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mouceen/Mapperatorinator-v30-LoRA-Arles-v1
Mapperatorinator-v30-LoRA-Arles-v1 is a machine learning model from mouceen. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This is a LoRA (Low-Rank Adaptation) fine-tune for the OliBomby/Mapperatorinator-v30 model. It has been trained on a custom dataset of beatmaps to generate maps in the "Arles" style.
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Updated Sep 26, 2025
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From the Hugging Face model README
This is a LoRA (Low-Rank Adaptation) fine-tune for the OliBomby/Mapperatorinator-v30 model. It has been trained on a custom dataset of beatmaps to generate maps in the "Arles" style.
OliBomby/Mapperatorinator-v30You can use this LoRA in two ways: by downloading the files locally or by loading it directly from the Hugging Face Hub. Best to use on songs that are hybrid bursts/short streams and jumps
Download and Structure Files: Download the adapter_config.json and adapter_model.safetensors files from this repository. Create a folder and place the files inside like this:
arles_lora/
├── adapter_config.json
└── adapter_model.safetensors
Run Inference: Use the lora_path argument to point to the local folder you created.
python inference.py \
audio_path='/path/to/your/audio.mp3' \
output_path='/path/to/your/output_folder' \
lora_path='/path/to/your/arles_lora' \
... # other arguments
You can load the LoRA directly from this repository without manually downloading the files. The script will handle it automatically.
lora_path argument and set it to the name of this repository.
python inference.py \
audio_path='/path/to/your/audio.mp3' \
output_path='/path/to/your/output_folder' \
lora_path='mouceen/Mapperatorinator-v30-LoRA-Arles-v1' \
... # other arguments