Training epochs: 8
Training steps: 6140
Learning rate: 1.5e-05
- Learning rate schedule: polynomial
- Warmup steps: 600
Max grad norm: 0.01
Effective batch size: 6
- Micro-batch size: 3
- Gradient accumulation steps: 1
- Number of GPUs: 2
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['flux_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])
Optimizer: optimi-lionweight_decay=1e-3
Trainable parameter precision: Pure BF16
Caption dropout probability: 25.0%

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-FLUX-V05-512

Repeats: 2
Total number of images: ~86
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-FLUX-V05-768

Repeats: 2
Total number of images: ~86
Total number of aspect buckets: 3
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-MIX-FLUX-V05-512

Repeats: 2
Total number of images: ~106
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-FLUX-V05-768

Repeats: 2
Total number of images: ~110
Total number of aspect buckets: 1
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-NEW_ORIGINAL-FLUX-V05-512

Repeats: 5
Total number of images: ~140
Total number of aspect buckets: 6
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-NEW_ORIGINAL-FLUX-V05-768

Repeats: 5
Total number of images: ~140
Total number of aspect buckets: 1
Resolution: 0.589824 megapixels
Cropped: True
Crop style: random
Crop aspect: closest
Used for regularisation data: No

BESCH-NEW_ORIGINAL-FLUX-V05-1024

Repeats: 5
Total number of images: ~140
Total number of aspect buckets: 4
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 = 'black-forest-labs/FLUX.1-dev'
adapter_repo_id = 'gattaplayer/besch-flux-lokr-lion-1.5e-5-bs6-v05'
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."


## 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,
    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(1121925699670477),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")