🔴 Video processors as a separate class (#35206)

* initial design

* update all video processors

* add tests

* need to add qwen2-vl (not tested yet)

* add qwen2-vl in auto map

* fix copies

* isort

* resolve confilicts kinda

* nit:

* qwen2-vl is happy now

* qwen2-5 happy

* other models are happy

* fix copies

* fix tests

* add docs

* CI green now?

* add more tests

* even more changes + tests

* doc builder fail

* nit

* Update src/transformers/models/auto/processing_auto.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* small update

* imports correctly

* dump, otherwise this is getting unmanagebale T-T

* dump

* update

* another update

* update

* tests

* move

* modular

* docs

* test

* another update

* init

* remove flakiness in tests

* fixup

* clean up and remove commented lines

* docs

* skip this one!

* last fix after rebasing

* run fixup

* delete slow files

* remove unnecessary tests + clean up a bit

* small fixes

* fix tests

* more updates

* docs

* fix tests

* update

* style

* fix qwen2-5-vl

* fixup

* fixup

* unflatten batch when preparing

* dump, come back soon

* add docs and fix some tests

* how to guard this with new dummies?

* chat templates in qwen

* address some comments

* remove `Fast` suffix

* fixup

* oops should be imported from transforms

* typo in requires dummies

* new model added with video support

* fixup once more

* last fixup I hope

* revert image processor name + comments

* oh, this is why fetch test is failing

* fix tests

* fix more tests

* fixup

* add new models: internvl, smolvlm

* update docs

* imprt once

* fix failing tests

* do we need to guard it here again, why?

* new model was added, update it

* remove testcase from tester

* fix tests

* make style

* not related CI fail, lets' just fix here

* mark flaky for now, filas 15 out of 100

* style

* maybe we can do this way?

* don't download images in setup class

---------

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

docs/source/en/_toctree.yml

@@ -39,6 +39,8 @@
       title: Tokenizers
     - local: image_processors
       title: Image processors
+    - local: video_processors
+      title: Video processors
     - local: backbones
       title: Backbones
     - local: feature_extractors
@@ -362,7 +364,9 @@
       title: Feature Extractor
     - local: main_classes/image_processor
       title: Image Processor
-    title: Main classes
+    - local: main_classes/video_processor
+      title: Video Processor
+    title: Main Classes
   - sections:
     - sections:
       - local: model_doc/albert

docs/source/en/image_processors.md

@@ -16,7 +16,7 @@ rendered properly in your Markdown viewer.
 
 # Image processors
 
-Image processors converts images into pixel values, tensors that represent image colors and size. The pixel values are inputs to a vision or video model. To ensure a pretrained model receives the correct input, an image processor can perform the following operations to make sure an image is exactly like the images a model was pretrained on.
+Image processors converts images into pixel values, tensors that represent image colors and size. The pixel values are inputs to a vision model. To ensure a pretrained model receives the correct input, an image processor can perform the following operations to make sure an image is exactly like the images a model was pretrained on.
 
 - [`~BaseImageProcessor.center_crop`] to resize an image
 - [`~BaseImageProcessor.normalize`] or [`~BaseImageProcessor.rescale`] pixel values

docs/source/en/main_classes/video_processor.md

@@ -0,0 +1,55 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+
+# Video Processor
+
+A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch. 
+
+The video processor extends the functionality of image processors by allowing Vision Large Language Models (VLMs) to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM.
+
+When adding a new VLM or updating an existing one to enable distinct video preprocessing, saving and reloading the processor configuration will store the video related arguments in a dedicated file named `video_preprocessing_config.json`. Don't worry if you haven't upadted your VLM, the processor will try to load video related configurations from a file named `preprocessing_config.json`.
+
+
+### Usage Example
+Here's an example of how to load a video processor with [`llava-hf/llava-onevision-qwen2-0.5b-ov-hf`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf) model:
+
+```python
+from transformers import AutoVideoProcessor
+
+processor = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
+```
+
+Currently, if using base image processor for videos, it processes video data by treating each frame as an individual image and applying transformations frame-by-frame. While functional, this approach is not highly efficient. Using `AutoVideoProcessor` allows us to take advantage of **fast video processors**, leveraging the [torchvision](https://pytorch.org/vision/stable/index.html) library. Fast processors handle the whole batch of videos at once, without iterating over each video or frame. These updates introduce GPU acceleration and significantly enhance processing speed, especially for tasks requiring high throughput.
+
+Fast video processors are available for all models and are loaded by default when an `AutoVideoProcessor` is initialized. When using a fast video processor, you can also set the `device` argument to specify the device on which the processing should be done. By default, the processing is done on the same device as the inputs if the inputs are tensors, or on the CPU otherwise. For even more speed improvement, we can compile the processor when using 'cuda' as device.
+
+```python
+import torch
+from transformers.video_utils import load_video
+from transformers import AutoVideoProcessor
+
+video = load_video("video.mp4")
+processor = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf", device="cuda")
+processor = torch.compile(processor)
+processed_video = processor(video, return_tensors="pt")
+```
+
+
+## BaseVideoProcessor
+
+[[autodoc]] video_processing_utils.BaseVideoProcessor
+

docs/source/en/model_doc/auto.md

@@ -74,6 +74,10 @@ Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
 
 [[autodoc]] AutoImageProcessor
 
+## AutoVideoProcessor
+
+[[autodoc]] AutoVideoProcessor
+
 ## AutoProcessor
 
 [[autodoc]] AutoProcessor

docs/source/en/model_doc/instructblipvideo.md

@@ -58,6 +58,12 @@ The attributes can be obtained from model config, as `model.config.num_query_tok
 
 [[autodoc]] InstructBlipVideoProcessor
 
+
+## InstructBlipVideoVideoProcessor
+
+[[autodoc]] InstructBlipVideoVideoProcessor
+    - preprocess
+
 ## InstructBlipVideoImageProcessor
 
 [[autodoc]] InstructBlipVideoImageProcessor

docs/source/en/model_doc/internvl.md

@@ -353,3 +353,7 @@ This example showcases how to handle a batch of chat conversations with interlea
 ## InternVLProcessor
 
 [[autodoc]] InternVLProcessor
+
+## InternVLVideoProcessor
+
+[[autodoc]] InternVLVideoProcessor

docs/source/en/model_doc/llava_next_video.md

@@ -262,6 +262,10 @@ model = LlavaNextVideoForConditionalGeneration.from_pretrained(
 
 [[autodoc]] LlavaNextVideoImageProcessor
 
+## LlavaNextVideoVideoProcessor
+
+[[autodoc]] LlavaNextVideoVideoProcessor
+
 ## LlavaNextVideoModel
 
 [[autodoc]] LlavaNextVideoModel

docs/source/en/model_doc/llava_onevision.md

@@ -303,6 +303,7 @@ model = LlavaOnevisionForConditionalGeneration.from_pretrained(
 ## LlavaOnevisionImageProcessor
 
 [[autodoc]] LlavaOnevisionImageProcessor
+    - preprocess
 
 ## LlavaOnevisionImageProcessorFast
 
@@ -313,6 +314,10 @@ model = LlavaOnevisionForConditionalGeneration.from_pretrained(
 
 [[autodoc]] LlavaOnevisionVideoProcessor
 
+## LlavaOnevisionVideoProcessor
+
+[[autodoc]] LlavaOnevisionVideoProcessor
+
 ## LlavaOnevisionModel
 
 [[autodoc]] LlavaOnevisionModel

docs/source/en/model_doc/qwen2_vl.md

@@ -287,6 +287,11 @@ model = Qwen2VLForConditionalGeneration.from_pretrained(
 [[autodoc]] Qwen2VLImageProcessor
     - preprocess
 
+## Qwen2VLVideoProcessor
+
+[[autodoc]] Qwen2VLVideoProcessor
+    - preprocess
+
 ## Qwen2VLImageProcessorFast
 
 [[autodoc]] Qwen2VLImageProcessorFast

docs/source/en/model_doc/smolvlm.md

@@ -197,6 +197,9 @@ print(generated_texts[0])
 [[autodoc]] SmolVLMImageProcessor
     - preprocess
 
+## SmolVLMVideoProcessor
+[[autodoc]] SmolVLMVideoProcessor
+    - preprocess
 
 ## SmolVLMProcessor
 [[autodoc]] SmolVLMProcessor

docs/source/en/model_doc/video_llava.md

@@ -211,6 +211,11 @@ model = VideoLlavaForConditionalGeneration.from_pretrained(
 
 [[autodoc]] VideoLlavaImageProcessor
 
+
+## VideoLlavaVideoProcessor
+
+[[autodoc]] VideoLlavaVideoProcessor
+
 ## VideoLlavaProcessor
 
 [[autodoc]] VideoLlavaProcessor

docs/source/en/video_processors.md

@@ -0,0 +1,49 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+
+# Video Processor
+
+A **Video Processor** is a utility responsible for preparing input features for video models, as well as handling the post-processing of their outputs. It provides transformations such as resizing, normalization, and conversion into PyTorch. 
+
+The video processor extends the functionality of image processors by allowing the models to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM.
+
+Use [`~BaseVideoProcessor.from_pretrained`] to load a video processors configuration (image size, whether to normalize and rescale, etc.) from a video model on the Hugging Face [Hub](https://hf.co) or local directory. The configuration for each pretrained model should be saved in a [video_preprocessor_config.json] file but older models might have the config saved in [preprocessor_config.json](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf/blob/main/preprocessor_config.json) file. Note that the latter is less preferred and will be removed in the future.
+
+
+### Usage Example
+Here's an example of how to load a video processor with [`llava-hf/llava-onevision-qwen2-0.5b-ov-hf`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf) model:
+
+```python
+from transformers import AutoVideoProcessor
+
+processor = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf")
+```
+
+Currently, if using base image processor for videos, it processes video data by treating each frame as an individual image and applying transformations frame-by-frame. While functional, this approach is not highly efficient. Using `AutoVideoProcessor` allows us to take advantage of **fast video processors**, leveraging the [torchvision](https://pytorch.org/vision/stable/index.html) library. Fast processors handle the whole batch of videos at once, without iterating over each video or frame. These updates introduce GPU acceleration and significantly enhance processing speed, especially for tasks requiring high throughput.
+
+Fast video processors are available for all models and are loaded by default when an `AutoVideoProcessor` is initialized. When using a fast video processor, you can also set the `device` argument to specify the device on which the processing should be done. By default, the processing is done on the same device as the inputs if the inputs are tensors, or on the CPU otherwise. For even more speed improvement, we can compile the processor when using 'cuda' as device.
+
+```python
+import torch
+from transformers.video_utils import load_video
+from transformers import AutoVideoProcessor
+
+video = load_video("video.mp4")
+processor = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf", device="cuda")
+processor = torch.compile(processor)
+processed_video = processor(video, return_tensors="pt")
+```