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StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

ICML 2023 · Image generation › Text-to-image diffusion · approach: Text-driven synthesis and semantic guidance · 61 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
redesign generator: StyleGAN2 backbone, residual convs, stronger conditioning (ablation)StyleGAN-XL text baseline · Zero-shot MS COCOFID and CLIP score: ~10% improvement in FID and CLIP scoreHelped
replace StyleGAN-XL discriminator with ViT-S DINO feature network and five minimalist heads (ablation)StyleGAN-T · Zero-shot MS COCOFID and CLIP score: further ~40% improvementHelped
add CLIP guidance loss during training (weight 0.2) (ablation)StyleGAN-T · Zero-shot MS COCOFID and CLIP score: further ~20% improvementHelped
fine-tune the text encoder in a secondary phase with frozen generator and CLIP guidance weight 50 (ablation)StyleGAN-T · Zero-shot MS COCOCLIP scoreHelped
use explicit truncation at inference (ablation)StyleGAN-T · Zero-shot MS COCOCLIP score: CLIP score pushed to 0.305Helped
overly strong CLIP guidance weight during primary training (ablation)StyleGAN-T · Zero-shot MS COCOFIDHurt
use 1D convolutions on tokens instead of 2D convolutions in discriminator heads (ablation)StyleGAN-T discriminator · Zero-shot MS COCOFIDNo clear effect
drop StyleGAN3 equivariance, use StyleGAN2 backbone (ablation)StyleGAN-T · Zero-shot MS COCOFIDHelped
increase generator depth with residual convs and layer scaleStyleGAN-T generator · training stabilitymode collapseHelped

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