mlsanityData to 7 Oct 2026

Scaling up GANs for Text-to-Image Synthesis

arXiv 2303.05511 ↗First posted 9 Mar 2023cs.CV cs.GR cs.LGcited by 492

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Cited by 201 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that na"Ively increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.

Key facts. Cited by 492 papers in the map (data through 7 Oct 2026). Builds on 92 of its 115 references that are in the map. First posted on arXiv 9 Mar 2023.

Builds on

92 of 115 references.

References · 92

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38 more
  1. 2022Scalable Diffusion Models with Transformerscited by 5,931
  2. 2022Classifier-Free Diffusion Guidancecited by 5,717
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
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  6. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
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50 most cited of 92 All 92 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 124; 2024: 182; 2025: 131; 2026: 55.

492 papers.

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  1. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,005
  2. 2023One-step Diffusion with Distribution Matching Distillationcited by 978
  3. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 946
  4. 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 914
  5. 2023PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesiscited by 854
  6. 2024Autoregressive Model Beats Diffusion: Llama for Scalable Image Generationcited by 760
  7. 2023Adversarial Diffusion Distillationcited by 741
  8. 2024Show-o: One Single Transformer to Unify Multimodal Understanding and Generationcited by 734
  9. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 666
  10. 2024Improved Distribution Matching Distillation for Fast Image Synthesiscited by 648
  11. 2025Mean Flows for One-step Generative Modelingcited by 524
  12. 2024Evaluating Text-to-Visual Generation with Image-to-Text Generationcited by 465
38 more
  1. 2024Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generationcited by 447
  2. 2023Analyzing and Improving the Training Dynamics of Diffusion Modelscited by 396
  3. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
  4. 2023Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Modelcited by 376
  5. 2023InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuningcited by 343
  6. 2024SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformerscited by 331
  7. 2024PixArt-Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generationcited by 326
  8. 2023ControlVideo: Training-free Controllable Text-to-Video Generationcited by 324
  9. 2023FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attentioncited by 313
  10. 2023Ablating Concepts in Text-to-Image Diffusion Modelscited by 312
  11. 2024GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generationcited by 298
  12. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
  13. 2023Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystackcited by 278
  14. 2024Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillationcited by 276
  15. 2024SDXL-Lightning: Progressive Adversarial Diffusion Distillationcited by 258
  16. 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 254
  17. 2023RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulationcited by 206
  18. 2023To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Nowcited by 204
  19. 2023Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelscited by 199
  20. 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
  21. 2023FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editingcited by 165
  22. 2023UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANscited by 165
  23. 2024Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Modelscited by 164
  24. 2023Grounded Text-to-Image Synthesis with Attention Refocusingcited by 163
  25. 2023Raising the Bar of AI-generated Image Detection with CLIPcited by 163
  26. 2023Dense Text-to-Image Generation with Attention Modulationcited by 162
  27. 2024JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generationcited by 162
  28. 2023Holistic Evaluation of Text-To-Image Modelscited by 160
  29. 2023Cache Me if You Can: Accelerating Diffusion Models through Block Cachingcited by 149
  30. 2023Localizing Object-level Shape Variations with Text-to-Image Diffusion Modelscited by 142
  31. 2025Diffusion Adversarial Post-Training for One-Step Video Generationcited by 142
  32. 2023RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Pathscited by 141
  33. 2024DreamBench++: A Human-Aligned Benchmark for Personalized Image Generationcited by 131
  34. 2024HART: Efficient Visual Generation with Hybrid Autoregressive Transformercited by 128
  35. 2024FORA: Fast-Forward Caching in Diffusion Transformer Accelerationcited by 127
  36. 2024InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimizationcited by 123
  37. 2023Intriguing properties of synthetic images: from generative adversarial networks to diffusion modelscited by 121
  38. 2023Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusioncited by 114

50 most cited of 492 All 492 (JSON)

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