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 2651–2675 of 6689 papers

TitleStatusHype
MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic AlignmentCode0
LumiPath -- Towards Real-time Physically-based Rendering on Embedded DevicesCode0
Mode Seeking Generative Adversarial Networks for Diverse Image SynthesisCode0
Cross-Domain Adversarial Auto-EncoderCode0
Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image GenerationCode0
Loss-Sensitive Generative Adversarial Networks on Lipschitz DensitiesCode0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
Longitudinal Causal Image SynthesisCode0
LR-GAN: Layered Recursive Generative Adversarial Networks for Image GenerationCode0
FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-designCode0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
FP4DiT: Towards Effective Floating Point Quantization for Diffusion TransformersCode0
LoFT: LoRA-fused Training Dataset Generation with Few-shot GuidanceCode0
LOGAN: Latent Optimisation for Generative Adversarial NetworksCode0
FoREST: Frame of Reference Evaluation in Spatial Reasoning TasksCode0
Forensic Iris Image SynthesisCode0
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and QuantizationCode0
Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit AssignmentCode0
Cyclic image generation using chaotic dynamicsCode0
LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention MapsCode0
Mask and Restore: Blind Backdoor Defense at Test Time with Masked AutoencoderCode0
Follow the Flow: On Information Flow Across Textual Tokens in Text-to-Image ModelsCode0
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image SynthesisCode0
Leveraging GAN Priors for Few-Shot Part SegmentationCode0
Covid-19 chest x-ray image generation using resnet-dcgan modelCode0
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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