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 3101–3125 of 6689 papers

TitleStatusHype
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation—0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
GLASS: Guided Latent Slot Diffusion for Object-Centric Learning—0
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance—0
Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform—0
Illustrious: an Open Advanced Illustration Model—0
Grand Challenge of 106-Point Facial Landmark Localization—0
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data—0
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models—0
KNN-Diffusion: Image Generation via Large-Scale Retrieval—0
Image Aesthetic Reasoning: A New Benchmark for Medical Image Screening with MLLMs—0
GIU-GANs: Global Information Utilization for Generative Adversarial Networks—0
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation—0
Image Augmentations for GAN Training—0
GIST: Towards Photorealistic Style Transfer via Multiscale Geometric Representations—0
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection—0
Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models—0
Gibbs Sampling with People—0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs—0
Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networks—0
Beyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering—0
Image Compression with Product Quantized Masked Image Modeling—0
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray 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