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Spread your wings - freedom
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fireYtail
fireYtail's Gallery (18)

Stable Diffusion: all samplers (direct comparison)

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Spread your wings - freedom
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Stable Diffusion: all samplers (direct comparison)
Nvidia GTX 1080ti Founders Edition (stock clock)
[/u]


Average generation time (for 100 steps)

Fast samplers, high resolution fix off: 56.26 seconds
Fast samplers, high resolution fix on: 77.30 seconds
Slow samplers, high resolution fix off: 110.03 seconds
Slow samplers, high resolution fix on: 113.95 seconds


NMKD Stable Diffusion GUI 1.7.1[/u]


Prompt:

a dragon, wings, scales, claws, majestic, proud, powerful, strong, big, beautiful, friendly, happy, playful, shadows, shading, reflections, art, Unreal Engine 4, Unreal Engine 5, digital, fantasy, fiction, fictional, depth, impressive, wallpaper, PNG, HDR, RTX, ray tracing, high quality, intrincate details, highly detailed, high resolution, HD, full HD, quad HD, ultra HD, 4K, 8K


Negative:

deformed, person, man, woman, pet, sleeping, ugly, weak, depression, depressing, anxiety, anxious, bored, boring, disgust, disgusting, horrible, trash, shit, shitty, weird, simple, generic, low budget, sketch, stock, DVD, CRT, grayscale, film grain, camera lens, 2D, analogic, gif, giphy, flat, blur, blurry, unfocused, distorsion, distorted, noise, low quality, low resolution


Image: none
Concept: none
Generation Steps: rows
Prompt Guidance (CFG Scale) 10.5
Seed: 24
Resolution: 576x576
High-Resolution Fix: No (but times are listed)
Sampler: columns
Generate Seamless (Tileable) Images: Disabled

Model: v1.5 full size CKPT (7.17 GB)
Run Upscaling: No
Run Face Restoration: No

Keywords
dragon 146,584, wings 29,904, claws 21,370, horns 20,993, ai generated 19,035, fire 11,520, ai 6,167, stable diffusion 4,366, test 1,207, comparison 606, testing 255
Details
Type: Picture Series
Published: 2 years ago
Rating: General

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AutoSnep
2 years ago
I think going past 50 steps is largely pointless with most samplers, especially the ancestral ones (they're designed to converge slowly, so you're getting random changes with the same image quality) and DPM++ variations (they're designed to produce meaningful output at 10 and converge around 25).

How did you measure time?
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