Rename 'CLIPFeatureExtractor' class to 'CLIPImageProcessor' (#2732)

The 'CLIPFeatureExtractor' class name has been renamed to 'CLIPImageProcessor' in order to comply with future deprecation. This commit includes the necessary changes to the affected files.

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -18,7 +18,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 import PIL
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
@@ -77,7 +77,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     vae_encoder: OnnxRuntimeModel
@@ -87,7 +87,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
     unet: OnnxRuntimeModel
     scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
     safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPFeatureExtractor
+    feature_extractor: CLIPImageProcessor
 
     _optional_components = ["safety_checker", "feature_extractor"]
 
@@ -100,7 +100,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
         unet: OnnxRuntimeModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -18,7 +18,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 import PIL
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
@@ -77,7 +77,7 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     vae_encoder: OnnxRuntimeModel
@@ -87,7 +87,7 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
     unet: OnnxRuntimeModel
     scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
     safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPFeatureExtractor
+    feature_extractor: CLIPImageProcessor
 
     _optional_components = ["safety_checker", "feature_extractor"]
 
@@ -100,7 +100,7 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
         unet: OnnxRuntimeModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -4,7 +4,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 import PIL
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
@@ -63,7 +63,7 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -75,7 +75,7 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
     unet: OnnxRuntimeModel
     scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
     safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPFeatureExtractor
+    feature_extractor: CLIPImageProcessor
 
     def __init__(
         self,
@@ -86,7 +86,7 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         unet: OnnxRuntimeModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -17,7 +17,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
@@ -76,7 +76,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -89,7 +89,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py

@@ -19,7 +19,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import numpy as np
 import torch
 from torch.nn import functional as F
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.attention_processor import Attention
@@ -183,7 +183,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -196,7 +196,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py

@@ -21,7 +21,7 @@ import numpy as np
 import PIL.Image
 import torch
 from torch import nn
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.controlnet import ControlNetOutput
@@ -174,7 +174,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -188,7 +188,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline):
         controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py

@@ -18,7 +18,7 @@ from typing import Callable, List, Optional, Union
 import PIL
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
@@ -53,7 +53,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     # TODO: feature_extractor is required to encode images (if they are in PIL format),
@@ -67,7 +67,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()
@@ -284,7 +284,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
                 The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
                 configuration of
                 [this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
-                `CLIPFeatureExtractor`
+                `CLIPImageProcessor`
             height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image.
             width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -19,7 +19,7 @@ import numpy as np
 import PIL
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...image_processor import VaeImageProcessor
@@ -115,7 +115,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -128,7 +128,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -19,7 +19,7 @@ import numpy as np
 import PIL
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
@@ -161,7 +161,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -174,7 +174,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -19,7 +19,7 @@ import numpy as np
 import PIL
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
@@ -105,7 +105,7 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["feature_extractor"]
@@ -119,7 +119,7 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

@@ -18,7 +18,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 import PIL
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
@@ -84,7 +84,7 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -97,7 +97,7 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -71,7 +71,7 @@ class StableDiffusionKDiffusionPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py

@@ -15,7 +15,7 @@ import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import DDIMScheduler, PNDMScheduler
@@ -75,7 +75,7 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -88,7 +88,7 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: DDIMScheduler,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py

@@ -23,7 +23,7 @@ import torch.nn.functional as F
 from transformers import (
     BlipForConditionalGeneration,
     BlipProcessor,
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPTextModel,
     CLIPTokenizer,
 )
@@ -297,7 +297,7 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
         requires_safety_checker (bool):
             Whether the pipeline requires a safety checker. We recommend setting it to True if you're using the
@@ -318,7 +318,7 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline):
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
         scheduler: Union[DDPMScheduler, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler],
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         safety_checker: StableDiffusionSafetyChecker,
         inverse_scheduler: DDIMInverseScheduler,
         caption_generator: BlipForConditionalGeneration,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py

@@ -17,7 +17,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 import torch.nn.functional as F
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
@@ -111,7 +111,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -124,7 +124,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py

@@ -17,7 +17,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import PIL
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from diffusers.utils.import_utils import is_accelerate_available
 
@@ -68,7 +68,7 @@ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline):
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
 
     Args:
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Feature extractor for image pre-processing before being encoded.
         image_encoder ([`CLIPVisionModelWithProjection`]):
             CLIP vision model for encoding images.
@@ -91,7 +91,7 @@ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline):
     """
 
     # image encoding components
-    feature_extractor: CLIPFeatureExtractor
+    feature_extractor: CLIPImageProcessor
     image_encoder: CLIPVisionModelWithProjection
 
     # image noising components
@@ -109,7 +109,7 @@ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline):
     def __init__(
         self,
         # image encoding components
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         image_encoder: CLIPVisionModelWithProjection,
         # image noising components
         image_normalizer: StableUnCLIPImageNormalizer,

src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py

@@ -5,7 +5,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
@@ -45,7 +45,7 @@ class StableDiffusionPipelineSafe(DiffusionPipeline):
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
@@ -59,7 +59,7 @@ class StableDiffusionPipelineSafe(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: SafeStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py

@@ -19,7 +19,7 @@ import PIL
 import torch
 from torch.nn import functional as F
 from transformers import (
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
     CLIPVisionModelWithProjection,
@@ -48,7 +48,7 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
         tokenizer (`CLIPTokenizer`):
             Tokenizer of class
             [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `image_encoder`.
         image_encoder ([`CLIPVisionModelWithProjection`]):
             Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of
@@ -73,7 +73,7 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
     text_proj: UnCLIPTextProjModel
     text_encoder: CLIPTextModelWithProjection
     tokenizer: CLIPTokenizer
-    feature_extractor: CLIPFeatureExtractor
+    feature_extractor: CLIPImageProcessor
     image_encoder: CLIPVisionModelWithProjection
     super_res_first: UNet2DModel
     super_res_last: UNet2DModel
@@ -87,7 +87,7 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
         text_encoder: CLIPTextModelWithProjection,
         tokenizer: CLIPTokenizer,
         text_proj: UnCLIPTextProjModel,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         image_encoder: CLIPVisionModelWithProjection,
         super_res_first: UNet2DModel,
         super_res_last: UNet2DModel,
@@ -264,7 +264,7 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
                 The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
                 configuration of
                 [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
-                `CLIPFeatureExtractor`. Can be left to `None` only when `image_embeddings` are passed.
+                `CLIPImageProcessor`. Can be left to `None` only when `image_embeddings` are passed.
             num_images_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             decoder_num_inference_steps (`int`, *optional*, defaults to 25):

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py

@@ -3,7 +3,7 @@ from typing import Callable, List, Optional, Union
 
 import PIL.Image
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
@@ -41,12 +41,12 @@ class VersatileDiffusionPipeline(DiffusionPipeline):
         safety_checker ([`StableDiffusionMegaSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
     tokenizer: CLIPTokenizer
-    image_feature_extractor: CLIPFeatureExtractor
+    image_feature_extractor: CLIPImageProcessor
     text_encoder: CLIPTextModel
     image_encoder: CLIPVisionModel
     image_unet: UNet2DConditionModel
@@ -57,7 +57,7 @@ class VersatileDiffusionPipeline(DiffusionPipeline):
     def __init__(
         self,
         tokenizer: CLIPTokenizer,
-        image_feature_extractor: CLIPFeatureExtractor,
+        image_feature_extractor: CLIPImageProcessor,
         text_encoder: CLIPTextModel,
         image_encoder: CLIPVisionModel,
         image_unet: UNet2DConditionModel,

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py

@@ -20,7 +20,7 @@ import PIL
 import torch
 import torch.utils.checkpoint
 from transformers import (
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
     CLIPVisionModelWithProjection,
@@ -55,7 +55,7 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
     """
     tokenizer: CLIPTokenizer
-    image_feature_extractor: CLIPFeatureExtractor
+    image_feature_extractor: CLIPImageProcessor
     text_encoder: CLIPTextModelWithProjection
     image_encoder: CLIPVisionModelWithProjection
     image_unet: UNet2DConditionModel
@@ -68,7 +68,7 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
     def __init__(
         self,
         tokenizer: CLIPTokenizer,
-        image_feature_extractor: CLIPFeatureExtractor,
+        image_feature_extractor: CLIPImageProcessor,
         text_encoder: CLIPTextModelWithProjection,
         image_encoder: CLIPVisionModelWithProjection,
         image_unet: UNet2DConditionModel,