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VARGPT-family/
VARGPT-v1.1

Name: VARGPT-v1.1
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VARGPT-family/VARGPT_datasets

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VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning

VARGPT-v1.1 (7B+2B) modeling understanding and generation as two distinct paradigms within a unified model: predicting the next token for visual understanding and predicting the next scale for visual generation.

We provide the simple generation process for using our model. For more details, you could refer to Github: VARGPT-v1.1.

Multimodal Understanding

Inference demo for Multimodal Understanding. You can execute the following code:

python

# 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))

Multimodal Generation

Inference demo for Text-to-Image Generation. You can execute the following code:

python

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))

Citation

To cite the datasets and model, please use the below:

This datasets are used in the paper VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning:

@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}, 
}