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 Settings
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/ComfyUI_temp_pugkg_00088_.png
text: '-'
base_model: ACE-Step/acestep-v15-xl-turbo
instance_prompt: null
license: cc-by-nc-sa-4.0
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.
Download model
Download them in the Files & versions tab.