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 301325 of 6689 papers

TitleStatusHype
GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned PolicyCode2
GPT4Point: A Unified Framework for Point-Language Understanding and GenerationCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
GrounDiT: Grounding Diffusion Transformers via Noisy Patch TransplantationCode2
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement LearningCode2
Beyond Self-attention: External Attention using Two Linear Layers for Visual TasksCode2
VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive ModelingCode2
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
GRPose: Learning Graph Relations for Human Image Generation with Pose PriorsCode2
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature FieldsCode2
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
GeoSynth: Contextually-Aware High-Resolution Satellite Image SynthesisCode2
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised MatchingCode2
Adversarial Latent AutoencodersCode2
Geodesic Diffusion Models for Medical Image-to-Image GenerationCode2
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image GenerationCode2
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
Compositional Transformers for Scene GenerationCode2
Generative Modeling by Estimating Gradients of the Data DistributionCode2
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech SynthesisCode2
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixCode2
Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image SynthesisCode2
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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