Instructions to use mdmachine/ACEStep-XL-Regrind-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mdmachine/ACEStep-XL-Regrind-V1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ACE-Step/acestep-v15-xl-turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mdmachine/ACEStep-XL-Regrind-V1") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
ACEStep XL Regrind V1

- Prompt
- -
Model description
Three-file resonance suppression package for ACE-Step XL Turbo. Reduces harmonic hum and resonance accumulation in long generations (60s+). Includes baked base model, VAE decoder regrind, and LoRA adapter.
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Model tree for mdmachine/ACEStep-XL-Regrind-V1
Base model
ACE-Step/acestep-v15-xl-turbo