HunyuanImage 3.0 Technical Report

Siyu Cao , Hangting Chen , Peng Chen , Yiji Cheng , Yutao Cui , Xinchi Deng , Ying Dong , Kipper Gong

show 66 more authors

Tianpeng Gu Xiusen Gu Tiankai Hang Duojun Huang Jie Jiang Zhengkai Jiang Weijie Kong Changlin Li Donghao Li Junzhe Li Xin Li Yang Li Zhenxi Li Zhimin Li Jiaxin Lin Linus Lucaz Liu Shu Liu Songtao Liu Yu Liu Yuhong Liu Yanxin Long Fanbin Lu Qinglin Lu Yuyang Peng Yuanbo Peng Xiangwei Shen Yixuan Shi Jiale Tao Yangyu Tao Qi Tian Pengfei Wan Chunyu Wang Kai Wang Lei Wang Linqing Wang Lucas Wang Qixun Wang Weiyan Wang Hao Wen Bing Wu Jianbing Wu Yue Wu Senhao Xie Fang Yang Miles Yang Xiaofeng Yang Xuan Yang Zhantao Yang Jingmiao Yu Zheng Yuan Chao Zhang Jian-Wei Zhang Peizhen Zhang Shi-Xue Zhang Tao Zhang Weigang Zhang Yepeng Zhang Yingfang Zhang Zihao Zhang Zijian Zhang Penghao Zhao Zhiyuan Zhao Xuefei Zhe Jianchen Zhu Zhao Zhong

Authors on Pith no claims yet

Pith reviewed 2026-05-16 01:56 UTC · model grok-4.3

classification 💻 cs.CV

keywords modelhunyuanimagemultimodalavailablebilliongenerationimageinference

0 comments

The pith

HunyuanImage 3.0 delivers an 80B-parameter MoE model unifying multimodal understanding and generation that matches prior state-of-the-art results while being fully open-sourced.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes building a single large AI system that can both read images and create new ones from text prompts. It uses a mixture-of-experts design so only a fraction of the total parameters run for each input, keeping inference efficient. Training happened in stages: first careful data cleaning, then progressive pre-training on mixed text and image data, followed by post-training that includes a native chain-of-thought process to improve reasoning about images. The final model contains more than 80 billion parameters overall but activates only about 13 billion for any given token. Automatic metrics and human raters judged how well generated images match the input text and how realistic they look. The authors report that the results are competitive with closed models from other labs. By publishing the full code and weights, the work turns a proprietary-scale system into a shared starting point for the research community.

Core claim

We successfully trained a Mixture-of-Experts (MoE) model comprising over 80 billion parameters in total, with 13 billion parameters activated per token during inference, making it the largest and most powerful open-source image generative model to date.

Load-bearing premise

That the reported human and automatic evaluations used representative test sets and unbiased raters, and that no undisclosed data or compute advantages explain the competitive results.

read the original abstract

We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with its image generation module publicly available. The achievement of HunyuanImage 3.0 relies on several key components, including meticulous data curation, advanced architecture design, a native Chain-of-Thoughts schema, progressive model pre-training, aggressive model post-training, and an efficient infrastructure that enables large-scale training and inference. With these advancements, we successfully trained a Mixture-of-Experts (MoE) model comprising over 80 billion parameters in total, with 13 billion parameters activated per token during inference, making it the largest and most powerful open-source image generative model to date. We conducted extensive experiments and the results of automatic and human evaluation of text-image alignment and visual quality demonstrate that HunyuanImage 3.0 rivals previous state-of-the-art models. By releasing the code and weights of HunyuanImage 3.0, we aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant multimodal ecosystem. All open source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanImage-3.0

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

HunyuanImage 3.0 is a straightforward technical report on training and open-sourcing an 80B MoE multimodal model with competitive image generation results, where the public release is the main practical value.

read the letter

The main thing to know is that this report describes Tencent's training of an 80 billion parameter MoE model with 13 billion active parameters per token. It unifies multimodal understanding and image generation in an autoregressive setup using native chain-of-thought, progressive pre-training, and post-training, then releases the code and weights publicly. The claim is that it matches prior state-of-the-art on text-image alignment and visual quality through extensive evaluations.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claims rest on standard deep-learning scaling practices and empirical outcomes from large-scale training; no new theoretical entities or derivations are introduced.

free parameters (2)

Total parameters
Chosen via scaling experiments to reach target performance within available compute.
Activated parameters per token
Selected as part of MoE routing design to balance quality and inference cost.

axioms (1)

domain assumption Next-token prediction on interleaved text-image tokens is sufficient to learn unified multimodal understanding and generation.
Invoked throughout the autoregressive framework description.

pith-pipeline@v0.9.0 · 5794 in / 1353 out tokens · 162887 ms · 2026-05-16T01:56:26.396669+00:00 · methodology

discussion (0)

Forward citations

Cited by 19 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Towards Realistic 3D Emission Materials: Dataset, Baseline, and Evaluation for Emission Texture Generation
cs.CV 2026-04 unverdicted novelty 8.0

The work creates the first dataset and baseline for generating emission textures on 3D objects to reproduce glowing materials from input images.
UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs
cs.CV 2026-04 unverdicted novelty 7.0

UniEditBench unifies image and video editing evaluation with a nine-plus-eight operation taxonomy and cost-effective 4B/8B distilled MLLM evaluators that align with human judgments.
Banana100: Breaking NR-IQA Metrics by 100 Iterative Image Replications with Nano Banana Pro
cs.CV 2026-04 unverdicted novelty 7.0

Banana100 dataset shows that none of 21 popular NR-IQA metrics consistently rate images degraded by 100 iterative edits lower than clean originals.
Qwen-Image-VAE-2.0 Technical Report
cs.CV 2026-05 unverdicted novelty 6.0

Qwen-Image-VAE-2.0 achieves state-of-the-art high-compression image reconstruction and superior diffusability for diffusion models, with a new text-rich document benchmark.
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
cs.CV 2026-05 unverdicted novelty 6.0

D-OPSD enables continuous supervised fine-tuning of few-step diffusion models via on-policy self-distillation where the model acts as both teacher (multimodal context) and student (text-only context) on its own roll-outs.
Refinement via Regeneration: Enlarging Modification Space Boosts Image Refinement in Unified Multimodal Models
cs.CV 2026-04 unverdicted novelty 6.0

Refinement via Regeneration (RvR) reformulates image refinement in unified multimodal models as conditional regeneration using prompt and semantic tokens from the initial image, yielding higher alignment scores than e...
Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
cs.CV 2026-04 unverdicted novelty 6.0

By requiring and using highly discriminative LLM text features, the work enables the first effective one-step text-conditioned image generation with MeanFlow.
SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing
cs.CV 2026-04 unverdicted novelty 6.0

SpatialEdit provides a benchmark, large synthetic dataset, and baseline model for precise object and camera spatial manipulations in images, with the model beating priors on spatial editing.
Gen-Searcher: Reinforcing Agentic Search for Image Generation
cs.CV 2026-03 unverdicted novelty 6.0

Gen-Searcher is the first search-augmented image generation agent trained with SFT followed by agentic RL using dual text and image rewards on custom datasets and the KnowGen benchmark.
HunyuanVideo 1.5 Technical Report
cs.CV 2025-11 unverdicted novelty 6.0

HunyuanVideo 1.5 delivers state-of-the-art open-source text-to-video and image-to-video generation with an 8.3B parameter DiT model featuring SSTA attention, glyph-aware encoding, and progressive training.
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
cs.CV 2026-05 unverdicted novelty 5.0

SenseNova-U1 presents native unified multimodal models that match top understanding VLMs while delivering strong performance in image generation, infographics, and interleaved tasks via the NEO-unify architecture.
InsHuman: Towards Natural and Identity-Preserving Human Insertion
cs.CV 2026-05 unverdicted novelty 5.0

InsHuman proposes Human-Background Adaptive Fusion, Face-to-Face ID-Preserving, and Bidirectional Data Pairing to enable natural human insertion in images without altering identity.
Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
cs.CV 2026-04 unverdicted novelty 5.0

Visual generation models are evolving from passive renderers to interactive agentic world modelers, but current systems lack spatial reasoning, temporal consistency, and causal understanding, with evaluations overemph...
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
cs.CV 2025-11 unverdicted novelty 5.0

Z-Image is an efficient 6B-parameter foundation model for image generation that rivals larger commercial systems in photorealism and bilingual text rendering through a new single-stream diffusion transformer and strea...
Qwen-Image-2.0 Technical Report
cs.CV 2026-05 unverdicted novelty 4.0

Qwen-Image-2.0 unifies high-fidelity image generation and precise editing by coupling Qwen3-VL with a Multimodal Diffusion Transformer, improving text rendering, photorealism, and complex prompt following over prior versions.
Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
cs.CV 2026-04 unverdicted novelty 4.0

Tstars-Tryon 1.0 is a deployed virtual try-on system claiming high robustness, photorealism, multi-reference flexibility, and near real-time speed for diverse fashion items.
Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation of Its Performance on Image Restoration Tasks
cs.CV 2026-04 unverdicted novelty 4.0

Nano Banana 2 delivers competitive perceptual quality on image restoration but produces over-enhanced results that diverge from input fidelity in ways standard metrics miss.
Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
cs.CV 2026-04 unverdicted novelty 3.0

Tstars-Tryon 1.0 is a robust, photorealistic virtual try-on system with multi-image support and near real-time speed, deployed at industrial scale on Taobao and accompanied by a released benchmark.
Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
cs.CV 2026-04 unverdicted novelty 3.0

Wan-Image is a unified multi-modal system that integrates LLMs and diffusion transformers to deliver professional-grade image generation features including complex typography, multi-subject consistency, and precise ed...

Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages · cited by 18 Pith papers · 13 internal anchors

[1]

Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

work page 2020
[2]

Denoising Diffusion Implicit Models

Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020

work page internal anchor Pith review Pith/arXiv arXiv 2010
[3]

Diffusion models beat gans on image synthesis

Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780–8794, 2021

work page 2021
[4]

Score-Based Generative Modeling through Stochastic Differential Equations

Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456, 2020

work page internal anchor Pith review Pith/arXiv arXiv 2011
[5]

High- resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022

work page 2022
[6]

Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35: 26565–26577, 2022

Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35: 26565–26577, 2022

work page 2022
[7]

Flow Matching for Generative Modeling

Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling.arXiv preprint arXiv:2210.02747, 2022

work page internal anchor Pith review Pith/arXiv arXiv 2022
[8]

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003, 2022

work page internal anchor Pith review Pith/arXiv arXiv 2022
[9]

Unsupervised representation learning from pre- trained diffusion probabilistic models.Advances in neural information processing systems, 35: 22117–22130, 2022

Zijian Zhang, Zhou Zhao, and Zhijie Lin. Unsupervised representation learning from pre- trained diffusion probabilistic models.Advances in neural information processing systems, 35: 22117–22130, 2022

work page 2022
[10]

Shiftddpms: Exploring conditional diffusion models by shifting diffusion trajectories

Zijian Zhang, Zhou Zhao, Jun Yu, and Qi Tian. Shiftddpms: Exploring conditional diffusion models by shifting diffusion trajectories. InProceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 3552–3560, 2023

work page 2023
[11]

Attention is all you need.Advances in neural information processing systems, 30, 2017

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

work page 2017
[13]

Seedream 4.0: Toward Next-generation Multimodal Image Generation

Team Seedream, Yunpeng Chen, Yu Gao, Lixue Gong, Meng Guo, Qiushan Guo, Zhiyao Guo, Xiaoxia Hou, Weilin Huang, Yixuan Huang, Xiaowen Jian, Huafeng Kuang, Zhichao Lai, Fanshi Li, Liang Li, Xiaochen Lian, Chao Liao, Liyang Liu, Wei Liu, Yanzuo Lu, Zhengxiong Luo, Tongtong Ou, Guang Shi, Yichun Shi, Shiqi Sun, Yu Tian, Zhi Tian, Peng Wang, Rui Wang, Xun Wang...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[14]

Nano banana, 2025

Google. Nano banana, 2025. URL https://developers.googleblog.com/en/introdu cing-gemini-2-5-flash-image

work page 2025
[15]

Gpt-image, 2025

OpenAI. Gpt-image, 2025. URL https://platform.openai.com/docs/models/gpt-i mage-1

work page 2025
[16]

Qwen-Image Technical Report

Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhengyi Wang, An Yang, Bowen Yu, Chen Cheng, Dayiheng Liu, Deqing Li, Hang Zhang, Hao Meng, Hu Wei, Jingyuan Ni, Kai Chen, Kuan Cao, Liang Peng, Lin Qu, Minggang Wu, Peng Wang, Shuting Yu, Tingkun...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[17]

Hunyuanimage 2.1, 2025

Tencent Hunyuan. Hunyuanimage 2.1, 2025. URL https://github.com/Tencent-Hunyu an/HunyuanImage-2.1. 14

work page 2025
[18]

Hunyuan-a13b

Tencent Hunyuan Team. Hunyuan-a13b. https://github.com/Tencent-Hunyuan/Hunyu an-A13B, 2024

work page 2024
[19]

Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35: 23716–23736, 2022

Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35: 23716–23736, 2022

work page 2022
[20]

Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

work page 2023
[21]

Improved baselines with visual instruction tuning

Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 26296–26306, 2024

work page 2024
[22]

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model.arXiv preprint arXiv:2408.11039, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[23]

Janusflow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation

Yiyang Ma, Xingchao Liu, Xiaokang Chen, Wen Liu, Chengyue Wu, Zhiyu Wu, Zizheng Pan, Zhenda Xie, Haowei Zhang, Xingkai Yu, et al. Janusflow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 7739–7751, 2025

work page 2025
[24]

Lareˆ 2: Latent reconstruction error based method for diffusion-generated image detection

Yunpeng Luo, Junlong Du, Ke Yan, and Shouhong Ding. Lareˆ 2: Latent reconstruction error based method for diffusion-generated image detection. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17006–17015, 2024

work page 2024
[25]

Aigi-holmes: Towards explainable and generaliz- able ai-generated image detection via multimodal large language models.arXiv preprint arXiv:2507.02664, 2025

Ziyin Zhou, Yunpeng Luo, Yuanchen Wu, Ke Sun, Jiayi Ji, Ke Yan, Shouhong Ding, Xiaoshuai Sun, Yunsheng Wu, and Rongrong Ji. Aigi-holmes: Towards explainable and generaliz- able ai-generated image detection via multimodal large language models.arXiv preprint arXiv:2507.02664, 2025

work page arXiv 2025
[26]

Dual data alignment makes ai-generated image detector easier generalizable.arXiv preprint arXiv:2505.14359, 2025

Ruoxin Chen, Junwei Xi, Zhiyuan Yan, Ke-Yue Zhang, Shuang Wu, Jingyi Xie, Xu Chen, Lei Xu, Isabel Guan, Taiping Yao, et al. Dual data alignment makes ai-generated image detector easier generalizable.arXiv preprint arXiv:2505.14359, 2025

work page arXiv 2025
[27]

Ic9600: A benchmark dataset for automatic image complexity assessment

Tinglei Feng, Yingjie Zhai, Jufeng Yang, Jie Liang, Deng-Ping Fan, Jing Zhang, Ling Shao, and Dacheng Tao. Ic9600: A benchmark dataset for automatic image complexity assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence, (01):1–17, 2023. doi: 10.1109/TPAMI.2022.3232328

work page doi:10.1109/tpami.2022.3232328 2023
[28]

Scalable diffusion models with transformers

William Peebles and Saining Xie. Scalable diffusion models with transformers. InProceedings of the IEEE/CVF international conference on computer vision, pages 4195–4205, 2023

work page 2023
[29]

Scaling rectified flow transformers for high-resolution image synthesis

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, and Robin Rombach. Scaling rectified flow transformers for high-resolution image synthesis. InICML, 2024. URLhttps://openreview.net/forum?id=FPnUhsQJ5B

work page 2024
[30]

Hunyuan-dit: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding,

Zhimin Li, Jianwei Zhang, Qin Lin, Jiangfeng Xiong, Yanxin Long, Xinchi Deng, Yingfang Zhang, Xingchao Liu, Minbin Huang, Zedong Xiao, Dayou Chen, Jiajun He, Jiahao Li, Wenyue Li, Chen Zhang, Rongwei Quan, Jianxiang Lu, Jiabin Huang, Xiaoyan Yuan, Xiaoxiao Zheng, Yixuan Li, Jihong Zhang, Chao Zhang, Meng Chen, Jie Liu, Zheng Fang, Weiyan Wang, Jinbao Xue,...

work page arXiv 2024
[31]

Flux, 2024

Black Forest Labs. Flux, 2024. URLhttps://blackforestlabs.ai/

work page 2024
[32]

Hunyuanvideo: A systematic framework for large video generative models,

Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Junkun Yuan, Yanxin Long, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai, Jianbing Wu, Jinbao Xue, Joey Wang, Kai Wang, Mengyang Liu, Pengyu Li, Shuai Li, ...

work page
[33]

URLhttps://arxiv.org/abs/2412.03603

work page internal anchor Pith review Pith/arXiv arXiv
[34]

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single trans- former to unify multimodal understanding and generation.arXiv preprint arXiv:2408.12528, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[35]

Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling.arXiv preprint arXiv:2501.17811, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[36]

Emerging Properties in Unified Multimodal Pretraining

Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining.arXiv preprint arXiv:2505.14683, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[37]

Mogao: An omni foundation model for interleaved multi-modal generation.arXiv preprint arXiv:2505.05472, 2025

Chao Liao, Liyang Liu, Xun Wang, Zhengxiong Luo, Xinyu Zhang, Wenliang Zhao, Jie Wu, Liang Li, Zhi Tian, and Weilin Huang. Mogao: An omni foundation model for interleaved multi-modal generation.arXiv preprint arXiv:2505.05472, 2025

work page arXiv 2025
[38]

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016

work page 2016
[39]

Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

work page 2024
[40]

Diffusion model alignment using direct preference optimization

Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8228–8238, 2024

work page 2024
[41]

MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE

Junzhe Li, Yutao Cui, Tao Huang, Yinping Ma, Chun Fan, Miles Yang, and Zhao Zhong. Mixgrpo: Unlocking flow-based grpo efficiency with mixed ode-sde.arXiv preprint arXiv:2507.21802, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[42]

Directly aligning the full diffusion trajectory with fine-grained human preference.arXiv preprint arXiv:2509.06942, 2025

Xiangwei Shen, Zhimin Li, Zhantao Yang, Shiyi Zhang, Yingfang Zhang, Donghao Li, Chunyu Wang, Qinglin Lu, and Yansong Tang. Directly aligning the full diffusion trajectory with fine-grained human preference.arXiv preprint arXiv:2509.06942, 2025

work page arXiv 2025
[43]

Mean Flows for One-step Generative Modeling

Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling.arXiv preprint arXiv:2505.13447, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[44]

T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.Advances in Neural Information Processing Systems, 36:78723–78747, 2023

Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.Advances in Neural Information Processing Systems, 36:78723–78747, 2023

work page 2023
[45]

Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152, 2023

Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152, 2023

work page 2023
[46]

Dreambench++: A human-aligned benchmark for personalized image generation.arXiv preprint arXiv:2406.16855, 2024

Yuang Peng, Yuxin Cui, Haomiao Tang, Zekun Qi, Runpei Dong, Jing Bai, Chunrui Han, Zheng Ge, Xiangyu Zhang, and Shu-Tao Xia. Dreambench++: A human-aligned benchmark for personalized image generation.arXiv preprint arXiv:2406.16855, 2024. 16

work page arXiv 2024