Instructions to use mdmachine/ACEStep-XL-Regrind-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mdmachine/ACEStep-XL-Regrind-V1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M

Use Docker

docker model run hf.co/mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M

LM Studio
Jan
Ollama
How to use mdmachine/ACEStep-XL-Regrind-V1 with Ollama:
```
ollama run hf.co/mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M
```

Unsloth Studio

How to use mdmachine/ACEStep-XL-Regrind-V1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mdmachine/ACEStep-XL-Regrind-V1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mdmachine/ACEStep-XL-Regrind-V1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mdmachine/ACEStep-XL-Regrind-V1 to start chatting

Atomic Chat new
Docker Model Runner
How to use mdmachine/ACEStep-XL-Regrind-V1 with Docker Model Runner:
```
docker model run hf.co/mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M
```

Lemonade

How to use mdmachine/ACEStep-XL-Regrind-V1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mdmachine/ACEStep-XL-Regrind-V1:Q4_K_M

Run and chat with the model

lemonade run user.ACEStep-XL-Regrind-V1-Q4_K_M

List all available models

lemonade list

ACEStep XL Regrind V1

Oobleck VAE · DiT Layer · Artifact Suppression Stack

Fine-tuned VAE decoder and DiT LoRA targeting the Oobleck hum artifact series at the weight level

Alexander Allan (MDMAchine) · A&E Concepts · CC BY-NC-SA 4.0

What is this?

ACEStep XL Turbo and the Oobleck VAE produce a characteristic harmonic artifact present in all generations. The artifact has two independent origins:

VAE decoder bias — static reconstruction bias baked into the Oobleck decoder during its original training. Present at consistent amplitude across all generations, independent of length. Characterized via Ableton EQ Eight direct measurement:

Frequency	Depth	Character
74.4 Hz	-1.43 dB	Sub fundamental, BPM-dependent
94.0 Hz	-1.86 dB	Upper sub resonance
654–669 Hz	-12 to -15 dB	Primary hum, BPM shift ~2Hz/octave
211 Hz	~-7 dB	3rd harmonic
1090 Hz	~-7 dB	5th harmonic

DiT body resonance — accumulates over the denoising trajectory, perceptible after ~60 seconds. Hypothesized to arise from ODE manifold shearing in early denoising steps creating resonant modes in FFN activations of the decoder layers.

Both components require independent treatment. Post-processing filters (like the hum notch in MD Audio VAE Tiled) address the symptom. This regrind addresses the cause at the weight level.

Files

VAE (`vae/`)

File	Size	Description
`vae/acestep_1.5_vae_Regrind_V10b-BF16.gguf`	337 MB	VAE V10b — full-strength regrind (bf16 gguf)
`vae/acestep_1.5_vae_Regrind_V10b.safetensors`	337 MB	VAE V10b — full-strength regrind (safetensors)
`vae/acestep_1.5_vae_Regrind_V10b_blend50-BF16.gguf`	337 MB	VAE V10b — 50% blend, conservative (bf16 gguf)
`vae/acestep_1.5_vae_Regrind_V10b_blend50.safetensors`	337 MB	VAE V10b — 50% blend, conservative (safetensors)
`vae/acestep_1.5_vae_Regrind_V9b-BF16.gguf`	337 MB	VAE V9b — full-strength (bf16 gguf, previous gen)
`vae/acestep_1.5_vae_Regrind_V9b_blend50-BF16.gguf`	337 MB	VAE V9b — 50% blend (bf16 gguf, previous gen)
`vae/acestep_1.5_vae_Regrind_V9b_blend50.safetensors`	337 MB	VAE V9b — 50% blend (safetensors, previous gen)
`vae/acestep_1.5_vae_Regrind_V7.safetensors`	337 MB	VAE V7 — legacy (safetensors)

DiT base model (`dit/`)

File	Size	Description
`dit/acestep_xl_turbo_Regrind_V1.safetensors`	9.97 GB	Full regrinded DiT base model (bf16)
`dit/acestep_1.5_xl_turbo_regrind_v1-Q6_K.gguf`	4.1 GB	DiT Q6_K quantized (acestep.cpp / HOT-Step)
`dit/acestep_1.5_xl_turbo_regrind_v1-Q4_K_M.gguf`	2.99 GB	DiT Q4_K_M quantized (acestep.cpp / HOT-Step)
`dit/acestep_1.5_xl_turbo_regrind_v1-Q8_0.gguf`	5.18 GB	DiT Q8_0 quantized (acestep.cpp / HOT-Step)

LoRA (`lora/`)

File	Size	Description
`lora/acestep_xl_turbo_lora_LayerRegrind_V7.safetensors`	393 MB	DiT layer regrind LoRA (strength 0.25–0.50)

Which VAE should I use?

Start with vae/acestep_1.5_vae_Regrind_V10b_blend50-BF16.gguf (or .safetensors for ComfyUI) — 50% blend between V10b regrind and the base decoder. Conservative, closest to the base model's original frequency response. Recommended for most material, especially anything spectrally dense or noisy.

Use vae/acestep_1.5_vae_Regrind_V10b-BF16.gguf for full-strength V10b suppression when the blend isn't aggressive enough.

V9b variants are the previous generation. Still valid if V10b causes any issues on your material.

V7 safetensors is the oldest format, kept for legacy loaders.

Recommended stack

DiT base:  dit/acestep_xl_turbo_Regrind_V1.safetensors
VAE:       vae/acestep_1.5_vae_Regrind_V10b_blend50.safetensors   (ComfyUI)
           vae/acestep_1.5_vae_Regrind_V10b_blend50-BF16.gguf     (acestep.cpp / HOT-Step)
LoRA:      lora/acestep_xl_turbo_lora_LayerRegrind_V7.safetensors  (strength 0.25–0.50)

On raw ACE-Step XL Turbo without the regrinded base: load the LoRA at 0.50–1.00. No trigger words. Standard ACE-Step prompting.

Pair with MD Audio VAE Tiled

For long-form generation (>30s), pair with the MD Audio VAE Tiled decoder node for ComfyUI. Handles BPM-synced tiling, dual-pass seam elimination, and post-decode spectral cleanup (Wiener denoiser, harmonic preservation crossfade, hum notch stack).

The regrind addresses artifact suppression at the weight level. MD Audio VAE Tiled addresses the decode mechanics. Both together give the cleanest output.

→ MD Audio VAE Tiled

Related tools

Repo	Description
MD Audio VAE Tiled	BPM-synced tiled VAE decoder — ComfyUI + C++ + Lua
STORM Sampler	Adaptive ODE solver, kills metallic hum at source
MD HAP Scheduler	Physics-derived sigma schedule, HAP+TPT
MD Causal Scheduler	Causal timestep scheduler

License

Model weights are licensed under CC BY-NC-SA 4.0.

These are derivative works of ACE-Step XL Turbo (Apache 2.0, ACE Studio / StepFun). Free for personal and research use. Commercial use requires a separate license — contact A&E Concepts.

Source code for associated ComfyUI nodes, C++ headers, and Lua plugins is licensed separately under GPL v3 in the repositories above.

Citation / acknowledgment

If you use these weights in your work, please credit:

Alexander Allan (MDMAchine) — ACEStep XL Regrind V1, A&E Concepts, 2026. https://huggingface.co/mdmachine/ACEStep-XL-Regrind-V1

Base model: ACE-Step v1.5 XL Turbo — ACE Studio / StepFun (Apache 2.0).

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GGUF

Model size

5B params

Architecture

acestep-dit

Hardware compatibility

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Model tree for mdmachine/ACEStep-XL-Regrind-V1

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