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 5501–5550 of 6689 papers

TitleStatusHype
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer—0
Drag-guided diffusion models for vehicle image generation—0
DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving—0
Visual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI)—0
Synthesizing beta-amyloid PET images from T1-weighted Structural MRI: A Preliminary Study—0
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN—0
DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition—0
DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images—0
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models—0
DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter—0
Boosting Medical Image Synthesis via Registration-guided Consistency and Disentanglement Learning—0
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
DreamCache: Finetuning-Free Lightweight Personalized Image Generation via Feature Caching—0
DreamCom: Finetuning Text-guided Inpainting Model for Image Composition—0
DreamCube: 3D Panorama Generation via Multi-plane Synchronization—0
Boosting Few-Shot Detection with Large Language Models and Layout-to-Image Synthesis—0
DreamDrone: Text-to-Image Diffusion Models are Zero-shot Perpetual View Generators—0
DreamEdit: Subject-driven Image Editing—0
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
DreamHuman: Animatable 3D Avatars from Text—0
DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation—0
DreamOmni: Unified Image Generation and Editing—0
DreamPolish: Domain Score Distillation With Progressive Geometry Generation—0
DreamRelation: Bridging Customization and Relation Generation—0
DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models—0
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling—0
DreamSparse: Escaping from Plato's Cave with 2D Frozen Diffusion Model Given Sparse Views—0
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models—0
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback—0
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation—0
DreamTuner: Single Image is Enough for Subject-Driven Generation—0
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion—0
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts—0
Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation—0
Dressing in the Wild by Watching Dance Videos—0
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving—0
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation—0
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision—0
Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis—0
DT2I: Dense Text-to-Image Generation from Region Descriptions—0
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation—0
Dual Adversarial Inference for Text-to-Image Synthesis—0
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis—0
Book Cover Synthesis from the Summary—0
Dual Contradistinctive Generative Autoencoder—0
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis—0
BodyMetric: Evaluating the Realism of Human Bodies in Text-to-Image Generation—0
Synthesizing Photorealistic Images with Deep Generative Learning—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting—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