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 526550 of 6689 papers

TitleStatusHype
Boosting Resolution Generalization of Diffusion Transformers with Randomized Positional Encodings0
Equivariant Image ModelingCode1
An Image-like Diffusion Method for Human-Object Interaction Detection0
DeLoRA: Decoupling Angles and Strength in Low-rank AdaptationCode1
TransAnimate: Taming Layer Diffusion to Generate RGBA Video0
Unseen from Seen: Rewriting Observation-Instruction Using Foundation Models for Augmenting Vision-Language NavigationCode1
Adoption of Watermarking Measures for AI-Generated Content and Implications under the EU AI Act0
TCFG: Tangential Damping Classifier-free Guidance0
Efficient Diffusion Training through Parallelization with Truncated Karhunen-Loève Expansion0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis0
TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation0
OMR-Diffusion:Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Intent Understanding0
FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation0
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation0
End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile RobotsCode0
Halton Scheduler For Masked Generative Image TransformerCode3
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens0
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content0
Zero-Shot Styled Text Image Generation, but Make It Autoregressive0
Leveraging Text-to-Image Generation for Handling Spurious Correlation0
EDiT: Efficient Diffusion Transformers with Linear Compressed Attention0
World Knowledge from AI Image Generation for Robot Control0
InfiniteYou: Flexible Photo Recrafting While Preserving Your IdentityCode7
Tokenize Image as a SetCode2
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation 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