Full Training Tutorial and Guide and Research For a FLUX Style
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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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Post
2444
Detailed Comparison of JoyCaption Alpha One vs JoyCaption Pre-Alpha — 10 Different Style Amazing Images — I think JoyCaption Alpha One is the very best image captioning model at the moment for model training — Works very fast and requires as low as 8.5 GB VRAM
Where To Download And Install
You can download our APP from here : https://www.patreon.com/posts/110613301
1-Click to install on Windows, RunPod and Massed Compute
Official APP is here where you can try : fancyfeast/joy-caption-alpha-one
Have The Following Features
Auto downloads meta-llama/Meta-Llama-3.1–8B into your Hugging Face cache folder and other necessary models into the installation folder
Use 4-bit quantization — Uses 8.5 GB VRAM Total
Overwrite existing caption file
Append new caption to existing caption
Remove newlines from generated captions
Cut off at last complete sentence
Discard repeating sentences
Don’t save processed image
Caption Prefix
Caption Suffix
Custom System Prompt (Optional)
Input Folder for Batch Processing
Output Folder for Batch Processing (Optional)
Fully supported Multi GPU captioning — GPU IDs (comma-separated, e.g., 0,1,2)
Batch Size — Batch captioning
Where To Download And Install
You can download our APP from here : https://www.patreon.com/posts/110613301
1-Click to install on Windows, RunPod and Massed Compute
Official APP is here where you can try : fancyfeast/joy-caption-alpha-one
Have The Following Features
Auto downloads meta-llama/Meta-Llama-3.1–8B into your Hugging Face cache folder and other necessary models into the installation folder
Use 4-bit quantization — Uses 8.5 GB VRAM Total
Overwrite existing caption file
Append new caption to existing caption
Remove newlines from generated captions
Cut off at last complete sentence
Discard repeating sentences
Don’t save processed image
Caption Prefix
Caption Suffix
Custom System Prompt (Optional)
Input Folder for Batch Processing
Output Folder for Batch Processing (Optional)
Fully supported Multi GPU captioning — GPU IDs (comma-separated, e.g., 0,1,2)
Batch Size — Batch captioning
Post
2178
I have done an extensive multi-GPU FLUX Full Fine Tuning / DreamBooth training experimentation on RunPod by using 2x A100–80 GB GPUs (PCIe) since this was commonly asked of me.
Full article here : https://medium.com/@furkangozukara/multi-gpu-flux-fu
Image 1
Image 1 shows that only first part of installation of Kohya GUI took 30 minutes on a such powerful machine on a very expensive Secure Cloud pod — 3.28 USD per hour
There was also part 2, so just installation took super time
On Massed Compute, it would take like 2–3 minutes
This is why I suggest you to use Massed Compute over RunPod, RunPod machines have terrible hard disk speeds and they are like lottery to get good ones
Image 2, 3 and 4
Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training
https://www.patreon.com/posts/kohya-flux-fine-112099700
Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Image 3 shows VRAM usage of this config when doing 2x Multi GPU training
Image 4 shows the GPUs of the Pod
Image 5 and 6
Image 5 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training
https://www.patreon.com/posts/kohya-flux-fine-112099700
Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Image 6 shows this setup used VRAM amount
Image 7 and 8
Image 7 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training and Gradient Checkpointing is disabled
https://www.patreon.com/posts/kohya-flux-fine-112099700
Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Image 8 shows this setup used VRAM amount
....
Full article here : https://medium.com/@furkangozukara/multi-gpu-flux-fu
Image 1
Image 1 shows that only first part of installation of Kohya GUI took 30 minutes on a such powerful machine on a very expensive Secure Cloud pod — 3.28 USD per hour
There was also part 2, so just installation took super time
On Massed Compute, it would take like 2–3 minutes
This is why I suggest you to use Massed Compute over RunPod, RunPod machines have terrible hard disk speeds and they are like lottery to get good ones
Image 2, 3 and 4
Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training
https://www.patreon.com/posts/kohya-flux-fine-112099700
Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Image 3 shows VRAM usage of this config when doing 2x Multi GPU training
Image 4 shows the GPUs of the Pod
Image 5 and 6
Image 5 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training
https://www.patreon.com/posts/kohya-flux-fine-112099700
Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Image 6 shows this setup used VRAM amount
Image 7 and 8
Image 7 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training and Gradient Checkpointing is disabled
https://www.patreon.com/posts/kohya-flux-fine-112099700
Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Image 8 shows this setup used VRAM amount
....
models
63
MonsterMMORPG/256_Imgs_FLUX_Fine_Tuning_Trainings_Best
Updated
MonsterMMORPG/Big_FLUX_Fine_Tuning_Experiments
Updated
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1
MonsterMMORPG/SECourses_SUPIR
Updated
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665
MonsterMMORPG/Patreon_SECourses_ROCKs
Updated
MonsterMMORPG/SECourses_Patreon_Rocks
Updated
MonsterMMORPG/CapFiles
Updated
MonsterMMORPG/Generative-AI
Updated
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3
MonsterMMORPG/mich
Updated
MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests
Updated
MonsterMMORPG/Stable-Diffusion
Updated
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5