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 376400 of 6689 papers

TitleStatusHype
ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation ModelsCode0
Wavelet-based Variational Autoencoders for High-Resolution Image Generation0
Beyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering0
Synthetic Data for Blood Vessel Network Extraction0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching0
Cobra: Efficient Line Art COlorization with BRoAder References0
ACE: Attentional Concept Erasure in Diffusion Models0
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale DatasetCode0
DMM: Building a Versatile Image Generation Model via Distillation-Based Model MergingCode1
InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer FrameworkCode5
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep LearningCode0
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction0
Omni^2: Unifying Omnidirectional Image Generation and Editing in an Omni Model0
ADT: Tuning Diffusion Models with Adversarial Supervision0
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models0
Seedream 3.0 Technical Report0
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Using LLMs as prompt modifier to avoid biases in AI image generators0
REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion TransformersCode3
GeoUni: A Unified Model for Generating Geometry Diagrams, Problems and Problem SolutionsCode1
Art3D: Training-Free 3D Generation from Flat-Colored Illustration0
Anchor Token Matching: Implicit Structure Locking for Training-free AR Image EditingCode1
InstructEngine: Instruction-driven Text-to-Image Alignment0
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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