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 3901–3925 of 6689 papers

TitleStatusHype
Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human MattingCode0
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data—0
Model-Agnostic Human Preference Inversion in Diffusion Models—0
Convergence of Continuous Normalizing Flows for Learning Probability Distributions—0
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation—0
CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned NormalizationCode0
MaGRITTe: Manipulative and Generative 3D Realization from Image, Topview and Text—0
Grid Diffusion Models for Text-to-Video Generation—0
Dependability Evaluation of Stable Diffusion with Soft Errors on the Model Parameters—0
FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models—0
FairRAG: Fair Human Generation via Fair Retrieval Augmentation—0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation—0
Frame by Familiar Frame: Understanding Replication in Video Diffusion Models—0
GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models—0
Synthetic Medical Imaging Generation with Generative Adversarial Networks For Plain Radiographs—0
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models—0
Detecting Origin Attribution for Text-to-Image Diffusion ModelsCode0
Imperceptible Protection against Style Imitation from Diffusion Models—0
QNCD: Quantization Noise Correction for Diffusion ModelsCode0
Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative ModelsCode0
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models—0
CPR: Retrieval Augmented Generation for Copyright Protection—0
Conditional Wasserstein Distances with Applications in Bayesian OT Flow MatchingCode0
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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