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 251300 of 6689 papers

TitleStatusHype
ModelScope Text-to-Video Technical ReportCode3
Emu: Generative Pretraining in MultimodalityCode3
Designing a Better Asymmetric VQGAN for StableDiffusionCode3
Personalize Segment Anything Model with One ShotCode3
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image GenerationCode3
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free VideosCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360^Code3
One Transformer Fits All Distributions in Multi-Modal Diffusion at ScaleCode3
Unlimited-Size Diffusion RestorationCode3
Composer: Creative and Controllable Image Synthesis with Composable ConditionsCode3
MultiDiffusion: Fusing Diffusion Paths for Controlled Image GenerationCode3
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image SynthesisCode3
MedSegDiff-V2: Diffusion based Medical Image Segmentation with TransformerCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360degCode3
Paint by Example: Exemplar-based Image Editing with Diffusion ModelsCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Deep Generative Models on 3D Representations: A SurveyCode3
On Distillation of Guided Diffusion ModelsCode3
Flow Matching for Generative ModelingCode3
All are Worth Words: A ViT Backbone for Diffusion ModelsCode3
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified FlowCode3
StyleGAN-Human: A Data-Centric Odyssey of Human GenerationCode3
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
Autoregressive Image Generation using Residual QuantizationCode3
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation ModelsCode3
MaskGIT: Masked Generative Image TransformerCode3
Alias-Free Generative Adversarial NetworksCode3
Zero-Shot Text-to-Image GenerationCode3
Improved Denoising Diffusion Probabilistic ModelsCode3
On Noise Injection in Generative Adversarial NetworksCode3
Generating Long Sequences with Sparse TransformersCode3
CharaConsist: Fine-Grained Consistent Character GenerationCode2
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group QuantizationCode2
Flow-Anchored Consistency ModelsCode2
Watermarking Autoregressive Image GenerationCode2
Marrying Autoregressive Transformer and Diffusion with Multi-Reference AutoregressionCode2
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand BetterCode2
OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image GenerationCode2
AliTok: Towards Sequence Modeling Alignment between Tokenizer and Autoregressive ModelCode2
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RLCode2
Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion ModelCode2
DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail PredictionCode2
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement LearningCode2
Scaling Diffusion Transformers Efficiently via μPCode2
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to RankCode2
Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image SynthesisCode2
Unified Continuous Generative ModelsCode2
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and SegmentationCode2
InstanceGen: Image Generation with Instance-level InstructionsCode2
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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