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 3851–3900 of 6689 papers

TitleStatusHype
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery—0
Multi-objective evolutionary GAN for tabular data synthesisCode0
Zero-shot detection of buildings in mobile LiDAR using Language Vision Model—0
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model—0
ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image SynthesisCode0
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models—0
Modeling Emotions and Ethics with Large Language ModelsCode0
EdgeRelight360: Text-Conditioned 360-Degree HDR Image Generation for Real-Time On-Device Video Portrait Relighting—0
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers—0
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling—0
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives—0
Semantic Approach to Quantifying the Consistency of Diffusion Model Image GenerationCode0
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image SynthesisCode0
ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation—0
Implicit and Explicit Language Guidance for Diffusion-based Visual Perception—0
Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image Models -- Technical Challenges and Implications for Monitoring and Verification—0
Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model—0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
High Noise Scheduling is a Must—0
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific SegmentationCode0
Automatic Controllable Colorization via Imagination—0
Mask-ControlNet: Higher-Quality Image Generation with An Additional Mask Prompt—0
SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing—0
StyleForge: Enhancing Text-to-Image Synthesis for Any Artistic Styles with Dual Binding—0
Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI—0
UniFL: Improve Latent Diffusion Model via Unified Feedback Learning—0
Strictly-ID-Preserved and Controllable Accessory Advertising Image Generation—0
ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model—0
Contextual Chart Generation for Cyber Deception—0
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models—0
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D ImagesCode0
Pixel-wise RL on Diffusion Models: Reinforcement Learning from Rich Feedback—0
Would Deep Generative Models Amplify Bias in Future Models?—0
GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis—0
Reference-Based 3D-Aware Image Editing with Triplanes—0
Multi Positive Contrastive Learning with Pose-Consistent Generated Images—0
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification—0
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation—0
MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment—0
On the Scalability of Diffusion-based Text-to-Image Generation—0
Many-to-many Image Generation with Auto-regressive Diffusion Models—0
Heat Death of Generative Models in Closed-Loop Learning—0
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models—0
Real, fake and synthetic faces - does the coin have three sides?—0
Condition-Aware Neural Network for Controlled Image Generation—0
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation—0
Action Detection via an Image Diffusion Process—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