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 2351–2375 of 6689 papers

TitleStatusHype
Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control—0
A Dataset and Model for Realistic License Plate DeblurringCode1
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation—0
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis—0
LTOS: Layout-controllable Text-Object Synthesis via Adaptive Cross-attention Fusions—0
Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models—0
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated ImagesCode1
DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving—0
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy—0
DensePANet: An improved generative adversarial network for photoacoustic tomography image reconstruction from sparse data—0
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion ModellingCode0
How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples—0
Generative Modelling with High-Order Langevin Dynamics—0
TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image GenerationCode1
EdgeFusion: On-Device Text-to-Image Generation—0
LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights—0
Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans—0
Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT Synthesis for Head and Neck Proton Treatment Planning—0
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications—0
Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
On the Scalability of GNNs for Molecular Graphs—0
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing PansharpeningCode1
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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