---
license: other
base_model: "flux/unknown-model"
tags:
  - flux
  - flux-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - not-for-all-audiences
  - lora
  - template:sd-lora
  - lycoris
inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
- text: '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'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
- text: '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'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_0.png
- text: '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.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_3_0.png
- text: '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'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_4_0.png
---

# simpletuner-lora

This is a LyCORIS adapter derived from [flux/unknown-model](https://huggingface.co/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](#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: 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:
```json
{
    "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


```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 = '/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")
```