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 27512775 of 6689 papers

TitleStatusHype
Variational Schrödinger Momentum Diffusion0
DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model0
RelightVid: Temporal-Consistent Diffusion Model for Video Relighting0
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation0
MetaDecorator: Generating Immersive Virtual Tours through Multimodality0
Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation0
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual PromptingCode0
StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces0
SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity0
Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest0
"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy0
PAID: A Framework of Product-Centric Advertising Image Design0
Training-Free Style and Content Transfer by Leveraging U-Net Skip Connections in Stable Diffusion 2.*0
Fully Guided Neural Schrödinger bridge for Brain MR image synthesis0
Towards Scalable Topological Regularizers0
LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention MapsCode0
PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics0
Binary Diffusion Probabilistic Model0
IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image ModelsCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation0
MSF: Efficient Diffusion Model Via Multi-Scale Latent Factorize0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback0
Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation0
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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