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 351375 of 6689 papers

TitleStatusHype
Next Patch Prediction for Autoregressive Visual GenerationCode2
Causal Diffusion Transformers for Generative ModelingCode2
Simple Guidance Mechanisms for Discrete Diffusion ModelsCode2
Financial Fine-tuning a Large Time Series ModelCode2
Diffusion-Enhanced Test-time Adaptation with Text and Image AugmentationCode2
LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural AnnotationsCode2
EMOv2: Pushing 5M Vision Model FrontierCode2
Proactive Agents for Multi-Turn Text-to-Image Generation Under UncertaintyCode2
ZipAR: Accelerating Auto-regressive Image Generation through Spatial LocalityCode2
Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image SynthesisCode2
TinyFusion: Diffusion Transformers Learned ShallowCode2
TextSSR: Diffusion-based Data Synthesis for Scene Text RecognitionCode2
OmniFlow: Any-to-Any Generation with Multi-Modal Rectified FlowsCode2
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation ModelsCode2
Playable Game GenerationCode2
TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian SplattingCode2
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion ModelsCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
Collaborative Decoding Makes Visual Auto-Regressive Modeling EfficientCode2
What Makes a Scene ? Scene Graph-based Evaluation and Feedback for Controllable GenerationCode2
AnyText2: Visual Text Generation and Editing With Customizable AttributesCode2
MMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation PerspectiveCode2
RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image GenerationCode2
From Text to Pose to Image: Improving Diffusion Model Control and QualityCode2
HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and ManipulationCode2
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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