davidrd123/Caravaggio-QuarterCrops-Flux-LoKr

Caravaggio-QuarterCrops-Flux-LoKr

This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.

No validation prompt was used during training.

None

Validation settings

• —CFG: 3.0
• —CFG Rescale: 0.0
• —Steps: 20
• —Sampler: FlowMatchEulerDiscreteScheduler
• —Seed: 42
• —Resolution: 768x1408
• —Skip-layer guidance:

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery:

<Gallery />

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

• —Training epochs: 5
• —Training steps: 9000
• —Learning rate: 8e-05
• —Learning rate schedule: constant
• —Warmup steps: 100
• —Max grad norm: 0.1
• —Effective batch size: 3
• —Micro-batch size: 3
• —Gradient accumulation steps: 1
• —Number of GPUs: 1
• —Gradient checkpointing: True
• —Prediction type: flow-matching (extra parameters=['shift=3', 'fluxguidancemode=constant', 'fluxguidancevalue=1.0', 'flowmatchingloss=compatible'])
• —Optimizer: adamw_bf16
• —Trainable parameter precision: Pure BF16
• —Caption dropout probability: 10.0%

• —SageAttention: Enabled inference

LyCORIS Config:

{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 16,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 16
            },
            "FeedForward": {
                "factor": 8
            }
        }
    }
}

Datasets

caravaggio-512

• —Repeats: 22
• —Total number of images: 45
• —Total number of aspect buckets: 4
• —Resolution: 0.262144 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

caravaggio-768

• —Repeats: 22
• —Total number of images: 45
• —Total number of aspect buckets: 4
• —Resolution: 0.589824 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

caravaggio-1024

• —Repeats: 11
• —Total number of images: 45
• —Total number of aspect buckets: 12
• —Resolution: 1.048576 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

caravaggio-1536

• —Repeats: 5
• —Total number of images: 45
• —Total number of aspect buckets: 12
• —Resolution: 2.359296 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None
• —Used for regularisation data: No

caravaggio-512-crop

• —Repeats: 11
• —Total number of images: 45
• —Total number of aspect buckets: 1
• —Resolution: 0.262144 megapixels
• —Cropped: True
• —Crop style: random
• —Crop aspect: square
• —Used for regularisation data: No

caravaggio-768-crop

• —Repeats: 11
• —Total number of images: 45
• —Total number of aspect buckets: 1
• —Resolution: 0.589824 megapixels
• —Cropped: True
• —Crop style: random
• —Crop aspect: square
• —Used for regularisation data: No

caravaggio-1024-crop

• —Repeats: 5
• —Total number of images: 45
• —Total number of aspect buckets: 1
• —Resolution: 1.048576 megapixels
• —Cropped: True
• —Crop style: random
• —Crop aspect: square
• —Used for regularisation data: No

Inference

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 = 'davidrd123/Caravaggio-QuarterCrops-Flux-LoKr'
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(42),
    width=768,
    height=1408,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")