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 2176–2200 of 6689 papers

TitleStatusHype
EdgeFusion: On-Device Text-to-Image Generation—0
Edge-Cloud Routing for Text-to-Image Model with Token-Level Multi-Metric Prediction—0
COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design—0
FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation—0
E-Commerce Inpainting with Mask Guidance in Controlnet for Reducing Overcompletion—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Coherent Zero-Shot Visual Instruction Generation—0
ECNet: Effective Controllable Text-to-Image Diffusion Models—0
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation—0
From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
From Noise to Nuance: Advances in Deep Generative Image Models—0
FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation—0
EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing—0
Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection—0
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models—0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation—0
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM—0
EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation—0
1.58-bit FLUX—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer—0
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation—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