LLaVA-OneVision-2

[!IMPORTANT] 🌟 The core work on this project is led by Glint Lab. For an interactive online experience, visit ve2s.ai.

A multimodal vision-language model that handles single images, multi-image, and video inputs, built on a Qwen3-8B language backbone with a OneVision-style vision encoder.

The model is distributed as a HuggingFace transformers checkpoint with custom code (trust_remote_code=True).

Requirements

Base (image + frame-sampling video)

pip install "transformers>=5.7.0" "torch>=2.4" pillow requests decord

Optional: codec video backend

The model ships a second video backend (video_backend="codec") that replaces uniform frame sampling with codec-aware canvas packing driven by motion vectors and bit-cost — typically yielding stronger long-video accuracy at the same token budget. To enable it you need two extra pieces:

# 1. The cv-preinfer CLI (PyPI: codec-video-prep) drives canvas extraction.
pip install codec-video-prep opencv-python

# 2. A working `ffmpeg` binary must be on PATH.
#    Verify with: ffmpeg -version

ffmpeg version: ffmpeg 4.4.x – 7.x is recommended.

The codec backend additionally needs POSIX flock (already present on Linux/macOS) for the on-disk result cache, and roughly 2 GB free disk under $ONLINE_CODEC_CACHE_DIR (defaults to $HF_HOME/online_codec) per processed video.

Quick start

The repository ships a ready-to-run demo_inference.py that covers both image and video paths.

# Image (default sample image; no auth required)
python demo_inference.py

# Image, custom file + prompt
python demo_inference.py --mode image --media /path/to/cat.jpg \
    --prompt "What is the cat doing?"

# Video (16 uniformly-sampled frames; max-pixels caps per-frame resolution for memory)
python demo_inference.py --mode video --media /path/to/clip.mp4 \
    --num-frames 16 --max-pixels 200704 \
    --prompt "Describe what happens in this video."

Programmatic use

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image

MODEL_ID = "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct"

processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID, trust_remote_code=True, dtype=torch.bfloat16, device_map="cuda",
).eval()

# ----- Image -----
image = Image.open("cat.jpg").convert("RGB")
messages = [{"role": "user", "content": [
    {"type": "image"},
    {"type": "text", "text": "Describe this image in detail."},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt", padding=True)
inputs = {k: v.to("cuda") if hasattr(v, "to") else v for k, v in inputs.items()}

out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(processor.tokenizer.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

# ----- Video -----
# Lower max_pixels if you hit OOM on long videos.
processor.video_processor.max_pixels = 200704

messages = [{"role": "user", "content": [
    {"type": "video"},
    {"type": "text", "text": "Describe what happens in this video."},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(
    text=[text], videos=["clip.mp4"], return_tensors="pt", padding=True,
    num_frames=16,  # exact frame count; or use target_fps / max_frames
)
inputs = {k: v.to("cuda") if hasattr(v, "to") else v for k, v in inputs.items()}
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(processor.tokenizer.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Video — codec backend (recommended for long videos)

The codec backend is exposed as a single processor kwarg (video_backend="codec"). Everything else — canvas extraction via cv-preinfer, on-disk caching, patch-position bookkeeping, chat-template rewriting — happens inside processor(...):

# Make sure: `pip install codec-video-prep opencv-python` and ffmpeg on PATH.
messages = [{"role": "user", "content": [
    {"type": "video"},
    {"type": "text", "text": "Describe what happens in this long video."},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = processor(
    text=[text],
    videos=["long_clip.mp4"],
    video_backend="codec",
    max_pixels=150000,          # per-canvas pixel budget; lower if OOM
    return_tensors="pt",
    padding=True,
    # Optional: override codec defaults from preprocessor_config.json
    # codec_config={"target_canvas": 32, "group_size": 32, "images_per_group": 4},
)
inputs = {k: v.to("cuda") if hasattr(v, "to") else v for k, v in inputs.items()}

out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(processor.tokenizer.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Defaults for the codec pipeline live in preprocessor_config.json under the "codec" key (target_canvas=32, group_size=32, images_per_group=4, patch=14, min_group_frames=8, max_group_frames=64); they can be overridden per call via codec_config={...}.

Short-video behaviour: if the input video has fewer frames than target_canvas requires (or fewer than min_group_frames), a UserWarning is emitted and inference proceeds with however many canvases cv-preinfer can actually form. For very short clips, falling back to the frame-sampling backend is usually a better choice.

Notes

• The vision tower is a OneVision-style encoder; the language backbone is Qwen3-8B.
• chat_template.jinja follows the Qwen3 chat format and emits ... placeholders; the processor expands them per-frame (frames backend) or per-canvas-patch-run (codec backend).
• Two video backends are availabl