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 1126–1150 of 6689 papers

TitleStatusHype
Multimodal Latent Language Modeling with Next-Token DiffusionCode0
Generative Modeling with Explicit MemoryCode0
CC-Diff: Enhancing Contextual Coherence in Remote Sensing Image Synthesis—0
Generative Zoo—0
LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural AnnotationsCode2
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion ModelsCode0
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models—0
Analyzing and Improving Model Collapse in Rectified Flow Models—0
Fast Prompt Alignment for Text-to-Image GenerationCode1
Learning Flow Fields in Attention for Controllable Person Image GenerationCode5
AsyncDSB: Schedule-Asynchronous Diffusion Schrödinger Bridge for Image Inpainting—0
Preference Adaptive and Sequential Text-to-Image Generation—0
CAP: Evaluation of Persuasive and Creative Image GenerationCode0
Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion ModelCode0
Buster: Implanting Semantic Backdoor into Text Encoder to Mitigate NSFW Content Generation—0
Moderating the Generalization of Score-based Generative Model—0
Fine-grained Text to Image Synthesis—0
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models—0
DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation—0
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics—0
A Generative Victim Model for Segmentation—0
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion TransformerCode1
Boosting Alignment for Post-Unlearning Text-to-Image Generative ModelsCode0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
Proactive Agents for Multi-Turn Text-to-Image Generation Under UncertaintyCode2
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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