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https://mlpc-ucsd.github.io/TokenCompose/

(CVPR 2024) 🧩 TokenCompose: Text-to-Image Diffusion with Token-level Supervision
https://mlpc-ucsd.github.io/TokenCompose/

artificial-intelligence computer-vision diffusion-models generative-ai image-generation latent-diffusion machine-learning multimodal stable-diffusion text-to-image

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(CVPR 2024) 🧩 TokenCompose: Text-to-Image Diffusion with Token-level Supervision

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README 

        



  


🧩 TokenCompose: Text-to-Image Diffusion with Token-level Supervision


  


    Zirui Wang1, 3

    ·

    Zhizhou Sha2, 3

    ·

    Zheng Ding3

    ·

    Yilin Wang2, 3

    ·

    Zhuowen Tu3

  


  


    1Princeton University

    ·

    2Tsinghua University

    ·

    3University of California, San Diego

  


  

  


    CVPR 2024

  


  

  


    

    Project done while Zirui Wang, Zhizhou Sha and Yilin Wang interned at UC San Diego.

  





  Project Page

  |

  arXiv

  |

  X (Twitter)




### Updates

*If you use our method and/or model for your research project, we are happy to provide cross-reference here in the updates.* :)



[04/04/2024] 🔥 Our training methodology is incorporated into [CoMat](https://arxiv.org/abs/2404.03653) which shows enhanced text-to-image attribute assignments.  

[02/26/2024] 🔥 TokenCompose is accepted to CVPR 2024!  

[02/20/2024] 🔥 TokenCompose is used as a base model from the [RealCompo](https://arxiv.org/abs/2402.12908) paper for enhanced compositionality.  



https://github.com/mlpc-ucsd/TokenCompose/assets/59942464/93feea16-4eac-49c3-b286-ee390a325b17





  A Stable Diffusion model finetuned with token-level consistency terms for enhanced multi-category instance composition and photorealism.










  Logo




  

    Method

    Multi-category Instance Composition

    Photorealism

    Efficiency

  



  

    

    Object Accuracy

    COCO

    ADE20K

    FID (COCO)

    FID (Flickr30K)

    Latency

  



  

    

    MG2

    MG3

    MG4

    MG5

    MG2

    MG3

    MG4

    MG5

  



  

    SD 1.4

    29.86

    90.721.33

    50.740.89

    11.680.45

    0.880.21

    89.810.40

    53.961.14

    16.521.13

    1.890.34

    20.88

    71.46

    7.540.17

  



  

    Composable

    27.83

    63.330.59

    21.871.01

    3.250.45

    0.230.18

    69.610.99

    29.960.84

    6.890.38

    0.730.22

    -

    75.57

    13.810.15

  



  

    Layout

    43.59

    93.220.69

    60.151.58

    19.490.88

    2.270.44

    96.050.34

    67.830.90

    21.931.34

    2.350.41

    -

    74.00

    18.890.20

  



  

    Structured

    29.64

    90.401.06

    48.641.32

    10.710.92

    0.680.25

    89.250.72

    53.051.20

    15.760.86

    1.740.49

    21.13

    71.68

    7.740.17

  



  

    Attn-Exct

    45.13

    93.640.76

    65.101.24

    28.010.90

    6.010.61

    91.740.49

    62.510.94

    26.120.78

    5.890.40

    -

    71.68

    25.434.89

  



  

    TokenCompose (Ours)

    52.15

    98.080.40

    76.161.04

    28.810.95

    3.280.48

    97.750.34

    76.931.09

    33.921.47

    6.210.62

    20.19

    71.13

    7.560.14

  



## 🆕 Models



| Stable Diffusion Version | Checkpoint 1 | Checkpoint 2 |

|:------------------------:|:------------:|:------------:|

| v1.4                     | [TokenCompose_SD14_A](https://huggingface.co/mlpc-lab/TokenCompose_SD14_A)         | [TokenCompose_SD14_B](https://huggingface.co/mlpc-lab/TokenCompose_SD14_B)         |

| v2.1                     | [TokenCompose_SD21_A](https://huggingface.co/mlpc-lab/TokenCompose_SD21_A)         | [TokenCompose_SD21_B](https://huggingface.co/mlpc-lab/TokenCompose_SD21_B)         |



Our finetuned models do not contain any extra modules and can be directly used in a standard diffusion model library (e.g., HuggingFace's Diffusers) by replacing the pretrained U-Net with our finetuned U-Net in a plug-and-play manner. We provide a [demo jupyter notebook](notebooks/example_usage.ipynb) which uses our model checkpoint to generate images. 



You can also use the following code to download our checkpoints and generate images:



```python

import torch

from diffusers import StableDiffusionPipeline



model_id = "mlpc-lab/TokenCompose_SD14_A"

device = "cuda"



pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)

pipe = pipe.to(device)



prompt = "A cat and a wine glass"

image = pipe(prompt).images[0]  

    

image.save("cat_and_wine_glass.png")

```



## 📊 MultiGen 



See [MultiGen](multigen/readme.md) for details.



  

    Method

    COCO

    ADE20K

  



  

    

    MG2

    MG3

    MG4

    MG5

    MG2

    MG3

    MG4

    MG5

  



  

    SD 1.4

    90.721.33

    50.740.89

    11.680.45

    0.880.21

    89.810.40

    53.961.14

    16.521.13

    1.890.34

  



  

    Composable

    63.330.59

    21.871.01

    3.250.45

    0.230.18

    69.610.99

    29.960.84

    6.890.38

    0.730.22

  



  

    Layout

    93.220.69

    60.151.58

    19.490.88

    2.270.44

    96.050.34

    67.830.90

    21.931.34

    2.350.41

  



  

    Structured

    90.401.06

    48.641.32

    10.710.92

    0.680.25

    89.250.72

    53.051.20

    15.760.86

    1.740.49

  



  

    Attn-Exct

    93.640.76

    65.101.24

    28.010.90

    6.010.61

    91.740.49

    62.510.94

    26.120.78

    5.890.40

  



  

    Ours

    98.080.40

    76.161.04

    28.810.95

    3.280.48

    97.750.34

    76.931.09

    33.921.47

    6.210.62

  



## 💻 Environment Setup



For those who want to use our codebase to **train your own diffusion models with token-level objectives**, follow the below instructions:



```bash

conda create -n TokenCompose python=3.8.5

conda activate TokenCompose

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

pip install -r requirements.txt

```



We have verified the environment setup using this specific package versions, but we expect that it will also work for newer versions too!



## 🛠️ Dataset Setup



If you want to use your own data, please refer to [preprocess_data](preprocess_data/readme.md) for details.



If you want to use our training data as examples or for research purposes, please follow the below instructions:



### 1. Setup the COCO Image Data



```bash

cd train/data

# download COCO train2017

wget http://images.cocodataset.org/zips/train2017.zip

unzip train2017.zip

rm train2017.zip

bash coco_data_setup.sh

```



After this step, you should have the following structure under the `train/data`  directory:



```

train/data/

    coco_gsam_img/

        train/

            000000000142.jpg

            000000000370.jpg

            ...

```



### 2. Setup Token-wise Grounded Segmentation Maps



Download COCO segmentation data from [Google Drive](https://drive.google.com/file/d/16uoQpfZ0O-NW92HuaCaFU8K4cGHHbv4R/view?usp=drive_link) and put it under `train/data` directory.



After this step, you should have the following structure under the `train/data` directory:



```

train/data/

    coco_gsam_img/

        train/

            000000000142.jpg

            000000000370.jpg

            ...

    coco_gsam_seg.tar

```



Then, run the following command to unzip the segmentation data:



```bash

cd train/data

tar -xvf coco_gsam_seg.tar

rm coco_gsam_seg.tar

```



After the setup, you should have the following structure under the `train/data` directory:



```

train/data/

    coco_gsam_img/

        train/

            000000000142.jpg

            000000000370.jpg

            ...

    coco_gsam_seg/

        000000000142/

            mask_000000000142_bananas.png

            mask_000000000142_bread.png

            ...

        000000000370/

            mask_000000000370_bananas.png

            mask_000000000370_bread.png

            ...

        ...

```



## 📈 Training 

We use wandb to log some curves and visualizations. Login to wandb before running the scripts.

```bash

wandb login

```

Then, to run TokenCompose, use the following command:



```bash

cd train

bash scripts/train.sh

```



The results will be saved under `train/results` directory.



## 🏷️ License



This repository is released under the [Apache 2.0](LICENSE) license. 



## 🙏 Acknowledgement



Our code is built upon [diffusers](https://github.com/huggingface/diffusers), [prompt-to-prompt](https://github.com/google/prompt-to-prompt), [VISOR](https://github.com/microsoft/VISOR), [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything), and [CLIP](https://github.com/openai/CLIP). We thank all these authors for their nicely open sourced code and their great contributions to the community.



## 📝 Citation



If you find our work useful, please consider citing:

```bibtex

@InProceedings{Wang2024TokenCompose,

    author    = {Wang, Zirui and Sha, Zhizhou and Ding, Zheng and Wang, Yilin and Tu, Zhuowen},

    title     = {TokenCompose: Text-to-Image Diffusion with Token-level Supervision},

    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

    month     = {June},

    year      = {2024},

    pages     = {8553-8564}

}

```