junwatu/dsp-repro-bundle

Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation

ICML 2026

Authors: Nan Bao, Yifan Zhao, Wenzhuang Wang, Jia Li

Environment Setup

We use two separate environments:

1. Main environment for core training and inference.

   conda create -n dsp python=3.10.20
   conda activate dsp
   pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
   pip install datasets==4.8.5 pillow==12.2.0 accelerate==1.13.0 transformers==5.8.1 diffusers==0.38.0 safetensors==0.8.0rc0 tensorboard==2.20.0 opencv-python==4.13.0.92 einops==0.8.2 imagesize==2.0.0 peft==0.19.1 ttach==0.0.3 ftfy==6.3.1 albumentations==2.0.8
   

2. Evaluation environment for MMDetection/MMEngine compatibility. It is used for evaluation with MMDetection/MMEngine due to strict version constraints, and also supports YOLO-based evaluation.

   conda create -n dsp-eval python=3.10.20
   conda activate dsp-eval
   conda install mkl==2023.1.0 numpy==1.26.4
   conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia
   pip install mmengine==0.10.7 tqdm==4.67.3 shapely==2.1.2 scipy==1.15.3 terminaltables==3.1.10 ultralytics==8.4.50 pycocotools==2.0.11 https://download.openmmlab.com/mmcv/dist/cu121/torch2.1.0/mmcv-2.1.0-cp310-cp310-manylinux1_x86_64.whl "numpy<2.0.0" "setuptools<70.0.0"
   

Set Environment Variables

Set the root path of this project:

export DSP_PROJECT_DIR=/path/to/DSP # replace with the actual path

It is recommended to add this line to ~/.bashrc or ~/.zshrc for persistence.

Pretrained Models Preparation

1. We use several pretrained models as external dependencies. Please download them manually from the following sources:

   • stable-diffusion-v1-5
   • clip-vit-large-patch14
   • dinov2_vitl14_pretrain.pth
   • ViT-B-16.pt

2. After downloading, organize the pretrained weights under ./pretrained as follows:

   pretrained
   ├── stable-diffusion-v1-5
   │   └── ...
   ├── clip-vit-large-patch14
   │   └── ...
   ├── dinov2_vitl14_pretrain.pth
   └── ViT-B-16.pt
   

   You may either copy or symlink the files. We recommend using symbolic links:

   ln -s /path/to/stable-diffusion-v1-5 ./pretrained/stable-diffusion-v1-5
   ln -s /path/to/clip-vit-large-patch14 ./pretrained/clip-vit-large-patch14
   ln -s /path/to/dinov2_vitl14_pretrain.pth ./pretrained/dinov2_vitl14_pretrain.pth
   ln -s /path/to/ViT-B-16.pt ./pretrained/ViT-B-16.pt
   

Data Preparation

1. We use several public datasets. Please download them manually from the following sources:

   • DIOR
   • RUOD
   • ExDark

2. Unzip the downloaded datasets and organize the external dataset directories as follows:

   DIOR-VOC
   ├── Annotations
   │   ├── Horizontal_Bounding_Boxes
   │   └── Oriented_Bounding_Boxes
   └── VOC2007
       ├── ImageSets
       │   ├── Layout
       │   ├── Main
       │   └── Segmentation
       └── JPEGImages
   

   RUOD
   ├── Environment_pic
   │   ├── blur
   │   ├── color
   │   └── light
   ├── Environmet_ANN
   ├── RUOD_ANN
   └── RUOD_pic
       ├── test
       └── train
   

   ExDark
   ├── annos
   ├── imageclasslist.txt
   └── images
   

3. Run data preprocessing scripts located in ./scripts/data_process, after updating all hard-coded paths (e.g., /path/to/DIOR_VOC, /path/to/RUOD, /path/to/ExDark) in the scripts to match the local setup. Execute them in order.

   The preprocessing outputs will be generated under ./data with the following structure:

   data
   ├── DIOR
   │   ├── dior_emb.pt
   │   ├── images -> /path/to/DIOR-VOC/VOC2007/JPEGImages
   │   ├── metadatas
   │   └── patches
   ├── EXDARK
   │   ├── exdark_emb.pt
   │   ├── images
   │   ├── metadatas
   │   └── patches
   └── RUOD
       ├── images -> /path/to/RUOD/RUOD_pic
       ├── metadatas
       ├── patches
       └── ruod_emb.pt
   

Training and Inference

We provide three example configurations in ./configs: dsp-dior.yaml, dsp-ruod.yaml, and dsp-exdark.yaml.

Argument Description:

• config: configuration file for model and dataset setup.
• metaseed: seed generator identifier for deterministic sampling.
• num_seed: number of sampling seeds for few-shot evaluation.
• k_shot: number of samples per category in few-shot setting.
• run_id: identifier for different runs.
• gpu_ids: GPU device indices for execution.
• iter: number of bootstrap iterations for FID.

Base Phase Training

bash train_base.sh --config "dsp-dior"
bash train_base.sh --config "dsp-ruod"
bash train_base.sh --config "dsp-exdark"

Novel Phase Training

bash train_novel.sh --config "dsp-dior" --metaseed "aaa" --num_seed 50 --k_shot "5" --run_id "1" --gpu_ids "0,1,2,3"
bash train_novel.sh --config "dsp-ruod" --metaseed "aaa" --num_seed 50 --k_shot "5" --run_id "1" --gpu_ids "0,1,2,3"
bash train_novel.sh --config "dsp-exdark" --metaseed "aaa" --num_seed 50 --k_shot "5" --run_id "1" --gpu_ids "0,1,2,3"

Inference

bash infer.sh --config "dsp-dior" --metaseed "aaa" --num_seed 50 --k_shot "5" --run_id "1" --ckpt "100" --gpu_ids "0,1,2,3" --max_infer_size 50
bash infer.sh --config "dsp-ruod" --metaseed "aaa" --num_seed 50 --k_shot "5" --run_id "1" --ckpt "100" --gpu_ids "0,1,2,3" --max_infer_size 50
bash infer.sh --config "dsp-exdark" --metaseed "aaa" --num_seed 50 --k_shot "5" --run_id "1" --ckpt "100" --gpu_ids "0,1,2,3" --max_infer_size 50

Evaluation

Preparation

Download the YOLO and Faster R-CNN weights from this link. Place them under ./pretrained. The expected directory structure is as follows:

pretrained
├── evaluation
│   ├── mmdet
│   │   ├── faster_rcnn_r50_fpn_1x-dior
│   │   │   └── epoch_12.pth
│   │   ├── faster_rcnn_r50_fpn_1x-exdark
│   │   │   └── epoch_12.pth
│   │   └── faster_rcnn_r50_fpn_1x-ruod
│   │       └── epoch_12.pth
│   └── yolo
│       └── best.pt
└── ... (pretrained models for training)

YOLO (mAP / AP50 / AP75)

Note: In yolo-wrapper-dior.sh, the --xml_folder path should be set to the DIOR annotation directory (/path/to/DIOR-VOC/Annotations/Horizontal_Bounding_Boxes).

cd $DSP_PROJECT_DIR/scripts/evaluation/yoloscore-dior
bash yolo-wrapper-dior.sh --config "dsp-dior" --metaseed "aaa" --num_seed 50 --ckpt "100" --k_shot "5" --run_id "1" --gpu_ids 0

Faster R-CNN (mAP / AP50 / AP75)

cd $DSP_PROJECT_DIR/scripts/evaluation/FasterRCNN_score-mmdet
bash test-wrapper-dior.sh --config "dsp-dior" --metaseed "aaa" --num_seed 50 --ckpt "100" --k_shot "5" --run_id "1" --gpu_ids 0
bash test-wrapper-ruod.sh --config "dsp-ruod" --metaseed "aaa" --num_seed 50 --ckpt "100" --k_shot "5" --run_id "1" --gpu_ids 0
bash test-wrapper-exdark.sh --config "dsp-exdark" --metaseed "aaa" --num_seed 50 --ckpt "100" --k_shot "5" --run_id "1" --gpu_ids 0

Bootstrap FID

cd $DSP_PROJECT_DIR/scripts/evaluation/bootstrap_fid
python boot_fid-dior.py --config dsp-dior -run_id 1 -num_seeds 50 --iter 50 --k_shot 5
python boot_fid-ruod.py --config dsp-ruod -run_id 1 -num_seeds 50 --iter 50 --k_shot 5
python boot_fid-exdark.py --config dsp-exdark -run_id 1 -num_seeds 50 --iter 50 --k_shot 5

Bootstrap FID results will be saved under ./metrics/BootstrapFID.

Detection Metric Summarization

cd $DSP_PROJECT_DIR/scripts/evaluation/summarize
bash summarize-wrapper.sh --config "dsp-dior" --k_shot "5" --run_id "1" --ckpt "100" --metaseed "aaa" --num_seed 50
bash summarize-wrapper.sh --config "dsp-ruod" --k_shot "5" --run_id "1" --ckpt "100" --metaseed "aaa" --num_seed 50
bash summarize-wrapper.sh --config "dsp-exdark" --k_shot "5" --run_id "1" --ckpt "100" --metaseed "aaa" --num_seed 50

Detection evaluation results (mAP / AP50 / AP75, YOLO and Faster R-CNN) will be summarized in ./metrics.

Acknowledgement

Our work is based on stable diffusion, diffusers, CLIP, DINOv2, CC-Diff, MIGC, GradCAM, and kmeans_pytorch. Thanks for these great projects!

Citation

If you find our work useful for your research, please cite the following paper.

@inproceedings{
    bao2026envisioning,
    title={Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation},
    author={Bao, Nan and Zhao, Yifan and Wang, Wenzhuang and Li, Jia},
    booktitle={Forty-third International Conference on Machine Learning},
    year={2026},
    url={https://openreview.net/forum?id=Jva4wVEySO}
}