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 13761400 of 6689 papers

TitleStatusHype
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing ImagesCode1
Correcting Diffusion Generation through ResamplingCode1
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
Illiterate DALL-E Learns to ComposeCode1
Foreground-Background Separation through Concept Distillation from Generative Image Foundation ModelsCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image TransitionCode1
Image Captions are Natural Prompts for Text-to-Image ModelsCode1
Anatomical Consistency and Adaptive Prior-informed Transformation for Multi-contrast MR Image Synthesis via Diffusion ModelCode1
Image Generation Diversity Issues and How to Tame ThemCode1
Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in Personalized Image GenerationCode1
SketchyCOCO: Image Generation from Freehand Scene SketchesCode1
Deep Spatial Transformation for Pose-Guided Person Image Generation and AnimationCode1
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic DataCode1
Fundamental Benefit of Alternating Updates in Minimax OptimizationCode1
Generative diffusion model with inverse renormalization group flowsCode1
FLAME Diffuser: Wildfire Image Synthesis using Mask Guided DiffusionCode1
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiesCode1
Text-Anchored Score Composition: Tackling Condition Misalignment in Text-to-Image Diffusion ModelsCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
Aligning Text to Image in Diffusion Models is Easier Than You ThinkCode1
CookGAN: Meal Image Synthesis from IngredientsCode1
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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