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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4376–4400 of 6689 papers

TitleStatusHype
Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory—0
A Generative Victim Model for Segmentation—0
Retrieval-Augmented Multimodal Language Modeling—0
XDLM: Cross-lingual Diffusion Language Model for Machine Translation—0
Retrieval-guided Cross-view Image Synthesis—0
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval—0
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data—0
Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data—0
UrbanGenAI: Reconstructing Urban Landscapes using Panoptic Segmentation and Diffusion Models—0
Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation—0
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare—0
X&Fuse: Fusing Visual Information in Text-to-Image Generation—0
Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis—0
Zero-shot Text-guided Infinite Image Synthesis with LLM guidance—0
Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward—0
Reward Guided Latent Consistency Distillation—0
Reward Incremental Learning in Text-to-Image Generation—0
RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images—0
RGBX: Image decomposition and synthesis using material- and lighting-aware diffusion models—0
Urban Scene Diffusion through Semantic Occupancy Map—0
Rhetorical Text-to-Image Generation via Two-layer Diffusion Policy Optimization—0
1.58-bit FLUX—0
Riemannian Diffusion Models—0
RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge—0
RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection—0
Show:102550
← PrevPage 176 of 268Next →
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