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 751775 of 6689 papers

TitleStatusHype
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs0
Diffusion Models without Classifier-free GuidanceCode2
Learning to Sample Effective and Diverse Prompts for Text-to-Image GenerationCode1
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond0
Multi-Faceted Multimodal Monosemanticity0
SWA-LDM: Toward Stealthy Watermarks for Latent Diffusion ModelsCode0
ManiTrend: Bridging Future Generation and Action Prediction with 3D Flow for Robotic Manipulation0
Designing a Conditional Prior Distribution for Flow-Based Generative Models0
Redistribute Ensemble Training for Mitigating Memorization in Diffusion ModelsCode0
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling0
When the LM misunderstood the human chuckled: Analyzing garden path effects in humans and language models0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation0
Detecting Malicious Concepts Without Image Generation in AIGC0
PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation0
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and ClassificationCode1
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation0
Ultrasound Image Generation using Latent Diffusion Models0
Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise RatioCode0
A Survey on Pre-Trained Diffusion Model Distillations0
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation0
ID-Cloak: Crafting Identity-Specific Cloaks Against Personalized Text-to-Image Generation0
SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion0
Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision0
FlexControl: Computation-Aware ControlNet with Differentiable Router for Text-to-Image GenerationCode0
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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