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 4351–4375 of 6689 papers

TitleStatusHype
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs—0
Hand-Object Interaction Image Generation—0
The Myth of Culturally Agnostic AI Models—0
Conditional Progressive Generative Adversarial Network for satellite image generation—0
Diffusion Probabilistic Model Made Slim—0
Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs—0
Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning—0
Efficient Video Prediction via Sparsely Conditioned Flow Matching—0
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background SegmentationCode0
SpaText: Spatio-Textual Representation for Controllable Image Generation—0
Unifying conditional and unconditional semantic image synthesis with OCO-GAN—0
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis from Monocular Image—0
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
More comprehensive facial inversion for more effective expression recognitionCode0
Shifted Diffusion for Text-to-image GenerationCode1
Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions—0
ReCo: Region-Controlled Text-to-Image Generation—0
CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields—0
Latent Video Diffusion Models for High-Fidelity Long Video GenerationCode2
TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape GenerationCode1
Inversion-Based Style Transfer with Diffusion ModelsCode2
Paint by Example: Exemplar-based Image Editing with Diffusion ModelsCode3
Rethinking Implicit Neural Representations for Vision Learners—0
Retrieval-Augmented Multimodal Language Modeling—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