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 4051–4075 of 6689 papers

TitleStatusHype
Pixel-wise RL on Diffusion Models: Reinforcement Learning from Rich Feedback—0
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions—0
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space—0
3D-aware Image Generation and Editing with Multi-modal Conditions—0
PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models—0
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation—0
Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN—0
A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic Information—0
Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation—0
Plug-and-Play Context Feature Reuse for Efficient Masked Generation—0
Plug-and-Play Controllable Generation for Discrete Masked Models—0
Plug-and-Play Diffusion Distillation—0
A multi-channel cycleGAN for CBCT to CT synthesis—0
Plug-and-Play Interpretable Responsible Text-to-Image Generation via Dual-Space Multi-facet Concept Control—0
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics—0
Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation—0
POEM: Precise Object-level Editing via MLLM control—0
Poetry2Image: An Iterative Correction Framework for Images Generated from Chinese Classical Poetry—0
POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation—0
PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals—0
PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation—0
PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation—0
3D-Aware Generative Model for Improved Side-View Image Synthesis—0
Point-Driven Interactive Text and Image Layer Editing Using Diffusion Models—0
PointT2I: LLM-based text-to-image generation via keypoints—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