Full Training Tutorial and Guide and Research For a FLUX Style
•
5
Furkan Gözükara
MonsterMMORPG
AI & ML interests
Check out my youtube page SECourses for Stable Diffusion tutorials. They will help you tremendously in every topic
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MonsterMMORPG/YoloModels
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1 day ago
MonsterMMORPG/Fix_FLUX_Bleeding_Experiments_with_DOP
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MonsterMMORPG's activity
replied to
their
post
4 days ago
@wikeeyang awesome. and thanks a lot for comment
reacted to
their
post with 🤯🤝👍🧠➕😎🤗❤️👀🚀🔥
4 days ago
Post
2316
Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training - check attached images in full size to see full details
You can download all configs and full instructions
> https://www.patreon.com/posts/112099700 - Fine Tuning post
> https://www.patreon.com/posts/110879657 - LoRA post
Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.
Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training
You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.
You can download all configs and full instructions > https://www.patreon.com/posts/112099700
The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute
The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute
You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates
This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1
Key thing to reduce VRAM usage is using block swap
Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well
Now you can do FP16 training with LoRAs on 24 GB and below GPUs
Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)
It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)
You can download all configs and full instructions
> https://www.patreon.com/posts/112099700 - Fine Tuning post
> https://www.patreon.com/posts/110879657 - LoRA post
Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.
Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training
You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.
You can download all configs and full instructions > https://www.patreon.com/posts/112099700
The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute
The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute
You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates
This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1
Key thing to reduce VRAM usage is using block swap
Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well
Now you can do FP16 training with LoRAs on 24 GB and below GPUs
Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)
It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)
posted
an
update
4 days ago
Post
2316
Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training - check attached images in full size to see full details
You can download all configs and full instructions
> https://www.patreon.com/posts/112099700 - Fine Tuning post
> https://www.patreon.com/posts/110879657 - LoRA post
Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.
Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training
You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.
You can download all configs and full instructions > https://www.patreon.com/posts/112099700
The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute
The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute
You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates
This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1
Key thing to reduce VRAM usage is using block swap
Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well
Now you can do FP16 training with LoRAs on 24 GB and below GPUs
Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)
It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)
You can download all configs and full instructions
> https://www.patreon.com/posts/112099700 - Fine Tuning post
> https://www.patreon.com/posts/110879657 - LoRA post
Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.
Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training
You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.
You can download all configs and full instructions > https://www.patreon.com/posts/112099700
The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute
The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute
You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates
This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1
Key thing to reduce VRAM usage is using block swap
Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well
Now you can do FP16 training with LoRAs on 24 GB and below GPUs
Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)
It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)
reacted to
maxiw's
post with 🤗🚀👍🔥❤️
8 days ago
Post
4489
I was curious to see what people post here on HF so I created a dataset with all HF Posts:
maxiw/hf-posts
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
Some interesting stats:
Top 5 Authors by Total Impressions:
-----------------------------------
@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)
Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions
Top 5 Most Used Reactions:
-------------------------
❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
-
- 9 replies
Wow I am surprised i am on the list :D