Ola Image

🚀 Ola-7B

Ola-7Bモデルは、騰訊、清華大学、南洋理工大学の関係者によって開発されたモデルです。このモデルはQwen2.5言語モデルをベースに、テキスト、画像、ビデオ、音声データを用いて学習されており、32Kトークンのコンテキストウィンドウを持っています。画像やビデオ、テキスト、音声を入力とし、テキストを出力することができます。また、Olaは任意の空間サイズと時間長のビジュアル入力をシームレスかつ効率的に処理するオンデマンドソリューションを提供します。

🚀 クイックスタート

モデルの使用方法

1. 音声エンコーダをこちらからダウンロードします。
2. config.json内のパスを音声エンコーダのローカルパスに置き換えます。

モデルの使用に関する詳細な情報は、GitHubリポジトリを参照してください。

コード例

import os
os.environ['LOWRES_RESIZE'] = '384x32'
os.environ['HIGHRES_BASE'] = '0x32'
os.environ['VIDEO_RESIZE'] = "0x64"
os.environ['VIDEO_MAXRES'] = "480"
os.environ['VIDEO_MINRES'] = "288"
os.environ['MAXRES'] = '1536'
os.environ['MINRES'] = '0'
os.environ['REGIONAL_POOL'] = '2x'
os.environ['FORCE_NO_DOWNSAMPLE'] = '1'
os.environ['LOAD_VISION_EARLY'] = '1'
os.environ['SKIP_LOAD_VIT'] = '1'
    

import gradio as gr
import torch
import re
from decord import VideoReader, cpu
from PIL import Image
import numpy as np
import transformers
import moviepy.editor as mp
from typing import Dict, Optional, Sequence, List
import librosa
import whisper
from ola.conversation import conv_templates, SeparatorStyle
from ola.model.builder import load_pretrained_model
from ola.utils import disable_torch_init
from ola.datasets.preprocess import tokenizer_image_token, tokenizer_speech_image_token, tokenizer_speech_question_image_token
from ola.mm_utils import get_model_name_from_path, KeywordsStoppingCriteria, process_anyres_video, process_anyres_highres_image_genli
from ola.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX, DEFAULT_SPEECH_TOKEN

model_path = ""
tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None)
model = model.to('cuda').eval()
model = model.bfloat16()

USE_SPEECH=False
cur_dir = os.path.dirname(os.path.abspath(__file__))


def load_audio(audio_file_name):
    speech_wav, samplerate = librosa.load(audio_file_name, sr=16000)
    if len(speech_wav.shape) > 1:
        speech_wav = speech_wav[:, 0]
    speech_wav = speech_wav.astype(np.float32)
    CHUNK_LIM = 480000
    SAMPLE_RATE = 16000
    speechs = []
    speech_wavs = []

    if len(speech_wav) <= CHUNK_LIM:
        speech = whisper.pad_or_trim(speech_wav)
        speech_wav = whisper.pad_or_trim(speech_wav)
        speechs.append(speech)
        speech_wavs.append(torch.from_numpy(speech_wav).unsqueeze(0))
    else:
        for i in range(0, len(speech_wav), CHUNK_LIM):
            chunk = speech_wav[i : i + CHUNK_LIM]
            if len(chunk) < CHUNK_LIM:
                chunk = whisper.pad_or_trim(chunk)
            speechs.append(chunk)
            speech_wavs.append(torch.from_numpy(chunk).unsqueeze(0))
    mels = []
    for chunk in speechs:
        chunk = whisper.log_mel_spectrogram(chunk, n_mels=128).permute(1, 0).unsqueeze(0)
        mels.append(chunk)

    mels = torch.cat(mels, dim=0)
    speech_wavs = torch.cat(speech_wavs, dim=0)
    if mels.shape[0] > 25:
        mels = mels[:25]
        speech_wavs = speech_wavs[:25]

    speech_length = torch.LongTensor([mels.shape[1]] * mels.shape[0])
    speech_chunks = torch.LongTensor([mels.shape[0]])
    return mels, speech_length, speech_chunks, speech_wavs

def extract_audio(videos_file_path):
    my_clip = mp.VideoFileClip(videos_file_path)
    return my_clip.audio

def ola_inference(multimodal, audio_path):
    visual, text = multimodal["files"][0], multimodal["text"]
    if visual.endswith("image2.png"):
        modality = "video"
        visual = f"{cur_dir}/case/case1.mp4"
    if visual.endswith(".mp4"):
        modality = "video"
    else:
        modality = "image"
    
    # input audio and video, do not parse audio in the video, else parse audio in the video
    if audio_path:
        USE_SPEECH = True
    elif modality == "video":
        USE_SPEECH = True
    else:
        USE_SPEECH = False
    
    speechs = []
    speech_lengths = []
    speech_wavs = []
    speech_chunks = []
    if modality == "video":
        vr = VideoReader(visual, ctx=cpu(0))
        total_frame_num = len(vr)
        fps = round(vr.get_avg_fps())
        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, 64, dtype=int)
        frame_idx = uniform_sampled_frames.tolist()
        spare_frames = vr.get_batch(frame_idx).asnumpy()
        video = [Image.fromarray(frame) for frame in spare_frames]
    else:
        image = [Image.open(visual)]
        image_sizes = [image[0].size]

    if USE_SPEECH and audio_path:
        audio_path = audio_path
        speech, speech_length, speech_chunk, speech_wav = load_audio(audio_path)
        speechs.append(speech.bfloat16().to('cuda'))
        speech_lengths.append(speech_length.to('cuda'))
        speech_chunks.append(speech_chunk.to('cuda'))
        speech_wavs.append(speech_wav.to('cuda'))
        print('load audio')
    elif USE_SPEECH and not audio_path:
        # parse audio in the video
        audio = extract_audio(visual)
        audio.write_audiofile("./video_audio.wav")
        video_audio_path = './video_audio.wav'
        speech, speech_length, speech_chunk, speech_wav = load_audio(video_audio_path)
        speechs.append(speech.bfloat16().to('cuda'))
        speech_lengths.append(speech_length.to('cuda'))
        speech_chunks.append(speech_chunk.to('cuda'))
        speech_wavs.append(speech_wav.to('cuda'))
    else:
        speechs = [torch.zeros(1, 3000, 128).bfloat16().to('cuda')]
        speech_lengths = [torch.LongTensor([3000]).to('cuda')]
        speech_wavs = [torch.zeros([1, 480000]).to('cuda')]
        speech_chunks = [torch.LongTensor([1]).to('cuda')]
    
    conv_mode = "qwen_1_5"
    if text:
        qs = text
    else:
        qs = ''
    if USE_SPEECH and audio_path:
        qs = DEFAULT_IMAGE_TOKEN + "\n" + "User's question in speech: " + DEFAULT_SPEECH_TOKEN + '\n'
    elif USE_SPEECH:
        qs = DEFAULT_SPEECH_TOKEN + DEFAULT_IMAGE_TOKEN + "\n" + qs
    else:
        qs = DEFAULT_IMAGE_TOKEN + "\n" + qs

    conv = conv_templates[conv_mode].copy()
    conv.append_message(conv.roles[0], qs)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    if USE_SPEECH and audio_path:
        input_ids = tokenizer_speech_question_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda')
    elif USE_SPEECH:
        input_ids = tokenizer_speech_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda')
    else:
        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to('cuda')

    if modality == "video":
        video_processed = []
        for idx, frame in enumerate(video):
            image_processor.do_resize = False
            image_processor.do_center_crop = False
            frame = process_anyres_video(frame, image_processor)

            if frame_idx is not None and idx in frame_idx:
                video_processed.append(frame.unsqueeze(0))
            elif frame_idx is None:
                video_processed.append(frame.unsqueeze(0))
        
        if frame_idx is None:
            frame_idx = np.arange(0, len(video_processed), dtype=int).tolist()
        
        video_processed = torch.cat(video_processed, dim=0).bfloat16().to("cuda")
        video_processed = (video_processed, video_processed)

        video_data = (video_processed, (384, 384), "video")
    else:
        image_processor.do_resize = False
        image_processor.do_center_crop = False
        image_tensor, image_highres_tensor = [], []
        for visual in image:
            image_tensor_, image_highres_tensor_ = process_anyres_highres_image_genli(visual, image_processor)
            image_tensor.append(image_tensor_)
            image_highres_tensor.append(image_highres_tensor_)
        if all(x.shape == image_tensor[0].shape for x in image_tensor):
            image_tensor = torch.stack(image_tensor, dim=0)
        if all(x.shape == image_highres_tensor[0].shape for x in image_highres_tensor):
            image_highres_tensor = torch.stack(image_highres_tensor, dim=0)
        if type(image_tensor) is list:
            image_tensor = [_image.bfloat16().to("cuda") for _image in image_tensor]
        else:
            image_tensor = image_tensor.bfloat16().to("cuda")
        if type(image_highres_tensor) is list:
            image_highres_tensor = [_image.bfloat16().to("cuda") for _image in image_highres_tensor]
        else:
            image_highres_tensor = image_highres_tensor.bfloat16().to("cuda")

    pad_token_ids = 151643

    attention_masks = input_ids.ne(pad_token_ids).long().to('cuda')
    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

    gen_kwargs = {}

    if "max_new_tokens" not in gen_kwargs:
        gen_kwargs["max_new_tokens"] = 1024
    if "temperature" not in gen_kwargs:
        gen_kwargs["temperature"] = 0.2
    if "top_p" not in gen_kwargs:
        gen_kwargs["top_p"] = None
    if "num_beams" not in gen_kwargs:
        gen_kwargs["num_beams"] = 1

    with torch.inference_mode():
        if modality == "video":
            output_ids = model.generate(
                inputs=input_ids,
                images=video_data[0][0],
                images_highres=video_data[0][1],
                modalities=video_data[2],
                speech=speechs,
                speech_lengths=speech_lengths,
                speech_chunks=speech_chunks,
                speech_wav=speech_wavs,
                attention_mask=attention_masks,
                use_cache=True,
                stopping_criteria=[stopping_criteria],
                do_sample=True if gen_kwargs["temperature"] > 0 else False,
                temperature=gen_kwargs["temperature"],
                top_p=gen_kwargs["top_p"],
                num_beams=gen_kwargs["num_beams"],
                max_new_tokens=gen_kwargs["max_new_tokens"],
            )
        else:
            output_ids = model.generate(
                inputs=input_ids,
                images=image_tensor,
                images_highres=image_highres_tensor,
                image_sizes=image_sizes,
                modalities=['image'],
                speech=speechs,
                speech_lengths=speech_lengths,
                speech_chunks=speech_chunks,
                speech_wav=speech_wavs,
                attention_mask=attention_masks,
                use_cache=True,
                stopping_criteria=[stopping_criteria],
                do_sample=True if gen_kwargs["temperature"] > 0 else False,
                temperature=gen_kwargs["temperature"],
                top_p=gen_kwargs["top_p"],
                num_beams=gen_kwargs["num_beams"],
                max_new_tokens=gen_kwargs["max_new_tokens"],
            )

    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
    outputs = outputs.strip()
    if outputs.endswith(stop_str):
        outputs = outputs[:-len(stop_str)]
    outputs = outputs.strip()
    return outputs, None

✨ 主な機能

• 画像、ビデオ、テキスト、音声を入力として受け取り、テキストを出力することができます。
• 任意の空間サイズと時間長のビジュアル入力をシームレスかつ効率的に処理することができます。

📦 インストール

1. 音声エンコーダをこちらからダウンロードします。
2. config.json内のパスを音声エンコーダのローカルパスに置き換えます。

📚 ドキュメント

モデルの使用に関する詳細な情報は、GitHubリポジトリを参照してください。

🔧 技術詳細

モデルアーキテクチャ

• アーキテクチャ：事前学習済みのOryx-ViT + Qwen2.5-7B
• データ：500万以上の画像/ビデオ/音声データの混合物を使用し、3段階で学習を行っています。
• 精度：BFloat16

ハードウェアとソフトウェア

• ハードウェア：64 * NVIDIA Tesla A100
• オーケストレーション：HuggingFace Trainer
• コード：Pytorch

📄 ライセンス

このモデルはApache-2.0ライセンスの下で提供されています。

引用

@article{liu2025ola,
title={Ola: Pushing the Frontiers of Omni-Modal Language Model with Progressive Modality Alignment},
author={Liu, Zuyan and Dong, Yuhao and Wang, Jiahui and Liu, Ziwei and Hu, Winston and Lu, Jiwen and Rao, Yongming},
journal={arXiv preprint arXiv:2502.04328},
year={2025}
}