Built on

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021), https://arxiv.org/abs/2103.00020
Original
2021
Earlier work this paper cites.
Hessel, J., Holtzman, A., Forbes, M., Bras, R.L., Choi, Y.: CLIPScore: A Reference-free Evaluation Metric for Image Captioning (Mar 2022). https://doi.org/10.48550/arXiv.2104.08718
2022
Earlier work this paper cites.
Li, C., Xu, H., Tian, J., Wang, W., Yan, M., Bi, B., Ye, J., Chen, H., Xu, G., Cao, Z., Zhang, J., Huang, S., Huang, F., Zhou, J., Si, L.: mplug: Effective and efficient vision-language learning by cross-modal skip-connections (2022), https://arxiv.org/abs/2205.12005
Original
2022
Earlier work this paper cites.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation (2022), https://arxiv.org/abs/2201.12086
Original
2022
Earlier work this paper cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis With Latent Diffusion Models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)
2022
Earlier work this paper cites.
Zhou, X., Koltun, V., Krähenbühl, P.: Simple multi-dataset detection (2022), https://arxiv.org/abs/2102.13086
Original
2022
Earlier work this paper cites.
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al.: Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf 2
2023
Earlier work this paper cites.
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., Li, Z.: PixArt-$ α $ \alpha\$ : Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis (Dec 2023). https://doi.org/10.48550/arXiv.2310.00426
2023
Earlier work this paper cites.
Ghosh, D., Hajishirzi, H., Schmidt, L.: Geneval: An object-focused framework for evaluating text-to-image alignment (2023), https://arxiv.org/abs/2310.11513
Original
2023
Earlier work this paper cites.
Hu, Y., Liu, B., Kasai, J., Wang, Y., Ostendorf, M., Krishna, R., Smith, N.A.: Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering (2023), https://arxiv.org/abs/2303.11897
Original
2023
Earlier work this paper cites.
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (Jul 2023). https://doi.org/10.48550/arXiv.2307.01952
2023
Earlier work this paper cites.
Wu, X., Hao, Y., Sun, K., Chen, Y., Zhu, F., Zhao, R., Li, H.: Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis (2023), https://arxiv.org/abs/2306.09341
Original
2023
Earlier work this paper cites.
Baldridge, J., Bauer, J., Bhutani, M., Brichtova, N., Bunner, A., Castrejon, L., Chan, K., Chen, Y., Dieleman, S., Du, Y., et al.: Imagen 3. arXiv preprint arXiv:2408.07009 (2024)
Original
2024
Earlier work this paper cites.
Chen, J., Ge, C., Xie, E., Wu, Y., Yao, L., Ren, X., Wang, Z., Luo, P., Lu, H., Li, Z.: PixArt- Σ \Sigma : Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation (Mar 2024). https://doi.org/10.48550/arXiv.2403.04692
2024
Earlier work this paper cites.
Chen, J., Wu, Y., Luo, S., Xie, E., Paul, S., Luo, P., Zhao, H., Li, Z.: PIXART- δ \delta : Fast and Controllable Image Generation with Latent Consistency Models (Jan 2024). https://doi.org/10.48550/arXiv.2401.05252
2024
Earlier work this paper cites.
Cho, J., Hu, Y., Garg, R., Anderson, P., Krishna, R., Baldridge, J., Bansal, M., Pont-Tuset, J., Wang, S.: Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-to-image generation (2024), https://arxiv.org/abs/2310.18235
Original
2024
Earlier work this paper cites.
Cui, S., Guo, J., An, X., Deng, J., Zhao, Y., Wei, X., Feng, Z.: IDAdapter: Learning mixed features for tuning-free personalization of text-to-image models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 950–959 (June 2024)
2024
Earlier work this paper cites.
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., Podell, D., Dockhorn, T., English, Z., Rombach, R.: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. In: Forty-First International Conference on Machine Learning (Jun 2024)
2024
Earlier work this paper cites.
Han, J., Liu, J., Jiang, Y., Yan, B., Zhang, Y., Yuan, Z., Peng, B., Liu, X.: Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis (2024)
2024
Earlier work this paper cites.
Hu, X., Wang, R., Fang, Y., Fu, B., Cheng, P., Yu, G.: Ella: Equip diffusion models with llm for enhanced semantic alignment (2024), https://arxiv.org/abs/2403.05135
Original
2024
Earlier work this paper cites.
Hurst, A., Lerer, A., Goucher, A.P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al.: Gpt-4o system card. arXiv preprint arXiv:2410.21276 (2024)
Original
2024
Earlier work this paper cites.
Imagen-Team-Google, Baldridge, J., Bauer, J., Bhutani, M., Others: Imagen 3 (Dec 2024). https://doi.org/10.48550/arXiv.2408.07009
2024
Earlier work this paper cites.
Labs, B.F.: Flux. https://github.com/black-forest-labs/flux (2024)
2024
Earlier work this paper cites.
Li, B., Lin, Z., Pathak, D., Li, J., Fei, Y., Wu, K., Ling, T., Xia, X., Zhang, P., Neubig, G., Ramanan, D.: Genai-bench: Evaluating and improving compositional text-to-visual generation (2024), https://arxiv.org/abs/2406.13743
Original
2024
Earlier work this paper cites.
Li, Z., Zhang, J., Lin, Q., Xiong, J., Long, Y., Deng, X., Zhang, Y., Liu, X., Huang, M., Xiao, Z., Chen, D., He, J., Li, J., Li, W., Zhang, C., Quan, R., Lu, J., Huang, J., Yuan, X., Zheng, X., Li, Y., Zhang, J., Zhang, C., Chen, M., Liu, J., Fang, Z., Wang, W., Xue, J., Tao, Y., Zhu, J., Liu, K., Lin, S., Sun, Y., Li, Y., Wang, D., Chen, M., Hu, Z., Xiao, X., Chen, Y., Liu, Y., Liu, W., Wang, D., Yang, Y., Jiang, J., Lu, Q.: Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding (May 2024). https://doi.org/10.48550/arXiv.2405.08748
2024
Earlier work this paper cites.
Lin, Z., Pathak, D., Li, B., Li, J., Xia, X., Neubig, G., Zhang, P., Ramanan, D.: Evaluating text-to-visual generation with image-to-text generation (2024), https://arxiv.org/abs/2404.01291
Original
2024
Earlier work this paper cites.
Somepalli, G., Gupta, A., Gupta, K., Palta, S., Goldblum, M., Geiping, J., Shrivastava, A., Goldstein, T.: Measuring style similarity in diffusion models (2024), https://arxiv.org/abs/2404.01292
Original
2024
Earlier work this paper cites.

Similar

Sun, P., Jiang, Y., Chen, S., Zhang, S., Peng, B., Luo, P., Yuan, Z.: Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation (Jun 2024). https://doi.org/10.48550/arXiv.2406.06525
2024
Cited alongside, same era.
Tian, K., Jiang, Y., Yuan, Z., Peng, B., Wang, L.: Visual autoregressive modeling: Scalable image generation via next-scale prediction (2024)
2024
Cited alongside, same era.
Wang, G., Wei, X., Liu, J., Zhang, R., Zhang, Y., Zhang, K., Chong, M., Zhang, S.: MR-MLLM: Mutual reinforcement of multimodal comprehension and vision perception (2024), https://arxiv.org/abs/2406.15768
Original
2024
Cited alongside, same era.
Wu, C., Chen, X., Wu, Z., Ma, Y., Liu, X., Pan, Z., Liu, W., Xie, Z., Yu, X., Ruan, C., Luo, P.: Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation (Oct 2024). https://doi.org/10.48550/arXiv.2410.13848
2024
Cited alongside, same era.

Then

Siméoni, O., Vo, H.V., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V., Szafraniec, M., Yi, S., Ramamonjisoa, M., Massa, F., Haziza, D., Wehrstedt, L., Wang, J., Darcet, T., Moutakanni, T., Sentana, L., Roberts, C., Vedaldi, A., Tolan, J., Brandt, J., Couprie, C., Mairal, J., Jégou, H., Labatut, P., Bojanowski, P.: Dinov3 (2025), https://arxiv.org/abs/2508.10104
Original
2025
Closest in time.
Sun, K., Fang, R., Duan, C., Liu, X., Liu, X.: T2i-reasonbench: Benchmarking reasoning-informed text-to-image generation (2025), https://arxiv.org/abs/2508.17472
Original
2025
Closest in time.
Team, M.: Midjourney. https://www.midjourney.com/ (2025)
2025
Closest in time.
Tong, C., Guo, Z., Zhang, R., Shan, W., Wei, X., Xing, Z., Li, H., Heng, P.A.: Delving into RL for image generation with CoT: A study on DPO vs. GRPO. In: Advances in Neural Information Processing Systems (2025)
2025
Closest in time.

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2025

TIIF-Bench: How Does Your T2I Model Follow Your Instructions?

Wei, Xinyu, Zhang, Jinrui, Wang, Zeqing et al.

Understand

The rapid advancements of Text-to-Image (T2I) models have ushered in a new phase of AI-generated content, marked by their growing ability to interpret and follow user instructions.

However, existing T2I model evaluation benchmarks fall short in limited prompt diversity and complexity, as well as coarse evaluation metrics, making it difficult to evaluate the fine-grained alignment performance between textual instructions and generated images.
In this paper, we present TIIF-Bench Text-to-Image Instruction Following Benchmark), aiming to systematically assess T2I models' ability in interpreting and following intricate textual instructions.
TIIF-Bench comprises 5,000 prompts organized along multiple dimensions and categorized into three levels of difficulty and complexity.

Built on

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021), https://arxiv.org/abs/2103.00020
Original
2021
Earlier work this paper cites.
Hessel, J., Holtzman, A., Forbes, M., Bras, R.L., Choi, Y.: CLIPScore: A Reference-free Evaluation Metric for Image Captioning (Mar 2022). https://doi.org/10.48550/arXiv.2104.08718
2022
Earlier work this paper cites.
Li, C., Xu, H., Tian, J., Wang, W., Yan, M., Bi, B., Ye, J., Chen, H., Xu, G., Cao, Z., Zhang, J., Huang, S., Huang, F., Zhou, J., Si, L.: mplug: Effective and efficient vision-language learning by cross-modal skip-connections (2022), https://arxiv.org/abs/2205.12005
Original
2022
Earlier work this paper cites.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation (2022), https://arxiv.org/abs/2201.12086
Original
2022
Earlier work this paper cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis With Latent Diffusion Models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)
2022
Earlier work this paper cites.
Zhou, X., Koltun, V., Krähenbühl, P.: Simple multi-dataset detection (2022), https://arxiv.org/abs/2102.13086
Original
2022
Earlier work this paper cites.
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al.: Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf 2
2023
Earlier work this paper cites.
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J., Luo, P., Lu, H., Li, Z.: PixArt-$ α $ \alpha\$ : Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis (Dec 2023). https://doi.org/10.48550/arXiv.2310.00426
2023
Earlier work this paper cites.
Ghosh, D., Hajishirzi, H., Schmidt, L.: Geneval: An object-focused framework for evaluating text-to-image alignment (2023), https://arxiv.org/abs/2310.11513
Original
2023
Earlier work this paper cites.
Hu, Y., Liu, B., Kasai, J., Wang, Y., Ostendorf, M., Krishna, R., Smith, N.A.: Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering (2023), https://arxiv.org/abs/2303.11897
Original
2023
Earlier work this paper cites.
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (Jul 2023). https://doi.org/10.48550/arXiv.2307.01952
2023
Earlier work this paper cites.
Wu, X., Hao, Y., Sun, K., Chen, Y., Zhu, F., Zhao, R., Li, H.: Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis (2023), https://arxiv.org/abs/2306.09341
Original
2023
Earlier work this paper cites.
Baldridge, J., Bauer, J., Bhutani, M., Brichtova, N., Bunner, A., Castrejon, L., Chan, K., Chen, Y., Dieleman, S., Du, Y., et al.: Imagen 3. arXiv preprint arXiv:2408.07009 (2024)
Original
2024
Earlier work this paper cites.
Chen, J., Ge, C., Xie, E., Wu, Y., Yao, L., Ren, X., Wang, Z., Luo, P., Lu, H., Li, Z.: PixArt- Σ \Sigma : Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation (Mar 2024). https://doi.org/10.48550/arXiv.2403.04692
2024
Earlier work this paper cites.
Chen, J., Wu, Y., Luo, S., Xie, E., Paul, S., Luo, P., Zhao, H., Li, Z.: PIXART- δ \delta : Fast and Controllable Image Generation with Latent Consistency Models (Jan 2024). https://doi.org/10.48550/arXiv.2401.05252
2024
Earlier work this paper cites.
Cho, J., Hu, Y., Garg, R., Anderson, P., Krishna, R., Baldridge, J., Bansal, M., Pont-Tuset, J., Wang, S.: Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-to-image generation (2024), https://arxiv.org/abs/2310.18235
Original
2024
Earlier work this paper cites.
Cui, S., Guo, J., An, X., Deng, J., Zhao, Y., Wei, X., Feng, Z.: IDAdapter: Learning mixed features for tuning-free personalization of text-to-image models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 950–959 (June 2024)
2024
Earlier work this paper cites.
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., Podell, D., Dockhorn, T., English, Z., Rombach, R.: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. In: Forty-First International Conference on Machine Learning (Jun 2024)
2024
Earlier work this paper cites.
Han, J., Liu, J., Jiang, Y., Yan, B., Zhang, Y., Yuan, Z., Peng, B., Liu, X.: Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis (2024)
2024
Earlier work this paper cites.
Hu, X., Wang, R., Fang, Y., Fu, B., Cheng, P., Yu, G.: Ella: Equip diffusion models with llm for enhanced semantic alignment (2024), https://arxiv.org/abs/2403.05135
Original
2024
Earlier work this paper cites.
Hurst, A., Lerer, A., Goucher, A.P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al.: Gpt-4o system card. arXiv preprint arXiv:2410.21276 (2024)
Original
2024
Earlier work this paper cites.
Imagen-Team-Google, Baldridge, J., Bauer, J., Bhutani, M., Others: Imagen 3 (Dec 2024). https://doi.org/10.48550/arXiv.2408.07009
2024
Earlier work this paper cites.
Labs, B.F.: Flux. https://github.com/black-forest-labs/flux (2024)
2024
Earlier work this paper cites.
Li, B., Lin, Z., Pathak, D., Li, J., Fei, Y., Wu, K., Ling, T., Xia, X., Zhang, P., Neubig, G., Ramanan, D.: Genai-bench: Evaluating and improving compositional text-to-visual generation (2024), https://arxiv.org/abs/2406.13743
Original
2024
Earlier work this paper cites.
Li, Z., Zhang, J., Lin, Q., Xiong, J., Long, Y., Deng, X., Zhang, Y., Liu, X., Huang, M., Xiao, Z., Chen, D., He, J., Li, J., Li, W., Zhang, C., Quan, R., Lu, J., Huang, J., Yuan, X., Zheng, X., Li, Y., Zhang, J., Zhang, C., Chen, M., Liu, J., Fang, Z., Wang, W., Xue, J., Tao, Y., Zhu, J., Liu, K., Lin, S., Sun, Y., Li, Y., Wang, D., Chen, M., Hu, Z., Xiao, X., Chen, Y., Liu, Y., Liu, W., Wang, D., Yang, Y., Jiang, J., Lu, Q.: Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding (May 2024). https://doi.org/10.48550/arXiv.2405.08748
2024
Earlier work this paper cites.
Lin, Z., Pathak, D., Li, B., Li, J., Xia, X., Neubig, G., Zhang, P., Ramanan, D.: Evaluating text-to-visual generation with image-to-text generation (2024), https://arxiv.org/abs/2404.01291
Original
2024
Earlier work this paper cites.
Somepalli, G., Gupta, A., Gupta, K., Palta, S., Goldblum, M., Geiping, J., Shrivastava, A., Goldstein, T.: Measuring style similarity in diffusion models (2024), https://arxiv.org/abs/2404.01292
Original
2024
Earlier work this paper cites.

Similar

Sun, P., Jiang, Y., Chen, S., Zhang, S., Peng, B., Luo, P., Yuan, Z.: Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation (Jun 2024). https://doi.org/10.48550/arXiv.2406.06525
2024
Cited alongside, same era.
Tian, K., Jiang, Y., Yuan, Z., Peng, B., Wang, L.: Visual autoregressive modeling: Scalable image generation via next-scale prediction (2024)
2024
Cited alongside, same era.
Wang, G., Wei, X., Liu, J., Zhang, R., Zhang, Y., Zhang, K., Chong, M., Zhang, S.: MR-MLLM: Mutual reinforcement of multimodal comprehension and vision perception (2024), https://arxiv.org/abs/2406.15768
Original
2024
Cited alongside, same era.
Wu, C., Chen, X., Wu, Z., Ma, Y., Liu, X., Pan, Z., Liu, W., Xie, Z., Yu, X., Ruan, C., Luo, P.: Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation (Oct 2024). https://doi.org/10.48550/arXiv.2410.13848
2024
Cited alongside, same era.

Then

Siméoni, O., Vo, H.V., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V., Szafraniec, M., Yi, S., Ramamonjisoa, M., Massa, F., Haziza, D., Wehrstedt, L., Wang, J., Darcet, T., Moutakanni, T., Sentana, L., Roberts, C., Vedaldi, A., Tolan, J., Brandt, J., Couprie, C., Mairal, J., Jégou, H., Labatut, P., Bojanowski, P.: Dinov3 (2025), https://arxiv.org/abs/2508.10104
Original
2025
Closest in time.
Sun, K., Fang, R., Duan, C., Liu, X., Liu, X.: T2i-reasonbench: Benchmarking reasoning-informed text-to-image generation (2025), https://arxiv.org/abs/2508.17472
Original
2025
Closest in time.
Team, M.: Midjourney. https://www.midjourney.com/ (2025)
2025
Closest in time.
Tong, C., Guo, Z., Zhang, R., Shan, W., Wei, X., Xing, Z., Li, H., Heng, P.A.: Delving into RL for image generation with CoT: A study on DPO vs. GRPO. In: Advances in Neural Information Processing Systems (2025)
2025
Closest in time.

Beyond the bibliography

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Wu, X., Yu, D., Huang, Y., Russakovsky, O., Arora, S.: Conceptmix: A compositional image generation benchmark with controllable difficulty (2024), https://arxiv.org/abs/2408.14339

Original

2024

Cited alongside, same era.

Xie, J., Mao, W., Bai, Z., Zhang, D.J., Wang, W., Lin, K.Q., Gu, Y., Chen, Z., Yang, Z., Shou, M.Z.: Show-o: One Single Transformer to Unify Multimodal Understanding and Generation (Oct 2024). https://doi.org/10.48550/arXiv.2408.12528

2024

Cited alongside, same era.

Zhuo, L., Du, R., Xiao, H., Li, Y., Liu, D., Huang, R., Liu, W., Zhao, L., Wang, F.Y., Ma, Z., Luo, X., Wang, Z., Zhang, K., Zhu, X., Liu, S., Yue, X., Liu, D., Ouyang, W., Liu, Z., Qiao, Y., Li, H., Gao, P.: Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT (Jun 2024). https://doi.org/10.48550/arXiv.2406.18583

2024

Cited alongside, same era.

Bai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., Ge, W., Guo, Z., Huang, Q., Huang, J., Huang, F., Hui, B., Jiang, S., Li, Z., Li, M., Li, M., Li, K., Lin, Z., Lin, J., Liu, X., Liu, J., Liu, C., Liu, Y., Liu, D., Liu, S., Lu, D., Luo, R., Lv, C., Men, R., Meng, L., Ren, X., Ren, X., Song, S., Sun, Y., Tang, J., Tu, J., Wan, J., Wang, P., Wang, P., Wang, Q., Wang, Y., Xie, T., Xu, Y., Xu, H., Xu, J., Yang, Z., Yang, M., Yang, J., Yang, A., Yu, B., Zhang, F., Zhang, H., Zhang, X., Zheng, B., Zhong, H., Zhou, J., Zhou, F., Zhou, J., Zhu, Y., Zhu, K.: Qwen3-vl technical report (2025), https://arxiv.org/abs/2511.21631

Original

2025

Cited alongside, same era.

Chang, J., Fang, Y., Xing, P., Wu, S., Cheng, W., Wang, R., Zeng, X., Yu, G., Chen, H.B.: Oneig-bench: Omni-dimensional nuanced evaluation for image generation (2025), https://arxiv.org/abs/2506.07977

Original

2025

Cited alongside, same era.

Chen, J., Xue, S., Zhao, Y., Yu, J., Paul, S., Chen, J., Cai, H., Xie, E., Han, S.: SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation (Mar 2025). https://doi.org/10.48550/arXiv.2503.09641

2025

Cited alongside, same era.

Chen, K., Lin, Z., Xu, Z., Shen, Y., Yao, Y., Rimchala, J., Zhang, J., Huang, L.: R2i-bench: Benchmarking reasoning-driven text-to-image generation (2025), https://arxiv.org/abs/2505.23493

Original

2025

Cited alongside, same era.

Cui, Y., Chen, H., Deng, H., Huang, X., Li, X., Liu, J., Liu, Y., Luo, Z., Wang, J., Wang, W., Wang, Y., Wang, C., Zhang, F., Zhao, Y., Pan, T., Li, X., Hao, Z., Ma, W., Chen, Z., Ao, Y., Huang, T., Wang, Z., Wang, X.: Emu3.5: Native multimodal models are world learners (2025), https://arxiv.org/abs/2510.26583

Original

2025

Cited alongside, same era.

Tschannen, M., Gritsenko, A., Wang, X., Naeem, M.F., Alabdulmohsin, I., Parthasarathy, N., Evans, T., Beyer, L., Xia, Y., Mustafa, B., Hénaff, O., Harmsen, J., Steiner, A., Zhai, X.: Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features (2025), https://arxiv.org/abs/2502.14786

Original

2025

Closest in time.

Wang, Y., Li, Z., Zang, Y., Wang, C., Lu, Q., Jin, C., Wang, J.: Unified multimodal chain-of-thought reward model through reinforcement fine-tuning (2025), https://arxiv.org/abs/2505.03318

Original

2025

Closest in time.

Wang, Z., Wei, X., Li, B., Guo, Z., Zhang, J., Wei, H., Wang, K., Zhang, L.: VideoVerse: Does your T2V generator have world model capability to synthesize videos? (2025), https://arxiv.org/abs/2510.08398

Original

2025

Closest in time.

Wang, Z., Zhang, S., Tang, C., Wang, K.: TimeCausality: Evaluating the causal ability in time dimension for vision language models (2025), https://arxiv.org/abs/2505.15435

Original

2025

Closest in time.

Wiles, O., Zhang, C., Albuquerque, I., Kajić, I., Wang, S., Bugliarello, E., Onoe, Y., Papalampidi, P., Ktena, I., Knutsen, C., Rashtchian, C., Nawalgaria, A., Pont-Tuset, J., Nematzadeh, A.: Revisiting text-to-image evaluation with gecko: On metrics, prompts, and human ratings (2025), https://arxiv.org/abs/2404.16820

Original

2025

Closest in time.

Wu, C., Li, J., Zhou, J., Lin, J., Gao, K., Yan, K., ming Yin, S., Bai, S., Xu, X., Chen, Y., Chen, Y., Tang, Z., Zhang, Z., Wang, Z., Yang, A., Yu, B., Cheng, C., Liu, D., Li, D., Zhang, H., Meng, H., Wei, H., Ni, J., Chen, K., Cao, K., Peng, L., Qu, L., Wu, M., Wang, P., Yu, S., Wen, T., Feng, W., Xu, X., Wang, Y., Zhang, Y., Zhu, Y., Wu, Y., Cai, Y., Liu, Z.: Qwen-image technical report (2025), https://arxiv.org/abs/2508.02324

Original

2025

Closest in time.

Xie, E., Chen, J., Zhao, Y., Yu, J., Zhu, L., Wu, C., Lin, Y., Zhang, Z., Li, M., Chen, J., Cai, H., Liu, B., Zhou, D., Han, S.: SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer (Mar 2025). https://doi.org/10.48550/arXiv.2501.18427

2025

Closest in time.

Xie, J., Yang, Z., Shou, M.Z.: Show-o2: Improved native unified multimodal models (2025), https://arxiv.org/abs/2506.15564

Original

2025

Closest in time.

Xin, Y., Yan, J., Qin, Q., Li, Z., Liu, D., Li, S., Huang, V.S.J., Zhou, Y., Zhang, R., Zhuo, L., Han, T., Sun, X., Luo, S., Wang, M., Fu, B., Cao, Y., Li, H., Zhai, G., Liu, X., Qiao, Y., Gao, P.: Lumina-mgpt 2.0: Stand-alone autoregressive image modeling (2025), https://arxiv.org/abs/2507.17801

Original

2025

Closest in time.

Xu, J., Huang, Y., Cheng, J., Yang, Y., Xu, J., Wang, Y., Duan, W., Yang, S., Jin, Q., Li, S., Teng, J., Yang, Z., Zheng, W., Liu, X., Ding, M., Zhang, X., Gu, X., Huang, S., Huang, M., Tang, J., Dong, Y.: Visionreward: Fine-grained multi-dimensional human preference learning for image and video generation (2025), https://arxiv.org/abs/2412.21059

Original

2025

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Yang, Z., Gu, T., Wang, J., Lin, F., Sheng, X., Chen, P., Li, L.: Longt2ibench: A benchmark for evaluating long text-to-image generation with graph-structured annotations (2025), https://arxiv.org/abs/2512.09271

Original

2025

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Ye, J., Jiang, D., Wang, Z., Zhu, L., Hu, Z., Huang, Z., He, J., Yan, Z., Yu, J., Li, H., He, C., Li, W.: Echo-4o: Harnessing the power of gpt-4o synthetic images for improved image generation (2025), https://arxiv.org/abs/2508.09987

Original

2025

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Zhang, R., Wei, X., Jiang, D., Guo, Z., Li, S., Zhang, Y., Tong, C., Liu, J., Zhou, A., Wei, B., Zhang, S., Gao, P., Li, C., Li, H.: MAVIS: Mathematical visual instruction tuning with an automatic data engine. In: International Conference on Learning Representations (2025)

2025

Closest in time.

Zhang, Y.F., Yang, H., Zhang, H., Shi, Y., Chen, Z., Tian, H., Fu, C., Wang, H., Wu, K., Cui, B., Wang, X., Pan, J., Wang, H., Zhang, Z., Wang, L.: Basereward: A strong baseline for multimodal reward model (2025), https://arxiv.org/abs/2509.16127

Original

2025

Closest in time.

Zhao, S., Wu, Q., Li, X., Zhang, B., Li, M., Qin, Q., Liu, D., Zhang, K., Li, H., Qiao, Y., Gao, P., Fu, B., Li, Z.: Lex-art: Rethinking text generation via scalable high-quality data synthesis (2025), https://arxiv.org/abs/2503.21749

Original

2025

Closest in time.

An, R., Yang, S., Guo, Z., Dai, W., Shen, Z., Li, H., Zhang, R., Wei, X., Li, G., Wu, W., Zhang, W.: GENIUS: Generative fluid intelligence evaluation suite (2026), https://arxiv.org/abs/2602.11144

Original

2026

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Google: nano-banana-2. https://deepmind.google/models/gemini-image/flash/ (2026)

2026

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Labs, B.F.: Flux.2. https://bfl.ai/models/flux-2 (2026)

2026

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Li, O., Wang, Y., Hu, X., Huang, H., Chen, R., Ou, J., Tao, X., Wan, P., Qi, X., Feng, F.: Easier painting than thinking: Can text-to-image models set the stage, but not direct the play? (2026), https://arxiv.org/abs/2509.03516

Original

2026

Closest in time.

Wang, S., Liu, S., Kuang, Y., Wei, X., Liu, Y., Li, Z., Man, Y., Chen, G., Tao, A., Liu, G., Kautz, J., Zhang, L., Yu, Z.: Locateanything: Fast and high-quality vision-language grounding with parallel box decoding (2026), https://arxiv.org/abs/2605.27365

Original

2026

Closest in time.

Wang, Y., Zang, Y., Li, H., Jin, C., Wang, J.: Unified reward model for multimodal understanding and generation (2026), https://arxiv.org/abs/2503.05236

Original

2026

Closest in time.

Wei, X., Cen, K., Wei, H., Guo, Z., Li, B., Wang, Z., Zhang, J., Zhang, L.: MICo-150K: A comprehensive dataset advancing multi-image composition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 29695–29706 (June 2026)

2026

Closest in time.