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 901925 of 6689 papers

TitleStatusHype
Edge-preserving noise for diffusion modelsCode1
Data Extrapolation for Text-to-image Generation on Small DatasetsCode1
KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion ModelsCode1
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused SurveyCode1
Realistic Evaluation of Model Merging for Compositional GeneralizationCode1
Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI GenerationCode1
Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion ModelsCode1
Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image TranslationCode1
VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image ModelsCode1
Evaluating Image Hallucination in Text-to-Image Generation with Question-AnsweringCode1
MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistanceCode1
Robust image representations with counterfactual contrastive learningCode1
Finetuning CLIP to Reason about Pairwise DifferencesCode1
One-Shot Learning for Pose-Guided Person Image Synthesis in the WildCode1
Improving Virtual Try-On with Garment-focused Diffusion ModelsCode1
Click2Mask: Local Editing with Dynamic Mask GenerationCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
Scribble-Guided Diffusion for Training-free Text-to-Image GenerationCode1
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative RefinementCode1
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiesCode1
iSeg: An Iterative Refinement-based Framework for Training-free SegmentationCode1
Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image EditingCode1
HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM PromptsCode1
AdaNAT: Exploring Adaptive Policy for Token-Based Image GenerationCode1
STEREO: Towards Adversarially Robust Concept Erasing from Text-to-Image Generation ModelsCode1
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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