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 2101–2125 of 6689 papers

TitleStatusHype
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion TransformerCode1
Generate Your Own Scotland: Satellite Image Generation Conditioned on MapsCode1
Image Super-Resolution with Text Prompt DiffusionCode1
Generating Person Images with Appearance-aware Pose StylizerCode1
LidarCLIP or: How I Learned to Talk to Point CloudsCode1
Generative Adversarial Text to Image SynthesisCode1
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular ValuesCode1
Generative diffusion model with inverse renormalization group flowsCode1
Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework—0
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation—0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers—0
ComfyGI: Automatic Improvement of Image Generation Workflows—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation—0
Efficient Training with Denoised Neural Weights—0
Efficient training for future video generation based on hierarchical disentangled representation of latent variables—0
Combining Transformer Generators with Convolutional Discriminators—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
SkyDiffusion: Ground-to-Aerial Image Synthesis with Diffusion Models and BEV Paradigm—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation—0
Affordance Diffusion: Synthesizing Hand-Object Interactions—0
Efficient Quantization Strategies for Latent Diffusion Models—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion—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