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 651675 of 6689 papers

TitleStatusHype
Flux Already Knows -- Activating Subject-Driven Image Generation without TrainingCode2
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and EditingCode2
Character-Adapter: Prompt-Guided Region Control for High-Fidelity Character CustomizationCode2
MDTv2: Masked Diffusion Transformer is a Strong Image SynthesizerCode2
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous TokensCode2
From Text to Pose to Image: Improving Diffusion Model Control and QualityCode2
Flow Matching in Latent SpaceCode2
Medical Vision Generalist: Unifying Medical Imaging Tasks in ContextCode2
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and QualityCode2
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory MatchingCode2
Agent Attention: On the Integration of Softmax and Linear AttentionCode2
Flow-Anchored Consistency ModelsCode2
FlexVAR: Flexible Visual Autoregressive Modeling without Residual PredictionCode2
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation CapabilitiesCode2
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow MatchingCode2
CharaConsist: Fine-Grained Consistent Character GenerationCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Denoising Diffusion Implicit ModelsCode2
Causal Diffusion Transformers for Generative ModelingCode2
Denoising Diffusion Probabilistic ModelsCode2
Flow-Guided Diffusion for Video InpaintingCode2
FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity RefinerCode2
GALIP: Generative Adversarial CLIPs for Text-to-Image SynthesisCode2
CapHuman: Capture Your Moments in Parallel UniversesCode2
Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion InferenceCode2
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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