Training epochs: 11
Training steps: 20000
Learning rate: 1e-06
Max grad norm: 0.01
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
Prediction type: flow-matching
Rescaled betas zero SNR: False
Optimizer: adamw_bf16
Precision: Pure BF16
Quantised: Yes: int8-quanto
Xformers: Not used
LyCORIS Config:

json

{
    "bypass_mode": true,
    "algo": "lokr",
    "multiplier": 1.0,
    "full_matrix": true,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 12,
    "apply_preset": {
        "target_module": [
            "JointTransformerBlock"
        ],
        "module_algo_map": {
            "FeedForward": {
                "factor": 1
            },
            "JointTransformerBlock": {
                "factor": 2
            }
        }
    }
}

Datasets

default_dataset_art

Repeats: 0
Total number of images: 1805
Total number of aspect buckets: 374
Resolution: 2048 px
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights

model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()

prompt = "loona from helluva boss."
negative_prompt = ''
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1024,
    height=1024,
    guidance_scale=5.5,
).images[0]
image.save("output.png", format="PNG")

jimmycarter/sd35-training-m-init

sd35-training-m-init

Validation settings

Training settings

Datasets

default_dataset_art

Inference

jimmycarter/
sd35-training-m-init