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 40514075 of 6689 papers

TitleStatusHype
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks0
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived DatasetCode0
Rethinking cluster-conditioned diffusion models for label-free image synthesisCode0
Disentangling representations of retinal images with generative modelsCode0
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras0
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models0
Block and Detail: Scaffolding Sketch-to-Image Generation0
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models0
Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System0
Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation0
Structure-Guided Adversarial Training of Diffusion Models0
CustomSketching: Sketch Concept Extraction for Sketch-based Image Synthesis and Editing0
T-HITL Effectively Addresses Problematic Associations in Image Generation and Maintains Overall Visual Quality0
Understanding Subjectivity through the Lens of Motivational Context in Model-Generated Image Satisfaction0
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion0
Multi-LoRA Composition for Image Generation0
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling0
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis0
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening0
Semantic Image Synthesis with Unconditional Generator0
Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models0
SDXL-Lightning: Progressive Adversarial Diffusion Distillation0
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image SynthesisCode0
Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention Control0
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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