sd35-training-m-init
This is a LyCORIS adapter derived from stabilityai/stable-diffusion-3.5-medium.
The main validation prompt used during training was:
loona from helluva boss.
Validation settings
- CFG:
5.5
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
None
- Seed:
42
- Resolution:
1024x1024
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
The text encoder was not trained.
You may reuse the base model text encoder for inference.
Training settings
- 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:
{
"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
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'
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")