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 4376–4400 of 6689 papers

TitleStatusHype
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image TranslationCode2
The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image GenerationCode1
Person Image Synthesis via Denoising Diffusion ModelCode2
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark—0
SinDiffusion: Learning a Diffusion Model from a Single Natural ImageCode2
DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter—0
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-TrainingCode1
SceneComposer: Any-Level Semantic Image Synthesis—0
TimbreCLIP: Connecting Timbre to Text and Images—0
Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images—0
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models—0
Exploring Discrete Diffusion Models for Image CaptioningCode1
IC3D: Image-Conditioned 3D Diffusion for Shape Generation—0
Single Stage Multi-Pose Virtual Try-On—0
Potential Auto-driving Threat: Universal Rain-removal Attack—0
GLAMI-1M: A Multilingual Image-Text Fashion DatasetCode1
UMFuse: Unified Multi View Fusion for Human Editing applications—0
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and GenerationCode2
Null-text Inversion for Editing Real Images using Guided Diffusion ModelsCode4
Conffusion: Confidence Intervals for Diffusion ModelsCode1
MAGE: MAsked Generative Encoder to Unify Representation Learning and Image SynthesisCode2
A Creative Industry Image Generation Dataset Based on Captions—0
Will Large-scale Generative Models Corrupt Future Datasets?Code0
Versatile Diffusion: Text, Images and Variations All in One Diffusion ModelCode6
Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models—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