README.md

Reward Guided Latent Consistency Distillation

🔥News

• (🔥New) 10/09/2024 We release the training codes!
• (🔥New) 05/28/2024 We release the model weights and the local gradio demo! The model weights can be download from here. We will release other model weights soon!
• 03/18/2024 Our repo for RG-LCD is created. We will release our codes and models very soon!! Please stay tuned!

🏭 Installation

pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 diffusers transformers accelerate gradio webdataset accelerate open_clip_torch gradio==3.48.0 

✅ Local gradio Demos (Text-to-Image):

Launch the gradio: (For MacOS users, need to set the device="mps" in app.py; For Intel GPU users, set device="xpu" in app.py)

python local_gradio/app.py --model_name MODEL_NAME

You can find the currently available models at here with the prefix RG-LCM. By default, MODEL_NAME is set to jiachenli-ucsb/RG-LCM-SD-2.1-768-HPSv2.1, which is ditilled from Stable Diffusion 2.1 with the reward feedback from HPSv2.1.

🏋️ Training commands

To perform RG-LCD with the HPSv2.1, we can run

accelerate launch main.py \
--output_dir=PATH_TO_LOG \
 --gradient_checkpointing \
 --use_8bit_adam \
 --enable_xformers_memory_efficient_attention \
 --resolution 768 \
 --allow_tf32 \
 --mixed_precision bf 16 \
 --train_shards_path_or_url "pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01090}.tar?download=true" \
 --optimize_reward_fn \
 --direct_optim_expert_reward \
 --reward_fn_name hpsv2 \
 --reward_scale 1

📃 Citation

@article{
    li2024reward,
    title={Reward Guided Latent Consistency Distillation},
    author={Jiachen Li and Weixi Feng and Wenhu Chen and William Yang Wang},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2024},
    url={https://openreview.net/forum?id=z116TO4LDT},
    note={Featured Certification}
}