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

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Showing 201–225 of 6689 papers

TitleStatusHype
MRI Image Generation Based on Text Prompts—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of NanoparticlesCode0
Taming Diffusion for Dataset Distillation with High RepresentativenessCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPOCode4
TensorAR: Refinement is All You Need in Autoregressive Image Generation—0
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment—0
FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-designCode0
Conditional Panoramic Image Generation via Masked Autoregressive Modeling—0
Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation—0
Forward-only Diffusion Probabilistic ModelsCode1
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement LearningCode2
Incorporating Visual Correspondence into Diffusion Model for Virtual Try-OnCode1
Creatively Upscaling Images with Global-Regional Priors—0
Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than ExtrapolationCode1
PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals—0
Generative AI for Autonomous Driving: A Review—0
Scaling Diffusion Transformers Efficiently via μPCode2
IA-T2I: Internet-Augmented Text-to-Image Generation—0
Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation—0
MMaDA: Multimodal Large Diffusion Language ModelsCode0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion—0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

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