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

Showing 11511175 of 6689 papers

TitleStatusHype
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image GenerationCode1
PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image GenerationCode1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision TasksCode1
Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image SynthesisCode1
Tell2Design: A Dataset for Language-Guided Floor Plan GenerationCode1
Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person ImagesCode1
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion ModelsCode1
ViT-Lens: Towards Omni-modal RepresentationsCode1
PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated ImagesCode1
Self-correcting LLM-controlled Diffusion ModelsCode1
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language ModelsCode1
BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray ImagesCode1
Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person RetrievalCode1
InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style AdviserCode1
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual NoiseCode1
Image Super-Resolution with Text Prompt DiffusionCode1
Paragraph-to-Image Generation with Information-Enriched Diffusion ModelCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
AutoStory: Generating Diverse Storytelling Images with Minimal Human EffortCode1
High-fidelity Person-centric Subject-to-Image SynthesisCode1
Scene Text Image Super-resolution based on Text-conditional Diffusion ModelsCode1
MAM-E: Mammographic synthetic image generation with diffusion modelsCode1
Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without NoiseCode1
Plum: Prompt Learning using MetaheuristicCode1
UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified