simpletuner-lora

This is a LyCORIS adapter derived from flux/unknown-model.

The main validation prompt used during training was:

The vehicle in the image is a luxury SUV with a bold and modern design. It features a large chrome-accented grille, sleek LED headlights, and sculpted body lines that enhance its aerodynamic look. The high ground clearance and large alloy wheels suggest off-road capability, while chrome trim and a panoramic sunroof add a touch of elegance. The rear is equipped with slim LED taillights and dual exhaust outlets, emphasizing both style and performance. | Length: 4663.0 mm | Width: 2050.0 mm | Height: 1145.0 mm | Wheelbase: 4000.0 mm

Validation settings

CFG: 3.0
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 1024x1024
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:

Prompt
unconditional (blank prompt)

Negative Prompt
blurry, cropped, ugly

Prompt
The vehicle in the image is a luxury SUV with a bold and modern design. It features a large chrome-accented grille, sleek LED headlights, and sculpted body lines that enhance its aerodynamic look. The high ground clearance and large alloy wheels suggest off-road capability, while chrome trim and a panoramic sunroof add a touch of elegance. The rear is equipped with slim LED taillights and dual exhaust outlets, emphasizing both style and performance. | Length: 3663.0 mm | Width: 2050.0 mm | Height: 1145.0 mm | Wheelbase: 3000.0 mm

Negative Prompt
blurry, cropped, ugly

Prompt
This image showcases the side of the luxury SUV, highlighting its bold and modern design. The sculpted body lines and high beltline create a dynamic and aerodynamic profile, while the large alloy wheels and high ground clearance emphasize its off-road capability. Chrome trim around the windows and a panoramic sunroof add a touch of sophistication. At the rear, the slim LED taillights extend towards the side, seamlessly integrating with the vehicle’s sleek silhouette. | Length: 4663.0 mm | Width: 2050.0 mm | Height: 1145.0 mm | Wheelbase: 4000.0 mm

Negative Prompt
blurry, cropped, ugly

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

Training settings

Training epochs: 0
Training steps: 1500
Learning rate: 0.0001
- Learning rate schedule: polynomial
- Warmup steps: 100
Max grad norm: 2.0
Effective batch size: 6
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 6
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 10.0%

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

cardatasets_w_wlh2

Repeats: 5
Total number of images: ~4746
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: True
Crop style: center
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 = '/data/shared_workspace/zgt/text2car_dataset/FLUX.1-dev'
adapter_repo_id = 'zhengzhou/simpletuner-lora'
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 = "The vehicle in the image is a luxury SUV with a bold and modern design. It features a large chrome-accented grille, sleek LED headlights, and sculpted body lines that enhance its aerodynamic look. The high ground clearance and large alloy wheels suggest off-road capability, while chrome trim and a panoramic sunroof add a touch of elegance. The rear is equipped with slim LED taillights and dual exhaust outlets, emphasizing both style and performance. | Length: 4663.0 mm | Width: 2050.0 mm | Height: 1145.0 mm | Wheelbase: 4000.0 mm"


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it 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=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")