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

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Showing 726–750 of 6689 papers

TitleStatusHype
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence—0
Diffusion Models for Tabular Data: Challenges, Current Progress, and Future DirectionsCode2
Culture-TRIP: Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement—0
RELICT: A Replica Detection Framework for Medical Image GenerationCode0
Fractal Generative ModelsCode5
DICEPTION: A Generalist Diffusion Model for Visual Perceptual TasksCode3
Iterative Flow Matching -- Path Correction and Gradual Refinement for Enhanced Generative Modeling—0
Unified Prompt Attack Against Text-to-Image Generation Models—0
High-resolution Rainy Image Synthesis: Learning from RenderingCode0
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation—0
One-step Diffusion Models with f-Divergence Distribution Matching—0
FlipConcept: Tuning-Free Multi-Concept Personalization for Text-to-Image Generation—0
Multi-Agent Multimodal Models for Multicultural Text to Image GenerationCode0
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
Generative Modeling of Individual Behavior at Scale—0
Improving the Diffusability of Autoencoders—0
DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models—0
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length—0
MagicGeo: Training-Free Text-Guided Geometric Diagram Generation—0
Flow-based generative models as iterative algorithms in probability space—0
IP-Composer: Semantic Composition of Visual Concepts—0
CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image GenerationCode2
Spherical Dense Text-to-Image Synthesis—0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Personalized Image Generation with Deep Generative Models: A Decade SurveyCode3
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified