Video-LLaVA

This model was released on 2023-11-16 and added to Hugging Face Transformers on 2024-05-15.

Video-LLaVA

Overview

Video-LLaVa is an open-source multimodal LLM trained by fine-tuning LlamA/Vicuna on multimodal instruction-following data generated by Llava1.5 and VideChat. It is an auto-regressive language model, based on the transformer architecture. Video-LLaVa unifies visual representations to the language feature space, and enables an LLM to perform visual reasoning capabilities on both images and videos simultaneously.

The Video-LLaVA model was proposed in Video-LLaVA: Learning United Visual Representation by Alignment Before Projection by Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munang Ning, Peng Jin, Li Yuan.

The abstract from the paper is the following:

The Large Vision-Language Model (LVLM) has enhanced the performance of various downstream tasks in visual-language understanding. Most existing approaches encode images and videos into separate feature spaces, which are then fed as inputs to large language models. However, due to the lack of unified tokenization for images and videos, namely misalignment before projection, it becomes challenging for a Large Language Model (LLM) to learn multi-modal interactions from several poor projection layers. In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. As a result, we establish a simple but robust LVLM baseline, Video-LLaVA, which learns from a mixed dataset of images and videos, mutually enhancing each other. Video-LLaVA achieves superior performances on a broad range of 9 image benchmarks across 5 image question-answering datasets and 4 image benchmark toolkits. Additionally, our Video-LLaVA also outperforms Video-ChatGPT by 5.8%, 9.9%, 18.6%, and 10.1% on MSRVTT, MSVD, TGIF, and ActivityNet, respectively. Notably, extensive experiments demonstrate that Video-LLaVA mutually benefits images and videos within a unified visual representation, outperforming models designed specifically for images or videos. We aim for this work to provide modest insights into the multi-modal inputs for the LLM

Usage tips

• We advise users to use padding_side=“left” when computing batched generation as it leads to more accurate results. Simply make sure to call processor.tokenizer.padding_side = “left” before generating.

• Note the model has not been explicitly trained to process multiple images/videos in the same prompt, although this is technically possible, you may experience inaccurate results.

• Note that the video inputs should have exactly 8 frames at the input, since the models were trained in that setting.

This model was contributed by RaushanTurganbay. The original code can be found here.

Note

LLaVA models after release v4.46 will raise warnings about adding processor.patch_size = {{patch_size}}, processor.num_additional_image_tokens = {{num_additional_image_tokens}} and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}. It is strongly recommended to add the attributes to the processor if you own the model checkpoint, or open a PR if it is not owned by you.

Adding these attributes means that LLaVA will try to infer the number of image tokens required per image and expand the text with as many <image> placeholders as there will be tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there will be failure when merging the embeddings. The attributes can be obtained from model config, as model.config.vision_config.patch_size or model.config.vision_feature_select_strategy. The num_additional_image_tokens should be 1 if the vision backbone adds a CLS token or 0 if nothing extra is added to the vision patches.

Usage example

Single Media Mode

The model can accept both images and videos as input. Here’s an example code for inference in half-precision (torch.float16):

import av
import torch
import numpy as np
from transformers import VideoLlavaForConditionalGeneration, VideoLlavaProcessor

def read_video_pyav(container, indices):
    '''
    Decode the video with PyAV decoder.
    Args:
        container (`av.container.input.InputContainer`): PyAV container.
        indices (`list[int]`): List of frame indices to decode.
    Returns:
        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
    '''
    frames = []
    container.seek(0)
    start_index = indices[0]
    end_index = indices[-1]
    for i, frame in enumerate(container.decode(video=0)):
        if i > end_index:
            break
        if i >= start_index and i in indices:
            frames.append(frame)
    return np.stack([x.to_ndarray(format="rgb24") for x in frames])

# Load the model in half-precision
model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf", dtype=torch.float16, device_map="auto")
processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")

# Load the video as an np.arrau, sampling uniformly 8 frames
video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
container = av.open(video_path)
total_frames = container.streams.video[0].frames
indices = np.arange(0, total_frames, total_frames / 8).astype(int)
video = read_video_pyav(container, indices)

# For better results, we recommend to prompt the model in the following format
prompt = "USER: <video>\nWhy is this funny? ASSISTANT:"
inputs = processor(text=prompt, videos=video, return_tensors="pt")

out = model.generate(**inputs, max_new_tokens=60)
processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)

For multiple turns conversation change the prompt format to:

"USER: <video>\nWhat do you see in this video? ASSISTANT: A baby reading a book. USER: Why is the it funny? ASSISTANT:"

Mixed Media Mode

The model can also generate from an interleaved image-video inputs. However note, that it was not trained in interleaved image-video setting which might affect the performance. Below is an example usage for mixed media input, add the following lines to the above code snippet:

from PIL import Image
import requests

# Generate from image and video mixed inputs
# Load and image and write a new prompt
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = "USER: <image>\nHow many cats are there in the image? ASSISTANT: There are two cats. USER: <video>\nWhy is this video funny? ASSISTANT:"

inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")

# Generate
generate_ids = model.generate(**inputs, max_length=50)
processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)

Model optimization

Quantization using Bitsandbytes for memory efficiency

The model can be loaded in lower bits, significantly reducing memory burden while maintaining the performance of the original model. his allows for efficient deployment on resource-constrained cases.

First make sure to install bitsandbytes by running pip install bitsandbytes and to have access to a GPU/accelerator that is supported by the library.

bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit this link.

We value your feedback to help identify bugs before the full release! Check out these docs for more details and feedback links.

Load the quantized model by simply adding BitsAndBytesConfig(../main_classes/quantization#transformers.BitsAndBytesConfig) as shown below:

from transformers import VideoLlavaForConditionalGeneration, BitsAndBytesConfig

# specify how to quantize the model
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf", quantization_config=quantization_config, device_map="auto")

Flash-Attention 2 to speed-up generation

Additionally, we can greatly speed-up model inference by using Flash Attention, which is a faster implementation of the attention mechanism used inside the model.

First, make sure to install the latest version of Flash Attention 2:

pip install -U flash-attn --no-build-isolation

Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the flash attention repository. FlashAttention-2 can only be used when a model is loaded in torch.float16 or torch.bfloat16.

To load and run a model using Flash Attention-2, simply add attn_implementation="flash_attention_2" when loading the model as follows:

from transformers import VideoLlavaForConditionalGeneration

model = VideoLlavaForConditionalGeneration.from_pretrained(
    "LanguageBind/Video-LLaVA-7B-hf",
    dtype=torch.float16,
    attn_implementation="flash_attention_2",
).to(0)

VideoLlavaConfig

[[autodoc]] VideoLlavaConfig

VideoLlavaImageProcessor

[[autodoc]] VideoLlavaImageProcessor

VideoLlavaVideoProcessor

[[autodoc]] VideoLlavaVideoProcessor

VideoLlavaProcessor

[[autodoc]] VideoLlavaProcessor

VideoLlavaModel

[[autodoc]] VideoLlavaModel

VideoLlavaForConditionalGeneration

[[autodoc]] VideoLlavaForConditionalGeneration - forward