transformers/docs/source/en/model_doc/llava_onevision.md

LLaVA-OneVision

Overview

The LLaVA-OneVision model was proposed in LLaVA-OneVision: Easy Visual Task Transfer by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li

LLaVA-OneVision is a Vision-Language Model that can generate text conditioned on one or several images/videos. The model consists of SigLIP vision encoder and a Qwen2 language backbone. The images are processed with anyres-9 technique where the image is split into 9 patches to better process high resolution images and capture as much details as possible. However, videos are pooled to a total sequence length of 196 tokens each frame for more memory efficient computation. LLaVA-OneVision is available in three sizes: 0.5B, 7B and 72B and achieves remarkable performance on benchmark evaluations.

The abstract from the paper is the following:

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVAOneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.

LLaVA-OneVision architecture. Taken from the original paper.

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.

• Llava-OneVision uses different number of patches for images and thus has to pad the inputs inside modeling code, aside from the padding done when processing the inputs. The default setting is "left-padding" if model is in eval() mode, otherwise "right-padding".

Formatting Prompts with Chat Templates

Each checkpoint is trained with a specific prompt format, depending on the underlying large language model backbone. To ensure correct formatting, use the processor’s apply_chat_template method.

Important:

• You must construct a conversation history — passing a plain string won't work.
• Each message should be a dictionary with "role" and "content" keys.
• The "content" should be a list of dictionaries for different modalities like "text" and "image".

Here’s an example of how to structure your input. We will use llava-onevision-qwen2-7b-si-hf and a conversation history of text and image. Each content field has to be a list of dicts, as follows:

from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-si-hf")

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What’s shown in this image?"},
        ],
    },
    {
        "role": "assistant",
        "content": [{"type": "text", "text": "This image shows a red stop sign."},]
    },
    {

        "role": "user",
        "content": [
            {"type": "text", "text": "Describe the image in more details."},
        ],
    },
]

text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)

# Note that the template simply formats your prompt, you still have to tokenize it and obtain pixel values for your images
print(text_prompt)
'<|im_start|>user\n<image>What is shown in this image?<|im_end|>\n<|im_start|>assistant\nPage showing the list of options.<|im_end|>'

🚀 Bonus: If you're using transformers>=4.49.0, you can also get a vectorized output from apply_chat_template. See the Usage Examples below for more details on how to use it.

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

Usage example

Single image inference

Here's how to load the model and perform inference in half-precision (torch.float16):

from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
import torch

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,
    device_map="cuda:0"
)

# prepare image and text prompt, using the appropriate prompt template
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": url},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    },
]
inputs = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
inputs = inputs.to("cuda:0", torch.float16)

# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
'user\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, also known as a spider chart or a star chart, which is used to compare multiple quantitative variables. Each axis represents a different variable, and the chart is filled with'

Multi image inference

LLaVa-OneVision can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). For that you have to use checkpoints with an "ov" suffix. For multi-image cases, we recommend using a nested list of images as input. Otherwise, every image will be patchified and consume a lot of memory. Here is how you can do it:

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

# Load the model in half-precision
model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, device_map="auto")
processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

# Prepare a batch of two prompts, where the first one is a multi-turn conversation and the second is not
conversation_1 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
            {"type": "text", "text": "What is shown in this image?"},
            ],
    },
    {
        "role": "assistant",
        "content": [
            {"type": "text", "text": "There is a red stop sign in the image."},
            ],
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
            {"type": "text", "text": "What about this image? How many cats do you see?"},
            ],
    },
]

conversation_2 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"},
            {"type": "text", "text": "What is shown in this image?"},
            ],
    },
]

inputs = processor.apply_chat_template(
    [conversation_1, conversation_2],
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    padding=True,
    return_tensors="pt"
).to(model.device, torch.float16)

# Generate
generate_ids = model.generate(**inputs, max_new_tokens=30)
processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
['user\n\nWhat is shown in this image?\nassistant\nThere is a red stop sign in the image.\nuser\n\nWhat about this image? How many cats do you see?\nassistant\ntwo', 'user\n\nWhat is shown in this image?\nassistant\n']

Video inference

LLaVa-OneVision also can perform inference with videos as input, where video frames are treated as multiple images. Here is how you can do it:

from huggingface_hub import hf_hub_download
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration

# Load the model in half-precision
model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype=torch.float16, device_map="auto")
processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")

video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
conversation = [
    {

        "role": "user",
        "content": [
            {"type": "video", "path": video_path},
            {"type": "text", "text": "Why is this video funny?"},
            ],
    },
]

inputs = processor.apply_chat_template(
    conversation,
    num_frames=8
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device, torch.float16)

out = model.generate(**inputs, max_new_tokens=60)
processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
["user\n\nWhy is this video funny?\nassistant\nThe video appears to be humorous because it shows a young child, who is wearing glasses and holding a book, seemingly reading with a serious and focused expression. The child's glasses are a bit oversized for their face, which adds a comical touch, as it's a common trope to see children wearing"]

Model optimization

Quantization using bitsandbytes

The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, pip install bitsandbytes and make sure 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.

Simply change the snippet above with:

from transformers import LlavaOnevisionForConditionalGeneration, 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 = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, device_map="auto")

Use Flash-Attention 2 to further speed-up generation

First make sure to install flash-attn. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:

from transformers import LlavaOnevisionForConditionalGeneration

model = LlavaOnevisionForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
    use_flash_attention_2=True
).to(0)

LlavaOnevisionConfig

autodoc LlavaOnevisionConfig

LlavaOnevisionProcessor

autodoc LlavaOnevisionProcessor

LlavaOnevisionImageProcessor

autodoc LlavaOnevisionImageProcessor - preprocess

LlavaOnevisionImageProcessorFast

autodoc LlavaOnevisionImageProcessorFast - preprocess

LlavaOnevisionVideoProcessor

autodoc LlavaOnevisionVideoProcessor

LlavaOnevisionVideoProcessor

autodoc LlavaOnevisionVideoProcessor

LlavaOnevisionModel

autodoc LlavaOnevisionModel

LlavaOnevisionForConditionalGeneration

autodoc LlavaOnevisionForConditionalGeneration - forward