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 326–350 of 6689 papers

TitleStatusHype
PixelHacker: Image Inpainting with Structural and Semantic ConsistencyCode3
Efficient Listener: Dyadic Facial Motion Synthesis via Action Diffusion—0
A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks—0
Inception: Jailbreak the Memory Mechanism of Text-to-Image Generation Systems—0
Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future DirectionsCode2
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models—0
DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition—0
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation—0
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models—0
Fast Autoregressive Models for Continuous Latent Generation—0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition—0
FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model—0
ePBR: Extended PBR Materials in Image Synthesis—0
Distilling semantically aware orders for autoregressive image generation—0
UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing—0
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image GenerationCode1
Emergence and Evolution of Interpretable Concepts in Diffusion Models—0
Twin Co-Adaptive Dialogue for Progressive Image Generation—0
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation—0
Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration—0
TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models—0
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale—0
Causal Disentanglement for Robust Long-tail Medical Image Generation—0
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens—0
REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-to-Image Diffusion Models—0
Show:102550
← PrevPage 14 of 268Next →

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