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.