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ViCo

arXiv License

ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation

teaser

⏳ To Do

  • Release inference code
  • Release pretrained models
  • Release training code
  • Quantitative evaluation code
  • Hugging Face demo

⚙️ Set-up

Create a conda environment vico using

conda env create -f environment.yaml
conda activate vico

⏬ Download

Download the pretrained stable diffusion v1-4 under models/ldm/stable-diffusion-v1.

We provide the pretrained checkpoints at 300, 350, and 400 steps of 8 objects. You can download the sample images and their corresponding pretrained checkpoints. You can also download the data of any object:

Object Sample images Checkpoints
barn image ckpt
batman image ckpt
clock image ckpt
dog7 image ckpt
monster toy image ckpt
pink sunglasses image ckpt
teddybear image ckpt
wooden pot image ckpt

Datasets are originally collected and provided by Textual Inversion, DreamBooth, and Custom Diffsuion. You can find all datasets used for quantitaive comparison in our paper.

🚀 Inference

Before running the inference command, please set:

  • REF_IMAGE_PATH: Path of the reference image. It can be any image in the samples like batman/1.jpg.
  • CHECKPOINT_PATH: Path of the checkpoint weight. Its subfolder should be similar to checkpoints/*-399.pt.
  • OUTPUT_PATH: Path of the generated images. For example, it can be like outputs/batman.
python scripts/vico_txt2img.py \
--ddim_eta 0.0  --n_samples 4  --n_iter 2  --scale 7.5  --ddim_steps 50  \
--ckpt_path models/ldm/stable-diffusion-v1/sd-v1-4.ckpt  \
--image_path REF_IMAGE_PATH \
--ft_path CHECKPOINT_PATH \
--load_step 399 \
--prompt "a photo of * on the beach" \
--outdir OUTPUT_PATH

You can specify load_step (300,350,400) and personalize prompt (a prefix "a photo of" usually makes better results).

💻 Training

Before running the training command, please set:

  • RUN_NAME: Your run name. Will be the name of the folder of logs.
  • GPUS_USED: GPUs you are using, e.g., "0,1,2,3". (4 RTX 3090 GPUs in my case)
  • TRAIN_DATA_ROOT: Path of your training images.
  • INIT_WORD: Initialize the word to represent your unique object, e.g., "dog" and "toy".
python main.py \
--base configs/stable-diffusion/v1-finetune.yaml -t  \
--actual_resume models/ldm/stable-diffusion-v1/sd-v1-4.ckpt  \
-n RUN_NAME \
--gpus  GPUS_USED \
--data_root TRAIN_DATA_ROOT \
--init_word INIT_WORD

📖 Citation

If you use this code in your research, please consider citing our paper:

@inproceedings{Hao2023ViCo,
  title={ViCo: Detail-Preserving Visual Condition for Personalized Text-to-Image Generation},
  author={Shaozhe Hao and Kai Han and Shihao Zhao and Kwan-Yee K. Wong},
  year={2023}
}

💐 Acknowledgements

This code repository is based on the great work of Textual Inversion. Thanks!