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 376–400 of 6689 papers

TitleStatusHype
ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation ModelsCode0
Wavelet-based Variational Autoencoders for High-Resolution Image Generation—0
Beyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering—0
Synthetic Data for Blood Vessel Network Extraction—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Cobra: Efficient Line Art COlorization with BRoAder References—0
ACE: Attentional Concept Erasure in Diffusion Models—0
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis—0
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 Reconstruction—0
Omni^2: Unifying Omnidirectional Image Generation and Editing in an Omni Model—0
ADT: Tuning Diffusion Models with Adversarial Supervision—0
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era—0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
Seedream 3.0 Technical Report—0
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Using LLMs as prompt modifier to avoid biases in AI image generators—0
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 Illustration—0
Anchor Token Matching: Implicit Structure Locking for Training-free AR Image EditingCode1
InstructEngine: Instruction-driven Text-to-Image Alignment—0
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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