Repository: NVlabs/OmniVinci
Branch: main
Commit: 9307faa70176
Files: 9
Total size: 51.3 KB
Directory structure:
gitextract_43fwejau/
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── environment_setup.sh
├── example_infer.py
├── example_mini_audio.py
├── example_mini_image.py
├── example_mini_video.py
└── pyproject.toml
================================================
FILE CONTENTS
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================================================
FILE: CONTRIBUTING.md
================================================
# Contributing Guide
Thank you for your interest in the project! We welcome all forms of contributions, including but not limited to:
- Bug reports
- Feature suggestions
- Documentation improvements
- Code fixes
- New features
## Development Process
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Developer Certificate of Origin
Version 1.1
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## Issue Reporting
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Thank you for contributing!
================================================
FILE: LICENSE
================================================
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Copyright 2025 Hanrong Ye
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================================================
FILE: README.md
================================================
# **OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM (ICLR 2026)**
[](https://arxiv.org/abs/2510.15870)
[](https://github.com/NVlabs/OmniVinci)
[](https://huggingface.co/nvidia/omnivinci)
[](https://nvlabs.github.io/OmniVinci)
[](https://youtu.be/w84pPuGFH4o?si=OUFhhiXeQbzil7gN)
[Hanrong Ye*†](https://sites.google.com/site/yhrspace/home), [Chao-Han Huck Yang†](https://huckiyang.github.io/), [Arushi Goel†](https://scholar.google.com/citations?user=tj08PZcAAAAJ&hl=en), [Wei Huang†](https://aaron-weihuang.com/), [Ligeng Zhu†](https://lzhu.me/), [Yuanhang Su†](https://scholar.google.com/citations?user=n335GwUAAAAJ&hl=en), [Sean Lin†](https://www.nvidia.com/en-us/), [An-Chieh Cheng†](https://www.anjiecheng.me/), [Zhen Wan†](https://scholar.google.com/citations?user=OH_1qwMAAAAJ&hl=en), [Jinchuan Tian†](https://jctian98.github.io/), [Yuming Lou†](https://github.com/Louym), [Dong Yang†](https://scholar.google.com/citations?user=PHvliUgAAAAJ&hl=en), [Zhijian Liu](https://zhijianliu.com/), [Yukang Chen](https://yukangchen.com/), [Ambrish Dantrey](https://www.nvidia.com/en-us/), [Ehsan Jahangiri](https://www.nvidia.com/en-us/), [Sreyan Ghosh](https://sreyan88.github.io/), [Daguang Xu](https://scholar.google.com/citations?user=r_VHYHAAAAAJ&hl=en), [Ehsan Hosseini Asl](https://scholar.google.com/citations?user=I9w3ON4AAAAJ&hl=en), [Danial Mohseni Taheri](https://danialtaheri.github.io/), [Vidya Murali](https://www.linkedin.com/in/vidya-n-murali/), [Sifei Liu](https://sifeiliu.net/), [Yao Lu](https://www.linkedin.com/in/yao-jason-lu-a0291938/), [Oluwatobi Olabiyi](https://www.linkedin.com/in/oluwatobi-olabiyi-08955123/), [Yu-Chiang Frank Wang](https://scholar.google.com/citations?user=HSGvdtoAAAAJ&hl=en), [Rafael Valle](https://rafaelvalle.github.io/), [Bryan Catanzaro](https://www.linkedin.com/in/bryancatanzaro/), [Andrew Tao](https://scholar.google.com/citations?user=Wel9l1wAAAAJ&hl=en), [Song Han](https://hanlab.mit.edu/songhan), [Jan Kautz](https://jankautz.com/), [Hongxu Yin*^†](https://hongxu-yin.github.io/), [Pavlo Molchanov^](https://www.pmolchanov.com/)
**NVIDIA**
*Corresponding Author | †Core Contribution | ^Equal Advisory
Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world.
We introduce OmniVinci, an initiative to build a strong, open-source, omni-modal LLM.
We carefully study the design choices across model architecture and data curation.
For model architecture, we present three key innovations:
**(i)** OmniAlignNet for strengthening alignment between vision and audio embeddings in a shared omni-modal latent space;
**(ii)** Temporal Embedding Grouping for capturing relative temporal alignment between vision and audio signals; and
**(iii)** Constrained Rotary Time Embedding for encoding absolute temporal information in omni-modal embeddings.
We introduce a curation and synthesis pipeline that generates 24M single-modal and omni-modal conversations. We find that modalities reinforce one another in both perception and reasoning. Our model outperforms Qwen2.5-Omni with +19.05 on DailyOmni (cross-modal understanding), +1.7 on MMAR (audio), and +3.9 on Video-MME (vision), while using just 0.2T training tokens - a 6 times reduction compared to Qwen2.5-Omni’s 1.2T.
We finally demonstrate omni-modal advantages in downstream applications spanning robotics, medical AI, and smart factory.
| Model | Omni - Dailyomni | Omni - Worldsense | Audio - MMAU | Audio - MMAR | Vision - MVBench | Vision - Video-MME (w/o sub) |
|--------------|------------------|-------------------|--------------------------|--------------|------------------|------------------------------|
| Qwen2.5-Omni | 47.5 | 45.4 | 71.0 | 56.7 | 70.3 | 64.3 |
| **Ours** | **66.5** | **48.2** | **71.6** | **58.4** | **70.6** | **68.2** |
## News
- [x] **[2025 Oct 19] OmniVinci-9B** is released! It supports joint understanding of **vision, audio, and text**.
## Model Usage
### Inference
### Envirnoment setup
1. Download and cd huggingface repo
```
huggingface-cli download nvidia/omnivinci --local-dir ./omnivinci --local-dir-use-symlinks False
cd ./omnivinci
```
2. Install python environment (based on NVILA codebase)
```
bash ./environment_setup.sh omnivinci
```
### 🤗 Transformers Usage
#### Video (with audio) Inference Example:
```python
from transformers import AutoProcessor, AutoModel, AutoConfig,AutoModelForCausalLM
import torch
import os
# default: Load the model on the available device(s)
model_path = "./"
video_path = "xxx.mp4"
generation_kwargs = {"max_new_tokens": 1024, "max_length": 99999999}
load_audio_in_video = True
num_video_frames = 128
audio_length = "max_3600"
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path,
trust_remote_code=True,
torch_dtype="torch.float16",
device_map="auto")
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
generation_config = model.default_generation_config
generation_config.update(**generation_kwargs)
model.config.load_audio_in_video = load_audio_in_video
processor.config.load_audio_in_video = load_audio_in_video
if num_video_frames > 0:
model.config.num_video_frames = num_video_frames
processor.config.num_video_frames = num_video_frames
if audio_length != -1:
model.config.audio_chunk_length = audio_length
processor.config.audio_chunk_length = audio_length
conversation = [{
"role": "user",
"content": [
{"type": "video", "video":video_path},
{"type": "text", "text": "Assess the video, followed by a detailed description of it's video and audio contents."}
]
}]
text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = processor([text])
output_ids = model.generate(
input_ids=inputs.input_ids,
media=getattr(inputs, 'media', None),
media_config=getattr(inputs, 'media_config', None),
generation_config=generation_config,
)
print(processor.tokenizer.batch_decode(output_ids, skip_special_tokens=True))
```
- **For a audio and image inference examples please refer to ```example_mini_audio.py``` and ```example_mini_image.py```**
## Examples
https://github.com/user-attachments/assets/276d2735-2cdc-47c8-8fdb-787f77c5dddb
**Question**: "What are they talking about in detail?"
**Answer**: The video features Jensen Huang with gray hair and glasses, dressed in a black leather jacket, speaking into a microphone in a modern office setting. The background includes a glass wall and a green panel, with a blurred view of desks and chairs. Jensen Huang discusses the evolution of artificial intelligence, focusing on the impact of deep learning and the development of AI frameworks. He emphasizes the importance of data, computing power, and software methodologies in driving AI advancements. The conversation highlights the transformative potential of AI, particularly in perception and generative capabilities, and the need for a new approach to software development. Jensen Huang also mentions the creation of a new AI supercomputer, DGX-1, and its impact on the industry. The video concludes with Jensen Huang discussing the second phase of AI, generative AI, and its potential to revolutionize the way we understand and create information.
https://github.com/user-attachments/assets/19a03604-f6f8-4407-a93d-c078b621e940
**Question**: "Describe the video in detail."
**Answer**: The video begins with Jensen Huang in a modern, well-lit room with large windows and a view of greenery outside. He dressed in a black jacket and white pants, is seated at a table, writing a message on a black card with a gold pen. The message reads, 'To Robot, Enjoy Your New Brain!' followed by a signature. He then places the card on the table rand begins to open a large black gift box with a gold ribbon and bow. The scene transitions to a close-up of the gift box on the table, with the person's hand visible. The focus then shifts to a robot wearing a white hard hat with the 'NVIDIA' logo, standing in a workshop or industrial setting. The robot holds the same black gift box with the gold ribbon and bow, and it opens the box to reveal the black card with the message. The robot examines the card closely. The narrative continues with the robot, still in the workshop setting, holding the black gift box. The robot opens the box, revealing a sleek, white device with a black screen, nestled in crumpled black paper. The robot examines the device closely, then places it back into the box and closes it. The scene transitions to a different setting, where the robot is now in a modern office environment with green walls and multiple computer monitors. The robot stands behind the closed gift box, gesturing with its hands as if explaining or presenting something. The video wraps up with the robot in the modern office environment, gesturing with its hands. The scene transitions to a close-up of the robot's face, showing its detailed features and expressive eyes.
## Citation
Please consider to cite our paper and this framework, if they are helpful in your research.
```bibtex
@article{ye2025omnivinci,
title={OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM},
author={Ye, Hanrong and Yang, Chao-Han Huck and Goel, Arushi and Huang, Wei and Zhu, Ligeng and Su, Yuanhang and Lin, Sean and Cheng, An-Chieh and Wan, Zhen and Tian, Jinchuan and others},
journal={arXiv preprint arXiv:2510.15870},
year={2025}
}
```
================================================
FILE: environment_setup.sh
================================================
#!/usr/bin/env bash
set -e
CONDA_ENV=${1:-""}
if [ -n "$CONDA_ENV" ]; then
# This is required to activate conda environment
eval "$(conda shell.bash hook)"
conda create -n $CONDA_ENV python=3.10.14 -y
conda activate $CONDA_ENV
# This is optional if you prefer to use built-in nvcc
conda install -c nvidia cuda-toolkit=12.2 -y
else
echo "Skipping conda environment creation. Make sure you have the correct environment activated."
fi
# Using uv to speedup installations
pip install uv
alias uvp="uv pip"
echo "[INFO] Using python $(which python)"
echo "[INFO] Using pip $(which pip)"
echo "[INFO] Using uv $(which uv)"
# This is required to enable PEP 660 support
uv pip install --upgrade pip setuptools
# Install FlashAttention2
uv pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.5.8/flash_attn-2.5.8+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
# Install VILA
uv pip install -e ".[train,eval]"
# numpy introduce a lot dependencies issues, separate from pyproject.yaml
pip install numpy==1.26.4
# audio
uv pip install soundfile librosa openai-whisper ftfy
conda install -c conda-forge ffmpeg
uv pip install jiwer
# Downgrade protobuf to 3.20 for backward compatibility
uv pip install protobuf==3.20.*
# Replace transformers and deepspeed files
site_pkg_path=$(python -c 'import site; print(site.getsitepackages()[0])')
cp -rv ./transformers/modeling_utils.py $site_pkg_path/transformers/modeling_utils.py # for using qwen 2.5 omni checkpoint
# for benchmark adoption
uv pip install faiss-gpu-cu12
# Quantization requires the newest triton version, and introduce dependency issue
uv pip install triton==3.1.0 # we don't need this version if we do not use FP8LinearQwen2Config, QLlavaLlamaConfig, etc. It is not compatible with mamba-ssm.
uv pip install kaldiio
# for rotary embedding
uv pip install beartype
uv pip install pydantic==1.10.22
================================================
FILE: example_infer.py
================================================
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from transformers import AutoProcessor, AutoModel, AutoConfig, GenerationConfig
import torch
import os
import time
from pathlib import Path
from typing import List, Dict, Any, Optional, Union
import logging
import sys
os.environ["HF_HUB_OFFLINE"] = "1" # Use local cache for models
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def add_to_sys_path_direct(model_path):
"""Add model path directly to sys.path"""
if model_path not in sys.path:
sys.path.insert(0, model_path) # Insert at beginning for priority
print(f"✓ Added to sys.path: {model_path}")
else:
print(f"Already in sys.path: {model_path}")
class NVOmniVideoInference:
"""A class to handle NVOmni video model inference with improved error handling and flexibility."""
def __init__(self, model_path: str, torch_dtype="torch.float16", device_map="auto"):
"""
Initialize the NVOmni model for video inference.
Args:
model_path (str): Path to the model directory
torch_dtype: PyTorch data type for model weights
device_map (str): Device mapping strategy for model loading
"""
self.model_path = model_path
self.torch_dtype = torch_dtype
self.device_map = device_map
self.model = None
self.processor = None
self.config = None
self.device = None
self.load_model()
def validate_paths(self, model_path: str, video_path: str = None) -> bool:
"""Validate that required paths exist."""
if not Path(model_path).exists():
logger.error(f"Model path does not exist: {model_path}")
return False
if video_path and not Path(video_path).exists():
logger.error(f"Video path does not exist: {video_path}")
return False
return True
def load_model(self) -> bool:
"""Load the model, processor, and config with error handling."""
if not self.validate_paths(self.model_path):
return False
if True:
logger.info("Loading model configuration...")
self.config = AutoConfig.from_pretrained(self.model_path, trust_remote_code=True)
logger.info("Loading model...")
start_time = time.time()
self.model = AutoModel.from_pretrained(
self.model_path,
trust_remote_code=True,
torch_dtype=self.torch_dtype,
device_map=self.device_map,
low_cpu_mem_usage=True # More memory efficient loading
)#.to(eval(self.torch_dtype))
load_time = time.time() - start_time
logger.info(f"Model loaded in {load_time:.2f} seconds")
logger.info("Loading processor...")
self.processor = AutoProcessor.from_pretrained(self.model_path, trust_remote_code=True)
# Set device for single-device setups
if hasattr(self.model, 'device'):
self.device = self.model.device
else:
self.device = next(self.model.parameters()).device if self.model.parameters() else torch.device('cpu')
logger.info(f"Model successfully loaded on device: {self.device}")
self._print_model_info()
return True
def _print_model_info(self):
"""Print useful information about the loaded model."""
logger.info("=" * 50)
logger.info("MODEL INFORMATION")
logger.info("=" * 50)
if self.config:
logger.info(f"Model type: {getattr(self.config, 'model_type', 'Unknown')}")
logger.info(f"Hidden size: {getattr(self.config, 'hidden_size', 'Unknown')}")
if self.model and torch.cuda.is_available():
logger.info(f"GPU memory allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
logger.info(f"GPU memory reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")
def create_conversation(self, video_path: str, text_prompt: str) -> List[Dict[str, Any]]:
"""
Create a conversation format for the model.
Args:
video_path (str): Path to the video file
text_prompt (str): Text prompt for the model
Returns:
List[Dict]: Conversation in the expected format
"""
return [{
"role": "user",
"content": [
{"type": "video", "video": video_path},
{"type": "text", "text": text_prompt}
]
}]
@torch.inference_mode()
def generate_response(
self,
video_path: str,
text_prompt: str,
max_new_tokens: int = 256,
temperature: float = None,
top_p: float = None,
do_sample: bool = None,
num_video_frames: int = -1,
load_audio_in_video: bool = True,
audio_length: Union[int, str] = "max_3600",
) -> Optional[str]:
"""
Generate a response from the model given a video and text prompt.
Args:
video_path (str): Path to the video file
text_prompt (str): Text prompt for the model
max_new_tokens (int): Maximum number of new tokens to generate
temperature (float): Sampling temperature
top_p (float): Top-p sampling parameter
do_sample (bool): Whether to use sampling
custom_generation_config (GenerationConfig): Custom generation configuration
Returns:
Optional[str]: Generated response or None if failed
"""
if not self.model or not self.processor:
logger.error("Model or processor not loaded. Please initialize the model first.")
return None
if not self.validate_paths(self.model_path, video_path):
return None
# try:
if True:
logger.info(f"Processing video: {video_path}")
logger.info(f"Text prompt: {text_prompt}")
# Create conversation
conversation = self.create_conversation(video_path, text_prompt)
# Apply chat template
text = self.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
logger.info(f"Chat template applied")
# set model params
self.model.config.load_audio_in_video = load_audio_in_video
self.processor.config.load_audio_in_video = load_audio_in_video
if num_video_frames > 0:
self.model.config.num_video_frames = num_video_frames
self.processor.config.num_video_frames = num_video_frames
if audio_length != -1:
self.model.config.audio_chunk_length = audio_length
self.processor.config.audio_chunk_length = audio_length
logger.info(f"Model config - load_audio_in_video: {self.model.config.load_audio_in_video}, num_video_frames: {self.model.config.num_video_frames}, audio_chunk_length: {self.model.config.audio_chunk_length}")
# Process inputs
start_time = time.time()
inputs = self.processor([text])
# Move inputs to the correct device if needed
if hasattr(inputs, 'input_ids') and inputs.input_ids is not None:
inputs.input_ids = inputs.input_ids.to(self.device)
processing_time = time.time() - start_time
logger.info(f"Input processing completed in {processing_time:.2f} seconds")
logger.info("Generating response...")
start_time = time.time()
generation_kwargs = {"max_new_tokens": max_new_tokens, "max_length": 99999999}
if top_p is not None:
generation_kwargs["top_p"] = top_p
if do_sample is not None:
generation_kwargs["do_sample"] = do_sample
if temperature is not None:
generation_kwargs["temperature"] = temperature
generation_config = self.model.default_generation_config
generation_config.update(**generation_kwargs)
logger.info(f"Generation config: {generation_config.to_dict()}")
with torch.no_grad():
output_ids = self.model.generate(
input_ids=inputs.input_ids,
media=getattr(inputs, 'media', None),
media_config=getattr(inputs, 'media_config', None),
generation_config=generation_config,
)
generation_time = time.time() - start_time
logger.info(f"Generation completed in {generation_time:.2f} seconds")
# Decode response
response = self.processor.tokenizer.batch_decode(
output_ids,
skip_special_tokens=True
)[0]
return response
def batch_generate(
self,
video_text_pairs: List[tuple],
**generation_kwargs
) -> List[Optional[str]]:
"""
Generate responses for multiple video-text pairs.
Args:
video_text_pairs (List[tuple]): List of (video_path, text_prompt) tuples
**generation_kwargs: Arguments passed to generate_response
Returns:
List[Optional[str]]: List of generated responses
"""
responses = []
for i, (video_path, text_prompt) in enumerate(video_text_pairs):
logger.info(f"Processing batch item {i+1}/{len(video_text_pairs)}")
response = self.generate_response(video_path, text_prompt, **generation_kwargs)
responses.append(response)
# Clear cache between generations to manage memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
return responses
def main():
"""Main function demonstrating usage of the NVOmni model."""
# Configuration
MODEL_PATH = "./"
VIDEO_PATH = "xxx.mp4"
TEXT_PROMPT = "Assess the video, followed by a detailed description of it's video and audio contents."
num_video_frames=128
audio_length="max_3600"
load_audio_in_video=True
add_to_sys_path_direct(MODEL_PATH)
# Initialize the inference class
logger.info("Initializing NVOmni Video Inference...")
inferencer = NVOmniVideoInference(MODEL_PATH, torch_dtype="torch.float16")
if inferencer.model is None:
logger.error("Failed to initialize model. Exiting.")
return
# Generate response
logger.info("Starting inference...")
response = inferencer.generate_response(
video_path=VIDEO_PATH,
text_prompt=TEXT_PROMPT,
num_video_frames=num_video_frames,
load_audio_in_video=load_audio_in_video,
audio_length=audio_length,
max_new_tokens=1024,
)
if response:
print("\n" + "="*60)
print("GENERATED RESPONSE")
print("="*60)
print(response)
print("="*60)
else:
logger.error("Failed to generate response")
# Example of batch processing
if False:
logger.info("\nExample: Batch processing")
batch_pairs = [
(VIDEO_PATH, "What is happening in this video?"),
(VIDEO_PATH, "Describe the audio content of this video."),
]
batch_responses = inferencer.batch_generate(batch_pairs, max_new_tokens=128)
for i, (pair, response) in enumerate(zip(batch_pairs, batch_responses)):
print(f"\n--- Batch Response {i+1} ---")
print(f"Prompt: {pair[1]}")
print(f"Response: {response}")
if __name__ == "__main__":
main()
================================================
FILE: example_mini_audio.py
================================================
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
"""
Example script for audio transcription using the model.
This script demonstrates how to:
1. Load the model and processor
2. Configure audio processing parameters
3. Process audio input
4. Generate transcription output
Usage:
python example_mini_audio.py --model_path --audio_path
"""
from transformers import AutoProcessor, AutoModel, AutoConfig, AutoModelForCausalLM
import torch
import os
import argparse
# Configuration
parser = argparse.ArgumentParser(description="Audio transcription example")
parser.add_argument("--model_path", type=str, default="./", help="Path to the model")
parser.add_argument("--audio_path", type=str, required=True, help="Path to the audio file")
parser.add_argument("--max_new_tokens", type=int, default=1024, help="Maximum number of tokens to generate")
parser.add_argument("--num_video_frames", type=int, default=128, help="Number of video frames to process")
parser.add_argument("--audio_length", type=str, default="max_3600", help="Maximum audio length")
args = parser.parse_args()
model_path = args.model_path
audio_path = args.audio_path
generation_kwargs = {"max_new_tokens": args.max_new_tokens, "max_length": 99999999}
load_audio_in_video = True
num_video_frames = args.num_video_frames
audio_length = args.audio_length
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path,
trust_remote_code=True,
torch_dtype="torch.float16",
device_map="auto")
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
generation_config = model.default_generation_config
generation_config.update(**generation_kwargs)
model.config.load_audio_in_video = load_audio_in_video
processor.config.load_audio_in_video = load_audio_in_video
if num_video_frames > 0:
model.config.num_video_frames = num_video_frames
processor.config.num_video_frames = num_video_frames
if audio_length != -1:
model.config.audio_chunk_length = audio_length
processor.config.audio_chunk_length = audio_length
conversation = [{
"role": "user",
"content": [
{"type": "audio", "audio": audio_path},
{"type": "text", "text": "Transcribe the whole speech."}
]
}]
text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = processor([text])
output_ids = model.generate(
input_ids=inputs.input_ids,
media=getattr(inputs, 'media', None),
media_config=getattr(inputs, 'media_config', None),
generation_config=generation_config,
)
print(processor.tokenizer.batch_decode(output_ids, skip_special_tokens=True))
================================================
FILE: example_mini_image.py
================================================
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
"""
Example script for image understanding using the model.
This script demonstrates how to:
1. Load the model and processor
2. Process image input
3. Generate description output
Usage:
python example_mini_image.py --model_path --image_path
"""
from transformers import AutoProcessor, AutoModel, AutoConfig, AutoModelForCausalLM
import torch
import os
import argparse
# Configuration
parser = argparse.ArgumentParser(description="Image understanding example")
parser.add_argument("--model_path", type=str, default="./", help="Path to the model")
parser.add_argument("--image_path", type=str, required=True, help="Path to the image file")
parser.add_argument("--max_new_tokens", type=int, default=1024, help="Maximum number of tokens to generate")
parser.add_argument("--prompt", type=str, default="Describe the image in detail.", help="Text prompt for the model")
args = parser.parse_args()
model_path = args.model_path
image_path = args.image_path
generation_kwargs = {"max_new_tokens": args.max_new_tokens, "max_length": 99999999}
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
generation_config = model.default_generation_config
generation_config.update(**generation_kwargs)
conversation = [{
"role": "user",
"content": [
{"type": "image", "image": image_path},
{"type": "text", "text": args.prompt}
]
}]
text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = processor([text])
output_ids = model.generate(
input_ids=inputs.input_ids,
media=getattr(inputs, 'media', None),
media_config=getattr(inputs, 'media_config', None),
generation_config=generation_config,
)
print(processor.tokenizer.batch_decode(output_ids, skip_special_tokens=True))
================================================
FILE: example_mini_video.py
================================================
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
"""
Example script for video understanding using the model.
This script demonstrates how to:
1. Load the model and processor
2. Configure video and audio processing parameters
3. Process video input with optional audio
4. Generate description output
Usage:
python example_mini_video.py --model_path --video_path
"""
from transformers import AutoProcessor, AutoModel, AutoConfig, AutoModelForCausalLM
import torch
import os
import argparse
# Configuration
parser = argparse.ArgumentParser(description="Video understanding example")
parser.add_argument("--model_path", type=str, default="./", help="Path to the model")
parser.add_argument("--video_path", type=str, required=True, help="Path to the video file")
parser.add_argument("--max_new_tokens", type=int, default=1024, help="Maximum number of tokens to generate")
parser.add_argument("--num_video_frames", type=int, default=128, help="Number of video frames to process")
parser.add_argument("--audio_length", type=str, default="max_3600", help="Maximum audio length")
parser.add_argument("--prompt", type=str, default="What are they talking about in detail?", help="Text prompt for the model")
parser.add_argument("--load_audio", action="store_true", default=True, help="Load audio from video")
args = parser.parse_args()
model_path = args.model_path
video_path = args.video_path
generation_kwargs = {"max_new_tokens": args.max_new_tokens, "max_length": 99999999}
load_audio_in_video = args.load_audio
num_video_frames = args.num_video_frames
audio_length = args.audio_length
text_prompt = args.prompt
assert os.path.exists(video_path), f"Video path {video_path} does not exist."
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype=torch.float16,
device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
generation_config = model.default_generation_config
generation_config.update(**generation_kwargs)
model.config.load_audio_in_video = load_audio_in_video
processor.config.load_audio_in_video = load_audio_in_video
if num_video_frames > 0:
model.config.num_video_frames = num_video_frames
processor.config.num_video_frames = num_video_frames
if audio_length != -1:
model.config.audio_chunk_length = audio_length
processor.config.audio_chunk_length = audio_length
def forward_inference(video_path, text_prompt):
"""Run inference on video with text prompt."""
print(f"Text prompt: {text_prompt}")
print(f"Video path: {video_path}")
conversation = [{
"role": "user",
"content": [
{"type": "video", "video": video_path},
{"type": "text", "text": text_prompt}
]
}]
text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = processor([text])
output_ids = model.generate(
input_ids=inputs.input_ids,
media=getattr(inputs, 'media', None),
media_config=getattr(inputs, 'media_config', None),
generation_config=generation_config,
)
print(processor.tokenizer.batch_decode(output_ids, skip_special_tokens=True))
forward_inference(video_path, text_prompt)
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[project]
name = "vila"
version = "1.5.0"
description = "nvOmni"
readme = "README.md"
requires-python = ">=3.8"
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
]
dependencies = [
"torch==2.3.0", "torchvision==0.18.0",
"transformers==4.46.0", "tokenizers>=0.15.2", "sentencepiece==0.1.99", "shortuuid",
"accelerate==0.34.2", "peft>=0.9.0", "bitsandbytes==0.43.2",
"pydantic<2,>=1", "markdown2[all]", "numpy==1.26.4", "scikit-learn==1.2.2",
"gradio==3.35.2", "gradio_client==0.2.9",
"requests", "httpx", "uvicorn", "fastapi", "fire", "seaborn", "ring_flash_attn==0.1.1",
"einops==0.6.1", "einops-exts==0.0.4", "timm==0.9.12",
"openpyxl==3.1.2", "pytorchvideo==0.1.5", "decord==0.6.0",
"datasets==2.16.1", "openai==1.8.0", "webdataset==0.2.86",
"nltk==3.3", "pywsd==1.2.4", "opencv-python-headless==4.8.0.76",
"s2wrapper@git+https://github.com/bfshi/scaling_on_scales",
"tyro", "pytest", "pre-commit", "loguru", "hydra-core", "xgrammar"
]
[project.scripts]
vila-run = "llava.cli.run:main"
vila-eval = "llava.cli.eval:main"
vila-infer = "llava.cli.infer:main"
vila-upload = "llava.cli.upload2hf:main"
[project.optional-dependencies]
train = ["deepspeed==0.9.5", "ninja", "wandb"]
eval = ["word2number", "Levenshtein", "nltk", "pywsd"]
[project.urls]
"Homepage" = "https://hanlab.mit.edu/projects/vila"
"Bug Tracker" = "https://github.com/NVlabs/VILA/issues"
[tool.triton]
triton = {version = "3.0.0.post20240610003544", file = "https://aiinfra.pkgs.visualstudio.com/2692857e-05ef-43b4-ba9c-ccf1c22c437c/_packaging/07c94329-d4c3-4ad4-9e6b-f904a60032ec/pypi/download/triton-nightly/3.post20240610003544/triton_nightly-3.0.0.post20240610003544-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", sha256 = "ac2c36a49bf9c2bb780909b38096fb718f17efd78b88a1ca1d649f6d063cdc2c"}
[tool.black]
line-length = 120
[tool.isort]
profile = "black"
multi_line_output = 3
include_trailing_comma = true
force_grid_wrap = 0
use_parentheses = true
ensure_newline_before_comments = true
line_length = 120
[tool.setuptools.packages.find]
exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
[tool.wheel]
exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]