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