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Add V-JEPA 2 (#38746)
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* adding model and conversion scripts

* add imports to test vjepa conversion

* fix imports and make conversion work

* fix computation for short side

* replace attention with library attention function

* cleanup more attention classes

* remove config overrides

* add test cases, fix some of the failing ones

* fix the model outputs

* fix outputs of the model per review

* fix too big model test case

* fix styling __init__.py

* fix initialization test

* remove all asserts per review

* update sorting unsorting logic as per feedback

* remove is_video per review

* remove another is_video segment

* remove unwanted stuff

* small fixes

* add docstrings for the model

* revert adding vjepa2 config here

* update styling

* add config docstrings (wip)

* fix dpr issue

* removed test failing issues

* update styles

* merge predictor configs into main config

* remove processing code, add video processor

* remove permute which is not necessary now

* fix styles

* updated vjepa2 to be in video_processing_auto

* update comment for preprocessing

* test integration test and fix the outputs

* update test values, change test to look at repeated frames for a given image

* add a simple video processing test

* refactoring pixel_values_videos and upload ckpts to original

* fix torch_fx test cases

* remove unused config

* add all config docstrings

* add more integration tests

* add basic doc

* revert unwanted styling changes

* working make fixup

* Fix model_type in config

* update attention implementation to fit new hf standards

* fix the preprocessing logic, ensure it matches the original model

* remove use_rope logic, cleanup

* fix docstrings

* Further cleanup, update doc

* Fix model prefix

* fix get_vision_features

* VJEPA2Embeddings style refactor

* nit, style comment

* change modules default values

* Only `str` activation in config

* GradientCheckpointingLayer

* fixup

* fix conversion script

* Remove return_dict

* remove None return typehint

* Refactor VJEPA2Layer, remove use_SiLU

* Fix fx tests

* dpr -> drop_path_rates

* move *ModelOutput on top

* format docs bit

* update docs

* update docs

* update doc example

* remove prune_heads from model

* remove unused config params

* refactor embed signature

* Add vjepa to docs

* Fix config docstring

* update defaults

* Update docs/source/en/model_doc/vjepa2.md

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update docs/source/en/model_doc/vjepa2.md

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix import

* Min refactoring

* Update HUB_SOURCE and HUB_REPO in conversion script

* Add missing headers

* VJEPA -> V-JEPA in docs

* Add image to doc

* fix style

* fix init weights

* change checkpoint name in modeling tests

---------

Co-authored-by: Koustuv Sinha <koustuv.sinha@mail.mcgill.ca>
Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
Co-authored-by: Koustuv Sinha <koustuvsinha@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
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2025-06-11 15:00:08 +01:00
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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# V-JEPA 2
V-JEPA 2 is a self-supervised approach to training video encoders developed by FAIR, Meta. Using internet-scale video data, V-JEPA 2 attains state-of-the-art performance on motion understanding and human action anticipation tasks. V-JEPA 2-AC is a latent action-conditioned world model post-trained from V-JEPA 2 (using a small amount of robot trajectory interaction data) that solves robot manipulation tasks without environment-specific data collection or task-specific training or calibration.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vjepa.gif" alt="drawing" width="600"/>
</div>
You can find all original V-JEPA2 checkpoints under the [V-JEPA 2](https://huggingface.co/collections/facebook/v-jepa-2-6841bad8413014e185b497a6) collection.
This model was contributed by [koustuvs](https://huggingface.co/koustuvs), [yonigozlan](https://huggingface.co/yonigozlan) and [qubvel](https://huggingface.co/qubvel-hf). The original code can be found [here](https://github.com/facebookresearch/vjepa2).
## Usage example
The snippet below shows how to load the V-JEPA 2 model using the `AutoModel` class.
```py
import torch
from torchcodec.decoders import VideoDecoder
import numpy as np
processor = AutoVideoProcessor.from_pretrained("facebook/vjepa2-vitl-fpc64-256")
model = AutoModel.from_pretrained(
"facebook/vjepa2-vitl-fpc64-256",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="sdpa"
)
video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/archery/-Qz25rXdMjE_000014_000024.mp4"
vr = VideoDecoder(video_url)
frame_idx = np.arange(0, 64) # choosing some frames. here, you can define more complex sampling strategy
video = vr.get_frames_at(indices=frame_idx).data # T x C x H x W
video = processor(video, return_tensors="pt").to(model.device)
outputs = model(**video)
# V-JEPA 2 encoder outputs, same as calling `model.get_vision_features()`
encoder_outputs = outputs.last_hidden_state
# V-JEPA 2 predictor outputs
predictor_outputs = outputs.predictor_output.last_hidden_state
```
## VJEPA2Config
[[autodoc]] VJEPA2Config
## VJEPA2Model
[[autodoc]] VJEPA2Model
- forward
## VJEPA2VideoProcessor
[[autodoc]] VJEPA2VideoProcessor