🚀 VARGPT-v1.1：通过迭代指令调优和强化学习改进视觉自回归大型统一模型

VARGPT-v1.1是一个视觉自回归大型统一模型，它将理解和生成建模为统一模型中的两种不同范式，即通过预测下一个标记进行视觉理解，通过预测下一个尺度进行视觉生成。本项目提供了模型的简单使用示例，更多详情可参考GitHub仓库。

🚀 快速开始

VARGPT-v1.1 (7B + 2B) 将理解和生成建模为统一模型中的两种不同范式：通过预测下一个标记进行视觉理解，通过预测下一个尺度进行视觉生成。

我们提供了使用该模型的简单生成过程。如需更多详细信息，您可以参考 GitHub。

✨ 主要特性

• 多模态理解：能够对图像和文本等多模态信息进行理解和分析。
• 多模态生成：支持文本到图像的生成任务。

💻 使用示例

基础用法

多模态理解

以下是多模态理解的推理示例代码：

# Or execute the following code
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, AutoTokenizer
from vargpt_qwen_v1_1.modeling_vargpt_qwen2_vl import VARGPTQwen2VLForConditionalGeneration
from vargpt_qwen_v1_1.prepare_vargpt_v1_1 import prepare_vargpt_qwen2vl_v1_1 
from vargpt_qwen_v1_1.processing_vargpt_qwen2_vl import VARGPTQwen2VLProcessor
from patching_utils.patching import patching

model_id = "VARGPT-family/VARGPT-v1.1"

prepare_vargpt_qwen2vl_v1_1(model_id)

model = VARGPTQwen2VLForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float32, 
    low_cpu_mem_usage=True, 
).to(0)

patching(model)

tokenizer = AutoTokenizer.from_pretrained(model_id)
processor = VARGPTQwen2VLProcessor.from_pretrained(model_id)

# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image") 
conversation = [
    {
      "role": "user",
      "content": [
          {"type": "text", "text": "Please explain the meme in detail."},
          {"type": "image"},
        ],
    },
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "./assets/llava_bench_demo.png"
print(prompt)

raw_image = Image.open(image_file)
inputs = processor(images=[raw_image], text=prompt, return_tensors='pt').to(0, torch.float32)

output = model.generate(
    **inputs, 
    max_new_tokens=2048, 
    do_sample=False)

print(processor.decode(output[0], skip_special_tokens=True))

多模态生成

以下是文本到图像生成的推理示例代码：

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, AutoTokenizer
from vargpt_qwen_v1_1.modeling_vargpt_qwen2_vl import VARGPTQwen2VLForConditionalGeneration
from vargpt_qwen_v1_1.prepare_vargpt_v1_1 import prepare_vargpt_qwen2vl_v1_1 
from vargpt_qwen_v1_1.processing_vargpt_qwen2_vl import VARGPTQwen2VLProcessor
from patching_utils.patching import patching
model_id = "VARGPT-family/VARGPT-v1.1"

prepare_vargpt_qwen2vl_v1_1(model_id)

model = VARGPTQwen2VLForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float32,     
    low_cpu_mem_usage=True, 
).to(0)

patching(model)
tokenizer = AutoTokenizer.from_pretrained(model_id)
processor = VARGPTQwen2VLProcessor.from_pretrained(model_id)

conversation = [
    {
      "role": "user",
      "content": [
          {"type": "text", "text": "Can you depict a scene of A power metalalbum cover featuring a fantasy-style illustration witha white falcon."},
        ],
    },
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
print(prompt)

inputs = processor(text=prompt, return_tensors='pt').to(0, torch.float32)
model._IMAGE_GEN_PATH = "output.png"
output = model.generate(
    **inputs, 
    max_new_tokens=4096, 
    do_sample=False)

print(processor.decode(output[0][:-1], skip_special_tokens=True))

📚 详细文档

本项目使用的数据集和模型相关信息如下：

📄 许可证

本项目采用 Apache-2.0 许可证。

📚 引用

若要引用本项目的数据集和模型，请使用以下 BibTeX 格式：

@misc{zhuang2025vargptunifiedunderstandinggeneration,
      title={VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model}, 
      author={Xianwei Zhuang and Yuxin Xie and Yufan Deng and Liming Liang and Jinghan Ru and Yuguo Yin and Yuexian Zou},
      year={2025},
      eprint={2501.12327},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.12327}, 
}
@misc{zhuang2025vargptv11improvevisualautoregressive,
      title={VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning}, 
      author={Xianwei Zhuang and Yuxin Xie and Yufan Deng and Dongchao Yang and Liming Liang and Jinghan Ru and Yuguo Yin and Yuexian Zou},
      year={2025},
      eprint={2504.02949},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.02949}, 
}