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 551575 of 6689 papers

TitleStatusHype
SEED-Bench-2: Benchmarking Multimodal Large Language ModelsCode2
Text-Driven Image Editing via Learnable RegionsCode2
LLMGA: Multimodal Large Language Model based Generation AssistantCode2
Flow-Guided Diffusion for Video InpaintingCode2
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D GenerationCode2
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion ModelsCode2
The Chosen One: Consistent Characters in Text-to-Image Diffusion ModelsCode2
Matryoshka Diffusion ModelsCode2
A Pytorch Reproduction of Masked Generative Image TransformerCode2
LAMP: Learn A Motion Pattern for Few-Shot-Based Video GenerationCode2
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training ParadigmCode2
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion ModelsCode2
Mini-DALLE3: Interactive Text to Image by Prompting Large Language ModelsCode2
DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion modelCode2
Aligning Text-to-Image Diffusion Models with Reward BackpropagationCode2
MiniGPT-5: Interleaved Vision-and-Language Generation via Generative VokensCode2
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of CodeCode2
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of DiffusionCode2
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision GeneralistsCode2
Denoising Diffusion Bridge ModelsCode2
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction TuningCode2
Relay Diffusion: Unifying diffusion process across resolutions for image synthesisCode2
Residual Denoising Diffusion ModelsCode2
Dense Text-to-Image Generation with Attention ModulationCode2
Bayesian Flow NetworksCode2
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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