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

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Showing 2401–2425 of 6689 papers

TitleStatusHype
HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation—0
ForgetMe: Evaluating Selective Forgetting in Generative Models—0
SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models—0
Privacy Protection Against Personalized Text-to-Image Synthesis via Cross-image Consistency Constraints—0
Cobra: Efficient Line Art COlorization with BRoAder References—0
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis—0
Synthetic Data for Blood Vessel Network Extraction—0
Beyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering—0
Wavelet-based Variational Autoencoders for High-Resolution Image Generation—0
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction—0
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep LearningCode0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
ACE: Attentional Concept Erasure in Diffusion Models—0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale DatasetCode0
Seedream 3.0 Technical Report—0
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models—0
ADT: Tuning Diffusion Models with Adversarial Supervision—0
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era—0
Omni^2: Unifying Omnidirectional Image Generation and Editing in an Omni Model—0
Using LLMs as prompt modifier to avoid biases in AI image generators—0
Art3D: Training-Free 3D Generation from Flat-Colored Illustration—0
InstructEngine: Instruction-driven Text-to-Image Alignment—0
Towards Explainable Partial-AIGC Image Quality Assessment—0
seg2med: a bridge from artificial anatomy to multimodal medical images—0
Discriminator-Free Direct Preference Optimization for Video Diffusion—0
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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