Text To Image Generation On Cub

Métriques

FID

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
TLDM	6.72	Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
Attention-driven Generator (perceptual loss)	-	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
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
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
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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Text To Image Generation On Cub

Métriques

Résultats