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 4301–4350 of 6689 papers

TitleStatusHype
Generator Born from Classifier—0
FaceStudio: Put Your Face Everywhere in Seconds—0
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images—0
ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning—0
A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity MetricsCode0
InstructBooth: Instruction-following Personalized Text-to-Image Generation—0
A multi-channel cycleGAN for CBCT to CT synthesis—0
CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model—0
Stable Messenger: Steganography for Message-Concealed Image Generation—0
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models—0
Token Fusion: Bridging the Gap between Token Pruning and Token Merging—0
Generating Images of the M87* Black Hole Using GANsCode0
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models—0
Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Pipeline Enabling Zero-shot Classification for Bangla Handwritten Grapheme—0
Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation—0
DFU: scale-robust diffusion model for zero-shot super-resolution image generationCode0
HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation—0
Few-shot Image Generation via Style Adaptation and Content Preservation—0
Detailed Human-Centric Text Description-Driven Large Scene Synthesis—0
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation—0
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation—0
Anatomy and Physiology of Artificial Intelligence in PET Imaging—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
Diffusion Models Without Attention—0
ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs—0
S2ST: Image-to-Image Translation in the Seed Space of Latent Diffusion—0
Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery—0
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback—0
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models—0
Fair Text-to-Image Diffusion via Fair Mapping—0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop—0
A High-Quality Robust Diffusion Framework for Corrupted DatasetCode0
Unlocking Spatial Comprehension in Text-to-Image Diffusion Models—0
MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices—0
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering—0
Manifold Preserving Guided Diffusion—0
COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design—0
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—0
Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation—0
Check, Locate, Rectify: A Training-Free Layout Calibration System for Text-to-Image Generation—0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification—0
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation—0
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion—0
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation—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