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 6301–6325 of 6689 papers

TitleStatusHype
10,000+ Times Accelerated Robust Subset Selection (ARSS)—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
GVP: Generative Volumetric Primitives—0
HACA3: A Unified Approach for Multi-site MR Image Harmonization—0
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations—0
"Haet Bhasha aur Diskrimineshun": Phonetic Perturbations in Code-Mixed Hinglish to Red-Team LLMs—0
HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis—0
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion—0
HairNeRF: Geometry-Aware Image Synthesis for Hairstyle Transfer—0
Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships—0
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation—0
Hand1000: Generating Realistic Hands from Text with Only 1,000 Images—0
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances—0
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging—0
Hand-Object Interaction Image Generation—0
HanDrawer: Leveraging Spatial Information to Render Realistic Hands Using a Conditional Diffusion Model in Single Stage—0
Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models—0
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation—0
Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation—0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models—0
Harnessing Global-Local Collaborative Adversarial Perturbation for Anti-Customization—0
Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model—0
Voice command generation using Progressive Wavegans—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—0
Harvesting Visual Objects from Internet Images via Deep Learning Based Objectness Assessment—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