Batch size 30 AdamW vs Batch Size 1 Adafactor SDXL Training Comparison

<p style="margin-left:0px;">I was hanging OneTrainer Discord yesterday and saw one of the very old and experienced user comment. He was saying AdamW is better than Adafactor. So I have asked his config which you can see here : <a href="https://gist.github.com/FurkanGozukara/5e9ee7d2b2070abb9a173dab342e1221" rel="nofollow"><u>https://gist.github.com/FurkanGozukara/5e9ee7d2b2070abb9a173dab342e1221</u></a></p>
<p style="margin-left:0px;">I have done my AdamW training with this config on RTX A6000 GPU on Massed Compute. Used batch size 30 since I had my regular 15 training images and every epoch 15 reg images. So every epoch was total 30 images thus in 1 step with batch size 30, it was able to train 1 epoch. It consumed 47 GB VRAM.</p>
<p style="margin-left:0px;">The experimental used training dataset is shared below — it is a bad dataset with purpose because if works on bad dataset it will work even better on a better dataset — machine learning general rule — Garbage in, garbage out.</p>
<p style="margin-left:0px;">Why this dataset is bad? It has repeating background, repeating clothes, lacking poses.</p>
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<source sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" srcset="https://miro.medium.com/v2/resize:fit:640/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/1*wbLAMUrM5iQWLa3YtOI3nA.png 1400w" type="image/webp"/>
<source sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" srcset="https://miro.medium.com/v2/resize:fit:640/1*wbLAMUrM5iQWLa3YtOI3nA.png 640w, https://miro.medium.com/v2/resize:fit:720/1*wbLAMUrM5iQWLa3YtOI3nA.png 720w, https://miro.medium.com/v2/resize:fit:750/1*wbLAMUrM5iQWLa3YtOI3nA.png 750w, https://miro.medium.com/v2/resize:fit:786/1*wbLAMUrM5iQWLa3YtOI3nA.png 786w, https://miro.medium.com/v2/resize:fit:828/1*wbLAMUrM5iQWLa3YtOI3nA.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*wbLAMUrM5iQWLa3YtOI3nA.png 1100w, https://miro.medium.com/v2/resize:fit:1400/1*wbLAMUrM5iQWLa3YtOI3nA.png 1400w"/><img alt="" class="image_resized" height="199" src="https://miro.medium.com/v2/resize:fit:1313/1*wbLAMUrM5iQWLa3YtOI3nA.png" style="height:auto;width:680px;" width="700"/>
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<p style="margin-left:0px;">Then I did same dataset and concepts training with my config shared in below post. The training name is default:</p>
<p style="margin-left:0px;"><a href="https://www.patreon.com/posts/96028218" rel="nofollow"><u>https://www.patreon.com/posts/96028218</u></a></p>
<p style="margin-left:0px;">If you are not my patreon supporter entire config shown in below tutorial video:</p>
<p style="margin-left:0px;"><a href="https://youtu.be/0t5l6CP9eBg" rel="nofollow"><u>https://youtu.be/0t5l6CP9eBg</u></a></p>
<p style="margin-left:auto;"> </p>
<p style="margin-left:0px;">Since AdamW uses batch size 30, I have trained it up to 750 epochs because bigger batch size = lower LR effect. Moreover it had lower LR than usual and didn’t train Text Encoder. Saved a checkpoint every 150 epochs. 1 step duration was 11.5 second on RTX A6000 on Massed Compute. So with 1 step it trained 1 epoch. Watch above tutorial you will understand 15 train images + 15 reg images.</p>
<p style="margin-left:0px;">With my best config shared on Patreon and also in above YouTube tutorial, I did up to 150 epochs. Saved a checkpoint every 30 epochs. 1 step was taking 2 second thus every epoch was taking 60 seconds on RTX A6000 on Massed Compute (slower than usual for some reason). So total training times of both training was almost equal.</p>
<p style="margin-left:0px;">AdamW uses more VRAM than Adafactor. Speed depends on using batch size, xformers and such. In my training i don’t use xformers and i do batch size 1. Because lower batch size = better quality. Thus if you need speed go up in batch size until you hit maximum speed and no-more.</p>
<p style="margin-left:0px;">So here comes the results. My results are far superior in resemblance. You can see the full grid comparison file in below link : <a href="https://huggingface.co/MonsterMMORPG/Generative-AI/resolve/main/AdamW_vs_Adafactor.png?download=true" rel="noopener noreferrer"><u>https://huggingface.co/MonsterMMORPG/Generative-AI/resolve/main/AdamW_vs_Adafactor.png</u></a></p>
<p style="margin-left:0px;">The grid file is 321 MB and 11458 px vs 22772 px resolution. It compares 20 special prompts + 1 generic prompt so that you can see if model is totally cooked or not.</p>
<p style="margin-left:0px;">Here below 4 grids of the above comparison as JPG.</p>
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<source sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" srcset="https://miro.medium.com/v2/resize:fit:640/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/1*WexV4iY-FesdZ__FsrlIKg.jpeg 1400w" type="image/webp"/>
<source sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" srcset="https://miro.medium.com/v2/resize:fit:640/1*WexV4iY-FesdZ__FsrlIKg.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/1*WexV4iY-FesdZ__FsrlIKg.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/1*WexV4iY-FesdZ__FsrlIKg.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/1*WexV4iY-FesdZ__FsrlIKg.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/1*WexV4iY-FesdZ__FsrlIKg.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/1*WexV4iY-FesdZ__FsrlIKg.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1400/1*WexV4iY-FesdZ__FsrlIKg.jpeg 1400w"/><img alt="" class="image_resized" height="81" src="https://miro.medium.com/v2/resize:fit:1313/1*WexV4iY-FesdZ__FsrlIKg.jpeg" style="height:auto;width:680px;" width="700"/>
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<p style="margin-left:0px;">After 450 epoch AdamW training became totally cooked and the resemblance is still way behind the Adafactor training. I am yet once again to find a better config than my researched config. I have done over 150 full trainings to find my best config :)</p>
<p style="margin-left:0px;">Adafactor normally a dynamic LR optimizer but I use it in a special static way. My config is very powerful and allows you to train more or less until becomes cooked or undertrained.</p>
<p style="margin-left:0px;">I am open to consultations as well. You can join our discord channel and message me or find me on LinkedIn.</p>
<p style="margin-left:0px;"><a href="https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Patreon-Posts-Index.md" rel="nofollow"><u>Patreon exclusive posts index</u></a> to find our scripts easily, <a href="https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Generative-AI-Updates-And-News.md" rel="nofollow"><u>Patreon scripts updates history</u></a> to see which updates arrived to which scripts and amazing <a href="https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Amazing-Generative-AI-Scripts.md" rel="nofollow"><u>Patreon special generative scripts list</u></a> that you can use in any of your task.</p>
<p style="margin-left:0px;">Join discord to get help, chat, discuss and also tell me your discord username to get your special rank : <a href="https://discord.com/servers/software-engineering-courses-secourses-772774097734074388" rel="nofollow"><u>SECourses Discord</u></a></p>
<p style="margin-left:0px;">Please also Star, Watch and Fork our <a href="https://github.com/FurkanGozukara/Stable-Diffusion" rel="nofollow"><u>Stable Diffusion &amp; Generative AI</u></a> GitHub repository and join our <a href="https://www.reddit.com/r/SECourses/" rel="nofollow"><u>Reddit subreddit</u></a> and follow me on <a href="https://www.linkedin.com/in/furkangozukara/" rel="nofollow"><u>LinkedIn </u></a>(my real profile)</p>
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