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 3176–3200 of 6689 papers

TitleStatusHype
Mitigating Exposure Bias in Discriminator Guided Diffusion Models—0
Behavior Optimized Image Generation—0
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models—0
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression—0
High-fidelity Person-centric Subject-to-Image SynthesisCode1
Enhancing Object Coherence in Layout-to-Image SynthesisCode0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
The Chosen One: Consistent Characters in Text-to-Image Diffusion ModelsCode2
MAM-E: Mammographic synthetic image generation with diffusion modelsCode1
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
Scene Text Image Super-resolution based on Text-conditional Diffusion ModelsCode1
Privacy Threats in Stable Diffusion Models—0
Single-Image 3D Human Digitization with Shape-Guided Diffusion—0
UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANsCode1
Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale—0
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR ImagesCode0
Finding AI-Generated Faces in the Wild—0
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation—0
Plum: Prompt Learning using MetaheuristicCode1
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion—0
How do Minimum-Norm Shallow Denoisers Look in Function Space?—0
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis—0
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
ChatAnything: Facetime Chat with LLM-Enhanced Personas—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