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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

ICML 2022 · Image generation › Text-to-image diffusion · approach: Compositional text-to-image guidance · 989 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use classifier-free guidance instead of CLIP guidanceGLIDE text-to-image sampling · MS-COCO validation prompts 256×256photorealism Elo: -73.2 -> 82.7Helped
Use classifier-free guidance instead of CLIP guidanceGLIDE text-to-image sampling · MS-COCO validation prompts 256×256caption Elo: 29.3 -> 110.9Helped
Add classifier-free guidance (ablation)GLIDE · MS-COCO promptsphotorealism Elo: -23.9 -> 133.0Helped
Add classifier-free guidance (ablation)GLIDE · MS-COCO promptscaption Elo: -27.1 -> 140.5Helped
Add classifier-free guidance (ablation)GLIDE small · MS-COCO promptsphotorealism Elo: -131.8 -> 28.2Helped
Add classifier-free guidance (ablation)GLIDE small · MS-COCO promptscaption Elo: -136.4 -> 70.9Helped
Evaluate on a validation subset purged of images similar to training imagesGLIDE · MS-COCO 256×256 validationzero-shot FID: 12.24 -> 12.89Hurt
Scale the model from 300M to 3.5B parameters (ablation)GLIDE with classifier-free guidance · MS-COCO promptsphotorealism Elo: 28.2 -> 133.0Helped
Use classifier-free guidance rather than increasing model size (ablation)GLIDE · MS-COCO promptsphotorealism Elo: -23.9 -> 133.0Helped

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