mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2026-05-07 09:02:04 +00:00
cleanup, fixed a regression in music gen with codes due to instruct prompt change
This commit is contained in:
Concedo
parent
6143a75426
commit
349fc744e9
1 changed files with 5 additions and 538 deletions
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@ -24,60 +24,6 @@
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#include "./vae-enc.h"
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#include "otherarch/utils.h"
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// Minimal WAV writer (16-bit PCM stereo)
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static bool write_wav(const char * path, const float * audio, int T_audio, int sr) {
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FILE * f = fopen(path, "wb");
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if (!f) return false;
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int n_channels = 2;
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int bits = 16;
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int byte_rate = sr * n_channels * (bits / 8);
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int block_align = n_channels * (bits / 8);
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int data_size = T_audio * n_channels * (bits / 8);
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int file_size = 36 + data_size;
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fwrite("RIFF", 1, 4, f);
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fwrite(&file_size, 4, 1, f);
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fwrite("WAVE", 1, 4, f);
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fwrite("fmt ", 1, 4, f);
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int fmt_size = 16; fwrite(&fmt_size, 4, 1, f);
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short audio_fmt = 1; fwrite(&audio_fmt, 2, 1, f);
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short nc = (short)n_channels; fwrite(&nc, 2, 1, f);
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fwrite(&sr, 4, 1, f);
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fwrite(&byte_rate, 4, 1, f);
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short ba = (short)block_align; fwrite(&ba, 2, 1, f);
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short bp = (short)bits; fwrite(&bp, 2, 1, f);
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fwrite("data", 1, 4, f);
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fwrite(&data_size, 4, 1, f);
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for (int t = 0; t < T_audio; t++) {
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for (int c = 0; c < 2; c++) {
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float s = audio[c * T_audio + t];
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s = s < -1.0f ? -1.0f : (s > 1.0f ? 1.0f : s);
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short v = (short)(s * 32767.0f);
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fwrite(&v, 2, 1, f);
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}
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}
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fclose(f);
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return true;
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}
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static void print_usage(const char * prog) {
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fprintf(stderr,
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"Usage: %s --request <json...> --text-encoder <gguf> --dit <gguf> --vae <gguf> [options]\n\n"
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"Required:\n"
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" --request <json...> One or more request JSONs (from ace-qwen3 --request)\n"
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" --text-encoder <gguf> Text encoder GGUF file\n"
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" --dit <gguf> DiT GGUF file\n"
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" --vae <gguf> VAE GGUF file\n\n"
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"Batch:\n"
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" --batch <N> DiT variations per request (default: 1, max 9)\n\n"
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"Output naming: input.json -> input0.wav, input1.wav, ... (last digit = batch index)\n\n"
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"VAE tiling (memory control):\n"
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" --vae-chunk <N> Latent frames per tile (default: 256)\n"
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" --vae-overlap <N> Overlap frames per side (default: 64)\n\n"
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"Debug:\n"
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" --noise-file <path> Load noise from bf16 file (Philox RNG dump, batch=1 only)\n"
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" --dump <dir> Dump intermediate tensors\n", prog);
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}
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// Parse comma-separated codes string into vector
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static std::vector<int> parse_codes_string(const std::string & s) {
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std::vector<int> codes;
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@ -92,488 +38,6 @@ static std::vector<int> parse_codes_string(const std::string & s) {
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return codes;
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}
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/*
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int main(int argc, char ** argv) {
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if (argc < 2) { print_usage(argv[0]); return 1; }
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std::vector<const char *> request_paths;
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const char * text_enc_gguf = NULL;
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const char * dit_gguf = NULL;
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const char * vae_gguf = NULL;
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const char * dump_dir = NULL;
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const char * noise_file = NULL;
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int batch_n = 1;
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int vae_chunk = 256;
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int vae_overlap = 64;
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for (int i = 1; i < argc; i++) {
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if (strcmp(argv[i], "--request") == 0) {
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// Collect all following non-option args
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while (i+1 < argc && argv[i+1][0] != '-')
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request_paths.push_back(argv[++i]);
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}
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else if (strcmp(argv[i], "--text-encoder") == 0 && i+1 < argc) text_enc_gguf = argv[++i];
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else if (strcmp(argv[i], "--dit") == 0 && i+1 < argc) dit_gguf = argv[++i];
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else if (strcmp(argv[i], "--vae") == 0 && i+1 < argc) vae_gguf = argv[++i];
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else if (strcmp(argv[i], "--noise-file") == 0 && i+1 < argc) noise_file = argv[++i];
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else if (strcmp(argv[i], "--dump") == 0 && i+1 < argc) dump_dir = argv[++i];
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else if (strcmp(argv[i], "--batch") == 0 && i+1 < argc) batch_n = atoi(argv[++i]);
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else if (strcmp(argv[i], "--vae-chunk") == 0 && i+1 < argc) vae_chunk = atoi(argv[++i]);
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else if (strcmp(argv[i], "--vae-overlap") == 0 && i+1 < argc) vae_overlap = atoi(argv[++i]);
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else if (strcmp(argv[i], "--help") == 0 || strcmp(argv[i], "-h") == 0) {
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print_usage(argv[0]); return 0;
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} else {
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fprintf(stderr, "Unknown option: %s\n", argv[i]);
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print_usage(argv[0]); return 1;
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}
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}
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if (request_paths.empty()) {
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fprintf(stderr, "ERROR: --request required\n");
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print_usage(argv[0]); return 1;
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}
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if (batch_n < 1 || batch_n > 9) {
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fprintf(stderr, "ERROR: --batch must be 1..9\n"); return 1;
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}
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if (!dit_gguf) {
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fprintf(stderr, "ERROR: --dit required\n");
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print_usage(argv[0]); return 1;
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}
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if (!text_enc_gguf) {
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fprintf(stderr, "ERROR: --text-encoder required\n");
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print_usage(argv[0]); return 1;
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}
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const int FRAMES_PER_SECOND = 25;
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DebugDumper dbg;
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debug_init(&dbg, dump_dir);
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Timer timer;
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DiTGGMLConfig cfg;
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DiTGGML model = {};
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// Load DiT model (once for all requests)
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dit_ggml_init_backend(&model);
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fprintf(stderr, "[Load] Backend init: %.1f ms\n", timer.ms());
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timer.reset();
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if (!dit_ggml_load(&model, dit_gguf, cfg)) {
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fprintf(stderr, "FATAL: failed to load DiT model\n");
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return 1;
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}
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fprintf(stderr, "[Load] DiT weight load: %.1f ms\n", timer.ms());
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// Read DiT GGUF metadata + silence_latent tensor (once)
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bool is_turbo = false;
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std::vector<float> silence_full; // [15000, 64] f32
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{
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GGUFModel gf = {};
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if (gf_load(&gf, dit_gguf)) {
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is_turbo = gf_get_bool(gf, "acestep.is_turbo");
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const void * sl_data = gf_get_data(gf, "silence_latent");
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if (sl_data) {
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silence_full.resize(15000 * 64);
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memcpy(silence_full.data(), sl_data, 15000 * 64 * sizeof(float));
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fprintf(stderr, "[Load] silence_latent: [15000, 64] from GGUF\n");
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} else {
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fprintf(stderr, "FATAL: silence_latent tensor not found in %s\n", dit_gguf);
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gf_close(&gf);
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dit_ggml_free(&model);
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return 1;
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}
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gf_close(&gf);
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} else {
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fprintf(stderr, "FATAL: cannot reopen %s for metadata\n", dit_gguf);
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dit_ggml_free(&model);
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return 1;
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}
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}
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int Oc = cfg.out_channels; // 64
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int ctx_ch = cfg.in_channels - Oc; // 128
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// Load VAE model (once for all requests)
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VAEGGML vae = {};
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bool have_vae = false;
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if (vae_gguf) {
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timer.reset();
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vae_ggml_load(&vae, vae_gguf);
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fprintf(stderr, "[Load] VAE weights: %.1f ms\n", timer.ms());
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have_vae = true;
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}
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// Process each request
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for (int ri = 0; ri < (int)request_paths.size(); ri++) {
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const char * rpath = request_paths[ri];
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fprintf(stderr, "[Request %d/%d] %s (batch=%d)\n",
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ri + 1, (int)request_paths.size(), rpath, batch_n);
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// Compute output basename: strip .json suffix
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std::string basename(rpath);
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{
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size_t dot = basename.rfind(".json");
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if (dot != std::string::npos)
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basename = basename.substr(0, dot);
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}
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// Parse request JSON
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AceRequest req;
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request_init(&req);
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if (!request_parse(&req, rpath)) {
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fprintf(stderr, "ERROR: failed to parse %s, skipping\n", rpath);
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continue;
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}
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if (req.caption.empty()) {
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fprintf(stderr, "ERROR: caption is empty in %s, skipping\n", rpath);
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continue;
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}
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// Extract params
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const char * caption = req.caption.c_str();
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const char * lyrics = req.lyrics.empty() ? "[Instrumental]" : req.lyrics.c_str();
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char bpm_str[16] = "N/A";
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if (req.bpm > 0) snprintf(bpm_str, sizeof(bpm_str), "%d", req.bpm);
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const char * bpm = bpm_str;
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const char * keyscale = req.keyscale.empty() ? "N/A" : req.keyscale.c_str();
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const char * timesig = req.timesignature.empty() ? "N/A" : req.timesignature.c_str();
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const char * language = req.vocal_language.empty() ? "en" : req.vocal_language.c_str();
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float duration = req.duration > 0 ? req.duration : 120.0f;
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long long seed = req.seed;
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int num_steps = req.inference_steps > 0 ? req.inference_steps : 8;
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float guidance_scale = req.guidance_scale > 0 ? req.guidance_scale : 7.0f;
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float shift = req.shift > 0 ? req.shift : 1.0f;
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if (is_turbo && guidance_scale > 1.0f) {
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fprintf(stderr, "[Pipeline] WARNING: turbo model, forcing guidance_scale=1.0 (was %.1f)\n",
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guidance_scale);
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guidance_scale = 1.0f;
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}
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if (seed < 0) {
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std::random_device rd;
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seed = (long long)rd() << 32 | rd();
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if (seed < 0) seed = -seed;
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}
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fprintf(stderr, "[Pipeline] seed=%lld, steps=%d, guidance=%.1f, shift=%.1f, duration=%.1fs\n",
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seed, num_steps, guidance_scale, shift, duration);
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// Parse audio codes from request
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std::vector<int> codes_vec = parse_codes_string(req.audio_codes);
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if (!codes_vec.empty())
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fprintf(stderr, "[Pipeline] %zu audio codes (%.1fs @ 5Hz)\n",
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codes_vec.size(), (float)codes_vec.size() / 5.0f);
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// Build schedule: t_i = shift * t / (1 + (shift-1)*t) where t = 1 - i/steps
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std::vector<float> schedule(num_steps);
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for (int i = 0; i < num_steps; i++) {
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float t = 1.0f - (float)i / (float)num_steps;
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schedule[i] = shift * t / (1.0f + (shift - 1.0f) * t);
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}
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// T = number of 25Hz latent frames for DiT
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// When audio codes are present, T is determined by the codes.
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// Otherwise, T is derived from the requested duration.
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int T = codes_vec.empty()
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? (int)(duration * FRAMES_PER_SECOND)
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: (int)codes_vec.size() * 5;
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T = ((T + cfg.patch_size - 1) / cfg.patch_size) * cfg.patch_size;
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int S = T / cfg.patch_size;
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int enc_S = 0;
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fprintf(stderr, "[Pipeline] T=%d, S=%d\n", T, S);
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if (T > 15000) {
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fprintf(stderr, "ERROR: T=%d exceeds silence_latent max 15000, skipping\n", T);
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continue;
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}
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// Text encoding
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// 1. Load BPE tokenizer
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timer.reset();
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BPETokenizer tok;
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if (!load_bpe_from_gguf(&tok, text_enc_gguf)) {
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fprintf(stderr, "FATAL: failed to load tokenizer from %s\n", text_enc_gguf);
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dit_ggml_free(&model);
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if (have_vae) vae_ggml_free(&vae);
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return 1;
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}
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fprintf(stderr, "[Load] BPE tokenizer: %.1f ms\n", timer.ms());
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// 2. Build formatted prompts
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const char * instruction = "Generate audio semantic tokens based on the given conditions:";
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char metas[512];
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snprintf(metas, sizeof(metas),
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"- bpm: %s\n- timesignature: %s\n- keyscale: %s\n- duration: %d seconds\n",
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bpm, timesig, keyscale, (int)duration);
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std::string text_str = std::string("# Instruction\n")
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+ instruction + "\n\n"
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+ "# Caption\n" + caption + "\n\n"
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+ "# Metas\n" + metas + "<|endoftext|>\n";
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bool instrumental = (strcmp(lyrics, "[Instrumental]") == 0 || strcmp(lyrics, "[instrumental]") == 0);
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std::string lyric_str = std::string("# Languages\n") + language + "\n\n# Lyric\n"
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+ (instrumental ? "[Instrumental]" : lyrics) + "<|endoftext|>";
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// 3. Tokenize
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auto text_ids = bpe_encode(&tok, text_str.c_str(), true);
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auto lyric_ids = bpe_encode(&tok, lyric_str.c_str(), true);
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int S_text = (int)text_ids.size();
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int S_lyric = (int)lyric_ids.size();
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fprintf(stderr, "[Pipeline] caption: %d tokens, lyrics: %d tokens\n", S_text, S_lyric);
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// 4. Text encoder forward (caption only)
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timer.reset();
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Qwen3GGML text_enc = {};
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qwen3_init_backend(&text_enc);
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if (!qwen3_load_text_encoder(&text_enc, text_enc_gguf)) {
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fprintf(stderr, "FATAL: failed to load text encoder\n");
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dit_ggml_free(&model);
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if (have_vae) vae_ggml_free(&vae);
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return 1;
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}
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fprintf(stderr, "[Load] TextEncoder: %.1f ms\n", timer.ms());
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int H_text = text_enc.cfg.hidden_size; // 1024
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std::vector<float> text_hidden(H_text * S_text);
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timer.reset();
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qwen3_forward(&text_enc, text_ids.data(), S_text, text_hidden.data());
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fprintf(stderr, "[Encode] TextEncoder (%d tokens): %.1f ms\n", S_text, timer.ms());
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debug_dump_2d(&dbg, "text_hidden", text_hidden.data(), S_text, H_text);
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// 5. Lyric embedding (CPU vocab lookup from text encoder embed table)
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timer.reset();
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std::vector<float> lyric_embed(H_text * S_lyric);
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{
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GGUFModel gf_te = {};
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if (!gf_load(&gf_te, text_enc_gguf)) {
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fprintf(stderr, "FATAL: cannot reopen text encoder GGUF for lyric embed\n");
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dit_ggml_free(&model);
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if (have_vae) vae_ggml_free(&vae);
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return 1;
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}
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const void * embed_data = gf_get_data(gf_te, "embed_tokens.weight");
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if (!embed_data) {
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fprintf(stderr, "FATAL: embed_tokens.weight not found\n");
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gf_close(&gf_te);
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dit_ggml_free(&model);
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if (have_vae) vae_ggml_free(&vae);
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return 1;
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}
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qwen3_cpu_embed_lookup(embed_data, H_text,
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lyric_ids.data(), S_lyric,
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lyric_embed.data());
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gf_close(&gf_te);
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}
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fprintf(stderr, "[Encode] Lyric vocab lookup (%d tokens): %.1f ms\n", S_lyric, timer.ms());
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debug_dump_2d(&dbg, "lyric_embed", lyric_embed.data(), S_lyric, H_text);
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// 6. Condition encoder forward
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timer.reset();
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CondGGML cond = {};
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cond_ggml_init_backend(&cond);
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if (!cond_ggml_load(&cond, dit_gguf)) {
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fprintf(stderr, "FATAL: failed to load condition encoder\n");
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dit_ggml_free(&model);
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if (have_vae) vae_ggml_free(&vae);
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return 1;
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}
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fprintf(stderr, "[Load] ConditionEncoder: %.1f ms\n", timer.ms());
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// Silence feats for timbre input: first 750 frames (30s @ 25Hz)
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const int S_ref = 750;
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std::vector<float> silence_feats(S_ref * 64);
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memcpy(silence_feats.data(), silence_full.data(), S_ref * 64 * sizeof(float));
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timer.reset();
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std::vector<float> enc_hidden;
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cond_ggml_forward(&cond, text_hidden.data(), S_text,
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lyric_embed.data(), S_lyric,
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silence_feats.data(), S_ref,
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enc_hidden, &enc_S);
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fprintf(stderr, "[Encode] ConditionEncoder: %.1f ms, enc_S=%d\n", timer.ms(), enc_S);
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qwen3_free(&text_enc);
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cond_ggml_free(&cond);
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debug_dump_2d(&dbg, "enc_hidden", enc_hidden.data(), enc_S, 2048);
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// Context building
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// Silence latent for this T
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std::vector<float> silence(Oc * T);
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memcpy(silence.data(), silence_full.data(), (size_t)(Oc * T) * sizeof(float));
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// Decode audio codes if provided
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int decoded_T = 0;
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std::vector<float> decoded_latents;
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if (!codes_vec.empty()) {
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timer.reset();
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DetokGGML detok = {};
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if (!detok_ggml_load(&detok, dit_gguf, model.backend, model.cpu_backend)) {
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fprintf(stderr, "FATAL: failed to load detokenizer\n");
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dit_ggml_free(&model);
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if (have_vae) vae_ggml_free(&vae);
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return 1;
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}
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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;
|
||||
|
|
@ -928,8 +392,11 @@ std::string acestep_generate_audio(const music_generation_inputs inputs)
|
|||
music_dit_timer.reset();
|
||||
|
||||
// 2. Build formatted prompts
|
||||
// Same instruction for all modes. Cover differs only by context content (audio vs silence).
|
||||
const char * instruction = "Fill the audio semantic mask based on the given conditions:";
|
||||
std::string instruction = "Generate audio semantic tokens based on the given conditions:";
|
||||
if(have_cover)
|
||||
{
|
||||
instruction = "Fill the audio semantic mask 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",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue