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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VjYy0F2gZIPR"
},
"outputs": [],
"source": [
"%cd /content\n",
"!git clone -b dev https://github.com/camenduru/FluxMusic\n",
"%cd C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/FluxMusic\n",
"\n",
"!apt -y install -qq aria2\n",
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/audo/FluxMusic/resolve/main/musicflow_b.pt -d C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/FluxMusic -o musicflow_b.pt\n",
"\n",
"!pip install transformers diffusers accelerate einops soundfile progressbar unidecode phonemizer torchlibrosa ftfy pandas timm matplotlib numpy==1.26.4 thop flash-attn==2.6.3 sentencepiece"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NoTEt9Wto70D"
},
"outputs": [],
"source": [
"%cd C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter\n",
"\n",
"import os\n",
"import torch\n",
"import argparse\n",
"import math\n",
"from einops import rearrange, repeat\n",
"from PIL import Image\n",
"from diffusers import AutoencoderKL\n",
"from transformers import SpeechT5HifiGan\n",
"\n",
"from utils import load_t5, load_clap, load_ae\n",
"from train import RF\n",
"from constants import build_model\n",
"\n",
"def prepare(t5, clip, img, prompt):\n",
" bs, c, h, w = img.shape\n",
" if bs == 1 and not isinstance(prompt, str):\n",
" bs = len(prompt)\n",
"\n",
" img = rearrange(img, \"b c (h ph) (w pw) -> b (h w) (c ph pw)\", ph=2, pw=2)\n",
" if img.shape[0] == 1 and bs > 1:\n",
" img = repeat(img, \"1 ... -> bs ...\", bs=bs)\n",
"\n",
" img_ids = torch.zeros(h // 2, w // 2, 3)\n",
" img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]\n",
" img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]\n",
" img_ids = repeat(img_ids, \"h w c -> b (h w) c\", b=bs)\n",
"\n",
" if isinstance(prompt, str):\n",
" prompt = [prompt]\n",
" txt = t5(prompt)\n",
" if txt.shape[0] == 1 and bs > 1:\n",
" txt = repeat(txt, \"1 ... -> bs ...\", bs=bs)\n",
" txt_ids = torch.zeros(bs, txt.shape[1], 3)\n",
"\n",
" vec = clip(prompt)\n",
" if vec.shape[0] == 1 and bs > 1:\n",
" vec = repeat(vec, \"1 ... -> bs ...\", bs=bs)\n",
"\n",
" print(img_ids.size(), txt.size(), vec.size())\n",
" return img, {\n",
" \"img_ids\": img_ids.to(img.device),\n",
" \"txt\": txt.to(img.device),\n",
" \"txt_ids\": txt_ids.to(img.device),\n",
" \"y\": vec.to(img.device),\n",
" }\n",
"\n",
"version=\"base\"\n",
"seed=2024\n",
"prompt_file=\"C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/config/example.txt\"\n",
"\n",
"print('generate with MusicFlux')\n",
"torch.manual_seed(seed)\n",
"torch.set_grad_enabled(False)\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"latent_size = (256, 16)\n",
"\n",
"model = build_model(version).to(device)\n",
"local_path = 'C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/musicflow_b.pt'\n",
"state_dict = torch.load(local_path, map_location=lambda storage, loc: storage, weights_only=True)\n",
"model.load_state_dict(state_dict['ema'])\n",
"model.eval() # important!\n",
"diffusion = RF()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "B5ebyTmto70D"
},
"outputs": [],
"source": [
"t5 = load_t5(device, max_length=256)\n",
"clap = load_clap(device, max_length=256)\n",
"\n",
"vae = AutoencoderKL.from_pretrained('cvssp/audioldm2', subfolder=\"vae\").to(device)\n",
"vocoder = SpeechT5HifiGan.from_pretrained('cvssp/audioldm2', subfolder=\"vocoder\").to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xqG8Px6xo70D"
},
"outputs": [],
"source": [
"prompt_file=\"C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/config/example.txt\"\n",
"\n",
"with open(prompt_file, 'r') as f:\n",
" conds_txt = f.readlines()\n",
"L = len(conds_txt)\n",
"unconds_txt = [\"low quality, gentle\"] * L\n",
"print(L, conds_txt, unconds_txt)\n",
"\n",
"init_noise = torch.randn(L, 8, latent_size[0], latent_size[1]).cuda()\n",
"\n",
"STEPSIZE = 50\n",
"img, conds = prepare(t5, clap, init_noise, conds_txt)\n",
"_, unconds = prepare(t5, clap, init_noise, unconds_txt)\n",
"with torch.autocast(device_type='cuda'):\n",
" images = diffusion.sample_with_xps(model, img, conds=conds, null_cond=unconds, sample_steps = STEPSIZE, cfg = 7.0)\n",
"\n",
"print(images[-1].size(), )\n",
"\n",
"images = rearrange(\n",
" images[-1],\n",
" \"b (h w) (c ph pw) -> b c (h ph) (w pw)\",\n",
" h=128,\n",
" w=8,\n",
" ph=2,\n",
" pw=2,)\n",
"# print(images.size())\n",
"latents = 1 / vae.config.scaling_factor * images\n",
"mel_spectrogram = vae.decode(latents).sample\n",
"print(mel_spectrogram.size())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ytAXlAEdo70D"
},
"outputs": [],
"source": [
"!mkdir C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/FluxMusic/b_output\n",
"\n",
"for i in range(L):\n",
" x_i = mel_spectrogram[i]\n",
" if x_i.dim() == 4:\n",
" x_i = x_i.squeeze(1)\n",
" waveform = vocoder(x_i)\n",
" waveform = waveform[0].cpu().float().detach().numpy()\n",
" print(waveform.shape)\n",
" # import soundfile as sf\n",
" # sf.write('reconstruct.wav', waveform, samplerate=16000)\n",
" from scipy.io import wavfile\n",
" wavfile.write('C:/Users/Curt/Developer/AItools/AIaudio/AudioCreation/FluxMusicJupyter/FluxMusic/b_output/sample_' + str(i) + '.wav', 16000, waveform)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |