Training epochs: 17
Training steps: 10840
Learning rate: 2.5e-05
- Learning rate schedule: polynomial
- Warmup steps: 1084
Max grad norm: 0.01
Effective batch size: 6
- Micro-batch size: 6
- Gradient accumulation steps: 1
- Number of GPUs: 1
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['flux_schedule_auto_shift', 'shift=0.0', 'flux_use_uniform_schedule'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 10.0%

LyCORIS Config:

json

{
    "bypass_mode": true,
    "algo": "lokr",
    "multiplier": 1.0,
    "full_matrix": true,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 4,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "FeedForward": {
                "factor": 4
            },
            "Attention": {
                "factor": 2
            }
        }
    }
}

Datasets

BESCH-CROP-SD35M-V04-512

Repeats: 1
Total number of images: 99
Total number of aspect buckets: 3
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-CROP-SD35M-V04-768

Repeats: 1
Total number of images: 98
Total number of aspect buckets: 1
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-CROP-SD35M-V04-1024

Repeats: 1
Total number of images: 99
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-CROP-SD35M-V04-1280

Repeats: 1
Total number of images: 99
Total number of aspect buckets: 1
Resolution: 1.6384 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-MIX-SD35M-V04-512

Repeats: 1
Total number of images: 93
Total number of aspect buckets: 3
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-MIX-SD35M-V04-768

Repeats: 1
Total number of images: 90
Total number of aspect buckets: 8
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-MIX-SD35M-V04-1024

Repeats: 1
Total number of images: 88
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-MIX-SD35M-V04-1280

Repeats: 1
Total number of images: 85
Total number of aspect buckets: 8
Resolution: 1.6384 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-COVER-SD35M-V04-512

Repeats: 2
Total number of images: 86
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-COVER-SD35M-V04-768

Repeats: 2
Total number of images: 83
Total number of aspect buckets: 6
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-COVER-SD35M-V04-1024

Repeats: 2
Total number of images: 81
Total number of aspect buckets: 3
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-COVER-SD35M-V04-1280

Repeats: 2
Total number of images: 83
Total number of aspect buckets: 1
Resolution: 1.6384 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-COVER-SD35M-V04-1536

Repeats: 2
Total number of images: 83
Total number of aspect buckets: 1
Resolution: 2.359296 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-ORIGINAL-SD35M-V04-512

Repeats: 1
Total number of images: 68
Total number of aspect buckets: 2
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-ORIGINAL-SD35M-V04-768

Repeats: 1
Total number of images: 68
Total number of aspect buckets: 2
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-ORIGINAL-SD35M-V04-1024

Repeats: 1
Total number of images: 68
Total number of aspect buckets: 3
Resolution: 1.048576 megapixels
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


def download_adapter(repo_id: str):
    import os
    from huggingface_hub import hf_hub_download
    adapter_filename = "pytorch_lora_weights.safetensors"
    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
    os.makedirs(path_to_adapter, exist_ok=True)
    hf_hub_download(
        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
    )

    return path_to_adapter_file
    
model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_repo_id = 'gattaplayer/besch-style-st-sd35m-lokr-25e-6-bs6-v04'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()

prompt = "An astronaut is riding a horse through the jungles of Thailand."
negative_prompt = 'blurry, cropped, ugly'

## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
from optimum.quanto import quantize, freeze, qint8
quantize(pipeline.transformer, weights=qint8)
freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1121925699670477),
    width=832,
    height=1216,
    guidance_scale=4.0,
    skip_guidance_layers=[7, 8, 9],
).images[0]
image.save("output.png", format="PNG")