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 276–300 of 6689 papers

TitleStatusHype
Consistency Models Made EasyCode3
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion ModelsCode3
DDT: Decoupled Diffusion TransformerCode3
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
MultiDiffusion: Fusing Diffusion Paths for Controlled Image GenerationCode3
REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion TransformersCode3
Collaborative Neural Rendering using Anime Character SheetsCode2
Collaborative Decoding Makes Visual Auto-Regressive Modeling EfficientCode2
GrounDiT: Grounding Diffusion Transformers via Noisy Patch TransplantationCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept MatchingCode2
GRPose: Learning Graph Relations for Human Image Generation with Pose PriorsCode2
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
GPT4Point: A Unified Framework for Point-Language Understanding and GenerationCode2
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement LearningCode2
GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned PolicyCode2
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
CogView: Mastering Text-to-Image Generation via TransformersCode2
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech SynthesisCode2
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixCode2
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
GeoSynth: Contextually-Aware High-Resolution Satellite Image SynthesisCode2
Bayesian Flow NetworksCode2
Adversarial Supervision Makes Layout-to-Image Diffusion Models ThriveCode2
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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