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 6326–6350 of 6689 papers

TitleStatusHype
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability—0
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model—0
HeadRouter: A Training-free Image Editing Framework for MM-DiTs by Adaptively Routing Attention Heads—0
Voltage-Controlled Magnetoelectric Devices for Neuromorphic Diffusion Process—0
Heat Death of Generative Models in Closed-Loop Learning—0
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models—0
Heredity-aware Child Face Image Generation with Latent Space Disentanglement—0
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization—0
Heuristics for Image Generation from Scene Graphs—0
Autoencoding Video Latents for Adversarial Video Generation—0
Hide and Seek: How Does Watermarking Impact Face Recognition?—0
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models—0
Autoencoding Labeled Interpolator, Inferring Parameters From Image, And Image From Parameters—0
The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects—0
The Myth of Culturally Agnostic AI Models—0
Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation—0
The Neural Painter: Multi-Turn Image Generation—0
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models—0
Hierarchical Modes Exploring in Generative Adversarial Networks—0
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
Hierarchical Vision-Language Alignment for Text-to-Image Generation via Diffusion Models—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
High-Fidelity Diffusion-based Image Editing—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
High-Fidelity Guided Image Synthesis with Latent 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