Refactoring of ImageProcessorFast (#35069)

* add init and base image processing functions

* add add_fast_image_processor to transformers-cli

* add working fast image processor clip

* add fast image processor to doc, working tests

* remove "to be implemented" SigLip

* fix unprotected import

* fix unprotected vision import

* update ViTImageProcessorFast

* increase threshold slow fast ewuivalence

* add fast img blip

* add fast class in tests with cli

* improve cli

* add fast image processor convnext

* add LlavaPatchingMixin and fast image processor for llava_next and llava_onevision

* add device kwarg to ImagesKwargs for fast processing on cuda

* cleanup

* fix unprotected import

* group images by sizes and add batch processing

* Add batch equivalence tests, skip when center_crop is used

* cleanup

* update init and cli

* fix-copies

* refactor convnext, cleanup base

* fix

* remove patching mixins, add piped torchvision transforms for ViT

* fix unbatched processing

* fix f strings

* protect imports

* change llava onevision to class transforms (test)

* fix convnext

* improve formatting (following Pavel review)

* fix handling device arg

* improve cli

* fix

* fix inits

* Add distinction between preprocess and _preprocess, and support for arbitrary kwargs through valid_extra_kwargs

* uniformize qwen2_vl fast

* fix docstrings

* add add fast image processor llava

* remove min_pixels max_pixels from accepted size

* nit

* nit

* refactor fast image processors docstrings

* cleanup and remove fast class transforms

* update add fast image processor transformers cli

* cleanup docstring

* uniformize pixtral fast and  make _process_image explicit

* fix prepare image structure llava next/onevision

* Use typed kwargs instead of explicit args

* nit fix import Unpack

* clearly separate pops and gets in base preprocess. Use explicit typed kwargs

* make qwen2_vl preprocess arguments hashable

docs/source/en/model_doc/blip.md

@@ -61,6 +61,11 @@ The original code can be found [here](https://github.com/salesforce/BLIP).
 [[autodoc]] BlipImageProcessor
     - preprocess
 
+## BlipImageProcessorFast
+
+[[autodoc]] BlipImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/clip.md

@@ -251,6 +251,11 @@ The resource should ideally demonstrate something new instead of duplicating an
 [[autodoc]] CLIPImageProcessor
     - preprocess
 
+## CLIPImageProcessorFast
+
+[[autodoc]] CLIPImageProcessorFast
+    - preprocess
+
 ## CLIPFeatureExtractor
 
 [[autodoc]] CLIPFeatureExtractor

docs/source/en/model_doc/convnext.md

@@ -64,6 +64,11 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] ConvNextImageProcessor
     - preprocess
 
+## ConvNextImageProcessorFast
+
+[[autodoc]] ConvNextImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/deit.md

@@ -125,6 +125,11 @@ If you're interested in submitting a resource to be included here, please feel f
 [[autodoc]] DeiTImageProcessor
     - preprocess
 
+## DeiTImageProcessorFast
+
+[[autodoc]] DeiTImageProcessorFast
+    - preprocess
+
 <frameworkcontent>
 <pt>
 

docs/source/en/model_doc/llava.md

@@ -195,6 +195,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] LlavaImageProcessor
     - preprocess
 
+## LlavaImageProcessorFast
+
+[[autodoc]] LlavaImageProcessorFast
+    - preprocess
+
 ## LlavaProcessor
 
 [[autodoc]] LlavaProcessor

docs/source/en/model_doc/llava_next.md

@@ -288,6 +288,11 @@ model = AutoModelForImageTextToText.from_pretrained(
 [[autodoc]] LlavaNextImageProcessor
     - preprocess
 
+## LlavaNextImageProcessorFast
+
+[[autodoc]] LlavaNextImageProcessorFast
+    - preprocess
+
 ## LlavaNextProcessor
 
 [[autodoc]] LlavaNextProcessor

docs/source/en/model_doc/llava_onevision.md

@@ -100,8 +100,8 @@ import torch
 from PIL import Image
 import requests
 
-processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") 
-model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True) 
+processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
+model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True)
 model.to("cuda:0")
 
 # prepare image and text prompt, using the appropriate prompt template
@@ -298,8 +298,8 @@ First make sure to install flash-attn. Refer to the [original repository of Flas
 from transformers import LlavaOnevisionForConditionalGeneration
 
 model = LlavaOnevisionForConditionalGeneration.from_pretrained(
-    model_id, 
-    torch_dtype=torch.float16, 
+    model_id,
+    torch_dtype=torch.float16,
     low_cpu_mem_usage=True,
     use_flash_attention_2=True
 ).to(0)
@@ -318,6 +318,11 @@ model = LlavaOnevisionForConditionalGeneration.from_pretrained(
 
 [[autodoc]] LlavaOnevisionImageProcessor
 
+## LlavaOnevisionImageProcessorFast
+
+[[autodoc]] LlavaOnevisionImageProcessorFast
+    - preprocess
+
 ## LlavaOnevisionVideoProcessor
 
 [[autodoc]] LlavaOnevisionVideoProcessor

docs/source/en/model_doc/siglip.md

@@ -214,6 +214,11 @@ Below is an expected speedup diagram that compares inference time between the na
 [[autodoc]] SiglipImageProcessor
     - preprocess
 
+## SiglipImageProcessorFast
+
+[[autodoc]] SiglipImageProcessorFast
+    - preprocess
+
 ## SiglipProcessor
 
 [[autodoc]] SiglipProcessor