diffusers safetensors stable-diffusion cvpr text-to-image image-generation compositionality en arxiv:2112.10752 arxiv:2312.03626 license:creativeml-openrail-m autotrain_compatible endpoints_compatible diffusers:StableDiffusionPipeline region:us
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- cvpr
- text-to-image
- image-generation
- compositionality
---
# 🧩 TokenCompose SD21 Model Card
## 🎬CVPR 2024
[TokenCompose_SD21_B](https://mlpc-ucsd.github.io/TokenCompose/) is a [latent text-to-image diffusion model](https://arxiv.org/abs/2112.10752) finetuned from the [**Stable-Diffusion-v2-1**](https://huggingface.co/stabilityai/stable-diffusion-2-1) checkpoint at resolution 768x768 on the [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning) split of [COCO image-caption pairs](https://cocodataset.org/#download) for 32,000 steps with a learning rate of 5e-6. The training objective involves token-level grounding terms in addition to denoising loss for enhanced multi-category instance composition and photorealism. The "_A/B" postfix indicates different finetuning runs of the model using the same above configurations.

# 📄 Paper

Please follow [this](https://arxiv.org/abs/2312.03626) link.

# 🧨Example Usage

We strongly recommend using the [🤗Diffuser](https://github.com/huggingface/diffusers) library to run our model.

```python
import torch
from diffusers import StableDiffusionPipeline

model_id = "mlpc-lab/TokenCompose_SD21_B"
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")
```

# ⬆️Improvements over SD21

| Model               | Object Accuracy | MG3 COCO | MG4 COCO | MG5 COCO | MG3 ADE20K | MG4 ADE20K | MG5 ADE20K | FID COCO |
|---------------------|-----------------|----------|----------|----------|------------|------------|------------|----------|
| SD21                | 47.82           | 70.14    | 25.57    | 3.27     | 75.13      | 35.07      | 7.16       | 19.59    |
| TokenCompose (SD21) | 60.10           | 80.48    | 36.69    | 5.71     | 79.51      | 39.59      | 8.13       | 19.15    |

# 📰 Citation
```bibtex
@misc{wang2023tokencompose,
      title={TokenCompose: Grounding Diffusion with Token-level Supervision}, 
      author={Zirui Wang and Zhizhou Sha and Zheng Ding and Yilin Wang and Zhuowen Tu},
      year={2023},
      eprint={2312.03626},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```