F2FLDM

[Page] [Paper]

F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation
Man M. Ho, Shikha Dubey, Yosep Chong, Beatrice Knudsen, Tolga Tasdizen
In ArXiv, 2024.

Environment Setup

Create Anaconda Environment

To create the Anaconda environment, run the following command:

conda create -n f2fldm python numpy pillow
conda activate f2fldm

Install Dependencies

To install the required dependencies, execute the following commands:

PyTorch:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

Other packages:

pip install huggingface transformers diffusers pytorch-lightning==2.1.2 timm accelerate datasets

Replace official diffusers with our customized diffusers:

cd diffusers
pip install -e .

Run Frozen Section to FFPE Image Translation

To run the Frozen Section to FFPE Image Translation, use the following command:

CUDA_VISIBLE_DEVICES=0 python run.py \
    --input_path="images/testA" \
    --output_path="images/testA_out" \
    --sd_model_id="stabilityai/stable-diffusion-xl-base-1.0" \
    --lora_model_path="checkpoints" \
    --high_noise_frac=0.5 \ # Strength S
    --target_resolution=1024 \
    --unet_addition_embed_type="text_latent_addembeddingft" \
    --unet_add_embedding_input=4096 \
    --unet_add_embedding_output=1280 \
    --guidance_scale=12.0 \ # Guidance Scale GS
    --feat_model_name="DINO" \
    --et_model_name="cyclegan" \
    --et_model_path="embedding_translation/checkpoints/et_cyclegan.ckpt" \
    --reg="l0" \ # Regularization on Noise Difference, comment this line for not using it.
    --et_weight=0.25 # Embedding Translation Weight alpha

or

scripts/test.sh

Finetune pre-trained SDXL with LoRA for Frozen Section to FFPE Image Translation

To finetune the pre-trained SDXL model for Frozen Section to FFPE Image Translation, use the following command:

CUDA_VISIBLE_DEVICES=0 accelerate launch train.py \
  --pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
  --dataset_dir="images" \
  --train_data_dir="username/f2f_tcga_kidney_1024/train/metadata.csv" --caption_column="text" \
  --resolution=512 --random_flip --center_crop\
  --train_batch_size=1 \
  --rank=8 \ # LoRA rank
  --max_train_steps=150000 --checkpointing_steps=1000 \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=1337 \
  --output_dir="checkpoints" \
  --train_text_encoder \
  --disable_crop_top_lefts \
  --unet_addition_embed_type="text_latent_addembeddingft"

or

scripts/train.sh

Citation

If you find this work useful, please consider citing:

@article{ho2024f2fldm,
  title={F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation},
  author={Ho, Man M and Dubey, Shikha and Chong, Yosep and Knudsen, Beatrice and Tasdizen, Tolga},
  journal={arXiv preprint arXiv:2404.12650},
  year={2024}
}

Contact

If you have any questions, feel free to contact me at [email protected]