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HomeLLMsacestep v15 turbo continuous

acestep v15 turbo continuous

by ACE-Step

Open source · 635 downloads · 16 likes

1.5
(16 reviews)AudioAPI & Local
About

ACE-Step v1.5 is an open-source music generation model designed to deliver professional-grade performance on consumer-grade hardware. It can produce complete tracks in seconds, even on modest graphics cards, while ensuring secure commercial use thanks to training on legally compliant data. The model stands out for its ability to generate diverse compositions, from short loops to ten-minute tracks, with precise stylistic control and advanced editing features like voice-to-BGM conversion or cover regeneration. Its hybrid architecture, combining a language model with a diffusion transformer, ensures optimal coherence between prompts and results, even across more than 50 languages. Ideal for artists, producers, and content creators, it seamlessly integrates into creative workflows while respecting users' technical constraints.

Documentation

ACE-Step 1.5

Pushing the Boundaries of Open-Source Music Generation

Project | Hugging Face | ModelScope | Space Demo | Discord Tech Report

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Model Details

🚀 ACE-Step v1.5 is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware.

Key Features

  • 💰 Commercial-Ready: Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for commercial purposes.
  • 📚 Safe & Robust Training Data: The model is trained on a massive, legally compliant dataset consisting of:
    • Licensed Data: Professionally licensed music tracks.
    • Royalty-Free / No-Copyright Data: A vast collection of public domain and royalty-free music.
    • Synthetic Data: High-quality audio generated via advanced MIDI-to-Audio conversion.
  • ⚡ Extreme Speed: Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090.
  • 🖥️ Consumer Hardware Friendly: Runs locally with less than 4GB of VRAM.

Technical Capabilities

🌉 At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints—scaling from short loops to 10-minute compositions—while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). ⚡ Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. 🎚️

🔮 Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities—such as cover generation, repainting, and vocal-to-BGM conversion—while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. 🎸

  • Developed by: [ACE-STEP]
  • Model type: [Text2Music]
  • Language(s): [50+ languages]
  • License: [MIT]

Evaluation

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🏗️ Architecture

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🦁 Model Zoo

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DiT Models

DiT ModelPre-TrainingSFTRLCFGStepRefer audioText2MusicCoverRepaintExtractLegoCompleteQualityDiversityFine-TunabilityHugging Face
acestep-v15-base✅❌❌✅50✅✅✅✅✅✅✅MediumHighEasyLink
acestep-v15-sft✅✅❌✅50✅✅✅✅❌❌❌HighMediumEasyLink
acestep-v15-turbo✅✅❌❌8✅✅✅✅❌❌❌Very HighMediumMediumLink
acestep-v15-turbo-rl✅✅✅❌8✅✅✅✅❌❌❌Very HighMediumMediumTo be released

LM Models

LM ModelPretrain fromPre-TrainingSFTRLCoT metasQuery rewriteAudio UnderstandingComposition CapabilityCopy MelodyHugging Face
acestep-5Hz-lm-0.6BQwen3-0.6B✅✅✅✅✅MediumMediumWeak✅
acestep-5Hz-lm-1.7BQwen3-1.7B✅✅✅✅✅MediumMediumMedium✅
acestep-5Hz-lm-4BQwen3-4B✅✅✅✅✅StrongStrongStrong✅

🙏 Acknowledgements

This project is co-led by ACE Studio and StepFun.

📖 Citation

If you find this project useful for your research, please consider citing:

BibTeX
@misc{gong2026acestep,
	title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
	author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
	howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
	year={2026},
	note={GitHub repository}
}
Capabilities & Tags
transformerssafetensorsacestepfeature-extractionaudiomusictext2musictext-to-audiocustom_code
Links & Resources
Specifications
CategoryAudio
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Rating
1.5

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