sjdtree/README.md

FSJD: Accelerating Auto-regressive Text-to-Image Generation with Training-free
Speculative Jacobi Decoding

🚩 注意事项

如果你把workdir生成在了本地,不要把workdir的内容push到仓库!!!!

🚩 New Features/Updates

• ✅ Apr, 2025. 💥 SJD has been integrated into Lumina-mGPT2 and SimpleAR.
• ✅ Jan, 2025. 💥 SJD is accepted to ICLR 2025.
• ✅ Oct, 2024. Release SJD's code.

🚩 TODO List

• □ Integrating SJD into vLLM framework for further acceleration.

Installing the dependencies

Environment:

• Python 3.10
• CUDA 12.5
• Pytorch 2.5.1+cu124
• Transformers 4.47.1

Install from yaml:

conda env create -f environment.yaml

🚩 如果你想完成完整的数据集测试?

target model: lumina-gpt

cd ./fsjd
isp='random' #init的填充方式
method='speculative_jacobi' #sjd
num_init_new_token=16 #填充数量
benchmark_way='order' #测试集的载入方式
prompt="MSCOCO2017Val" #测试集的名字
num_images=2 #测试集的图片数量
slice='0-1000' #测试集的区间
output_path=/home/leihaodong/AAAI25/exp/FSJD/${prompt}/${method}# 测试结果的输出文件夹

python /home/leihaodong/AAAI25/fsjd/main.py \
  --output_path=$output_path \
  --isp=$isp \
  --method=$method \
  --num_init_new_token=$num_init_new_token \
  --benchmark_way=$benchmark_way \
  --prompt=$prompt \
  --num_images=$num_images \
  --slice=$slice

target model: lumina-gpt

benchmark 为 parti-prompt

cd /home/leihaodong/AAAI25/fsjd
gpu_ids=0,3
dataset_name=parti
dataset_anno_file=/home/leihaodong/AAAI25/fsjd/data/prompts/PartiPrompts.tsv
model_name=leloy/Anole-7b-v0.1-hf
max_num_new_tokens=16
multi_token_init_scheme=random
seed=42
target_size=1024
prefix_token_sampler_scheme=speculative_jacobi
temperature=1.0

output_path=/home/leihaodong/AAAI25/exp/SJD/${dataset_name}_anole_${temperature}/${prefix_token_sampler_scheme}
mkdir -p ${output_path}

nohup python eval_model.py \
 --gpu_ids $gpu_ids \
 --dataset_name $dataset_name \
 --dataset_anno_file $dataset_anno_file \
 --model_name $model_name \
 --max_num_new_tokens $max_num_new_tokens \
 --multi_token_init_scheme $multi_token_init_scheme \
 --seed $seed \
 --target_size $target_size \
 --prefix_token_sampler_scheme $prefix_token_sampler_scheme \
 --temperature $temperature \
 --output_dir $output_path \
 --return_accl True \
 --num_images 0 > ${output_path}.log 2>&1 &

Lumina-mGPT

CUDA_VISIBLE_DEVICES=0 python test_lumina_mgpt.py

Emu3

CUDA_VISIBLE_DEVICES=0 python test_emu3.py

LlamaGen

CUDA_VISIBLE_DEVICES=0 python test_llamagen.py

Acknowledge

Our code is based on Lumina-mGPT, Emu3, LlamaGen, Anole, and CLLM. We would like to express our gratitude to Tianwei Xiong for his assistance.

Citation

@article{teng2024accelerating,
  title={Accelerating auto-regressive text-to-image generation with training-free speculative jacobi decoding},
  author={Teng, Yao and Shi, Han and Liu, Xian and Ning, Xuefei and Dai, Guohao and Wang, Yu and Li, Zhenguo and Liu, Xihui},
  journal={arXiv preprint arXiv:2410.01699},
  year={2024}
}