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

Showing 41764200 of 6689 papers

TitleStatusHype
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning0
Learning Diffusion Texture Priors for Image Restoration0
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts0
Vector Graphics Generation via Mutually Impulsed Dual-domain Diffusion0
AnyScene: Customized Image Synthesis with Composited Foreground0
MAGICK: A Large-scale Captioned Dataset from Matting Generated Images using Chroma Keying0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
Countering Personalized Text-to-Image Generation with Influence Watermarks0
One-dimensional Adapter to Rule Them All: Concepts Diffusion Models and Erasing Applications0
ZeroRF: Fast Sparse View 360deg Reconstruction with Zero Pretraining0
Taming Stable Diffusion for Text to 360 Panorama Image Generation0
TextNeRF: A Novel Scene-Text Image Synthesis Method based on Neural Radiance FieldsCode0
Check Locate Rectify: A Training-Free Layout Calibration System for Text-to-Image Generation0
Text-conditional Attribute Alignment across Latent Spaces for 3D Controllable Face Image Synthesis0
DiffMorph: Text-less Image Morphing with Diffusion Models0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation0
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution0
Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect DetectionCode0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
GazeCLIP: Towards Enhancing Gaze Estimation via Text Guidance0
CycleGAN Models for MRI Image Translation0
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion0
RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and Detail Accuracy through Hierarchical Transformers and Progressive Refinement0
Prompt Expansion for Adaptive Text-to-Image Generation0
A Recipe for Scaling up Text-to-Video Generation with Text-free Videos0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified