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 2951–2975 of 6689 papers

TitleStatusHype
Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey—0
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model InferenceCode2
Color Agnostic Cross-Spectral Disparity EstimationCode0
VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and GenerationCode1
DreamDrone: Text-to-Image Diffusion Models are Zero-shot Perpetual View Generators—0
ArchiGuesser -- AI Art Architecture Educational GameCode0
VideoLCM: Video Latent Consistency Model—0
Local Conditional Controlling for Text-to-Image Diffusion Models—0
VaLID: Variable-Length Input Diffusion for Novel View Synthesis—0
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian SplattingCode2
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition TimeCode0
LIME: Localized Image Editing via Attention Regularization in Diffusion Models—0
PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion—0
Agent Attention: On the Integration of Softmax and Linear AttentionCode2
ZeroRF: Fast Sparse View 360° Reconstruction with Zero Pretraining—0
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation—0
FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection—0
Fast Sampling Through The Reuse Of Attention Maps In Diffusion Models—0
DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images—0
Diffusion-based Blind Text Image Super-ResolutionCode1
SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance—0
SpeedUpNet: A Plug-and-Play Adapter Network for Accelerating Text-to-Image Diffusion Models—0
The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven InitializationCode1
Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework—0
Clockwork Diffusion: Efficient Generation With Model-Step DistillationCode1
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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