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 4851–4900 of 6689 papers

TitleStatusHype
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models—0
Conceptwm: A Diffusion Model Watermark for Concept Protection—0
Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration—0
Conditional Adversarial Generative Flow for Controllable Image Synthesis—0
Conditional Adversarial Synthesis of 3D Facial Action Units—0
Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation—0
Squeezing Large-Scale Diffusion Models for Mobile—0
Conditional Diffusion Model for Longitudinal Medical Image Generation—0
Causal Future Prediction in a Minkowski Space-Time—0
VideoLCM: Video Latent Consistency Model—0
Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation—0
Conditional Generative Data-free Knowledge Distillation—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
VideoMAR: Autoregressive Video Generatio with Continuous Tokens—0
Conditional Image Generation with One-Vs-All Classifier—0
Conditional Image Generation with Pretrained Generative Model—0
SSGVS: Semantic Scene Graph-to-Video Synthesis—0
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation—0
Causal Disentanglement for Robust Long-tail Medical Image Generation—0
Conditional Invertible Neural Networks for Guided Image Generation—0
Conditional MoCoGAN for Zero-Shot Video Generation—0
IMAGE-ALCHEMY: Advancing subject fidelity in personalised text-to-image generation—0
Conditional Panoramic Image Generation via Masked Autoregressive Modeling—0
Conditional Progressive Generative Adversarial Network for satellite image generation—0
Conditional Score Guidance for Text-Driven Image-to-Image Translation—0
Conditional Spoken Digit Generation with StyleGAN—0
Conditional Stochastic Interpolation for Generative Learning—0
Conditional Synthetic Food Image Generation—0
Conditional Text Image Generation with Diffusion Models—0
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training—0
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM—0
Condition-Aware Neural Network for Controlled Image Generation—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
CATVis: Context-Aware Thought Visualization—0
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Connecting GANs, MFGs, and OT—0
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image—0
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis—0
Considering Race a Problem of Transfer Learning—0
CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization—0
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis—0
Consistency Posterior Sampling for Diverse Image Synthesis—0
Consistency Regularization for Generative Adversarial Networks—0
Category-based Galaxy Image Generation via Diffusion Models—0
ConsistentAvatar: Learning to Diffuse Fully Consistent Talking Head Avatar with Temporal Guidance—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