This is an automated archive made by the Lemmit Bot.
The original was posted on /r/stablediffusion by /u/terminusresearchorg on 2024-10-24 21:33:58+00:00.
We used industry-standard dataset to train SD 3.5 and quantify its trainability on a single concept, 1boy.
full guide:
example model:
huggingface:
Hardware; 3x 4090
Training time, a cpl hours
Config:
- Learning rate: 1e-05
- Number of images: 15
- Max grad norm: 0.01
- Effective batch size: 3
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 3
- Optimizer: optimi-lion
- Precision: Pure BF16
- Quantised: No
Total used was about 18GB VRAM over the whole run. with int8-quanto it comes down to like 11gb needed.
LyCORIS config:
{
"bypass_mode": true,
"algo": "lokr",
"multiplier": 1.0,
"full_matrix": true,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 12,
"apply_preset": {
"target_module": [
"Attention"
],
"module_algo_map": {
"Attention": {
"factor": 6
}
}
}
}
See hugging face hub link for more config info.