Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
stable-diffusion
cvpr
image-generation
compositionality
Instructions to use mlpc-lab/TokenCompose_SD14_B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mlpc-lab/TokenCompose_SD14_B 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_SD14_B", 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 SD14 Model Card | |
| ## 🎬CVPR 2024 | |
| [TokenCompose_SD14_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-v1-4**](https://huggingface.co/CompVis/stable-diffusion-v1-4) checkpoint at resolution 512x512 on the [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning) split of [COCO image-caption pairs](https://cocodataset.org/#download) for 24,000 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_SD14_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 SD14 | |
| <table> | |
| <tr> | |
| <th rowspan="3" align="center">Method</th> | |
| <th colspan="9" align="center">Multi-category Instance Composition</th> | |
| <th colspan="2" align="center">Photorealism</th> | |
| <th colspan="1" align="center">Efficiency</th> | |
| </tr> | |
| <tr> | |
| <!-- <th align="center"> </th> --> | |
| <th rowspan="2" align="center">Object Accuracy</th> | |
| <th colspan="4" align="center">COCO</th> | |
| <th colspan="4" align="center">ADE20K</th> | |
| <th rowspan="2" align="center">FID (COCO)</th> | |
| <th rowspan="2" align="center">FID (Flickr30K)</th> | |
| <th rowspan="2" align="center">Latency</th> | |
| </tr> | |
| <tr> | |
| <!-- <th align="center"> </th> --> | |
| <th align="center">MG2</th> | |
| <th align="center">MG3</th> | |
| <th align="center">MG4</th> | |
| <th align="center">MG5</th> | |
| <th align="center">MG2</th> | |
| <th align="center">MG3</th> | |
| <th align="center">MG4</th> | |
| <th align="center">MG5</th> | |
| </tr> | |
| <tr> | |
| <td align="center"><a href="https://huggingface.co/CompVis/stable-diffusion-v1-4">SD 1.4</a></td> | |
| <td align="center">29.86</td> | |
| <td align="center">90.72<sub>1.33</sub></td> | |
| <td align="center">50.74<sub>0.89</sub></td> | |
| <td align="center">11.68<sub>0.45</sub></td> | |
| <td align="center">0.88<sub>0.21</sub></td> | |
| <td align="center">89.81<sub>0.40</sub></td> | |
| <td align="center">53.96<sub>1.14</sub></td> | |
| <td align="center">16.52<sub>1.13</sub></td> | |
| <td align="center">1.89<sub>0.34</sub></td> | |
| <td align="center"><u>20.88</u></td> | |
| <td align="center"><u>71.46</u></td> | |
| <td align="center"><b>7.54</b><sub>0.17</sub></td> | |
| </tr> | |
| <tr> | |
| <td align="center"><a href="https://github.com/mlpc-ucsd/TokenCompose"><strong>TokenCompose (Ours)</strong></a></td> | |
| <td align="center"><b>52.15</b></td> | |
| <td align="center"><b>98.08</b><sub>0.40</sub></td> | |
| <td align="center"><b>76.16</b><sub>1.04</sub></td> | |
| <td align="center"><b>28.81</b><sub>0.95</sub></td> | |
| <td align="center"><u>3.28</u><sub>0.48</sub></td> | |
| <td align="center"><b>97.75</b><sub>0.34</sub></td> | |
| <td align="center"><b>76.93</b><sub>1.09</sub></td> | |
| <td align="center"><b>33.92</b><sub>1.47</sub></td> | |
| <td align="center"><b>6.21</b><sub>0.62</sub></td> | |
| <td align="center"><b>20.19</b></td> | |
| <td align="center"><b>71.13</b></td> | |
| <td align="center"><b>7.56</b><sub>0.14</sub></td> | |
| </tr> | |
| </table> | |
| # 📰 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} | |
| } | |
| ``` |