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 23762400 of 6689 papers

TitleStatusHype
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance0
Adversarial Identity Injection for Semantic Face Image Synthesis0
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery0
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation?Code1
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model0
Modeling Emotions and Ethics with Large Language ModelsCode0
EdgeRelight360: Text-Conditioned 360-Degree HDR Image Generation for Real-Time On-Device Video Portrait Relighting0
Multi-objective evolutionary GAN for tabular data synthesisCode0
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers0
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models0
ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image SynthesisCode0
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models0
Zero-shot detection of buildings in mobile LiDAR using Language Vision Model0
Magic Clothing: Controllable Garment-Driven Image SynthesisCode5
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation0
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language ModelsCode4
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling0
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives0
Semantic Approach to Quantifying the Consistency of Diffusion Model Image GenerationCode0
Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model0
Latent Guard: a Safety Framework for Text-to-image GenerationCode2
Taming Stable Diffusion for Text to 360° Panorama Image GenerationCode3
Implicit and Explicit Language Guidance for Diffusion-based Visual Perception0
ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-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