Update CLIPFeatureExtractor to CLIPImageProcessor and DPTFeatureExtractor to DPTImageProcessor (#9002)

* fix: update `CLIPFeatureExtractor` to `CLIPImageProcessor` in codebase

* `make style && make quality`

* Update `DPTFeatureExtractor` to `DPTImageProcessor` in codebase

* `make style`

---------

Co-authored-by: Aryan <aryan@huggingface.co>

docs/source/en/using-diffusers/custom_pipeline_overview.md

@@ -289,9 +289,9 @@ scheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder="sche
 3. Load an image processor:
 
 ```python
-from transformers import CLIPFeatureExtractor
+from transformers import CLIPImageProcessor
 
-feature_extractor = CLIPFeatureExtractor.from_pretrained(pipe_id, subfolder="feature_extractor")
+feature_extractor = CLIPImageProcessor.from_pretrained(pipe_id, subfolder="feature_extractor")
 ```
 
 <Tip warning={true}>

docs/source/en/using-diffusers/inference_with_tcd_lora.md

@@ -212,14 +212,14 @@ TCD-LoRA is very versatile, and it can be combined with other adapter types like
 import torch
 import numpy as np
 from PIL import Image
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+from transformers import DPTImageProcessor, DPTForDepthEstimation
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
 from diffusers.utils import load_image, make_image_grid
 from scheduling_tcd import TCDScheduler
 
 device = "cuda"
 depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
 
 def get_depth_map(image):
     image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)

docs/source/ko/using-diffusers/loading.md

@@ -307,7 +307,7 @@ print(pipeline)
 
 위의 코드 출력 결과를 확인해보면, `pipeline`은 [`StableDiffusionPipeline`]의 인스턴스이며, 다음과 같이 총 7개의 컴포넌트로 구성된다는 것을 알 수 있습니다.
 
-- `"feature_extractor"`: [`~transformers.CLIPFeatureExtractor`]의 인스턴스
+- `"feature_extractor"`: [`~transformers.CLIPImageProcessor`]의 인스턴스
 - `"safety_checker"`: 유해한 컨텐츠를 스크리닝하기 위한 [컴포넌트](https://github.com/huggingface/diffusers/blob/e55687e1e15407f60f32242027b7bb8170e58266/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L32)
 - `"scheduler"`: [`PNDMScheduler`]의 인스턴스
 - `"text_encoder"`: [`~transformers.CLIPTextModel`]의 인스턴스

docs/source/ko/using-diffusers/textual_inversion_inference.md

@@ -24,7 +24,7 @@ import PIL
 from PIL import Image
 
 from diffusers import StableDiffusionPipeline
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 
 def image_grid(imgs, rows, cols):

examples/community/README.md

@@ -1435,9 +1435,9 @@ import requests
 import torch
 from diffusers import DiffusionPipeline
 from PIL import Image
-from transformers import CLIPFeatureExtractor, CLIPModel
+from transformers import CLIPImageProcessor, CLIPModel
 
-feature_extractor = CLIPFeatureExtractor.from_pretrained(
+feature_extractor = CLIPImageProcessor.from_pretrained(
     "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
 )
 clip_model = CLIPModel.from_pretrained(
@@ -2122,7 +2122,7 @@ import torch
 import open_clip
 from open_clip import SimpleTokenizer
 from diffusers import DiffusionPipeline
-from transformers import CLIPFeatureExtractor, CLIPModel
+from transformers import CLIPImageProcessor, CLIPModel
 
 
 def download_image(url):
@@ -2130,7 +2130,7 @@ def download_image(url):
     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
 
 # Loading additional models
-feature_extractor = CLIPFeatureExtractor.from_pretrained(
+feature_extractor = CLIPImageProcessor.from_pretrained(
     "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
 )
 clip_model = CLIPModel.from_pretrained(

examples/community/clip_guided_images_mixing_stable_diffusion.py

@@ -7,7 +7,7 @@ import PIL.Image
 import torch
 from torch.nn import functional as F
 from torchvision import transforms
-from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -86,7 +86,7 @@ class CLIPGuidedImagesMixingStableDiffusion(DiffusionPipeline, StableDiffusionMi
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
         scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         coca_model=None,
         coca_tokenizer=None,
         coca_transform=None,

examples/community/clip_guided_stable_diffusion_img2img.py

@@ -7,7 +7,7 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 from torchvision import transforms
-from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -32,9 +32,9 @@ EXAMPLE_DOC_STRING = """
         import torch
         from diffusers import DiffusionPipeline
         from PIL import Image
-        from transformers import CLIPFeatureExtractor, CLIPModel
+        from transformers import CLIPImageProcessor, CLIPModel
 
-        feature_extractor = CLIPFeatureExtractor.from_pretrained(
+        feature_extractor = CLIPImageProcessor.from_pretrained(
             "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
         )
         clip_model = CLIPModel.from_pretrained(
@@ -139,7 +139,7 @@ class CLIPGuidedStableDiffusion(DiffusionPipeline, StableDiffusionMixin):
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
         scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
     ):
         super().__init__()
         self.register_modules(

examples/community/mixture_canvas.py

@@ -9,7 +9,7 @@ import torch
 from numpy import exp, pi, sqrt
 from torchvision.transforms.functional import resize
 from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
@@ -275,7 +275,7 @@ class StableDiffusionCanvasPipeline(DiffusionPipeline, StableDiffusionMixin):
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler],
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
     ):
         super().__init__()
         self.register_modules(

examples/community/mixture_tiling.py

@@ -15,7 +15,7 @@ from diffusers.utils import logging
 
 try:
     from ligo.segments import segment
-    from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+    from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 except ImportError:
     raise ImportError("Please install transformers and ligo-segments to use the mixture pipeline")
 
@@ -144,7 +144,7 @@ class StableDiffusionTilingPipeline(DiffusionPipeline, StableDiffusionExtrasMixi
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler],
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
     ):
         super().__init__()
         self.register_modules(

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py

@@ -189,7 +189,7 @@ class StableDiffusionXLControlNetAdapterPipeline(
         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`.
     """
 

examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -332,7 +332,7 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
         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_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
             Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config

examples/community/pipeline_zero1to3.py

@@ -9,7 +9,7 @@ import numpy as np
 import PIL.Image
 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
@@ -87,7 +87,7 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
         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`.
         cc_projection ([`CCProjection`]):
             Projection layer to project the concated CLIP features and pose embeddings to the original CLIP feature size.
@@ -102,7 +102,7 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         cc_projection: CCProjection,
         requires_safety_checker: bool = True,
     ):

examples/community/regional_prompting_stable_diffusion.py

@@ -3,7 +3,7 @@ from typing import Dict, Optional
 
 import torch
 import torchvision.transforms.functional as FF
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import StableDiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -69,7 +69,7 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__(

examples/community/stable_diffusion_ipex.py

@@ -18,7 +18,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import intel_extension_for_pytorch as ipex
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers.configuration_utils import FrozenDict
 from diffusers.loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
@@ -86,7 +86,7 @@ class StableDiffusionIPEXPipeline(
         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`.
     """
 
@@ -100,7 +100,7 @@ class StableDiffusionIPEXPipeline(
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

examples/community/stable_diffusion_tensorrt_img2img.py

@@ -42,7 +42,7 @@ from polygraphy.backend.trt import (
     network_from_onnx_path,
     save_engine,
 )
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict, deprecate
@@ -679,7 +679,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(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`.
     """
 
@@ -693,7 +693,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: DDIMScheduler,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
         stages=["clip", "unet", "vae", "vae_encoder"],

examples/community/stable_diffusion_tensorrt_inpaint.py

@@ -42,7 +42,7 @@ from polygraphy.backend.trt import (
     network_from_onnx_path,
     save_engine,
 )
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict, deprecate
@@ -683,7 +683,7 @@ class TensorRTStableDiffusionInpaintPipeline(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`.
     """
 
@@ -697,7 +697,7 @@ class TensorRTStableDiffusionInpaintPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: DDIMScheduler,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
         stages=["clip", "unet", "vae", "vae_encoder"],

examples/community/stable_diffusion_tensorrt_txt2img.py

@@ -42,7 +42,7 @@ from polygraphy.backend.trt import (
     network_from_onnx_path,
     save_engine,
 )
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict, deprecate
@@ -595,7 +595,7 @@ class TensorRTStableDiffusionPipeline(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`.
     """
 
@@ -609,7 +609,7 @@ class TensorRTStableDiffusionPipeline(DiffusionPipeline):
         unet: UNet2DConditionModel,
         scheduler: DDIMScheduler,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
         stages=["clip", "unet", "vae"],

examples/research_projects/controlnet/train_controlnet_webdataset.py

@@ -43,7 +43,7 @@ from PIL import Image
 from torch.utils.data import default_collate
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import AutoTokenizer, DPTFeatureExtractor, DPTForDepthEstimation, PretrainedConfig
+from transformers import AutoTokenizer, DPTForDepthEstimation, DPTImageProcessor, PretrainedConfig
 from webdataset.tariterators import (
     base_plus_ext,
     tar_file_expander,
@@ -205,7 +205,7 @@ class Text2ImageDataset:
         pin_memory: bool = False,
         persistent_workers: bool = False,
         control_type: str = "canny",
-        feature_extractor: Optional[DPTFeatureExtractor] = None,
+        feature_extractor: Optional[DPTImageProcessor] = None,
     ):
         if not isinstance(train_shards_path_or_url, str):
             train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
@@ -1011,7 +1011,7 @@ def main(args):
         controlnet = pre_controlnet
 
     if args.control_type == "depth":
-        feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+        feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
         depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas")
         depth_model.requires_grad_(False)
     else:

examples/research_projects/gligen/demo.ipynb

@@ -45,7 +45,7 @@
     "    UniPCMultistepScheduler,\n",
     "    EulerDiscreteScheduler,\n",
     ")\n",
-    "from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n",
+    "from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n",
     "# pretrained_model_name_or_path = 'masterful/gligen-1-4-generation-text-box'\n",
     "\n",
     "pretrained_model_name_or_path = '/root/data/zhizhonghuang/checkpoints/models--masterful--gligen-1-4-generation-text-box/snapshots/d2820dc1e9ba6ca082051ce79cfd3eb468ae2c83'\n",

examples/research_projects/rdm/pipeline_rdm.py

@@ -4,7 +4,7 @@ from typing import Callable, List, Optional, Union
 import torch
 from PIL import Image
 from retriever import Retriever, normalize_images, preprocess_images
-from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -47,7 +47,7 @@ class RDMPipeline(DiffusionPipeline, StableDiffusionMixin):
         scheduler ([`SchedulerMixin`]):
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        feature_extractor ([`CLIPFeatureExtractor`]):
+        feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
@@ -65,7 +65,7 @@ class RDMPipeline(DiffusionPipeline, StableDiffusionMixin):
             EulerAncestralDiscreteScheduler,
             DPMSolverMultistepScheduler,
         ],
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         retriever: Optional[Retriever] = None,
     ):
         super().__init__()

examples/research_projects/rdm/retriever.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from datasets import Dataset, load_dataset
 from PIL import Image
-from transformers import CLIPFeatureExtractor, CLIPModel, PretrainedConfig
+from transformers import CLIPImageProcessor, CLIPModel, PretrainedConfig
 
 from diffusers import logging
 
@@ -20,7 +20,7 @@ def normalize_images(images: List[Image.Image]):
     return images
 
 
-def preprocess_images(images: List[np.array], feature_extractor: CLIPFeatureExtractor) -> torch.Tensor:
+def preprocess_images(images: List[np.array], feature_extractor: CLIPImageProcessor) -> torch.Tensor:
     """
     Preprocesses a list of images into a batch of tensors.
 
@@ -95,14 +95,12 @@ class Index:
     def build_index(
         self,
         model=None,
-        feature_extractor: CLIPFeatureExtractor = None,
+        feature_extractor: CLIPImageProcessor = None,
         torch_dtype=torch.float32,
     ):
         if not self.index_initialized:
             model = model or CLIPModel.from_pretrained(self.config.clip_name_or_path).to(dtype=torch_dtype)
-            feature_extractor = feature_extractor or CLIPFeatureExtractor.from_pretrained(
+            feature_extractor = feature_extractor or CLIPImageProcessor.from_pretrained(self.config.clip_name_or_path)
-                self.config.clip_name_or_path
-            )
             self.dataset = get_dataset_with_emb_from_clip_model(
                 self.dataset,
                 model,
@@ -136,7 +134,7 @@ class Retriever:
         index: Index = None,
         dataset: Dataset = None,
         model=None,
-        feature_extractor: CLIPFeatureExtractor = None,
+        feature_extractor: CLIPImageProcessor = None,
     ):
         self.config = config
         self.index = index or self._build_index(config, dataset, model=model, feature_extractor=feature_extractor)
@@ -148,7 +146,7 @@ class Retriever:
         index: Index = None,
         dataset: Dataset = None,
         model=None,
-        feature_extractor: CLIPFeatureExtractor = None,
+        feature_extractor: CLIPImageProcessor = None,
         **kwargs,
     ):
         config = kwargs.pop("config", None) or IndexConfig.from_pretrained(retriever_name_or_path, **kwargs)
@@ -156,7 +154,7 @@ class Retriever:
 
     @staticmethod
     def _build_index(
-        config: IndexConfig, dataset: Dataset = None, model=None, feature_extractor: CLIPFeatureExtractor = None
+        config: IndexConfig, dataset: Dataset = None, model=None, feature_extractor: CLIPImageProcessor = None
     ):
         dataset = dataset or load_dataset(config.dataset_name)
         dataset = dataset[config.dataset_set]

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -76,13 +76,13 @@ EXAMPLE_DOC_STRING = """
         >>> import numpy as np
         >>> from PIL import Image
 
-        >>> from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+        >>> from transformers import DPTImageProcessor, DPTForDepthEstimation
         >>> from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
         >>> from diffusers.utils import load_image
 
 
         >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
-        >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+        >>> feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
         >>> controlnet = ControlNetModel.from_pretrained(
         ...     "diffusers/controlnet-depth-sdxl-1.0-small",
         ...     variant="fp16",

src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.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, FlaxControlNetModel, FlaxUNet2DConditionModel
 from ...schedulers import (
@@ -149,7 +149,7 @@ class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
             FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
         ],
         safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         dtype: jnp.dtype = jnp.float32,
     ):
         super().__init__()

src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py

@@ -16,7 +16,7 @@ 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 ....image_processor import VaeImageProcessor
 from ....loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
@@ -66,8 +66,8 @@ class StableDiffusionModelEditingPipeline(
             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 more details
             about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPFeatureExtractor`]):
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPFeatureExtractor` to extract features from generated images; used as inputs to the `safety_checker`.
+            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
         with_to_k ([`bool`]):
             Whether to edit the key projection matrices along with the value projection matrices.
         with_augs ([`list`]):
@@ -86,7 +86,7 @@ class StableDiffusionModelEditingPipeline(
         unet: UNet2DConditionModel,
         scheduler: SchedulerMixin,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
         with_to_k: bool = True,
         with_augs: list = AUGS_CONST,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -20,7 +20,7 @@ import numpy as np
 import PIL.Image
 import torch
 from packaging import version
-from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation
+from transformers import CLIPTextModel, CLIPTokenizer, DPTForDepthEstimation, DPTImageProcessor
 
 from ...configuration_utils import FrozenDict
 from ...image_processor import PipelineImageInput, VaeImageProcessor
@@ -111,7 +111,7 @@ class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoader
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         depth_estimator: DPTForDepthEstimation,
-        feature_extractor: DPTFeatureExtractor,
+        feature_extractor: DPTImageProcessor,
     ):
         super().__init__()
 

src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py

@@ -18,7 +18,7 @@ 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 ...image_processor import VaeImageProcessor
 from ...loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
@@ -138,7 +138,7 @@ class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
         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_gligen/pipeline_stable_diffusion_gligen_text_image.py

@@ -19,7 +19,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import PIL.Image
 import torch
 from transformers import (
-    CLIPFeatureExtractor,
+    CLIPImageProcessor,
     CLIPProcessor,
     CLIPTextModel,
     CLIPTokenizer,
@@ -193,7 +193,7 @@ class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionM
         unet: UNet2DConditionModel,
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py

@@ -19,7 +19,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import numpy as np
 import PIL.Image
 import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...image_processor import VaeImageProcessor
 from ...loaders import StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
@@ -209,7 +209,7 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline, StableDiffusionMixin):
         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`.
     """
 
@@ -225,7 +225,7 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline, StableDiffusionMixin):
         adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        feature_extractor: CLIPImageProcessor,
         requires_safety_checker: bool = True,
     ):
         super().__init__()

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -237,7 +237,7 @@ class StableDiffusionXLAdapterPipeline(
         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`.
     """
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -26,8 +26,8 @@ from transformers import (
     CLIPTextModel,
     CLIPTokenizer,
     DPTConfig,
-    DPTFeatureExtractor,
     DPTForDepthEstimation,
+    DPTImageProcessor,
 )
 
 from diffusers import (
@@ -145,9 +145,7 @@ class StableDiffusionDepth2ImgPipelineFastTests(
             backbone_featmap_shape=[1, 384, 24, 24],
         )
         depth_estimator = DPTForDepthEstimation(depth_estimator_config).eval()
-        feature_extractor = DPTFeatureExtractor.from_pretrained(
+        feature_extractor = DPTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DPTForDepthEstimation")
-            "hf-internal-testing/tiny-random-DPTForDepthEstimation"
-        )
 
         components = {
             "unet": unet,