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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1175 of 6689 papers

TitleStatusHype
BiDM: Pushing the Limit of Quantization for Diffusion ModelsCode1
Bidirectional Consistency ModelsCode1
An Empirical Study of GPT-4o Image Generation CapabilitiesCode1
Image Generation from Scene GraphsCode1
Conditional Image Generation by Conditioning Variational Auto-EncodersCode1
Image Generation Diversity Issues and How to Tame ThemCode1
Harnessing LLM to Attack LLM-Guarded Text-to-Image ModelsCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image SynthesisCode1
Image Generation for Efficient Neural Network Training in Autonomous Drone RacingCode1
Image Generation From Small Datasets via Batch Statistics AdaptationCode1
BézierSketch: A generative model for scalable vector sketchesCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
Anchor Token Matching: Implicit Structure Locking for Training-free AR Image EditingCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Image-Based Virtual Try-On: A SurveyCode1
DMM: Building a Versatile Image Generation Model via Distillation-Based Model MergingCode1
Adaptive Weighted Discriminator for Training Generative Adversarial NetworksCode1
An Attentive-based Generative Model for Medical Image SynthesisCode1
Beyond Surface Statistics: Scene Representations in a Latent Diffusion ModelCode1
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentationCode1
Image Captions are Natural Prompts for Text-to-Image ModelsCode1
Image Generation With Neural Cellular AutomatasCode1
Show:102550
← PrevPage 47 of 268Next →
All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified