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 276300 of 6689 papers

TitleStatusHype
CtrLoRA: An Extensible and Efficient Framework for Controllable Image GenerationCode3
AnimeGamer: Infinite Anime Life Simulation with Next Game State PredictionCode3
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
Image and Video Tokenization with Binary Spherical QuantizationCode3
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible CostCode3
AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image GenerationCode3
Collaborative Neural Rendering using Anime Character SheetsCode2
Collaborative Decoding Makes Visual Auto-Regressive Modeling EfficientCode2
GrounDiT: Grounding Diffusion Transformers via Noisy Patch TransplantationCode2
CogView: Mastering Text-to-Image Generation via TransformersCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept MatchingCode2
GRPose: Learning Graph Relations for Human Image Generation with Pose PriorsCode2
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
GPT4Point: A Unified Framework for Point-Language Understanding and GenerationCode2
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned PolicyCode2
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image GenerationCode2
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature FieldsCode2
Closed-Form Factorization of Latent Semantics in GANsCode2
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement LearningCode2
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
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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