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 351–375 of 6689 papers

TitleStatusHype
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens—0
Leveraging Generative AI Models to Explore Human Identity—0
Towards Explainable Fake Image Detection with Multi-Modal Large Language ModelsCode0
Cross-attention for State-based model RWKV-7—0
PRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
Towards NSFW-Free Text-to-Image Generation via Safety-Constraint Direct Preference Optimization—0
Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis—0
Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity—0
Point-Driven Interactive Text and Image Layer Editing Using Diffusion Models—0
Entropy Rectifying Guidance for Diffusion and Flow Models—0
MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection—0
SupResDiffGAN a new approach for the Super-Resolution taskCode1
U-Shape Mamba: State Space Model for faster diffusionCode1
Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing—0
Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion—0
POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation—0
Personalized Text-to-Image Generation with Auto-Regressive ModelsCode1
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
ForgetMe: Evaluating Selective Forgetting in Generative Models—0
Science-T2I: Addressing Scientific Illusions in Image Synthesis—0
ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation ModelsCode0
Enhancing Person-to-Person Virtual Try-On with Multi-Garment Virtual Try-OffCode2
HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation—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