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 5051–5075 of 6689 papers

TitleStatusHype
CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models—0
CustomSketching: Sketch Concept Extraction for Sketch-based Image Synthesis and Editing—0
CustomText: Customized Textual Image Generation using Diffusion Models—0
CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers—0
Anomaly Anything: Promptable Unseen Visual Anomaly Generation—0
Cut and Continuous Paste towards Real-time Deep Fall Detection—0
View Extrapolation of Human Body from a Single Image—0
Cycle-Consistent Inverse GAN for Text-to-Image Synthesis—0
CycleGAN Models for MRI Image Translation—0
CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection—0
Cycle Generative Adversarial Networks Algorithm With Style Transfer For Image Generation—0
Stochastic Conditional Generative Networks with Basis Decomposition—0
IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts—0
StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces—0
Stomach 3D Reconstruction Based on Virtual Chromoendoscopic Image Generation—0
"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection—0
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens—0
A Deep and Tractable Density Estimator—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
Storybooth: Training-free Multi-Subject Consistency for Improved Visual Storytelling—0
DAFT-GAN: Dual Affine Transformation Generative Adversarial Network for Text-Guided Image Inpainting—0
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection—0
DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake Texture Detection—0
DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks—0
DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images—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