Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis

This is the official repository of Frido. We now support training and testing for text-to-image, layout-to-image, scene-graph-to-image, and label-to-image on COCO/VG/OpenImage. Please stay tune there!

Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
Wan-Cyuan Fan, Yen-Chun Chen, DongDong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank Wang

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☀️Important updates

• [Nov 7, 2024] Microsoft’s blob storage no longer allow anonymous downloads per the latest company wise security policy. The pretrained-weights may not be available now. We are fixing this issue.
• [Nov 12, 2024] All checkpoints are available for download in Google Drive.

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☀️News

We provide a web version of demo here to help researchers to better understand our work. This web demo contains multiple animations to explain th diffusion and denoising processes of Frido and more qualitative experimental results. Hope it's useful!

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🐧TODO

Frido codebase

• Training code
• Training scrpits
• Inference code
• Inference scripts
• Inference model weights setup
• Evaluation code and scripts
• Auto setup datasets
• Auto download model weights
• PLMS sampling tools
• Web demo and framework animation

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Machine environment

• Ubuntu version: 18.04.5 LTS
• CUDA version: 11.6
• Testing GPU: Nvidia Tesla V100

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Requirements

A conda environment named frido can be created and activated with:

conda env create -f environment.yaml
conda activate frido

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Datasets setup

We provide two approaches to set up the datasets:

🎶 Auto-download

To automatically download datasets and save it into the default path (../), please use following script:

bash tools/datasets/download_coco.sh
bash tools/datasets/download_vg.sh
bash tools/datasets/download_openimage.sh

🎶 Manual setup

COCO 2014 split (T2I)

• We use COCO 2014 splits for text-to-image task, which can be downloaded from official COCO website.

• Please create a folder name 2014 and collect the downloaded data and annotations as follows.

  COCO 2014 file structure

  >2014
  ├── annotations
  │   └── captions_val2014.json
  │   └── ...
  └── val2014
     └── COCO_val2014_000000000073.jpg
     └── ... 
  

COCO-stuff 2017

Standard split (Layout2I & Label2I)

• We follow TwFA and LAMA to perform layout-to-image experiment on COCO-stuff 2017, which can be downloaded from official COCO website.

• Please create a folder name 2017 and collect the downloaded data and annotations as follows.

  COCO-stuff 2017 split file structure

  >2017
  ├── annotations
  │   └── captions_val2017.json
  │   └── ...
  └── val2017
     └── 000000000872.jpg
     └── ... 
  

Segmentation challenge split (Layout2I & SG2I)

• We follow LDM and HCSS to perform layout-to-image experiment on COCO-stuff segmentation challenge split, which can be downloaded from official COCO website.

• Please make sure the deprecated-challenge2017 folder is downloaded and saved in annotations dir.

• Please create a folder name 2017 and collect the downloaded data and annotations as follows.

  COCO 2017 Segmentation challenge split file structure

  >2017
  ├── annotations
  │   └── deprecated-challenge2017
  │        └── train-ids.txt
  │        └── val-ids.txt
  │   └── captions_val2017.json
  │   └── ...
  └── val2017
     └── 000000000872.jpg
     └── ... 
  

Visual Genome (Layout2I & SG2I)

• We follow TwFA and LAMA to perform layout-to-image experiments on Visual Genome.
• Also, we follow Sg2Im and CanonicalSg2Im to conduct scene-graph-to-image experiments on Visual Genome.
• Firstly, please use the download scripts in Sg2Im to download and pre-process the Visual Genome dataset.
• Secondly, Please use the script TODO.py to generate coco-style vg.json for both two tasks, as shown below:

python3 TODO.py [VG_DIR_PATH]

• Please create a folder name vg and collect the downloaded data and annotations as follows.

  Visual Genome file structure

  >vg
  ├── VG_100K
  │   └── captions_val2017.json
  │   └── ...
  └── objects.json
  └── train_coco_style.json
  └── train.json
  └── ...
  

OpenImage (Layout2I)

• We follow LDM and HCSS to perform layout-to-image experiment on OpenImage, which can be downloaded from official OpenImage website.

• Please create a folder name openimage and collect the downloaded data and annotations as follows.

  OpenImage file structure

  >openimage
  ├── train
  │   └── data
  │   │    └── *.jpg
  │   └── labels
  │   │    └── masks
  │   │    └── detections.csv
  │   └── metadata
  │   │    └── classes.csv
  │   │    └── image_id.csv
  │   │    └── ...
  ├── validation
  │   └── data
  │   └── labels
  │   └── metadata
  └── info.json
  

File structure for dataset and code

Please make sure that the file structure is the same as the following. Or, you might modify the config file to match the corresponding paths.

File structure

>datasets
├── coco
│   └── 2014
│        └── annotations
│        └── val2014
│        └── ...
│   └── 2017
│        └── annotations
│        └── val2017
│        └── ...
├── vg
├── openimage
>Frido
└── configs
│   └── frido
│   └── ... 
└── exp
│   └── t2i
│        └── frido_f16f8_coco
│             └── checkpoints
│                  └── model.ckpt
│   └── layout2i
│   └── ...
└── frido
└── scripts
└── tools
└── ...

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Download pre-trained models

Microsoft’s blob storage no longer allow anonymous downloads per the latest company wise security policy. The pretrained-weights may not be available by the following script. Please kindly download the checkpoints from Google Drive.

The following table describs tasks and models that are currently available. To auto-download (using azcopy) all model checkpoints of Frido, please use following command:

bash tools/download.sh

You may also download them manually from the download links shown below.

Task	Dataset	FID	Link (TODO)	Comments
Text-to-image	COCO 2014	11.24	Google drive
Text-to-image (mini)	COCO 2014	64.85	Google drive	1000 images of mini-val; FID was calculated against corresponding GT images.
Text-to-image	COCO 2014	10.74	Google drive	CLIP encoder from stable diffusion (not CLIP re-ranking)
Scene-graph-to-image	COCO-stuff 2017	46.11	Google drive	Data preprocessing same as sg2im.
Scene-graph-to-image	Visual Genome	31.61	Google drive	Data preprocessing same as sg2im.
Label-to-image	COCO-stuff	27.65	Google drive	2-30 instances
Label-to-image	COCO-stuff	47.39	Google drive	3-8 instances
Layout-to-image	COCO (finetuned from OpenImage)	37.14	Google drive	FID calculated on 2,048 val images.
Layout-to-image (mini)	COCO (finetuned from OpenImage)	121.23	Google drive	320 images of mini-val; FID was calculated against corresponding GT images.
Layout-to-image	OpenImage	29.04	Google drive	FID calculated on 2,048 val images.
Layout-to-image	Visual Genome	17.24	Google drive	DDIM 250 steps. Wegiths initialized from coco-f8f4.

The mini-versions are for quick testing and reproducing, which can be done within 1 hours on 1V100. High FID is expected. To evaluate generation quality, full validation / test split needs to be run.*

FID scores were evaluated by using torch-fidelity. The scores may slightly fluctuate due to the inherent initial random noise of diffusion models.

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🌲Inference Frido

We now provide scripts for testing Frido.

Quick Start

Please checkout the jupyter notebook demo.ipynb for a simple demo on text-to-image generation for COCO.

Once the datasets and model weights are properly set up, one may test Frido by the following commands.

Text-to-image

# for full validation:
bash tools/frido/eval_t2i.sh

# for mini-val:
bash tools/frido/eval_t2i_minival.sh

• Default output folder will be exp/t2i/frido_f16f8/samples

Layout-to-image

# for full validation:
bash tools/frido/eval_layout2i.sh

# for mini-val:
bash tools/frido/eval_layout2i_minival.sh

Default output folder will be exp/layout2i/frido_f8f4/samples

(Optional) You can modify the script by adding following augments.

• -o [OUTPUT_PATH] : to change the output folder path.

• -c [INT] : number of steps for ddim and fastdpm sampling. (default=200)

Multi-GPU testing

We provide code for multiple GPUs testing. Please refer to scripts of tools/eval_t2i_multiGPU.sh

For example, 4-gpu inference can be run by the following.

bash eval_t2i_multiGPU.sh 4

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🌱Train Frido

We provide some sample scripts for training Frido.

Once the datasets and model weights are properly set up, one may test Frido by the following commands.

MS-VQGAN

bash tools/msvqgan/train_msvqgan_f16f8_coco.sh

• Default output folder will be exp_my/msvqgan/logs/msvqgan_f16f8_coco
• The sample script is tested on single V100. Please modify the batch-size and learning rate if using other types of GPU.

Frido

bash tools/frido/train_t2i_f16f8_coco.sh

• Default output folder will be exp_my/frido/t2i/logs/frido_f16f8_coco

(Optional) You can modify the script by adding following augments. bold denotes default settings.

• -t [True/False] : to switch between training and testing mode. Note that this only support testing without classifier-free guidance (CFG). For CFG testing, please refer to inference frido.
• -log_dir [LOG_DIR_PATH] : to change the logs folder path.
• -scale_lr [True/False] : to allow model to auto-adjust learning rate by the total GPUs you used.
• -autoresume [True/False] : to enable auto-resume if detect existing checkpoints in log_dir.
• -save_top_k [INT] : Only save top K of model checkpoints based on monitor setting in the config. (default: 10)

Multi-GPU training

For multi-GPU training, please modify the augmentation of --gpus in the training scripts as follows.

For single GPU training,

python main.py --base [CONFIGS] -t True --gpus 1 -log_dir [LOG_DIR] -n [EXP_NAME]

For 8 GPUs training,

python main.py --base [CONFIGS] -t True --gpus 0,1,2,3,4,5,6,7 -log_dir [LOG_DIR] -n [EXP_NAME]

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Evaluation

FID & SceneFID

FID scores were evaluated by using torch-fidelity.

After running inference, FID score can be computed by the following command:

fidelity --gpu 0 --fid --input2 [GT_FOLDER] --input1 [PRED_FOLDER]

Example:

fidelity --gpu 0 --fid --input2 exp/t2i/frido_f16f8/samples/.../img/inputs --input1 exp/t2i/frido_f16f8/samples/.../img/sample

CLIPscore

Please refer to EMNLP 2021 CLIPScore.

• paper: CLIPScore: A Reference-free Evaluation Metric for Image Captioning
• GitHub: clipscore

Detection score (YOLO)

We use YOLOv4 as pre-trained detector to calculate the detection score. Please refer to YOLOv4

• paper YOLOv4
• GitHub: darknet

IS/Precision/Recall

We use the scripts in ADM to calculate the IS, precision, and recall.

• paper ADM.
• GitHub: guided-diffusion

PSNR/SSIM

To evaluate the reconstruction performance, we use the PSNR and SSIM. The scripts can be found in the following python packages.

• GitHub: image-similarity-measures

Acknowledgement

We build Frido codebase heavily on the codebase of Latent Diffusion Model (LDM) and VQGAN. We sincerely thank the authors for open-sourcing!

Citation

If you find this code useful for your research, please consider citing:

@inproceedings{fan2022frido,
  title={Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis},
  author={Fan, Wan-Cyuan and Chen, Yen-Chun and Chen, Dongdong and Cheng, Yu and Yuan, Lu and Wang, Yu-Chiang Frank},
  booktitle={AAAI},
  year={2023}
}

License

MIT