FIND

Code for paper: FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models (ACMMM 2024) (Arxiv) (Website).

Env Installation

Create a conda environment and install required modules.

conda env create -n find python=3.8
conda activate find
pip install -r requirements.txt

Install ImageReward module.

bash install_image_reward.sh

Inference

python dist_train2.py --max_train_steps 150 --p_batch_size 1 --reward_weight 10 --kl_weight 0.01  --learning_rate 1e-3 --single_flag 1 --single_prompt "a green rabbit" --gradient_accumulation_steps 1 --clip_norm 0.1 --g_batch_size 1 --multi_gpu 0 --v_flag 1 --seed 99999

Explanation of the arguments:

• p_batch_size: batch size for policy training.
• reward_weight: weight for the reward term in the policy loss
• kl_weight: weight for the KL term in the policy loss
• learning_rate: learning rate for the policy network
• single_flag: whether to train on a single prompt
• single_prompt: the single prompt to train on
• gradient_accumulation_steps: number of gradient accumulation steps
• clip_norm: gradient clipping norm
• g_batch_size: batch size for generation. Batch size 12 can be used for a single A100 GPU.
• multi_gpu: whether to use multiple GPUs.
• v_flag: whether to use value learning.

Bibtex

@inproceedings{chen2024find,
      title={FIND: Fine-tuning Initial Noise Distribution with Policy Optimization for Diffusion Models},
      author={Chen, Changgu and Yang, Libing and Yang, Xiaoyan and Chen, Lianggangxu and He, Gaoqi and Wang, Changbo and Li, Yang},
      booktitle={ACM Multimedia 2024}
    }