|
--- |
|
license: creativeml-openrail-m |
|
--- |
|
|
|
### Trigger words |
|
|
|
``` |
|
Anisphia, Euphyllia, Tilty, OyamaMahiro, OyamaMihari |
|
by onono imoko, by momoko, by mochizuki kei, by kantoku, by ke-ta |
|
aniscreen, fanart |
|
``` |
|
|
|
For `0324_all_aniscreen_tags`, I accidentally tag all the character images with `aniscreen`. |
|
For `0325_aniscreen_fanart_styles`, things are done correctly (anime screenshots tagged as `aniscreen`, fanart tagged as `fanart`). |
|
|
|
|
|
### Settings |
|
|
|
Default settings are |
|
- loha net dim 8, conv dim 4, alpha 1 |
|
- lr 2e-4 constant scheduler throuout |
|
- Adam8bit |
|
- resolution 512 |
|
- clip skip 1 |
|
|
|
Names of the files suggest how the setting is changed with respect to this default setup. |
|
The configuration json files can otherwsie be found in the `config` subdirectories that lies in each folder. |
|
However, some experiments concern the effect of tags for which I regenerate the txt file and the difference can not be seen from the configuration file in this case. |
|
For now this concerns `05tag` for which tags are only used with probability 0.5. |
|
|
|
### Some observations |
|
|
|
For a thorough comparaison please refer to the `generated_samples` folder. |
|
|
|
#### Captioning |
|
|
|
Dataset, in general, is the most important out of all. |
|
The common wisdom that we should prune anything that we want to be attach to the trigger word is exactly the way to go for. |
|
No tags at all is terrible, especially for style training. |
|
Having all the tags remove the traits from subjects if these tags are not used during sampling (not completely true but more or less the case). |
|
|
|
 |
|
|
|
|
|
#### Others |
|
|
|
1. I barely see any difference for training at clip skip 1 and 2. |
|
2. Setting text encoder learning rate to be half of that of unet makes training two times slower while I cannot see how it helps. |
|
3. The difference between lora, locon, and loha are very subtle. |
|
4. Training at higher resolution helps generating more complex backgrounds etc, but it is very time-consuming and most of the time it isn't worth it (simpler to just switch base model) unless this is exactly the goal of the lora you're training. |
|
|
|
### Datasets |
|
|
|
Here is the composition of the datasets |
|
``` |
|
17_characters~fanart~OyamaMihari: 53 |
|
19_characters~fanart~OyamaMahiro+OyamaMihari: 47 |
|
1_artists~kantoku: 2190 |
|
24_characters~fanart~Anisphia: 37 |
|
28_characters~screenshots~Anisphia+Tilty: 24 |
|
2_artists~ke-ta: 738 |
|
2_artists~momoko: 762 |
|
2_characters~screenshots~Euphyllia: 235 |
|
3_characters~fanart~OyamaMahiro: 299 |
|
3_characters~screenshots~Anisphia: 217 |
|
3_characters~screenshots~OyamaMahiro: 210 |
|
3_characters~screenshots~OyamaMahiro+OyamaMihari: 199 |
|
3_characters~screenshots~OyamaMihari: 177 |
|
4_characters~screenshots~Anisphia+Euphyllia: 165 |
|
57_characters~fanart~Euphyllia: 16 |
|
5_artists~mochizuki_kei: 426 |
|
5_artists~onono_imoko: 373 |
|
7_characters~screenshots~Tilty: 95 |
|
9_characters~fanart~Anisphia+Euphyllia: 97 |
|
``` |
