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 326350 of 6689 papers

TitleStatusHype
PixelHacker: Image Inpainting with Structural and Semantic ConsistencyCode3
Efficient Listener: Dyadic Facial Motion Synthesis via Action Diffusion0
A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks0
Inception: Jailbreak the Memory Mechanism of Text-to-Image Generation Systems0
Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future DirectionsCode2
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models0
DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition0
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation0
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models0
Fast Autoregressive Models for Continuous Latent Generation0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition0
FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model0
ePBR: Extended PBR Materials in Image Synthesis0
Distilling semantically aware orders for autoregressive image generation0
UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing0
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image GenerationCode1
Emergence and Evolution of Interpretable Concepts in Diffusion Models0
Twin Co-Adaptive Dialogue for Progressive Image Generation0
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation0
Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration0
TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models0
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale0
Causal Disentanglement for Robust Long-tail Medical Image Generation0
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens0
REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-to-Image Diffusion Models0
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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