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 33263350 of 6689 papers

TitleStatusHype
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT ImagesCode1
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent DiffusionCode4
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance KernelCode0
Kosmos-G: Generating Images in Context with Multimodal Large Language ModelsCode0
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF0
Efficient-3DiM: Learning a Generalizable Single-image Novel-view Synthesizer in One Day0
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance FieldsCode1
GETAvatar: Generative Textured Meshes for Animatable Human Avatars0
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts0
Predicated Diffusion: Predicate Logic-Based Attention Guidance for Text-to-Image Diffusion Models0
GenCO: Generating Diverse Designs with Combinatorial Constraints0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
MiniGPT-5: Interleaved Vision-and-Language Generation via Generative VokensCode2
TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling0
ImagenHub: Standardizing the evaluation of conditional image generation modelsCode1
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of CodeCode2
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP0
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of DiffusionCode2
Counterfactual Image Generation for adversarially robust and interpretable Classifiers0
Completing Visual Objects via Bridging Generation and Segmentation0
A Comprehensive Review of Generative AI in Healthcare0
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision GeneralistsCode2
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image SynthesisCode4
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image SynthesisCode0
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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