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kakao-enterprise/vits-ljs). Each adapter captures a different emotional style and is stored alongside its adapter configuration.vits_lora_neutral/ — neutral speech adapter (contains adapter_model.safetensors and adapter_config.json)vits_lora_happy/ — happy speech adaptervits_lora_sad/ — sad speech adaptervits_lora_angry/ — angry speech adaptervits_lora_surprise/ — surprise speech adapter*.safetensors — LoRA adapter weights (tracked with Git LFS; see .gitattributes).adapter_config.json — PEFT-compatible adapter configuration (generated by PEFT).pip install transformers accelerate peft safetensors soundfile phonemizer1from transformers import AutoModel, AutoProcessor
2from peft import PeftModel, PeftConfig
3
4# Load base model and processor (replace with your exact VITS classes)
5base = AutoModel.from_pretrained("kakao-enterprise/vits-ljs")
6# Wrap with PEFT adapter
7peft_model = PeftModel.from_pretrained(base, "./vits_lora_happy")
8
9# Use the model as you normally would for inference
10# (model-specific generation/inference code not included here).gitattributes lists extensions tracked by LFS (e.g. *.safetensors). If you clone the repo and don't have LFS installed, install it or download the weights manually from the release/storage.peft_version in adapter_config.json). They are compatible with peft 0.19.x and later but should work with newer releases as well.username/vits-lora-happy).huggingface_hub and login: huggingface-cli login.vits_lora_happy) using the hub API or git:1cd vits_lora_happy
2git init
3git remote add origin https://huggingface.co/username/vits-lora-happy
4git add .
5git commit -m "Add vits LoRA happy adapter"
6git lfs install --local
7git push origin mainhuggingface_hub python API to upload individual files if you prefer.kakao-enterprise/vits-ljs. Respect the original model license when redistributing or publishing derivative weights.