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 3751–3775 of 6689 papers

TitleStatusHype
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
Cones 2: Customizable Image Synthesis with Multiple SubjectsCode2
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Evaluating the feasibility of using Generative Models to generate Chest X-Ray DataCode1
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
DualVAE: Controlling Colours of Generated and Real Images—0
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
Towards Accurate Post-training Quantization for Diffusion ModelsCode1
Translation-Enhanced Multilingual Text-to-Image Generation—0
Nested Diffusion Processes for Anytime Image GenerationCode1
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection—0
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold RobustnessCode0
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models—0
Alteration-free and Model-agnostic Origin Attribution of Generated Images—0
Controllable Text-to-Image Generation with GPT-4—0
BRICS: Bi-level feature Representation of Image CollectionS—0
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-DenoisingCode2
TaleCrafter: Interactive Story Visualization with Multiple CharactersCode2
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion PathsCode0
Conditional Score Guidance for Text-Driven Image-to-Image Translation—0
Alignment-free HDR Deghosting with Semantics Consistent Transformer—0
Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?—0
Text-to-image Editing by Image Information Removal—0
Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion InferenceCode2
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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