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 37763800 of 6689 papers

TitleStatusHype
Im-Promptu: In-Context Composition from Image PromptsCode0
Generating Images with Multimodal Language ModelsCode2
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings0
High-Fidelity Image Compression with Score-based Generative Models0
StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human0
Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models0
Improved Visual Story Generation with Adaptive Context Modeling0
Mindstorms in Natural Language-Based Societies of Mind0
Parallel Sampling of Diffusion ModelsCode1
Are Diffusion Models Vision-And-Language Reasoners?Code1
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images0
ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image0
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion ModelsCode1
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion ModelsCode2
Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks0
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing0
LayoutGPT: Compositional Visual Planning and Generation with Large Language ModelsCode2
Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion ModelsCode0
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion ModelsCode1
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion0
Vision + Language Applications: A SurveyCode4
Visual Programming for Text-to-Image Generation and Evaluation0
Transferring Visual Attributes from Natural Language to Verified Image Generation0
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image GenerationCode0
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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