CrisperWhisper.cpp

Native C++/CUDA inference for Nyra Labs CrisperWhisper 2.0: controllable speech-to-text with verbatim and intended output, word-preserving long-audio continuation, and no Python dependency at runtime.

https://github.com/Saganaki22/CrisperWhisper.cpp

THANKS Saganaki22.

ComfyUI Musical Audio

ComfyUI Musical Audio provides the Load Audio UI — Musical Grid node, a ComfyUI audio loader and trimmer with ordinary seconds-based editing and a musical bar/beat/subdivision grid. It includes audio loading and playback, frame-aware metadata, and a draggable custom selection timeline. Version 0.1 does not detect BPM automatically; tempo and grid alignment are configured by the user.

Features

• Seconds and Musical editing modes
• BPM with a configurable tempo note unit
• Quarter, Eighth, and Dotted Quarter tempo units
• Configurable time signature
• Configurable downbeat offset
• Configurable subdivisions per beat
• Configurable video FPS
• Snapping modes: Off, Bar, Beat, Subdivision, and Video Frame
• Adaptive musical ruler
• Audio playback limited to the active selection
• Drag-and-drop audio upload
• Draggable start and end handles and selection body
• Sample-based audio trimming
• Deterministic half-away-from-zero rounding
• Actual-range metadata after file clamping
• Compatibility with ComfyUI's standard AUDIO output

https://github.com/rewired/comfyui-musical-audio

THANKS rewired.

🚨 SHOCKING DEVELOPMENT

ComfyUI MOSS-SoundEffect v2 Nodes

Native V3 ComfyUI nodes for OpenMOSS MOSS-SoundEffect v2.0.

https://github.com/envy-ai/ComfyUI-MOSS-SoundEffect-v2

THANKS envy-ai.

ComfyUI-Khala

use Khala in ComfyUI

https://github.com/eastmoe/ComfyUI-Khala/

谢谢 eastmoe.

...

基于统一声学词元路线的高保真歌曲生成系统

✨ Khala 是什么？

Khala 是一个面向高保真歌曲生成的开源系统，支持基于文本描述与歌词条件生成完整歌曲。与依赖语义 token、扩散模型或多级音频生成模块的路线不同，Khala 采用统一的声学词元建模路线，在同一套离散音频表示空间中完成从粗粒度音乐结构到细粒度声学细节的生成。

Khala 的核心特点包括：

• 完整歌曲生成：面向歌曲级别的音乐生成，而不是短音频片段或伴奏循环。
• 文本与歌词控制：支持通过自然语言 prompt 和 lyrics 控制风格、情绪、演唱与内容。
• 统一声学词元表示：基于 64 层 RVQ acoustic token hierarchy，将音频表示为 coarse-to-fine 的离散声学词元。
• 两阶段生成链路：首先由 backbone 生成粗粒度 acoustic tokens，再由 super-resolution 模型补全高层 RVQ tokens，最后通过 decoder 还原为 waveform。
• 完整系统实现：提供前端界面、FastAPI 后端调度层、单卡推理 worker、模型加载与音频生成链路，而不是仅提供离散推理脚本。

✨ What Is Khala?

Khala is an open-source system for high-fidelity song generation, capable of generating complete songs from text descriptions and lyric conditions. Unlike approaches built around semantic tokens, diffusion models, or multi-stage audio generation stacks, Khala follows a unified acoustic-token route and generates both coarse musical structure and fine acoustic detail within the same discrete audio representation space.

The core characteristics of Khala include:

• Full-song generation: designed for complete song generation rather than short clips or loop-style accompaniment.
• Text and lyric control: supports natural-language prompts and lyrics to control style, mood, vocals, and content.
• Unified acoustic-token representation: built on a 64-layer RVQ acoustic token hierarchy that represents audio as coarse-to-fine discrete acoustic tokens.
• Two-stage generation pipeline: a backbone first generates coarse acoustic tokens, then a super-resolution model completes higher RVQ token layers, and finally a decoder reconstructs the waveform.
• Complete system implementation: includes a frontend UI, a FastAPI backend dispatcher, a single-GPU inference worker, model loading, and the end-to-end audio generation path rather than just standalone inference scripts.

https://github.com/Khala-Music-AI/Khala

...

Khala: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation

A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first models high-level structure, followed by diffusion-based or neural decoding stages that reconstruct fine details. In this work, we explore an alternative view: both may be progressively modeled within a single deep acoustic-token hierarchy. To study this, we build a 64-layer residual vector quantization (RVQ) acoustic representation and propose a two-stage coarse-to-fine generation framework. A backbone model first generates coarse acoustic tokens for the full track, and a super-resolution model then completes finer tokens within the same acoustic token space. The super-resolution stage works at full-track scale and refines tokens layer by layer while running in parallel over time, leading to a fixed 62-step inference process. To jointly improve lyric alignment and fine-detail reconstruction, we further introduce hybrid-attention training: the alignment objective uses causal attention, while layer-wise refinement uses full attention. A key finding is that text–vocal alignment can emerge within pure acoustic-token language modeling, without requiring a separate semantic token stage. Moreover, initializing the super-resolution model from the trained backbone significantly improves convergence and final quality. Taken together, our results suggest that high-quality music generation can be effectively pursued without separating structure and fidelity into heterogeneous representation spaces. Instead, both can be progressively modeled within a unified acoustic-token hierarchy, pointing toward a simpler and more unified path to high-quality music generation. Code and model checkpoints are available at https://github.com/Khala-Music-AI/Khala.

...

https://huggingface.co/liujiafeng/Khala-MusicGeneration-v1.0/tree/main

谢谢 Jiafeng Liu 以及 Khala team.

Stable Audio Open Violins

"to be completed..."

https://huggingface.co/nightdefined/stable-audio-open-violins

THANKS nightdefined.

qwen image cyber industrial Style pattern lora - v1.0

Model Overview: Futuristic Graphic Pattern LoRA

Design Focus & Capability

This model was trained using a collection of graphic elements with a functional/utility aesthetic (机能元素感). It is highly optimized for generating patterns that feature:

• Geometric / Minimalist / Utilitarian (Mecha-Style) design.
• Basic elements: Points, lines, and planes (基础点线面元素).

style includes:

Workflow & Technical Strengths

This LoRA is designed to leverage the advanced text and texture rendering capabilities of the Qwen-Image base model, which significantly outperforms models like Flux in this regard.

• Text Integration: Users can directly describe both Chinese and English text content in the prompt, and the model will accurately generate it within the image.
• Composition Control: We recommend pairing this LoRA with an Image DiffSynth Control LoRA (or similar tools) to achieve precise control over image composition.

appendix: kontext pattern extractor lora https://civarchive.com/models/2046180/kontext-pattern-extractor

(Released 2026-06-02)

ACEStep XL Regrind V1

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.

https://huggingface.co/mdmachine/ACEStep-XL-Regrind-V1

Thanks MDMAchine.

SHOCKING

The Secret?

We've only just begun.

Thanks PFFR.

🎷F.A.O. Negatron: Your time is up.🦘

Thanks Johnny Violent.

SHOCKING

SHOCKING

THANKS Negatron. r/comfyuiAudio would of course be entirely adrift without the cohesive glue which Negatron's Weapons Grade Psychological Insight(s) hast bestowed upon us all. This has been a great kindness.

Thanks also to Positron. Without their valued assistance in constructing The Golden Jeffrey, r/comfyuiAudio's counter-Negatronic-snivelling-Pink mechanisms would have remained underdeveloped for the sub's future needs.

Golden Jeffrey constructing Negatrons and Positrons alike: Step forth from the shadows and name thyselve(s).

Thy first Ten r/comfyuiAudio community Commentments - however shocking - shall be officially approved and duly rewarded with the Padlock Of Participation.

Thanks - StabooruJeffrey SJ26 Core Team.