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 2751–2775 of 6689 papers

TitleStatusHype
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion ModelsCode3
ColorSwap: A Color and Word Order Dataset for Multimodal EvaluationCode1
ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12—0
Noise Map Guidance: Inversion with Spatial Context for Real Image EditingCode1
Text2Street: Controllable Text-to-image Generation for Street Views—0
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!Code0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective FinetuningCode2
Training-Free Consistent Text-to-Image GenerationCode2
Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?—0
InstanceDiffusion: Instance-level Control for Image GenerationCode4
IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR imagesCode1
M^3Face: A Unified Multi-Modal Multilingual Framework for Human Face Generation and Editing—0
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image EditingCode4
Separable Multi-Concept Erasure from Diffusion Models—0
Diffusion Cross-domain RecommendationCode1
Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples—0
Variational Quantum Circuits Enhanced Generative Adversarial Network—0
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models—0
Neural Language of Thought Models—0
On the Multi-modal Vulnerability of Diffusion ModelsCode1
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Can MLLMs Perform Text-to-Image In-Context Learning?Code1
Cross-view Masked Diffusion Transformers for Person Image SynthesisCode2
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic DataCode0
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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