Diffusion models have been very successful at generating image conditioned on textual inputs. Recently quite a few methods proposed to augment them with the ability to take semantic maps as conditioning, like SpaText, Paint with Words (in the ediff-I paper), ControlNet, and T2I-Adapter.

In this post, we evaluate these different methods on the COCO dataset, and check how these novel methods perform in comparison with traditional train-based methods like OASIS and SDM.

We consider three evaluation scenarios: The first setting is the classical semantic image synthesis framework, where we give as input a complete segmentation map (which might contain unlabelled pixels, generally discarded), and the task is to generate a real image whose segmentation mask will be identical. The metrics are:

  • mIoU between input mask and detected mask (to detect the mask, we use a state-of-the-art ViT-Adapter segmentation network)
  • FID using the entire COCO validation set as reference set, at resolution 512;
  • CLIP, the average CLIP similarity between the generated image and conditioning text prompt.

Here are the results:

Method mIoU FID CLIP
Stable Diffusion 11.7 23.6 31.8
SpaText 16.8 19.8 30.0
T2I-Adapter 33.3 17.2 31.5
Paint with Words 21.2 36.2 29.4
OASIS 52.1 15.0 N/A
SDM 49.3 17.2 N/A
MultiDiffusion 15.8 69.3 24.9

Stable Diffusion refers to classical text-to image generation, without conditioning on masks. We note that the zero-shot method Paint-with words has a much better mIoU score than without conditioning (21.2 vs 11.7), but still very far from classical train-based methods that reach ~50 mIoU.

We also evaluate these methods similarly to SpaText: we remove masks covering less than 5% of the image, and consider \(1 \leq K \leq 3\) conditioning masks for each generated image.

Method mIoU FID CLIP
Stable Diffusion 11.7 23.6 31.8
Spatext 23.8 16.2 30.2
T2I-Adapter 31.6 19.2 30.6
Paint with Words 23.8 25.8 29.6
OASIS 41.4 46.8 N/A
SDM 29.3 65.3 N/A
MultiDiffusion 24.8 22.9 29.4

In this setting, we observe that train-based methods OASIS and SDM break, because they have not been trained to take only a few masks as conditioning information. This is why zero-shot methods like Paint-with words are more interesting. Spatext has a very good FID since it is the setup on which it has been trained.

Finally, we consider the intermediate setup where we consider as conditioning masks all masks covering more than 5% of the generated image.

Method mIoU FID CLIP
Stable Diffusion 11.7 23.6 31.8
Spatext 19.2 18.9 30.1
T2I-Adapter 35.1 17.8 31.3
Paint with Words 23.5 35.0 29.5
OASIS 53.7 18.2 N/A
SDM 41.7 28.6 N/A
MultiDiffusion 22.3 48.4 24.9

OASIS and SDM work much better, because the conditioning information covers most of the image.

Overall, on the three different settings, the best method seems to be T2I-Adapter, which adapts diffusion models to take spatial layout masks as conditioning information by fine-tuning adaptation layers on the COCO dataset. However, for free-form text conditioning, Paint with words offers a more flexible interface.


[1] Denoising diffusion probabilistic models, Ho, Jonathan and Jain, Ajay and Abbeel, Pieter, NeurIPS, 2020

[2] SpaText: Spatio-Textual Representation for Controllable Image Generation, Avrahami, Omri and Hayes, Thomas and Gafni, Oran and Gupta, Sonal and Taigman, Yaniv and Parikh, Devi and Lischinski, Dani and Fried, Ohad and Yin, Xi, arXiv preprint arXiv:2211.14305, 2022

[3] ediffi: Text-to-image diffusion models with an ensemble of expert denoisers, Balaji, Yogesh and Nah, Seungjun and Huang, Xun and Vahdat, Arash and Song, Jiaming and Kreis, Karsten and Aittala, Miika and Aila, Timo and Laine, Samuli and Catanzaro, Bryan and others, arXiv preprint arXiv:2211.01324, 2022

[4] Adding conditional control to text-to-image diffusion models, Zhang, Lvmin and Agrawala, Maneesh, arXiv preprint arXiv:2302.05543, 2023

[5] T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models, Mou, Chong and Wang, Xintao and Xie, Liangbin and Zhang, Jian and Qi, Zhongang and Shan, Ying and Qie, Xiaohu, arXiv preprint arXiv:2302.08453, 2023

[6] High-resolution image synthesis with latent diffusion models, Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bjorn, CVPR, 2022

[7] You only need adversarial supervision for semantic image synthesis, Sushko, Vadim and Schonfeld, Edgar and Zhang, Dan and Gall, Juergen and Schiele, Bernt and Khoreva, Anna, ICLR 2021

[8] Semantic image synthesis via diffusion models, Wang, Weilun and Bao, Jianmin and Zhou, Wengang and Chen, Dongdong and Chen, Dong and Yuan, Lu and Li, Houqiang, arXiv preprint arXiv:2207.00050, 2022

[9] Vision transformer adapter for dense predictions, Chen, Zhe and Duan, Yuchen and Wang, Wenhai and He, Junjun and Lu, Tong and Dai, Jifeng and Qiao, Yu, ICLR 2023