🧩 TokenCompose SD14 Model Card

🎬CVPR 2024

TokenCompose_SD14_A is a latent text-to-image diffusion model finetuned from the Stable-Diffusion-v1-4 checkpoint at resolution 512x512 on the VSR split of COCO image-caption pairs 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

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🧨Example Usage

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

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")

⬆️Improvements over SD14

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
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

📰 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}
}
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