add full README with API docs, MCP, CLI, architecture

allow custom LoRA values in dropdown (API clients)

log ace-server restart, show output for adapter debugging

fix: forward adapter to synth request, default LM to 1.7B

fix: adapter saved to clean dir, LM dropdown no 'Default', on-demand download

copy train_engine.py into Docker image

Side-Step training engine, tested locally on CPU

switch back to 1.7B LM (fastest at 269s, 0.6B was 936s)

swap LM to 0.6B Q8_0 for speed test

swap LM 4B->1.7B Q8_0 for faster CPU inference

add LoRA adapter dropdown to inference UI

default mp3, remove format selector, increase LM timeout to 900s

remove accelerate (causes meta tensors), clean up patches

fix meta tensor crash: force low_cpu_mem_usage=False and float32 for CPU

disable flash_sdp on CPU, force attn_implementation=sdpa for training

add granular logging + full stderr to diagnose preprocessing hang

fix: use float32 not bfloat16 for CPU training (bf16 deadlocks on CPU)

redirect training subprocess stderr to log file for debugging

add einops + vector_quantize_pytorch for model loading

run training as detached subprocess to survive Gradio session timeout

use bfloat16 precision for training to halve RAM usage

stop ace-server during training to free RAM, restart after, add log visibility

pre-download training checkpoints at build time

pin diffusers==0.30.3 for torch 2.4.x compat

pin torchaudio==2.4.0 (before torchcodec default backend)

force uninstall torchcodec (torchaudio dependency, broken on Ubuntu 22.04)

monkey-patch torchaudio.load to use soundfile backend

remove torchcodec (needs FFmpeg 5.x), keep soundfile backend

add torchcodec + ffmpeg for torchaudio audio loading

add soundfile + libsndfile1 for torchaudio backend

add all training deps: diffusers lightning numpy tensorboard

add torchaudio dep for training preprocess

add loguru dep for training preprocess

install gradio[mcp] for mcp_server support

add git to runtime image for ace-step source clone

add LoRA training, fix css kwarg

full app: CLI + Gradio + training placeholder + MCP

rewrite app.py for ace-server HTTP API, no torch

copy all shared libs including versioned symlinks

add pkg-config for BLAS detection

switch to acestep.cpp GGUF: XL turbo Q4_K_M + 4B LM Q5_K_M

clickable title

slim title

compact grid UI, 4B default, show training model

force float32 on training model for CPU

fix: LoRA rank param is 'r' not 'rank'

fix training: use LoRAConfigV2 instead of Union type alias

add torchcodec for training audio loading

step 7: real app.py

step 6: +numba (all deps)