Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
stable-diffusion
cvpr
image-generation
compositionality
Instructions to use mlpc-lab/TokenCompose_SD21_A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mlpc-lab/TokenCompose_SD21_A with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mlpc-lab/TokenCompose_SD21_A", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| 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_A](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_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") | |
| ``` | |
| # ⬆️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 | |
| @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} | |
| } | |
| ``` |