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 18011825 of 6689 papers

TitleStatusHype
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image GenerationCode1
Dissecting and Mitigating Diffusion Bias via Mechanistic InterpretabilityCode1
Harnessing LLM to Attack LLM-Guarded Text-to-Image ModelsCode1
Censored Sampling of Diffusion Models Using 3 Minutes of Human FeedbackCode1
CFG++: Manifold-constrained Classifier Free Guidance for Diffusion ModelsCode1
CONFIG: Controllable Neural Face Image GenerationCode1
Freestyle Layout-to-Image SynthesisCode1
Causal Inference via Style Transfer for Out-of-distribution GeneralisationCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic DataCode1
ConditionVideo: Training-Free Condition-Guided Text-to-Video GenerationCode1
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
ForkGAN: Seeing into the Rainy NightCode1
Seeing a Rose in Five Thousand WaysCode1
Distribution-Aware Data Expansion with Diffusion ModelsCode1
Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language ModelsCode1
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image GenerationCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
Forward-only Diffusion Probabilistic ModelsCode1
Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodlesCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image TranslationCode1
Conditional Vector Graphics Generation for Music Cover ImagesCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded SamplingCode1
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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