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 2251–2275 of 6689 papers

TitleStatusHype
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models—0
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation—0
DT2I: Dense Text-to-Image Generation from Region Descriptions—0
Articulate That Object Part (ATOP): 3D Part Articulation from Text and Motion Personalization—0
A Novel Evaluation Framework for Image2Text Generation—0
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision—0
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation—0
Opt-In Art: Learning Art Styles Only from Few Examples—0
CLIP Model for Images to Textual Prompts Based on Top-k Neighbors—0
Edit360: 2D Image Edits to 3D Assets from Any Angle—0
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving—0
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining—0
gCoRF: Generative Compositional Radiance Fields—0
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts—0
Dressing in the Wild by Watching Dance Videos—0
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion—0
DreamTuner: Single Image is Enough for Subject-Driven Generation—0
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation—0
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback—0
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models—0
CLIPAG: Towards Generator-Free Text-to-Image Generation—0
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time—0
DreamSparse: Escaping from Plato's Cave with 2D Frozen Diffusion Model Given Sparse Views—0
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling—0
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs—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