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 5101–5125 of 6689 papers

TitleStatusHype
DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model—0
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks—0
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations—0
Decentralized Learning of Generative Adversarial Networks from Non-iid Data—0
GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks—0
Decoder-Only LLMs are Better Controllers for Diffusion Models—0
Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts—0
The Nuts and Bolts of Adopting Transformer in GANs—0
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas—0
Decomposed evaluations of geographic disparities in text-to-image models—0
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition—0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networks—0
Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models—0
Adding Additional Control to One-Step Diffusion with Joint Distribution Matching—0
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection—0
Streamlining Image Editing with Layered Diffusion Brushes—0
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
AdaWCT: Adaptive Whitening and Coloring Style Injection—0
An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion—0
Decoupling Training-Free Guided Diffusion by ADMM—0
DEEP ADVERSARIAL FORWARD MODEL—0
Deep Algorithm Unrolling for Biomedical Imaging—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—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