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 4901–4950 of 6689 papers

TitleStatusHype
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—0
Consistent Image Layout Editing with Diffusion Models—0
Constant Rate Schedule: Constant-Rate Distributional Change for Efficient Training and Sampling in Diffusion Models—0
Constrained Generative Adversarial Networks for Interactive Image Generation—0
Constrained Image Generation Using Binarized Neural Networks with Decision Procedures—0
Constructing Dreams using Generative AI—0
Stable Diffusion Exposed: Gender Bias from Prompt to Image—0
Content-Aware Preserving Image Generation—0
Content-Conditioned Generation of Stylized Free hand Sketches—0
ContentV: Efficient Training of Video Generation Models with Limited Compute—0
Context-Aware Autoregressive Models for Multi-Conditional Image Generation—0
Instance-Aware Image Completion—0
Stable Diffusion for Data Augmentation in COCO and Weed Datasets—0
Context Diffusion: In-Context Aware Image Generation—0
Contextual Chart Generation for Cyber Deception—0
Stable Diffusion is a Natural Cross-Modal Decoder for Layered AI-generated Image Compression—0
Video Perception Models for 3D Scene Synthesis—0
Stable Diffusion with Continuous-time Neural Network—0
Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach—0
StableGarment: Garment-Centric Generation via Stable Diffusion—0
Continuous Speech Synthesis using per-token Latent Diffusion—0
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning—0
Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction—0
ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet—0
Stable Messenger: Steganography for Message-Concealed Image Generation—0
ADIR: Adaptive Diffusion for Image Reconstruction—0
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
Contrastive Learning-Enhanced Trajectory Matching for Small-Scale Dataset Distillation—0
Stable Rivers: A Case Study in the Application of Text-to-Image Generative Models for Earth Sciences—0
Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models—0
Categorical EHR Imputation with Generative Adversarial Nets—0
Video Prediction by Modeling Videos as Continuous Multi-Dimensional Processes—0
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields—0
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models—0
Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion—0
Controllable Human Image Generation with Personalized Multi-Garments—0
Controllable Image Generation via Collage Representations—0
Controllable Image Generation With Composed Parallel Token Prediction—0
Controllable Image Synthesis of Industrial Data Using Stable Diffusion—0
Controllable Image Synthesis via SegVAE—0
StackGAN: Facial Image Generation Optimizations—0
Cross Modality Medical Image Synthesis for Improving Liver Segmentation—0
Staging E-Commerce Products for Online Advertising using Retrieval Assisted Image Generation—0
Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion—0
Controllable Radiance Fields for Dynamic Face Synthesis—0
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy—0
Controllable Text-to-Image Generation with GPT-4—0
ControlVAE: Controllable Variational Autoencoder—0
Controlled and Conditional Text to Image Generation with Diffusion Prior—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