Just another shitty experiments.

Trained on: illustriousXL_v01;

Artist: gaku.

Recommendations:

• str: ~0.8;

• cfg: 5;

• sampler: euler a, 28 steps;

• hires fix: 1.5x, 14 steps, 0.4 denoise;

• trigger word: gaku artstyle;

• have a bias/overtrain/bad-captions towards sweat and hair intakes so add it in the negative for scene that don't need them.

The idea is to put higher rank on q and v (in this case 128 rank) and train all layers possible ("module type table: {'LoConModule': 1058, 'NormModule': 256}" reported by kohyass) with lower rank, then using rs_lora to scale the different in rank.

Settings:

dim 4, alpha 1, rs_lora=True, train_norm=true,

target name:

enable_conv = true

unet_target_module = [
]
unet_target_name = [
  "^.*to.(q|v).*$",
  "^.*to.(k|out).*$",
  "^.*ff.*$",
  "^.*proj.(in|out).*$",
  "^(?!.*(proj.(in|out)|attn|ff)).*$",
]
text_encoder_target_module = [
]
text_encoder_target_name = [
  "^.*(q|v).proj.*$",
  "^.*(k|out).proj.*$",
  "^.*mlp.*$",
]

[name_algo_map]
  [name_algo_map."^.*to.(q|v).*$"]
    algo = "lora"
    dim = 128
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true
  
  [name_algo_map."^.*to.(k|out).*$"]
    algo = "lora"
    dim = 16
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true
	
  [name_algo_map."^.*ff.*$"]
    algo = "lora"
    dim = 8
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true
  
  [name_algo_map."^.*proj.(in|out).*$"]
    algo = "lora"
    dim = 4
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true
  
  [name_algo_map."^(?!.*(proj.(in|out)|attn|ff)).*$"]
    algo = "lora"
    dim = 4
    alpha = 1
    rs_lora = true
  
  [name_algo_map."^.*(q|v).proj.*$"]
    algo = "lora"
    dim = 128
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true
  
  [name_algo_map."^.*(k|out).proj.*$"]
    algo = "lora"
    dim = 16
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true
  
  [name_algo_map."^.*mlp.*$"]
    algo = "lora"
    dim = 8
    alpha = 1
    rs_lora = true
    dora_wd = true
    wd_on_output = true