mlpc-lab/TokenCompose_SD21_A
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🧩 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
📰 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}
}