SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

Showing 76100 of 6689 papers

TitleStatusHype
Consistency ModelsCode5
Scalable Diffusion Models with TransformersCode5
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic ModelsCode5
DreamFusion: Text-to-3D using 2D DiffusionCode5
MoVQ: Modulating Quantized Vectors for High-Fidelity Image GenerationCode5
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven GenerationCode5
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual InversionCode5
XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT ModulationCode4
Ming-Omni: A Unified Multimodal Model for Perception and GenerationCode4
UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and GenerationCode4
ImgEdit: A Unified Image Editing Dataset and BenchmarkCode4
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPOCode4
Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-TuningCode4
Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal InteractionCode4
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoTCode4
Lumina-Image 2.0: A Unified and Efficient Image Generative FrameworkCode4
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image GenerationCode4
Unified Reward Model for Multimodal Understanding and GenerationCode4
Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by StepCode4
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANsCode4
The GAN is dead; long live the GAN! A Modern GAN BaselineCode4
Autoregressive Video Generation without Vector QuantizationCode4
Taming Scalable Visual Tokenizer for Autoregressive Image GenerationCode4
One Diffusion to Generate Them AllCode4
Region-Aware Text-to-Image Generation via Hard Binding and Soft RefinementCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified