metadata

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 is a latent text-to-image diffusion model finetuned from the Stable-Diffusion-v2-1 checkpoint at resolution 768x768 on the VSR split of COCO image-caption pairs 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 link.

🧨Example Usage

We strongly recommend using the 🤗Diffuser library to run our model.

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

@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}
}