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Full Fine Tuning of FLUX yields way better results than LoRA training as expected, overfitting and bleeding reduced a lot
Full Fine Tuning of FLUX yields way better results than LoRA training as expected, overfitting and bleeding reduced a lot
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Configs and Full Experiments
Configs and Full Experiments
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Full configs and grid files shared here : 
Full configs and grid files shared here : 
link
https://www.patreon.com/posts/kohya-flux-fine-112099700
https://www.patreon.com/posts/kohya-flux-fine-112099700
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Details
Details
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I am still rigorously testing different hyperparameters and comparing impact of each one to find the best workflow
I am still rigorously testing different hyperparameters and comparing impact of each one to find the best workflow
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So far done 16 different full trainings and completing 8 more at the moment
So far done 16 different full trainings and completing 8 more at the moment
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I am using my poor overfit 15 images dataset for experimentation (4th image)
I am using my poor overfit 15 images dataset for experimentation (4th image)
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I have already proven that when I use a better dataset it becomes many times betters and generate expressions perfectly
I have already proven that when I use a better dataset it becomes many times betters and generate expressions perfectly
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Here example case : 
Here example case : 
link
https://www.reddit.com/r/FluxAI/comments/1ffz9uc/tried_expressions_with_flux_lora_training_with_my/
https://www.reddit.com/r/FluxAI/comments/1ffz9uc/tried_expressions_with_flux_lora_training_with_my/
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Conclusions
Conclusions
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When the results are analyzed, Fine Tuning is way lesser overfit and more generalized and better quality
When the results are analyzed, Fine Tuning is way lesser overfit and more generalized and better quality
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In first 2 images, it is able to change hair color and add beard much better, means lesser overfit
In first 2 images, it is able to change hair color and add beard much better, means lesser overfit
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In the third image, you will notice that the armor is much better, thus lesser overfit
In the third image, you will notice that the armor is much better, thus lesser overfit
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I noticed that the environment and clothings are much lesser overfit and better quality
I noticed that the environment and clothings are much lesser overfit and better quality
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Disadvantages
Disadvantages
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Kohya still doesn’t have FP8 training, thus 24 GB GPUs gets a huge speed drop
Kohya still doesn’t have FP8 training, thus 24 GB GPUs gets a huge speed drop
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Moreover, 48 GB GPUs has to use Fused Back Pass optimization, thus have some speed drop
Moreover, 48 GB GPUs has to use Fused Back Pass optimization, thus have some speed drop
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16 GB GPUs gets way more aggressive speed drop due to lack of FP8
16 GB GPUs gets way more aggressive speed drop due to lack of FP8
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Clip-L and T5 trainings still not supported
Clip-L and T5 trainings still not supported
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Speeds
Speeds
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Rank 1 Fast Config — uses 27.5 GB VRAM, 6.28 second / it (LoRA is 4.85 second / it)
Rank 1 Fast Config — uses 27.5 GB VRAM, 6.28 second / it (LoRA is 4.85 second / it)
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Rank 1 Slower Config — uses 23.1 GB VRAM, 14.12 second / it (LoRA is 4.85 second / it)
Rank 1 Slower Config — uses 23.1 GB VRAM, 14.12 second / it (LoRA is 4.85 second / it)
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Rank 1 Slowest Config — uses 15.5 GB VRAM, 39 second / it (LoRA is 6.05 second / it)
Rank 1 Slowest Config — uses 15.5 GB VRAM, 39 second / it (LoRA is 6.05 second / it)
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Final Info
Final Info
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Saved checkpoints are FP16 and thus 23.8 GB (no Clip-L or T5 trained)
Saved checkpoints are FP16 and thus 23.8 GB (no Clip-L or T5 trained)
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According to the Kohya, applied optimizations doesn’t change quality so all configs are ranked as Rank 1 at the moment
According to the Kohya, applied optimizations doesn’t change quality so all configs are ranked as Rank 1 at the moment
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I am still testing whether these optimizations make any impact on quality or not
I am still testing whether these optimizations make any impact on quality or not
Full Fine Tuning of FLUX yields way better results than LoRA training as expected, overfitting and bleeding reduced a lot

Configs and Full Experiments
Full configs and grid files shared here : https://www.patreon.com/posts/kohya-flux-fine-112099700

Details
I am still rigorously testing different hyperparameters and comparing impact of each one to find the best workflow
So far done 16 different full trainings and completing 8 more at the moment
I am using my poor overfit 15 images dataset for experimentation (4th image)
I have already proven that when I use a better dataset it becomes many times betters and generate expressions perfectly
Here example case : https://www.reddit.com/r/FluxAI/comments/1ffz9uc/tried_expressions_with_flux_lora_training_with_my/
Conclusions
When the results are analyzed, Fine Tuning is way lesser overfit and more generalized and better quality
In first 2 images, it is able to change hair color and add beard much better, means lesser overfit
In the third image, you will notice that the armor is much better, thus lesser overfit
I noticed that the environment and clothings are much lesser overfit and better quality
Disadvantages
Kohya still doesn’t have FP8 training, thus 24 GB GPUs gets a huge speed drop
Moreover, 48 GB GPUs has to use Fused Back Pass optimization, thus have some speed drop
16 GB GPUs gets way more aggressive speed drop due to lack of FP8
Clip-L and T5 trainings still not supported
Speeds
Rank 1 Fast Config — uses 27.5 GB VRAM, 6.28 second / it (LoRA is 4.85 second / it)
Rank 1 Slower Config — uses 23.1 GB VRAM, 14.12 second / it (LoRA is 4.85 second / it)
Rank 1 Slowest Config — uses 15.5 GB VRAM, 39 second / it (LoRA is 6.05 second / it)
Final Info
Saved checkpoints are FP16 and thus 23.8 GB (no Clip-L or T5 trained)
According to the Kohya, applied optimizations doesn’t change quality so all configs are ranked as Rank 1 at the moment
I am still testing whether these optimizations make any impact on quality or not
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