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koboldcpp/otherarch/acestep/dit-vae.cpp
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// dit.cpp: ACEStep music generation via ggml (dit-vae binary)
//
// Usage: ./dit-vae [options]
// See --help for full option list.
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <cmath>
#include <vector>
#include <chrono>
#include <random>
#include "ggml.h"
#include "ggml-backend.h"
#include "./dit.h"
#include "./vae.h"
#include "./qwen3.h"
#include "./tokenizer.h"
#include "./cond.h"
#include "./bpe.h"
#include "./debug.h"
#include "./request.h"
// Minimal WAV writer (16-bit PCM stereo)
static bool write_wav(const char * path, const float * audio, int T_audio, int sr) {
FILE * f = fopen(path, "wb");
if (!f) return false;
int n_channels = 2;
int bits = 16;
int byte_rate = sr * n_channels * (bits / 8);
int block_align = n_channels * (bits / 8);
int data_size = T_audio * n_channels * (bits / 8);
int file_size = 36 + data_size;
fwrite("RIFF", 1, 4, f);
fwrite(&file_size, 4, 1, f);
fwrite("WAVE", 1, 4, f);
fwrite("fmt ", 1, 4, f);
int fmt_size = 16; fwrite(&fmt_size, 4, 1, f);
short audio_fmt = 1; fwrite(&audio_fmt, 2, 1, f);
short nc = (short)n_channels; fwrite(&nc, 2, 1, f);
fwrite(&sr, 4, 1, f);
fwrite(&byte_rate, 4, 1, f);
short ba = (short)block_align; fwrite(&ba, 2, 1, f);
short bp = (short)bits; fwrite(&bp, 2, 1, f);
fwrite("data", 1, 4, f);
fwrite(&data_size, 4, 1, f);
for (int t = 0; t < T_audio; t++) {
for (int c = 0; c < 2; c++) {
float s = audio[c * T_audio + t];
s = s < -1.0f ? -1.0f : (s > 1.0f ? 1.0f : s);
short v = (short)(s * 32767.0f);
fwrite(&v, 2, 1, f);
}
}
fclose(f);
return true;
}
static void print_usage(const char * prog) {
fprintf(stderr,
"Usage: %s --request <json...> --text-encoder <gguf> --dit <gguf> --vae <gguf> [options]\n\n"
"Required:\n"
" --request <json...> One or more request JSONs (from ace-qwen3 --request)\n"
" --text-encoder <gguf> Text encoder GGUF file\n"
" --dit <gguf> DiT GGUF file\n"
" --vae <gguf> VAE GGUF file\n\n"
"Batch:\n"
" --batch <N> DiT variations per request (default: 1, max 9)\n\n"
"Output naming: input.json -> input0.wav, input1.wav, ... (last digit = batch index)\n\n"
"VAE tiling (memory control):\n"
" --vae-chunk <N> Latent frames per tile (default: 256)\n"
" --vae-overlap <N> Overlap frames per side (default: 64)\n\n"
"Debug:\n"
" --noise-file <path> Load noise from bf16 file (Philox RNG dump, batch=1 only)\n"
" --dump <dir> Dump intermediate tensors\n", prog);
}
// Parse comma-separated codes string into vector
static std::vector<int> parse_codes_string(const std::string & s) {
std::vector<int> codes;
if (s.empty()) return codes;
const char * p = s.c_str();
while (*p) {
while (*p == ',' || *p == ' ') p++;
if (!*p) break;
codes.push_back(atoi(p));
while (*p && *p != ',') p++;
}
return codes;
}
/*
int main(int argc, char ** argv) {
if (argc < 2) { print_usage(argv[0]); return 1; }
std::vector<const char *> request_paths;
const char * text_enc_gguf = NULL;
const char * dit_gguf = NULL;
const char * vae_gguf = NULL;
const char * dump_dir = NULL;
const char * noise_file = NULL;
int batch_n = 1;
int vae_chunk = 256;
int vae_overlap = 64;
for (int i = 1; i < argc; i++) {
if (strcmp(argv[i], "--request") == 0) {
// Collect all following non-option args
while (i+1 < argc && argv[i+1][0] != '-')
request_paths.push_back(argv[++i]);
}
else if (strcmp(argv[i], "--text-encoder") == 0 && i+1 < argc) text_enc_gguf = argv[++i];
else if (strcmp(argv[i], "--dit") == 0 && i+1 < argc) dit_gguf = argv[++i];
else if (strcmp(argv[i], "--vae") == 0 && i+1 < argc) vae_gguf = argv[++i];
else if (strcmp(argv[i], "--noise-file") == 0 && i+1 < argc) noise_file = argv[++i];
else if (strcmp(argv[i], "--dump") == 0 && i+1 < argc) dump_dir = argv[++i];
else if (strcmp(argv[i], "--batch") == 0 && i+1 < argc) batch_n = atoi(argv[++i]);
else if (strcmp(argv[i], "--vae-chunk") == 0 && i+1 < argc) vae_chunk = atoi(argv[++i]);
else if (strcmp(argv[i], "--vae-overlap") == 0 && i+1 < argc) vae_overlap = atoi(argv[++i]);
else if (strcmp(argv[i], "--help") == 0 || strcmp(argv[i], "-h") == 0) {
print_usage(argv[0]); return 0;
} else {
fprintf(stderr, "Unknown option: %s\n", argv[i]);
print_usage(argv[0]); return 1;
}
}
if (request_paths.empty()) {
fprintf(stderr, "ERROR: --request required\n");
print_usage(argv[0]); return 1;
}
if (batch_n < 1 || batch_n > 9) {
fprintf(stderr, "ERROR: --batch must be 1..9\n"); return 1;
}
if (!dit_gguf) {
fprintf(stderr, "ERROR: --dit required\n");
print_usage(argv[0]); return 1;
}
if (!text_enc_gguf) {
fprintf(stderr, "ERROR: --text-encoder required\n");
print_usage(argv[0]); return 1;
}
const int FRAMES_PER_SECOND = 25;
DebugDumper dbg;
debug_init(&dbg, dump_dir);
Timer timer;
DiTGGMLConfig cfg;
DiTGGML model = {};
// Load DiT model (once for all requests)
dit_ggml_init_backend(&model);
fprintf(stderr, "[Load] Backend init: %.1f ms\n", timer.ms());
timer.reset();
if (!dit_ggml_load(&model, dit_gguf, cfg)) {
fprintf(stderr, "FATAL: failed to load DiT model\n");
return 1;
}
fprintf(stderr, "[Load] DiT weight load: %.1f ms\n", timer.ms());
// Read DiT GGUF metadata + silence_latent tensor (once)
bool is_turbo = false;
std::vector<float> silence_full; // [15000, 64] f32
{
GGUFModel gf = {};
if (gf_load(&gf, dit_gguf)) {
is_turbo = gf_get_bool(gf, "acestep.is_turbo");
const void * sl_data = gf_get_data(gf, "silence_latent");
if (sl_data) {
silence_full.resize(15000 * 64);
memcpy(silence_full.data(), sl_data, 15000 * 64 * sizeof(float));
fprintf(stderr, "[Load] silence_latent: [15000, 64] from GGUF\n");
} else {
fprintf(stderr, "FATAL: silence_latent tensor not found in %s\n", dit_gguf);
gf_close(&gf);
dit_ggml_free(&model);
return 1;
}
gf_close(&gf);
} else {
fprintf(stderr, "FATAL: cannot reopen %s for metadata\n", dit_gguf);
dit_ggml_free(&model);
return 1;
}
}
int Oc = cfg.out_channels; // 64
int ctx_ch = cfg.in_channels - Oc; // 128
// Load VAE model (once for all requests)
VAEGGML vae = {};
bool have_vae = false;
if (vae_gguf) {
timer.reset();
vae_ggml_load(&vae, vae_gguf);
fprintf(stderr, "[Load] VAE weights: %.1f ms\n", timer.ms());
have_vae = true;
}
// Process each request
for (int ri = 0; ri < (int)request_paths.size(); ri++) {
const char * rpath = request_paths[ri];
fprintf(stderr, "[Request %d/%d] %s (batch=%d)\n",
ri + 1, (int)request_paths.size(), rpath, batch_n);
// Compute output basename: strip .json suffix
std::string basename(rpath);
{
size_t dot = basename.rfind(".json");
if (dot != std::string::npos)
basename = basename.substr(0, dot);
}
// Parse request JSON
AceRequest req;
request_init(&req);
if (!request_parse(&req, rpath)) {
fprintf(stderr, "ERROR: failed to parse %s, skipping\n", rpath);
continue;
}
if (req.caption.empty()) {
fprintf(stderr, "ERROR: caption is empty in %s, skipping\n", rpath);
continue;
}
// Extract params
const char * caption = req.caption.c_str();
const char * lyrics = req.lyrics.empty() ? "[Instrumental]" : req.lyrics.c_str();
char bpm_str[16] = "N/A";
if (req.bpm > 0) snprintf(bpm_str, sizeof(bpm_str), "%d", req.bpm);
const char * bpm = bpm_str;
const char * keyscale = req.keyscale.empty() ? "N/A" : req.keyscale.c_str();
const char * timesig = req.timesignature.empty() ? "N/A" : req.timesignature.c_str();
const char * language = req.vocal_language.empty() ? "en" : req.vocal_language.c_str();
float duration = req.duration > 0 ? req.duration : 120.0f;
long long seed = req.seed;
int num_steps = req.inference_steps > 0 ? req.inference_steps : 8;
float guidance_scale = req.guidance_scale > 0 ? req.guidance_scale : 7.0f;
float shift = req.shift > 0 ? req.shift : 1.0f;
if (is_turbo && guidance_scale > 1.0f) {
fprintf(stderr, "[Pipeline] WARNING: turbo model, forcing guidance_scale=1.0 (was %.1f)\n",
guidance_scale);
guidance_scale = 1.0f;
}
if (seed < 0) {
std::random_device rd;
seed = (long long)rd() << 32 | rd();
if (seed < 0) seed = -seed;
}
fprintf(stderr, "[Pipeline] seed=%lld, steps=%d, guidance=%.1f, shift=%.1f, duration=%.1fs\n",
seed, num_steps, guidance_scale, shift, duration);
// Parse audio codes from request
std::vector<int> codes_vec = parse_codes_string(req.audio_codes);
if (!codes_vec.empty())
fprintf(stderr, "[Pipeline] %zu audio codes (%.1fs @ 5Hz)\n",
codes_vec.size(), (float)codes_vec.size() / 5.0f);
// Build schedule: t_i = shift * t / (1 + (shift-1)*t) where t = 1 - i/steps
std::vector<float> schedule(num_steps);
for (int i = 0; i < num_steps; i++) {
float t = 1.0f - (float)i / (float)num_steps;
schedule[i] = shift * t / (1.0f + (shift - 1.0f) * t);
}
// T = number of 25Hz latent frames for DiT
// When audio codes are present, T is determined by the codes.
// Otherwise, T is derived from the requested duration.
int T = codes_vec.empty()
? (int)(duration * FRAMES_PER_SECOND)
: (int)codes_vec.size() * 5;
T = ((T + cfg.patch_size - 1) / cfg.patch_size) * cfg.patch_size;
int S = T / cfg.patch_size;
int enc_S = 0;
fprintf(stderr, "[Pipeline] T=%d, S=%d\n", T, S);
if (T > 15000) {
fprintf(stderr, "ERROR: T=%d exceeds silence_latent max 15000, skipping\n", T);
continue;
}
// Text encoding
// 1. Load BPE tokenizer
timer.reset();
BPETokenizer tok;
if (!load_bpe_from_gguf(&tok, text_enc_gguf)) {
fprintf(stderr, "FATAL: failed to load tokenizer from %s\n", text_enc_gguf);
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
fprintf(stderr, "[Load] BPE tokenizer: %.1f ms\n", timer.ms());
// 2. Build formatted prompts
const char * instruction = "Generate audio semantic tokens based on the given conditions:";
char metas[512];
snprintf(metas, sizeof(metas),
"- bpm: %s\n- timesignature: %s\n- keyscale: %s\n- duration: %d seconds\n",
bpm, timesig, keyscale, (int)duration);
std::string text_str = std::string("# Instruction\n")
+ instruction + "\n\n"
+ "# Caption\n" + caption + "\n\n"
+ "# Metas\n" + metas + "<|endoftext|>\n";
bool instrumental = (strcmp(lyrics, "[Instrumental]") == 0 || strcmp(lyrics, "[instrumental]") == 0);
std::string lyric_str = std::string("# Languages\n") + language + "\n\n# Lyric\n"
+ (instrumental ? "[Instrumental]" : lyrics) + "<|endoftext|>";
// 3. Tokenize
auto text_ids = bpe_encode(&tok, text_str.c_str(), true);
auto lyric_ids = bpe_encode(&tok, lyric_str.c_str(), true);
int S_text = (int)text_ids.size();
int S_lyric = (int)lyric_ids.size();
fprintf(stderr, "[Pipeline] caption: %d tokens, lyrics: %d tokens\n", S_text, S_lyric);
// 4. Text encoder forward (caption only)
timer.reset();
Qwen3GGML text_enc = {};
qwen3_init_backend(&text_enc);
if (!qwen3_load_text_encoder(&text_enc, text_enc_gguf)) {
fprintf(stderr, "FATAL: failed to load text encoder\n");
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
fprintf(stderr, "[Load] TextEncoder: %.1f ms\n", timer.ms());
int H_text = text_enc.cfg.hidden_size; // 1024
std::vector<float> text_hidden(H_text * S_text);
timer.reset();
qwen3_forward(&text_enc, text_ids.data(), S_text, text_hidden.data());
fprintf(stderr, "[Encode] TextEncoder (%d tokens): %.1f ms\n", S_text, timer.ms());
debug_dump_2d(&dbg, "text_hidden", text_hidden.data(), S_text, H_text);
// 5. Lyric embedding (CPU vocab lookup from text encoder embed table)
timer.reset();
std::vector<float> lyric_embed(H_text * S_lyric);
{
GGUFModel gf_te = {};
if (!gf_load(&gf_te, text_enc_gguf)) {
fprintf(stderr, "FATAL: cannot reopen text encoder GGUF for lyric embed\n");
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
const void * embed_data = gf_get_data(gf_te, "embed_tokens.weight");
if (!embed_data) {
fprintf(stderr, "FATAL: embed_tokens.weight not found\n");
gf_close(&gf_te);
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
qwen3_cpu_embed_lookup(embed_data, H_text,
lyric_ids.data(), S_lyric,
lyric_embed.data());
gf_close(&gf_te);
}
fprintf(stderr, "[Encode] Lyric vocab lookup (%d tokens): %.1f ms\n", S_lyric, timer.ms());
debug_dump_2d(&dbg, "lyric_embed", lyric_embed.data(), S_lyric, H_text);
// 6. Condition encoder forward
timer.reset();
CondGGML cond = {};
cond_ggml_init_backend(&cond);
if (!cond_ggml_load(&cond, dit_gguf)) {
fprintf(stderr, "FATAL: failed to load condition encoder\n");
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
fprintf(stderr, "[Load] ConditionEncoder: %.1f ms\n", timer.ms());
// Silence feats for timbre input: first 750 frames (30s @ 25Hz)
const int S_ref = 750;
std::vector<float> silence_feats(S_ref * 64);
memcpy(silence_feats.data(), silence_full.data(), S_ref * 64 * sizeof(float));
timer.reset();
std::vector<float> enc_hidden;
cond_ggml_forward(&cond, text_hidden.data(), S_text,
lyric_embed.data(), S_lyric,
silence_feats.data(), S_ref,
enc_hidden, &enc_S);
fprintf(stderr, "[Encode] ConditionEncoder: %.1f ms, enc_S=%d\n", timer.ms(), enc_S);
qwen3_free(&text_enc);
cond_ggml_free(&cond);
debug_dump_2d(&dbg, "enc_hidden", enc_hidden.data(), enc_S, 2048);
// Context building
// Silence latent for this T
std::vector<float> silence(Oc * T);
memcpy(silence.data(), silence_full.data(), (size_t)(Oc * T) * sizeof(float));
// Decode audio codes if provided
int decoded_T = 0;
std::vector<float> decoded_latents;
if (!codes_vec.empty()) {
timer.reset();
DetokGGML detok = {};
if (!detok_ggml_load(&detok, dit_gguf, model.backend, model.cpu_backend)) {
fprintf(stderr, "FATAL: failed to load detokenizer\n");
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
fprintf(stderr, "[Load] Detokenizer: %.1f ms\n", timer.ms());
int T_5Hz = (int)codes_vec.size();
int T_25Hz_codes = T_5Hz * 5;
decoded_latents.resize(T_25Hz_codes * Oc);
timer.reset();
int ret = detok_ggml_decode(&detok, codes_vec.data(), T_5Hz, decoded_latents.data());
if (ret < 0) {
fprintf(stderr, "FATAL: detokenizer decode failed\n");
dit_ggml_free(&model);
if (have_vae) vae_ggml_free(&vae);
return 1;
}
fprintf(stderr, "[Context] Detokenizer: %.1f ms\n", timer.ms());
decoded_T = T_25Hz_codes < T ? T_25Hz_codes : T;
debug_dump_2d(&dbg, "detok_output", decoded_latents.data(), T_25Hz_codes, Oc);
detok_ggml_free(&detok);
}
// Build single context: [T, ctx_ch] = src_latents[64] + mask_ones[64]
std::vector<float> context_single(T * ctx_ch);
for (int t = 0; t < T; t++) {
const float * src = (t < decoded_T)
? decoded_latents.data() + t * Oc
: silence.data() + t * Oc;
for (int c = 0; c < Oc; c++)
context_single[t * ctx_ch + c] = src[c];
for (int c = 0; c < Oc; c++)
context_single[t * ctx_ch + Oc + c] = 1.0f;
}
// Replicate context for N batch samples (all identical)
std::vector<float> context(batch_n * T * ctx_ch);
for (int b = 0; b < batch_n; b++)
memcpy(context.data() + b * T * ctx_ch, context_single.data(),
T * ctx_ch * sizeof(float));
// Generate N noise samples
std::vector<float> noise(batch_n * Oc * T);
if (noise_file && batch_n == 1) {
// Load pre-generated Philox noise from bf16 file (batch=1 only)
FILE * nf = fopen(noise_file, "rb");
if (!nf) { fprintf(stderr, "FATAL: cannot open noise file %s\n", noise_file); return 1; }
fseek(nf, 0, SEEK_END);
int total_bf16 = (int)(ftell(nf) / sizeof(uint16_t));
fseek(nf, 0, SEEK_SET);
int T_file = total_bf16 / Oc;
if (T_file < T) {
fprintf(stderr, "FATAL: noise file too short: T_file=%d < T=%d\n", T_file, T);
fclose(nf); return 1;
}
std::vector<uint16_t> bf16_all(Oc * T);
if (fread(bf16_all.data(), sizeof(uint16_t), Oc * T, nf) != (size_t)(Oc * T)) {
fprintf(stderr, "FATAL: noise file read error\n");
fclose(nf); return 1;
}
fclose(nf);
for (int i = 0; i < Oc * T; i++) {
uint32_t w = (uint32_t)bf16_all[i] << 16;
float v; memcpy(&v, &w, 4);
noise[i] = v;
}
fprintf(stderr, "[Context] loaded noise from %s: [%d, %d] bf16\n",
noise_file, T, Oc);
} else {
// Generate N noise samples with seeds: seed, seed+1, ..., seed+N-1
for (int b = 0; b < batch_n; b++) {
std::mt19937 rng((uint32_t)(seed + b));
std::normal_distribution<float> normal(0.0f, 1.0f);
float * dst = noise.data() + b * Oc * T;
for (int i = 0; i < Oc * T; i++)
dst[i] = normal(rng);
fprintf(stderr, "[Context Batch%d] noise seed=%lld\n", b, seed + b);
}
}
// DiT Generate
std::vector<float> output(batch_n * Oc * T);
// Debug dumps (sample 0)
debug_dump_2d(&dbg, "noise", noise.data(), T, Oc);
debug_dump_2d(&dbg, "context", context.data(), T, ctx_ch);
fprintf(stderr, "[DiT] Starting: T=%d, S=%d, enc_S=%d, steps=%d, batch=%d\n",
T, S, enc_S, num_steps, batch_n);
timer.reset();
dit_ggml_generate(&model, noise.data(), context.data(), enc_hidden.data(),
enc_S, T, batch_n, num_steps, schedule.data(), output.data(),
guidance_scale, &dbg);
fprintf(stderr, "[DiT] Total generation: %.1f ms (%.1f ms/sample)\n",
timer.ms(), timer.ms() / batch_n);
debug_dump_2d(&dbg, "dit_output", output.data(), T, Oc);
// VAE Decode + Write WAVs
if (have_vae) {
int T_latent = T;
int T_audio_max = T_latent * 1920;
std::vector<float> audio(2 * T_audio_max);
for (int b = 0; b < batch_n; b++) {
float * dit_out = output.data() + b * Oc * T;
timer.reset();
int T_audio = vae_ggml_decode_tiled(&vae, dit_out, T_latent, audio.data(),
T_audio_max, vae_chunk, vae_overlap);
if (T_audio < 0) {
fprintf(stderr, "[VAE Batch%d] ERROR: decode failed\n", b);
continue;
}
fprintf(stderr, "[VAE Batch%d] Decode: %.1f ms\n", b, timer.ms());
// Peak normalization to -1.0 dB
{
float peak = 0.0f;
int n_samples = 2 * T_audio;
for (int i = 0; i < n_samples; i++) {
float a = audio[i] < 0 ? -audio[i] : audio[i];
if (a > peak) peak = a;
}
if (peak > 1e-6f) {
const float target_amp = powf(10.0f, -1.0f / 20.0f);
float gain = target_amp / peak;
for (int i = 0; i < n_samples; i++)
audio[i] *= gain;
}
}
// Write WAV: basename + batch_index + .wav
char wav_path[1024];
snprintf(wav_path, sizeof(wav_path), "%s%d.wav", basename.c_str(), b);
if (b == 0) debug_dump_2d(&dbg, "vae_audio", audio.data(), 2, T_audio);
if (write_wav(wav_path, audio.data(), T_audio, 48000)) {
fprintf(stderr, "[VAE Batch%d] Wrote %s: %d samples (%.2fs @ 48kHz stereo)\n",
b, wav_path, T_audio, (float)T_audio / 48000.0f);
} else {
fprintf(stderr, "[VAE Batch%d] FATAL: failed to write %s\n", b, wav_path);
}
}
}
fprintf(stderr, "[Request %d/%d] Done\n", ri + 1, (int)request_paths.size());
}
if (have_vae) vae_ggml_free(&vae);
dit_ggml_free(&model);
fprintf(stderr, "[Pipeline] All done\n");
return 0;
}
*/
//kcpp stuff
static DiTGGML acestep_dit = {};
static bool acestep_dit_others_loaded = false;
static bool acestep_dit_core_loaded = false;
static DiTGGMLConfig music_dit_cfg;
static Timer music_dit_timer;
static bool is_turbo = false;
static VAEGGML vae = {};
static BPETokenizer music_tok;
static Qwen3GGML music_text_enc = {};
static GGUFModel gf_te = {};
static const void * musice_te_embed_data = nullptr;
static CondGGML music_cond = {};
static std::vector<float> silence_full; // [15000, 64] f32
static DetokGGML detok = {};
static bool acestep_dit_lowvram = false;
static std::string acestep_music_embd_path = "";
static std::string acestep_music_dit_path = "";
static std::string acestep_music_vae_path = "";
void unload_acestep_dit_core()
{
if(acestep_dit_core_loaded)
{
acestep_dit_core_loaded = false;
dit_ggml_free(&acestep_dit);
}
}
void unload_acestep_dit_others()
{
if(acestep_dit_others_loaded)
{
acestep_dit_others_loaded = false;
vae_ggml_free(&vae);
gf_close(&gf_te);
cond_ggml_free(&music_cond);
detok_ggml_free(&detok);
qwen3_free(&music_text_enc);
}
}
bool load_acestep_dit(std::string music_embd_path, std::string music_dit_path, std::string music_vae_path, bool lowvram)
{
if(acestep_dit_others_loaded || acestep_dit_core_loaded)
{
unload_acestep_dit_core();
unload_acestep_dit_others();
}
acestep_dit_others_loaded = false;
acestep_dit_core_loaded = false;
const char * text_enc_gguf = music_embd_path.c_str();
const char * dit_gguf = music_dit_path.c_str();
const char * vae_gguf = music_vae_path.c_str();
acestep_dit_lowvram = lowvram;
acestep_music_embd_path = music_embd_path;
acestep_music_dit_path = music_dit_path;
acestep_music_vae_path = music_vae_path;
// Load DiT model (once for all requests)
dit_ggml_init_backend(&acestep_dit);
fprintf(stderr, "[Load] Backend init: %.1f ms\n", music_dit_timer.ms());
music_dit_timer.reset();
if (!dit_ggml_load(&acestep_dit, dit_gguf, music_dit_cfg)) {
fprintf(stderr, "FATAL: failed to load DiT model\n");
return false;
}
fprintf(stderr, "[Load] DiT weight load: %.1f ms\n", music_dit_timer.ms());
// Read DiT GGUF metadata + silence_latent tensor (once)
is_turbo = false;
{
GGUFModel gf = {};
if (gf_load(&gf, dit_gguf)) {
is_turbo = gf_get_bool(gf, "acestep.is_turbo");
const void * sl_data = gf_get_data(gf, "silence_latent");
if (sl_data) {
silence_full.resize(15000 * 64);
memcpy(silence_full.data(), sl_data, 15000 * 64 * sizeof(float));
fprintf(stderr, "[Load] silence_latent: [15000, 64] from GGUF\n");
} else {
fprintf(stderr, "FATAL: silence_latent tensor not found in %s\n", dit_gguf);
return false;
}
gf_close(&gf);
} else {
fprintf(stderr, "FATAL: cannot reopen %s for metadata\n", dit_gguf);
return false;
}
}
// Load VAE model (once for all requests)
music_dit_timer.reset();
vae_ggml_load(&vae, vae_gguf);
fprintf(stderr, "[Load] VAE weights: %.1f ms\n", music_dit_timer.ms());
music_dit_timer.reset();
if (!load_bpe_from_gguf(&music_tok, text_enc_gguf)) {
fprintf(stderr, "FATAL: failed to load music tokenizer from %s\n", text_enc_gguf);
return false;
}
fprintf(stderr, "[Load] BPE tokenizer: %.1f ms\n", music_dit_timer.ms());
// Text encoder forward (caption only)
music_dit_timer.reset();
qwen3_init_backend(&music_text_enc);
if (!qwen3_load_text_encoder(&music_text_enc, text_enc_gguf)) {
fprintf(stderr, "FATAL: failed to load text encoder\n");
return false;
}
fprintf(stderr, "[Load] TextEncoder: %.1f ms\n", music_dit_timer.ms());
if (!gf_load(&gf_te, text_enc_gguf)) {
fprintf(stderr, "FATAL: cannot reopen text encoder GGUF for lyric embed\n");
return false;
}
musice_te_embed_data = gf_get_data(gf_te, "embed_tokens.weight");
if (!musice_te_embed_data) {
fprintf(stderr, "FATAL: embed_tokens.weight not found\n");
return false;
}
// Condition encoder forward
music_dit_timer.reset();
cond_ggml_init_backend(&music_cond);
if (!cond_ggml_load(&music_cond, dit_gguf)) {
fprintf(stderr, "FATAL: failed to load condition encoder\n");
return false;
}
fprintf(stderr, "[Load] ConditionEncoder: %.1f ms\n", music_dit_timer.ms());
music_dit_timer.reset();
if (!detok_ggml_load(&detok, dit_gguf, acestep_dit.backend, acestep_dit.cpu_backend)) {
fprintf(stderr, "FATAL: failed to load detokenizer\n");
return false;
}
fprintf(stderr, "[Load] Detokenizer: %.1f ms\n", music_dit_timer.ms());
acestep_dit_others_loaded = true;
acestep_dit_core_loaded = true;
return true;
}
std::string acestep_generate_audio(const music_generation_inputs inputs)
{
if(!acestep_dit_others_loaded && !acestep_dit_core_loaded && acestep_music_dit_path!="")
{
printf("\nRuntime reload Music DiT model...\n");
bool ok = load_acestep_dit(acestep_music_embd_path, acestep_music_dit_path, acestep_music_vae_path, acestep_dit_lowvram);
if(!ok)
{
printf("\nERROR: Acestep DiT load fail\n");
return "";
}
}
// Parse request JSON
AceRequest req;
std::string injson = inputs.input_json;
request_init(&req);
if (!request_parse_from_str(&req, injson)) {
fprintf(stderr, "ERROR: failed to parse music gen request\n");
return "";
}
if (req.caption.empty()) {
req.caption = "An interesting song";
}
req.thinking = false;
req.inference_steps = (req.inference_steps>100?100:req.inference_steps); //clamp to 100
req.duration = (req.duration>420?420:req.duration); //clamp to 7 min
const int FRAMES_PER_SECOND = 25;
int Oc = music_dit_cfg.out_channels; // 64
int ctx_ch = music_dit_cfg.in_channels - Oc; // 128
int batch_n = 1;
int vae_chunk = 256;
int vae_overlap = 64;
// Extract params
const char * caption = req.caption.c_str();
const char * lyrics = req.lyrics.empty() ? "[Instrumental]" : req.lyrics.c_str();
char bpm_str[16] = "N/A";
if (req.bpm > 0) snprintf(bpm_str, sizeof(bpm_str), "%d", req.bpm);
const char * bpm = bpm_str;
const char * keyscale = req.keyscale.empty() ? "N/A" : req.keyscale.c_str();
const char * timesig = req.timesignature.empty() ? "N/A" : req.timesignature.c_str();
const char * language = req.vocal_language.empty() ? "en" : req.vocal_language.c_str();
float duration = req.duration > 0 ? req.duration : 120.0f;
long long seed = req.seed;
int num_steps = req.inference_steps > 0 ? req.inference_steps : 8;
float guidance_scale = req.guidance_scale > 0 ? req.guidance_scale : 7.0f;
float shift = req.shift > 0 ? req.shift : 1.0f;
if (is_turbo && guidance_scale > 1.0f) {
fprintf(stderr, "[Pipeline] WARNING: turbo model, forcing guidance_scale=1.0 (was %.1f)\n",
guidance_scale);
guidance_scale = 1.0f;
}
if (seed <= 0 || seed==0xFFFFFFFF)
{
seed = (((uint32_t)time(NULL)) % 1000000u);
}
fprintf(stderr, "[Pipeline] seed=%lld, steps=%d, guidance=%.1f, shift=%.1f, duration=%.1fs\n",
seed, num_steps, guidance_scale, shift, duration);
// Parse audio codes from request
std::vector<int> codes_vec = parse_codes_string(req.audio_codes);
if (!codes_vec.empty())
fprintf(stderr, "[Pipeline] %zu audio codes (%.1fs @ 5Hz)\n",
codes_vec.size(), (float)codes_vec.size() / 5.0f);
// Build schedule: t_i = shift * t / (1 + (shift-1)*t) where t = 1 - i/steps
std::vector<float> schedule(num_steps);
for (int i = 0; i < num_steps; i++) {
float t = 1.0f - (float)i / (float)num_steps;
schedule[i] = shift * t / (1.0f + (shift - 1.0f) * t);
}
// T = number of 25Hz latent frames for DiT
// When audio codes are present, T is determined by the codes.
// Otherwise, T is derived from the requested duration.
int T = codes_vec.empty()
? (int)(duration * FRAMES_PER_SECOND)
: (int)codes_vec.size() * 5;
T = ((T + music_dit_cfg.patch_size - 1) / music_dit_cfg.patch_size) * music_dit_cfg.patch_size;
int S = T / music_dit_cfg.patch_size;
int enc_S = 0;
fprintf(stderr, "[Pipeline] T=%d, S=%d\n", T, S);
if (T > 15000) {
fprintf(stderr, "ERROR: T=%d exceeds silence_latent max 15000, skipping\n", T);
return "";
}
// Text encoding
// 1. Load BPE tokenizer
music_dit_timer.reset();
// 2. Build formatted prompts
const char * instruction = "Generate audio semantic tokens based on the given conditions:";
char metas[512];
snprintf(metas, sizeof(metas),
"- bpm: %s\n- timesignature: %s\n- keyscale: %s\n- duration: %d seconds\n",
bpm, timesig, keyscale, (int)duration);
std::string text_str = std::string("# Instruction\n")
+ instruction + "\n\n"
+ "# Caption\n" + caption + "\n\n"
+ "# Metas\n" + metas + "<|endoftext|>\n";
bool instrumental = (strcmp(lyrics, "[Instrumental]") == 0 || strcmp(lyrics, "[instrumental]") == 0);
std::string lyric_str = std::string("# Languages\n") + language + "\n\n# Lyric\n"
+ (instrumental ? "[Instrumental]" : lyrics) + "<|endoftext|>";
// 3. Tokenize
auto text_ids = bpe_encode(&music_tok, text_str.c_str(), true);
auto lyric_ids = bpe_encode(&music_tok, lyric_str.c_str(), true);
int S_text = (int)text_ids.size();
int S_lyric = (int)lyric_ids.size();
fprintf(stderr, "[Pipeline] caption: %d tokens, lyrics: %d tokens\n", S_text, S_lyric);
int H_text = music_text_enc.cfg.hidden_size; // 1024
std::vector<float> text_hidden(H_text * S_text);
music_dit_timer.reset();
qwen3_forward(&music_text_enc, text_ids.data(), S_text, text_hidden.data());
fprintf(stderr, "[Encode] TextEncoder (%d tokens): %.1f ms\n", S_text, music_dit_timer.ms());
// 5. Lyric embedding (CPU vocab lookup from text encoder embed table)
music_dit_timer.reset();
std::vector<float> lyric_embed(H_text * S_lyric);
qwen3_cpu_embed_lookup(musice_te_embed_data, H_text,
lyric_ids.data(), S_lyric,
lyric_embed.data());
fprintf(stderr, "[Encode] Lyric vocab lookup (%d tokens): %.1f ms\n", S_lyric, music_dit_timer.ms());
// Silence feats for timbre input: first 750 frames (30s @ 25Hz)
const int S_ref = 750;
std::vector<float> silence_feats(S_ref * 64);
memcpy(silence_feats.data(), silence_full.data(), S_ref * 64 * sizeof(float));
music_dit_timer.reset();
std::vector<float> enc_hidden;
cond_ggml_forward(&music_cond, text_hidden.data(), S_text,
lyric_embed.data(), S_lyric,
silence_feats.data(), S_ref,
enc_hidden, &enc_S);
fprintf(stderr, "[Encode] ConditionEncoder: %.1f ms, enc_S=%d\n", music_dit_timer.ms(), enc_S);
// Context building
// Silence latent for this T
// std::vector<float> silence(Oc * T);
// memcpy(silence.data(), silence_full.data(), (size_t)(Oc * T) * sizeof(float));
// Decode audio codes if provided
int decoded_T = 0;
std::vector<float> decoded_latents;
if (!codes_vec.empty()) {
int T_5Hz = (int)codes_vec.size();
int T_25Hz_codes = T_5Hz * 5;
decoded_latents.resize(T_25Hz_codes * Oc);
music_dit_timer.reset();
int ret = detok_ggml_decode(&detok, codes_vec.data(), T_5Hz, decoded_latents.data());
if (ret < 0) {
fprintf(stderr, "FATAL: music detokenizer decode failed\n");
return "";
}
fprintf(stderr, "[Context] Detokenizer: %.1f ms\n", music_dit_timer.ms());
decoded_T = T_25Hz_codes < T ? T_25Hz_codes : T;
}
// Build single context: [T, ctx_ch] = src_latents[64] + mask_ones[64]
std::vector<float> context_single(T * ctx_ch);
for (int t = 0; t < T; t++) {
const float * src = (t < decoded_T)
? decoded_latents.data() + t * Oc
: silence_full.data() + (t - decoded_T) * Oc;
for (int c = 0; c < Oc; c++)
context_single[t * ctx_ch + c] = src[c];
for (int c = 0; c < Oc; c++)
context_single[t * ctx_ch + Oc + c] = 1.0f;
}
// Replicate context for N batch samples (all identical)
std::vector<float> context(batch_n * T * ctx_ch);
for (int b = 0; b < batch_n; b++)
{
memcpy(context.data() + b * T * ctx_ch, context_single.data(), T * ctx_ch * sizeof(float));
}
// Generate N noise samples
std::vector<float> noise(batch_n * Oc * T);
{
// Generate N noise samples with seeds: seed, seed+1, ..., seed+N-1
for (int b = 0; b < batch_n; b++) {
std::mt19937 rng((uint32_t)(seed + b));
std::normal_distribution<float> normal(0.0f, 1.0f);
float * dst = noise.data() + b * Oc * T;
for (int i = 0; i < Oc * T; i++)
dst[i] = normal(rng);
fprintf(stderr, "[Context Batch%d] noise seed=%lld\n", b, seed + b);
}
}
// DiT Generate
std::vector<float> output(batch_n * Oc * T);
fprintf(stderr, "[DiT] Starting: T=%d, S=%d, enc_S=%d, steps=%d, batch=%d\n",
T, S, enc_S, num_steps, batch_n);
music_dit_timer.reset();
dit_ggml_generate(&acestep_dit, noise.data(), context.data(), enc_hidden.data(),
enc_S, T, batch_n, num_steps, schedule.data(), output.data(),
guidance_scale);
fprintf(stderr, "[DiT] Total generation: %.1f ms (%.1f ms/sample)\n",
music_dit_timer.ms(), music_dit_timer.ms() / batch_n);
// VAE Decode + Write WAVs
int T_latent = T;
int T_audio_max = T_latent * 1920;
std::vector<float> audio(2 * T_audio_max);
int b = 0;
float * dit_out = output.data() + b * Oc * T;
if(acestep_dit_lowvram)
{
unload_acestep_dit_core();
}
music_dit_timer.reset();
int T_audio = vae_ggml_decode_tiled(&vae, dit_out, T_latent, audio.data(), T_audio_max, vae_chunk, vae_overlap);
if (T_audio < 0) {
fprintf(stderr, "[VAE] ERROR: decode failed\n");
return "";
}
fprintf(stderr, "[VAE] Decode: %.1f ms\n", music_dit_timer.ms());
// Peak normalization to -1.0 dB
{
float peak = 0.0f;
int n_samples = 2 * T_audio;
for (int i = 0; i < n_samples; i++) {
float a = audio[i] < 0 ? -audio[i] : audio[i];
if (a > peak) peak = a;
}
if (peak > 1e-6f) {
const float target_amp = powf(10.0f, -1.0f / 20.0f);
float gain = target_amp / peak;
for (int i = 0; i < n_samples; i++)
audio[i] *= gain;
}
}
// std::string opath = "egghenlo.wav";
// if (write_wav(opath.c_str(), audio.data(), T_audio, 48000)) {
// fprintf(stderr, "[VAE Batch%d] Wrote %s: %d samples (%.2fs @ 48kHz stereo)\n",
// b, opath.c_str(), T_audio, (float)T_audio / 48000.0f);
// } else {
// fprintf(stderr, "[VAE Batch%d] FATAL: failed to write %s\n", b, opath.c_str());
// }
// output wav
float muslen = (float)T_audio / 48000.0f;
std::string finalb64;
if(inputs.stereo)
{
finalb64 = save_stereo_wav16_base64(audio,T_audio,48000);
} else {
std::vector<float> mono = mix_planar_stereo_to_mono(audio.data(), T_audio);
std::vector<float> resampled_buf = resample_wav(mono,48000,32000);
finalb64 = save_wav16_base64(resampled_buf, 32000);
}
if(acestep_dit_lowvram)
{
unload_acestep_dit_others();
}
fprintf(stderr, "[Request Done: Music Length %.2fs]\n",muslen);
return finalb64;
}
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