                FutureWarning,
+            )
+            do_normalize = False
+
+        if do_normalize:
+            image = self.normalize(image)
+
+        return image
+
+    def postprocess(
+        self,
+        image,
+        output_type: str = "pil",
+    ):
+        if isinstance(image, torch.Tensor) and output_type == "pt":
+            return image
+
+        image = self.pt_to_numpy(image)
+
+        if output_type == "np":
+            return image
+        elif output_type == "pil":
+            return self.numpy_to_pil(image)
+        else:
+            raise ValueError(f"Unsupported output_type {output_type}.")

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -24,6 +24,7 @@ from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
 from diffusers.utils import is_accelerate_available, is_accelerate_version
 
 from ...configuration_utils import FrozenDict
+from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor, replace_example_docstring
@@ -192,7 +193,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             new_config = dict(unet.config)
             new_config["sample_size"] = 64
             unet._internal_dict = FrozenDict(new_config)
-
         self.register_modules(
             vae=vae,
             text_encoder=text_encoder,
@@ -203,7 +203,11 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             feature_extractor=feature_extractor,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(
+            requires_safety_checker=requires_safety_checker,
+        )
 
     def enable_sequential_cpu_offload(self, gpu_id=0):
         r"""
@@ -415,21 +419,17 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         return prompt_embeds
 
     def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+        feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+        safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
         image, has_nsfw_concept = self.safety_checker(
             images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
         )
-        else:
-            has_nsfw_concept = None
         return image, has_nsfw_concept
 
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 
     def prepare_extra_step_kwargs(self, generator, eta):
@@ -663,7 +663,7 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         )
 
         # 4. Preprocess image
-        image = preprocess(image)
+        image = self.image_processor.preprocess(image)
 
         # 5. set timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -703,15 +703,26 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)
 
-        # 9. Post-processing
+        if output_type not in ["latent", "pt", "np", "pil"]:
+            deprecation_message = (
+                f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
+                "`pil`, `np`, `pt`, `latent`"
+            )
+            deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
+            output_type = "np"
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+
         image = self.decode_latents(latents)
 
-        # 10. Run safety checker
+        if self.safety_checker is not None:
             image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            has_nsfw_concept = False
 
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload last model to CPU
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -22,6 +22,7 @@ from packaging import version
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
+from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -119,7 +120,6 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
     """
     _optional_components = ["safety_checker", "feature_extractor"]
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.__init__
     def __init__(
         self,
         vae: AutoencoderKL,
@@ -196,7 +196,6 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             new_config = dict(unet.config)
             new_config["sample_size"] = 64
             unet._internal_dict = FrozenDict(new_config)
-
         self.register_modules(
             vae=vae,
             text_encoder=text_encoder,
@@ -207,7 +206,11 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             feature_extractor=feature_extractor,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(
+            requires_safety_checker=requires_safety_checker,
+        )
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
     def enable_sequential_cpu_offload(self, gpu_id=0):
@@ -422,24 +425,18 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
 
         return prompt_embeds
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
     def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+        feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+        safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
         image, has_nsfw_concept = self.safety_checker(
             images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
         )
-        else:
-            has_nsfw_concept = None
         return image, has_nsfw_concept
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents).sample
         image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
@@ -674,7 +671,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         )
 
         # 4. Preprocess image
-        image = preprocess(image)
+        image = self.image_processor.preprocess(image)
 
         # 5. set timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -714,15 +711,26 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)
 
-        # 9. Post-processing
+        if output_type not in ["latent", "pt", "np", "pil"]:
+            deprecation_message = (
+                f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
+                "`pil`, `np`, `pt`, `latent`"
+            )
+            deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
+            output_type = "np"
+
+        if output_type == "latent":
+            image = latents
+            has_nsfw_concept = None
+
         image = self.decode_latents(latents)
 
-        # 10. Run safety checker
+        if self.safety_checker is not None:
             image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            has_nsfw_concept = False
 
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload last model to CPU
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:

tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py

@@ -21,7 +21,13 @@ import numpy as np
 import torch
 from transformers import XLMRobertaTokenizer
 
-from diffusers import AltDiffusionImg2ImgPipeline, AutoencoderKL, PNDMScheduler, UNet2DConditionModel
+from diffusers import (
+    AltDiffusionImg2ImgPipeline,
+    AutoencoderKL,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.image_processor import VaeImageProcessor
 from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
     RobertaSeriesConfig,
     RobertaSeriesModelWithTransformation,
@@ -128,6 +134,7 @@ class AltDiffusionImg2ImgPipelineFastTests(unittest.TestCase):
             safety_checker=None,
             feature_extractor=self.dummy_extractor,
         )
+        alt_pipe.image_processor = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=False)
         alt_pipe = alt_pipe.to(device)
         alt_pipe.set_progress_bar_config(disable=None)
 
@@ -191,6 +198,7 @@ class AltDiffusionImg2ImgPipelineFastTests(unittest.TestCase):
             safety_checker=None,
             feature_extractor=self.dummy_extractor,
         )
+        alt_pipe.image_processor = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=False)
         alt_pipe = alt_pipe.to(torch_device)
         alt_pipe.set_progress_bar_config(disable=None)
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -30,6 +30,7 @@ from diffusers import (
     StableDiffusionImg2ImgPipeline,
     UNet2DConditionModel,
 )
+from diffusers.image_processor import VaeImageProcessor
 from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
@@ -94,19 +95,33 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
         }
         return components
 
-    def get_dummy_inputs(self, device, seed=0):
+    def get_dummy_inputs(self, device, seed=0, input_image_type="pt", output_type="np"):
         image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
         if str(device).startswith("mps"):
             generator = torch.manual_seed(seed)
         else:
             generator = torch.Generator(device=device).manual_seed(seed)
+
+        if input_image_type == "pt":
+            input_image = image
+        elif input_image_type == "np":
+            input_image = image.cpu().numpy().transpose(0, 2, 3, 1)
+        elif input_image_type == "pil":
+            input_image = image.cpu().numpy().transpose(0, 2, 3, 1)
+            input_image = VaeImageProcessor.numpy_to_pil(input_image)
+        else:
+            raise ValueError(f"unsupported input_image_type {input_image_type}.")
+
+        if output_type not in ["pt", "np", "pil"]:
+            raise ValueError(f"unsupported output_type {output_type}")
+
         inputs = {
             "prompt": "A painting of a squirrel eating a burger",
-            "image": image,
+            "image": input_image,
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": output_type,
         }
         return inputs
 
@@ -114,6 +129,7 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()
         sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe.image_processor = VaeImageProcessor(vae_scale_factor=sd_pipe.vae_scale_factor, do_normalize=False)
         sd_pipe = sd_pipe.to(device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -130,6 +146,7 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()
         sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe.image_processor = VaeImageProcessor(vae_scale_factor=sd_pipe.vae_scale_factor, do_normalize=False)
         sd_pipe = sd_pipe.to(device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -148,6 +165,7 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()
         sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe.image_processor = VaeImageProcessor(vae_scale_factor=sd_pipe.vae_scale_factor, do_normalize=False)
         sd_pipe = sd_pipe.to(device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -169,6 +187,7 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
             beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
         )
         sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe.image_processor = VaeImageProcessor(vae_scale_factor=sd_pipe.vae_scale_factor, do_normalize=False)
         sd_pipe = sd_pipe.to(device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -197,6 +216,36 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
     def test_attention_slicing_forward_pass(self):
         return super().test_attention_slicing_forward_pass()
 
+    @skip_mps
+    def test_pt_np_pil_outputs_equivalent(self):
+        device = "cpu"
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        output_pt = sd_pipe(**self.get_dummy_inputs(device, output_type="pt"))[0]
+        output_np = sd_pipe(**self.get_dummy_inputs(device, output_type="np"))[0]
+        output_pil = sd_pipe(**self.get_dummy_inputs(device, output_type="pil"))[0]
+
+        assert np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max() <= 1e-4
+        assert np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max() <= 1e-4
+
+    @skip_mps
+    def test_image_types_consistent(self):
+        device = "cpu"
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        output_pt = sd_pipe(**self.get_dummy_inputs(device, input_image_type="pt"))[0]
+        output_np = sd_pipe(**self.get_dummy_inputs(device, input_image_type="np"))[0]
+        output_pil = sd_pipe(**self.get_dummy_inputs(device, input_image_type="pil"))[0]
+
+        assert np.abs(output_pt - output_np).max() <= 1e-4
+        assert np.abs(output_pil - output_np).max() <= 1e-2
+
 
 @slow
 @require_torch_gpu
@@ -219,7 +268,7 @@ class StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):
             "num_inference_steps": 3,
             "strength": 0.75,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -426,7 +475,7 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
             "num_inference_steps": 50,
             "strength": 0.75,
             "guidance_scale": 7.5,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 

tests/test_image_processor.py

+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import PIL
+import torch
+
+from diffusers.image_processor import VaeImageProcessor
+
+
+class ImageProcessorTest(unittest.TestCase):
+    @property
+    def dummy_sample(self):
+        batch_size = 1
+        num_channels = 3
+        height = 8
+        width = 8
+
+        sample = torch.rand((batch_size, num_channels, height, width))
+
+        return sample
+
+    def to_np(self, image):
+        if isinstance(image[0], PIL.Image.Image):
+            return np.stack([np.array(i) for i in image], axis=0)
+        elif isinstance(image, torch.Tensor):
+            return image.cpu().numpy().transpose(0, 2, 3, 1)
+        return image
+
+    def test_vae_image_processor_pt(self):
+        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
+
+        input_pt = self.dummy_sample
+        input_np = self.to_np(input_pt)
+
+        for output_type in ["pt", "np", "pil"]:
+            out = image_processor.postprocess(
+                image_processor.preprocess(input_pt),
+                output_type=output_type,
+            )
+            out_np = self.to_np(out)
+            in_np = (input_np * 255).round() if output_type == "pil" else input_np
+            assert (
+                np.abs(in_np - out_np).max() < 1e-6
+            ), f"decoded output does not match input for output_type {output_type}"
+
+    def test_vae_image_processor_np(self):
+        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
+        input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
+
+        for output_type in ["pt", "np", "pil"]:
+            out = image_processor.postprocess(image_processor.preprocess(input_np), output_type=output_type)
+
+            out_np = self.to_np(out)
+            in_np = (input_np * 255).round() if output_type == "pil" else input_np
+            assert (
+                np.abs(in_np - out_np).max() < 1e-6
+            ), f"decoded output does not match input for output_type {output_type}"
+
+    def test_vae_image_processor_pil(self):
+        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
+
+        input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)
+        input_pil = image_processor.numpy_to_pil(input_np)
+
+        for output_type in ["pt", "np", "pil"]:
+            out = image_processor.postprocess(image_processor.preprocess(input_pil), output_type=output_type)
+            for i, o in zip(input_pil, out):
+                in_np = np.array(i)
+                out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round()
+                assert (
+                    np.abs(in_np - out_np).max() < 1e-6
+                ), f"decoded output does not match input for output_type {output_type}"
+
+    def test_preprocess_input_3d(self):
+        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
+
+        input_pt_4d = self.dummy_sample
+        input_pt_3d = input_pt_4d.squeeze(0)
+
+        out_pt_4d = image_processor.postprocess(
+            image_processor.preprocess(input_pt_4d),
+            output_type="np",
+        )
+        out_pt_3d = image_processor.postprocess(
+            image_processor.preprocess(input_pt_3d),
+            output_type="np",
+        )
+
+        input_np_4d = self.to_np(self.dummy_sample)
+        input_np_3d = input_np_4d.squeeze(0)
+
+        out_np_4d = image_processor.postprocess(
+            image_processor.preprocess(input_np_4d),
+            output_type="np",
+        )
+        out_np_3d = image_processor.postprocess(
+            image_processor.preprocess(input_np_3d),
+            output_type="np",
+        )
+
+        assert np.abs(out_pt_4d - out_pt_3d).max() < 1e-6
+        assert np.abs(out_np_4d - out_np_3d).max() < 1e-6
+
+    def test_preprocess_input_list(self):
+        image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
+
+        input_pt_4d = self.dummy_sample
+        input_pt_list = list(input_pt_4d)
+
+        out_pt_4d = image_processor.postprocess(
+            image_processor.preprocess(input_pt_4d),
+            output_type="np",
+        )
+
+        out_pt_list = image_processor.postprocess(
+            image_processor.preprocess(input_pt_list),
+            output_type="np",
+        )
+
+        input_np_4d = self.to_np(self.dummy_sample)
+        list(input_np_4d)
+
+        out_np_4d = image_processor.postprocess(
+            image_processor.preprocess(input_pt_4d),
+            output_type="np",
+        )
+
+        out_np_list = image_processor.postprocess(
+            image_processor.preprocess(input_pt_list),
+            output_type="np",
+        )
+
+        assert np.abs(out_pt_4d - out_pt_list).max() < 1e-6
+        assert np.abs(out_np_4d - out_np_list).max() < 1e-6