How to use
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
from transformers import AutoProcessor,Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
import torch
# We recommend enabling flash_attention_2 for better acceleration and memory saving.
model_dir = "Qwen2.5-VL-7B-Instruct-sft"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_dir,
torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
device_map="auto",
)
model.eval()
processor = AutoProcessor.from_pretrained(model_dir)
def format(images, text):
content = []
for img in images:
content.append({"type": "image", "image": img})
content.append({"type": "text", "text": text})
messages = [
{
"role": "user",
"content": content,
}
]
return messages
imgs = ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]
text = "Describe this image."
messages = format(imgs, text)
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,add_vision_id=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0].strip()
Citation
@misc{wang2025cigeval,
title={A Unified Agentic Framework for Evaluating Conditional Image Generation},
author={Jifang Wang and Xue Yang and Longyue Wang and Zhenran Xu and Yiyu Wang and Yaowei Wang and Weihua Luo and Kaifu Zhang and Baotian Hu and Min Zhang},
year={2025},
eprint={2504.07046},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.07046},
}
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