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Accueil
SOTA
Génération d'images à partir du texte
Text To Image Generation On Cub
Text To Image Generation On Cub
Métriques
FID
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
FID
Paper Title
Repository
TLDM
6.72
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
-
Attention-driven Generator (perceptual loss)
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Controllable Text-to-Image Generation
-
MirrorGAN
-
MirrorGAN: Learning Text-to-image Generation by Redescription
-
GALIP
10.08
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis
-
GAWWN
67.22
Learning What and Where to Draw
-
AttnGAN
-
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN+CL
16.34
Improving Text-to-Image Synthesis Using Contrastive Learning
-
VQ-Diffusion-F
10.32
Vector Quantized Diffusion Model for Text-to-Image Synthesis
-
Lafite
10.48
LAFITE: Towards Language-Free Training for Text-to-Image Generation
-
StackGAN-v2
15.3
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
-
Swinv2-Imagen
9.78
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation
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VQ-Diffusion-S
12.97
Vector Quantized Diffusion Model for Text-to-Image Synthesis
-
StackGAN-v1
51.89
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
-
DM-GAN
-
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
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RAT-Diffusion
6.36
Data Extrapolation for Text-to-image Generation on Small Datasets
-
StackGAN
-
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
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VQ-Diffusion-B
11.94
Vector Quantized Diffusion Model for Text-to-Image Synthesis
-
RAT-GAN
10.21
Recurrent Affine Transformation for Text-to-image Synthesis
-
DM-GAN+CL
14.38
Improving Text-to-Image Synthesis Using Contrastive Learning
-
DF-GAN
-
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
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0 of 20 row(s) selected.
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