
This is a somewhat successful attempt to create an asari model for Stable Diffusion with Dreambooth. It is not generally useable.
I leave my notes here in case it helps someone get one step closer to generating Mass Effect stuff.

## asari3: Dreambooth with 53 samples

![sample for 3_2_8100](sample_32_01.png)
![sample for 3_3_13500](sample_33_01.png)

Tokens trained: asari, purple, blue, lightblue, green, teal, armor

The number starts on 3 because early experiments were met with absolute failure, which made me seek help on the SD discord where
I got some great advice from [Guizmus (link to HF page)](https://huggingface.co/Guizmus) and this is the result of
putting that into practice.

The sample set is mostly in-game screenshots with minimal labeling. The games are not great at face, clothing and
armor variety, which leads to overfitting facial features and not shaking off clothing/armor details in the token.
No class/regularization images were used.

* asari3_2_8100: 150 epochs at LR 1E-6
* asari3_3_8100: 150 epochs at LR 5E-7
* asari3_3_13500: above + 100 epochs at LR 1E-7 

Some possible improvements
* The ME: Legendary Edition games has a visual issue with asari eyes, which does show in the results. Maybe find a mod or graphics settings that reduce that and redo parts of the dataset.
* Label the images' clothes to mitigate the dataset's poor variety.
* Try to work in regularization again. It doesn't have too much bleed-over aside from some peculiar haircuts, so this might not be very helpful.
* Expand the dataset with realistic-style fanart.

### asari3_4: Simplifying it
This was a training done with only the asari label on every image. This is the same 9 pictures generated with the models starting from 
the least trained (45 epochs) until the last increment of 45 that was possible before the overtraining artifacts set in.

There is a loss of color control: markings aren't easy to colorize by prompt, and multi-color faces are more common.

![training montage](training_montage.jpg)

## Notes

This is *not* related to the Asari Diffusion LoRa model on CivitAI.
{"license": "creativeml-openrail-m"}
license:creativeml-openrail-m region:us
TAGS
#license-creativeml-openrail-m #region-us 


This is a somewhat successful attempt to create an asari model for Stable Diffusion with Dreambooth. It is not generally useable.
I leave my notes here in case it helps someone get one step closer to generating Mass Effect stuff.

## asari3: Dreambooth with 53 samples

!sample for 3_2_8100
!sample for 3_3_13500

Tokens trained: asari, purple, blue, lightblue, green, teal, armor

The number starts on 3 because early experiments were met with absolute failure, which made me seek help on the SD discord where
I got some great advice from Guizmus (link to HF page) and this is the result of
putting that into practice.

The sample set is mostly in-game screenshots with minimal labeling. The games are not great at face, clothing and
armor variety, which leads to overfitting facial features and not shaking off clothing/armor details in the token.
No class/regularization images were used.

* asari3_2_8100: 150 epochs at LR 1E-6
* asari3_3_8100: 150 epochs at LR 5E-7
* asari3_3_13500: above + 100 epochs at LR 1E-7 

Some possible improvements
* The ME: Legendary Edition games has a visual issue with asari eyes, which does show in the results. Maybe find a mod or graphics settings that reduce that and redo parts of the dataset.
* Label the images' clothes to mitigate the dataset's poor variety.
* Try to work in regularization again. It doesn't have too much bleed-over aside from some peculiar haircuts, so this might not be very helpful.
* Expand the dataset with realistic-style fanart.

### asari3_4: Simplifying it
This was a training done with only the asari label on every image. This is the same 9 pictures generated with the models starting from 
the least trained (45 epochs) until the last increment of 45 that was possible before the overtraining artifacts set in.

There is a loss of color control: markings aren't easy to colorize by prompt, and multi-color faces are more common.

!training montage

## Notes

This is *not* related to the Asari Diffusion LoRa model on CivitAI.
## asari3: Dreambooth with 53 samples

!sample for 3_2_8100
!sample for 3_3_13500

Tokens trained: asari, purple, blue, lightblue, green, teal, armor

The number starts on 3 because early experiments were met with absolute failure, which made me seek help on the SD discord where
I got some great advice from Guizmus (link to HF page) and this is the result of
putting that into practice.

The sample set is mostly in-game screenshots with minimal labeling. The games are not great at face, clothing and
armor variety, which leads to overfitting facial features and not shaking off clothing/armor details in the token.
No class/regularization images were used.

* asari3_2_8100: 150 epochs at LR 1E-6
* asari3_3_8100: 150 epochs at LR 5E-7
* asari3_3_13500: above + 100 epochs at LR 1E-7 

Some possible improvements
* The ME: Legendary Edition games has a visual issue with asari eyes, which does show in the results. Maybe find a mod or graphics settings that reduce that and redo parts of the dataset.
* Label the images' clothes to mitigate the dataset's poor variety.
* Try to work in regularization again. It doesn't have too much bleed-over aside from some peculiar haircuts, so this might not be very helpful.
* Expand the dataset with realistic-style fanart. ### asari3_4: Simplifying it
This was a training done with only the asari label on every image. This is the same 9 pictures generated with the models starting from 
the least trained (45 epochs) until the last increment of 45 that was possible before the overtraining artifacts set in.

There is a loss of color control: markings aren't easy to colorize by prompt, and multi-color faces are more common.

!training montage ## Notes

This is *not* related to the Asari Diffusion LoRa model on CivitAI.
TAGS
#license-creativeml-openrail-m #region-us 
 ## asari3: Dreambooth with 53 samples

!sample for 3_2_8100
!sample for 3_3_13500

Tokens trained: asari, purple, blue, lightblue, green, teal, armor

The number starts on 3 because early experiments were met with absolute failure, which made me seek help on the SD discord where
I got some great advice from Guizmus (link to HF page) and this is the result of
putting that into practice.

The sample set is mostly in-game screenshots with minimal labeling. The games are not great at face, clothing and
armor variety, which leads to overfitting facial features and not shaking off clothing/armor details in the token.
No class/regularization images were used.

* asari3_2_8100: 150 epochs at LR 1E-6
* asari3_3_8100: 150 epochs at LR 5E-7
* asari3_3_13500: above + 100 epochs at LR 1E-7 

Some possible improvements
* The ME: Legendary Edition games has a visual issue with asari eyes, which does show in the results. Maybe find a mod or graphics settings that reduce that and redo parts of the dataset.
* Label the images' clothes to mitigate the dataset's poor variety.
* Try to work in regularization again. It doesn't have too much bleed-over aside from some peculiar haircuts, so this might not be very helpful.
* Expand the dataset with realistic-style fanart. ### asari3_4: Simplifying it
This was a training done with only the asari label on every image. This is the same 9 pictures generated with the models starting from 
the least trained (45 epochs) until the last increment of 45 that was possible before the overtraining artifacts set in.

There is a loss of color control: markings aren't easy to colorize by prompt, and multi-color faces are more common.

!training montage ## Notes

This is *not* related to the Asari Diffusion LoRa model on CivitAI.
TAGS
#license-creativeml-openrail-m #region-us 
## asari3: Dreambooth with 53 samples

!sample for 3_2_8100
!sample for 3_3_13500

Tokens trained: asari, purple, blue, lightblue, green, teal, armor

The number starts on 3 because early experiments were met with absolute failure, which made me seek help on the SD discord where
I got some great advice from Guizmus (link to HF page) and this is the result of
putting that into practice.

The sample set is mostly in-game screenshots with minimal labeling. The games are not great at face, clothing and
armor variety, which leads to overfitting facial features and not shaking off clothing/armor details in the token.
No class/regularization images were used.

* asari3_2_8100: 150 epochs at LR 1E-6
* asari3_3_8100: 150 epochs at LR 5E-7
* asari3_3_13500: above + 100 epochs at LR 1E-7 

Some possible improvements
* The ME: Legendary Edition games has a visual issue with asari eyes, which does show in the results. Maybe find a mod or graphics settings that reduce that and redo parts of the dataset.
* Label the images' clothes to mitigate the dataset's poor variety.
* Try to work in regularization again. It doesn't have too much bleed-over aside from some peculiar haircuts, so this might not be very helpful.
* Expand the dataset with realistic-style fanart.### asari3_4: Simplifying it
This was a training done with only the asari label on every image. This is the same 9 pictures generated with the models starting from 
the least trained (45 epochs) until the last increment of 45 that was possible before the overtraining artifacts set in.

There is a loss of color control: markings aren't easy to colorize by prompt, and multi-color faces are more common.

!training montage## Notes

This is *not* related to the Asari Diffusion LoRa model on CivitAI.