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PiMPStudiosPiMPStudios/ACE-Step-1.5/.github/copilot-instructions.md

ACE-Step-1.5

Copilot instructions0 starsChanged 7 months agoMITView on GitHub

ACE-Step 1.5 is an open-source music foundation model combining a Language Model (LM) as a planner with a Diffusion Transformer (DiT) for audio synthesis. It generates commercial-grade music on consumer hardware (< 4GB VRAM). CRITICAL: Supports CUDA, ROCm, Intel XPU, MPS, MLX, and CPU. When fixing bugs or adding features: - DO NOT alter non-target platform paths unless explicitly required - Changes to CUDA code should not affect MPS/XPU/CPU paths - Use gpu_config.py for hardware detection and configuration

2 other projects use a version of this.

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Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
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The file

We split this copilot-instructions.md into the rules it gives an agent

4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use gpu_config.py for hardware detection and configuration

    line 24
  2. Distinctive:

    All user-facing strings must use i18n translation keys

    line 45
  3. Very specific:

    Logging: Use loguru logger (not print() except CLI output)

    line 55
  4. Very specific:

    Dependencies: Use uv add <package> to add to pyproject.toml

    line 56

≈ “Common” is an estimate: at least 1 in 5 of the 3,779 Copilot instructions files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
copilot-instructions.md

ACE-Step 1.5 - GitHub Copilot Instructions

Project Overview

ACE-Step 1.5 is an open-source music foundation model combining a Language Model (LM) as a planner with a Diffusion Transformer (DiT) for audio synthesis. It generates commercial-grade music on consumer hardware (< 4GB VRAM).

Tech Stack

  • Python 3.11-3.12 (ROCm on Windows requires 3.12; other platforms use 3.11)
  • PyTorch 2.7+ with CUDA 12.8 (Windows/Linux), MPS (macOS ARM64)
  • Transformers 4.51.0-4.57.x for LLM inference
  • Diffusers for diffusion models
  • Gradio 6.2.0 for web UI
  • FastAPI + Uvicorn for REST API server
  • uv for dependency management
  • MLX (Apple Silicon native acceleration, macOS ARM64)
  • nano-vllm (optimized LLM inference, non-macOS ARM64)

Multi-Platform Support

CRITICAL: Supports CUDA, ROCm, Intel XPU, MPS, MLX, and CPU. When fixing bugs or adding features:

  • DO NOT alter non-target platform paths unless explicitly required
  • Changes to CUDA code should not affect MPS/XPU/CPU paths
  • Use gpu_config.py for hardware detection and configuration

Code Organization

Main Entry Points

  • acestep/acestep_v15_pipeline.py - Gradio UI pipeline
  • acestep/api_server.py - REST API server
  • cli.py - Command-line interface
  • acestep/model_downloader.py - Model downloader

Core Modules

  • acestep/handler.py - Audio generation handler (AceStepHandler)
  • acestep/llm_inference.py - LLM handler for text processing
  • acestep/inference.py - Generation logic and parameters
  • acestep/gpu_config.py - Hardware detection and GPU configuration
  • acestep/audio_utils.py - Audio processing utilities
  • acestep/constants.py - Global constants

UI & Internationalization

  • acestep/gradio_ui/ - Gradio interface components
  • acestep/gradio_ui/i18n.py - i18n system (50+ languages)
  • All user-facing strings must use i18n translation keys

Training

  • acestep/training/ - LoRA training pipeline
  • acestep/dataset/ - Dataset handling

Key Conventions

  • Python style: PEP 8, 4 spaces, double quotes for strings
  • Naming: snake_case functions/variables, PascalCase classes, UPPER_SNAKE_CASE constants
  • Logging: Use loguru logger (not print() except CLI output)
  • Dependencies: Use uv add <package> to add to pyproject.toml

Performance

  • Target: 4GB VRAM - minimize memory allocations
  • Lazy load models when needed
  • Batch operations supported (up to 8 songs)

Additional Resources

  • AGENTS.md: Detailed guidance for AI coding agents
  • CONTRIBUTING.md: Contribution workflow and guidelines

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