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.

examples/community/stable_diffusion_mega.py

@@ -2,7 +2,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import PIL.Image
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -47,7 +47,7 @@ class StableDiffusionMegaPipeline(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`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
@@ -60,7 +60,7 @@ class StableDiffusionMegaPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

examples/community/text_inpainting.py

@@ -3,7 +3,7 @@ from typing import Callable, List, Optional, Union
 import PIL
 import torch
 from transformers import (
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPSegForImageSegmentation,
     CLIPSegProcessor,
     CLIPTextModel,
@@ -52,7 +52,7 @@ class TextInpainting(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`.
     """
 
@@ -66,7 +66,7 @@ class TextInpainting(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
     ):
         super().__init__()
 

examples/community/unclip_image_interpolation.py

@@ -5,7 +5,7 @@ import PIL
 import torch
 from torch.nn import functional as F
 from transformers import (
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
     CLIPVisionModelWithProjection,
@@ -50,7 +50,7 @@ class UnCLIPImageInterpolationPipeline(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
@@ -75,7 +75,7 @@ class UnCLIPImageInterpolationPipeline(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
@@ -90,7 +90,7 @@ class UnCLIPImageInterpolationPipeline(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,
@@ -270,7 +270,7 @@ class UnCLIPImageInterpolationPipeline(DiffusionPipeline):
                 The images to use for the image interpolation. Only accepts a list of two PIL Images or 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` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.
+                `CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.
             steps (`int`, *optional*, defaults to 5):
                 The number of interpolation images to generate.
             decoder_num_inference_steps (`int`, *optional*, defaults to 25):

examples/community/wildcard_stable_diffusion.py

@@ -6,7 +6,7 @@ from dataclasses import dataclass
 from typing import Callable, Dict, List, Optional, Union
 
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
@@ -104,7 +104,7 @@ class WildcardStableDiffusionPipeline(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/CompVis/stable-diffusion-v1-4) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
@@ -116,7 +116,7 @@ class WildcardStableDiffusionPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
     ):
         super().__init__()
 

examples/dreambooth/train_dreambooth_flax.py

@@ -22,7 +22,7 @@ from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
 from diffusers import (
     FlaxAutoencoderKL,
@@ -652,7 +652,7 @@ def main():
             tokenizer=tokenizer,
             scheduler=scheduler,
             safety_checker=safety_checker,
-            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
         )
 
         outdir = os.path.join(args.output_dir, str(step)) if step else args.output_dir

examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py

@@ -23,7 +23,7 @@ from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
@@ -632,7 +632,7 @@ def main():
                 tokenizer=tokenizer,
                 scheduler=PNDMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
                 safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
-                feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+                feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
             )
             pipeline.save_pretrained(args.output_dir)
         # Save the newly trained embeddings

examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py

@@ -25,7 +25,7 @@ from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
 from diffusers import (
     FlaxAutoencoderKL,
@@ -640,7 +640,7 @@ def main():
             tokenizer=tokenizer,
             scheduler=scheduler,
             safety_checker=safety_checker,
-            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
         )
 
         pipeline.save_pretrained(

examples/text_to_image/train_text_to_image_flax.py

@@ -20,7 +20,7 @@ from flax.training.common_utils import shard
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
 from diffusers import (
     FlaxAutoencoderKL,
@@ -567,7 +567,7 @@ def main():
             tokenizer=tokenizer,
             scheduler=scheduler,
             safety_checker=safety_checker,
-            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
         )
 
         pipeline.save_pretrained(

examples/textual_inversion/textual_inversion_flax.py

@@ -25,7 +25,7 @@ from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
 from diffusers import (
     FlaxAutoencoderKL,
@@ -667,7 +667,7 @@ def main():
             tokenizer=tokenizer,
             scheduler=scheduler,
             safety_checker=safety_checker,
-            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
         )
 
         pipeline.save_pretrained(

scripts/convert_versatile_diffusion_to_diffusers.py

@@ -19,7 +19,7 @@ from argparse import Namespace
 
 import torch
 from transformers import (
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
     CLIPVisionModelWithProjection,
@@ -774,7 +774,7 @@ if __name__ == "__main__":
         vae.load_state_dict(converted_vae_checkpoint)
 
     tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    image_feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-large-patch14")
+    image_feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
     text_encoder = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
     image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
 

src/diffusers/pipelines/README.md

@@ -7,9 +7,9 @@ components - all of which are needed to have a functioning end-to-end diffusion
 As an example, [Stable Diffusion](https://huggingface.co/blog/stable_diffusion) has three independently trained models:
 - [Autoencoder](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/models/vae.py#L392)
 - [Conditional Unet](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/models/unet_2d_condition.py#L12)
-- [CLIP text encoder](https://huggingface.co/docs/transformers/v4.21.2/en/model_doc/clip#transformers.CLIPTextModel)
+- [CLIP text encoder](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel)
 - a scheduler component, [scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py), 
-- a [CLIPFeatureExtractor](https://huggingface.co/docs/transformers/v4.21.2/en/model_doc/clip#transformers.CLIPFeatureExtractor),
+- a [CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor),
 - as well as a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py).
 All of these components are necessary to run stable diffusion in inference even though they were trained 
 or created independently from each other.

src/diffusers/pipelines/alt_diffusion/pipeline_alt_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, XLMRobertaTokenizer
+from transformers import CLIPImageProcessor, XLMRobertaTokenizer
 
 from diffusers.utils import is_accelerate_available, is_accelerate_version
 
@@ -73,7 +73,7 @@ class AltDiffusionPipeline(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"]
@@ -86,7 +86,7 @@ class AltDiffusionPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -19,7 +19,7 @@ import numpy as np
 import PIL
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
+from transformers import CLIPImageProcessor, XLMRobertaTokenizer
 
 from diffusers.utils import is_accelerate_available, is_accelerate_version
 
@@ -112,7 +112,7 @@ class AltDiffusionImg2ImgPipeline(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"]
@@ -125,7 +125,7 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py

@@ -18,7 +18,7 @@ from typing import Callable, List, Optional, Union
 import numpy as np
 import PIL
 import torch
-from transformers import CLIPFeatureExtractor
+from transformers import CLIPImageProcessor
 
 from diffusers.utils import is_accelerate_available
 
@@ -156,7 +156,7 @@ class PaintByExamplePipeline(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 initial images (if they are in PIL format),
@@ -170,7 +170,7 @@ class PaintByExamplePipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = False,
     ):
         super().__init__()

src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py

@@ -3,7 +3,7 @@ from itertools import repeat
 from typing import Callable, List, Optional, Union
 
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
@@ -84,7 +84,7 @@ class SemanticStableDiffusionPipeline(DiffusionPipeline):
         safety_checker ([`Q16SafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
             Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
@@ -98,7 +98,7 @@ class SemanticStableDiffusionPipeline(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_cycle_diffusion.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 diffusers.utils import is_accelerate_available, is_accelerate_version
 
@@ -142,7 +142,7 @@ class CycleDiffusionPipeline(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/CompVis/stable-diffusion-v1-4) 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"]
@@ -155,7 +155,7 @@ class CycleDiffusionPipeline(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_flax_stable_diffusion.py

@@ -24,7 +24,7 @@ from flax.jax_utils import unreplicate
 from flax.training.common_utils import shard
 from packaging import version
 from PIL import Image
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
 
 from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
 from ...schedulers import (
@@ -103,7 +103,7 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
         safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
             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`.
     """
 
@@ -117,7 +117,7 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
             FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
         ],
         safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         dtype: jnp.dtype = jnp.float32,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py

@@ -23,7 +23,7 @@ from flax.core.frozen_dict import FrozenDict
 from flax.jax_utils import unreplicate
 from flax.training.common_utils import shard
 from PIL import Image
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
 
 from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
 from ...schedulers import (
@@ -127,7 +127,7 @@ class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
         safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
             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`.
     """
 
@@ -141,7 +141,7 @@ class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
             FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
         ],
         safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         dtype: jnp.dtype = jnp.float32,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py

@@ -24,7 +24,7 @@ from flax.jax_utils import unreplicate
 from flax.training.common_utils import shard
 from packaging import version
 from PIL import Image
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel
+from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
 
 from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
 from ...schedulers import (
@@ -124,7 +124,7 @@ class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):
         safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
             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`.
     """
 
@@ -138,7 +138,7 @@ class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):
             FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
         ],
         safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         dtype: jnp.dtype = jnp.float32,
     ):
         super().__init__()

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -17,7 +17,7 @@ from typing import Callable, List, Optional, Union
 
 import numpy as np
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
@@ -38,7 +38,7 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline):
     unet: OnnxRuntimeModel
     scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
     safety_checker: OnnxRuntimeModel
-    feature_extractor: CLIPFeatureExtractor
+    feature_extractor: CLIPImageProcessor
 
     _optional_components = ["safety_checker", "feature_extractor"]
 
@@ -51,7 +51,7 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline):
         unet: OnnxRuntimeModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()
@@ -333,7 +333,7 @@ class StableDiffusionOnnxPipeline(OnnxStableDiffusionPipeline):
         unet: OnnxRuntimeModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: OnnxRuntimeModel,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
     ):
         deprecation_message = "Please use `OnnxStableDiffusionPipeline` instead of `StableDiffusionOnnxPipeline`."
         deprecate("StableDiffusionOnnxPipeline", "1.0.0", deprecation_message)