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 5551–5575 of 6689 papers

TitleStatusHype
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation—0
Synthesizing Photorealistic Virtual Humans Through Cross-modal Disentanglement—0
Dual Supervised Learning—0
DualVAE: Controlling Colours of Generated and Real Images—0
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion—0
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—0
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations—0
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection—0
DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation—0
Dynamically Grown Generative Adversarial Networks—0
DynamicControl: Adaptive Condition Selection for Improved Text-to-Image Generation—0
BodyGAN: General-Purpose Controllable Neural Human Body Generation—0
Dynamic Dual-Output Diffusion Models—0
DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability—0
Dynamic Neural Style Transfer for Artistic Image Generation using VGG19—0
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce—0
Blur, Noise, and Compression Robust Generative Adversarial Networks—0
Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model—0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
Synthetic Convolutional Features for Improved Semantic Segmentation—0
E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors—0
Synthetic CT Generation from MRI Using Improved DualGAN—0
Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing—0
EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer—0
EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation—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