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| import gc | |
| import logging | |
| from argparse import ArgumentParser | |
| from datetime import datetime | |
| from fractions import Fraction | |
| from pathlib import Path | |
| import gradio as gr | |
| import torch | |
| import torchaudio | |
| from mmaudio.eval_utils import (ModelConfig, VideoInfo, all_model_cfg, generate, load_image, | |
| load_video, make_video, setup_eval_logging) | |
| from mmaudio.model.flow_matching import FlowMatching | |
| from mmaudio.model.networks import MMAudio, get_my_mmaudio | |
| from mmaudio.model.sequence_config import SequenceConfig | |
| from mmaudio.model.utils.features_utils import FeaturesUtils | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| torch.backends.cudnn.allow_tf32 = True | |
| log = logging.getLogger() | |
| device = 'cpu' | |
| if torch.cuda.is_available(): | |
| device = 'cuda' | |
| elif torch.backends.mps.is_available(): | |
| device = 'mps' | |
| else: | |
| log.warning('CUDA/MPS are not available, running on CPU') | |
| dtype = torch.bfloat16 | |
| model: ModelConfig = all_model_cfg['large_44k_v2'] | |
| model.download_if_needed() | |
| output_dir = Path('./output/gradio') | |
| setup_eval_logging() | |
| def get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]: | |
| seq_cfg = model.seq_cfg | |
| net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval() | |
| net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True)) | |
| log.info(f'Loaded weights from {model.model_path}') | |
| feature_utils = FeaturesUtils(tod_vae_ckpt=model.vae_path, | |
| synchformer_ckpt=model.synchformer_ckpt, | |
| enable_conditions=True, | |
| mode=model.mode, | |
| bigvgan_vocoder_ckpt=model.bigvgan_16k_path, | |
| need_vae_encoder=False) | |
| feature_utils = feature_utils.to(device, dtype).eval() | |
| return net, feature_utils, seq_cfg | |
| net, feature_utils, seq_cfg = get_model() | |
| def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int, | |
| cfg_strength: float, duration: float): | |
| rng = torch.Generator(device=device) | |
| if seed >= 0: | |
| rng.manual_seed(seed) | |
| else: | |
| rng.seed() | |
| fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) | |
| video_info = load_video(video, duration) | |
| clip_frames = video_info.clip_frames | |
| sync_frames = video_info.sync_frames | |
| duration = video_info.duration_sec | |
| clip_frames = clip_frames.unsqueeze(0) | |
| sync_frames = sync_frames.unsqueeze(0) | |
| seq_cfg.duration = duration | |
| net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) | |
| audios = generate(clip_frames, | |
| sync_frames, [prompt], | |
| negative_text=[negative_prompt], | |
| feature_utils=feature_utils, | |
| net=net, | |
| fm=fm, | |
| rng=rng, | |
| cfg_strength=cfg_strength) | |
| audio = audios.float().cpu()[0] | |
| current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S') | |
| output_dir.mkdir(exist_ok=True, parents=True) | |
| video_save_path = output_dir / f'{current_time_string}.mp4' | |
| make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate) | |
| gc.collect() | |
| return video_save_path | |
| def image_to_audio(image: gr.Image, prompt: str, negative_prompt: str, seed: int, num_steps: int, | |
| cfg_strength: float, duration: float): | |
| rng = torch.Generator(device=device) | |
| if seed >= 0: | |
| rng.manual_seed(seed) | |
| else: | |
| rng.seed() | |
| fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) | |
| image_info = load_image(image) | |
| clip_frames = image_info.clip_frames | |
| sync_frames = image_info.sync_frames | |
| clip_frames = clip_frames.unsqueeze(0) | |
| sync_frames = sync_frames.unsqueeze(0) | |
| seq_cfg.duration = duration | |
| net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) | |
| audios = generate(clip_frames, | |
| sync_frames, [prompt], | |
| negative_text=[negative_prompt], | |
| feature_utils=feature_utils, | |
| net=net, | |
| fm=fm, | |
| rng=rng, | |
| cfg_strength=cfg_strength, | |
| image_input=True) | |
| audio = audios.float().cpu()[0] | |
| current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S') | |
| output_dir.mkdir(exist_ok=True, parents=True) | |
| video_save_path = output_dir / f'{current_time_string}.mp4' | |
| video_info = VideoInfo.from_image_info(image_info, duration, fps=Fraction(1)) | |
| make_video(video_info, video_save_path, audio, sampling_rate=seq_cfg.sampling_rate) | |
| gc.collect() | |
| return video_save_path | |
| def text_to_audio(prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float, | |
| duration: float): | |
| rng = torch.Generator(device=device) | |
| if seed >= 0: | |
| rng.manual_seed(seed) | |
| else: | |
| rng.seed() | |
| fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps) | |
| clip_frames = sync_frames = None | |
| seq_cfg.duration = duration | |
| net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len) | |
| audios = generate(clip_frames, | |
| sync_frames, [prompt], | |
| negative_text=[negative_prompt], | |
| feature_utils=feature_utils, | |
| net=net, | |
| fm=fm, | |
| rng=rng, | |
| cfg_strength=cfg_strength) | |
| audio = audios.float().cpu()[0] | |
| current_time_string = datetime.now().strftime('%Y%m%d_%H%M%S') | |
| output_dir.mkdir(exist_ok=True, parents=True) | |
| audio_save_path = output_dir / f'{current_time_string}.flac' | |
| torchaudio.save(audio_save_path, audio, seq_cfg.sampling_rate) | |
| gc.collect() | |
| return audio_save_path | |
| video_to_audio_tab = gr.Interface( | |
| fn=video_to_audio, | |
| description=""" Video-to-Audio | |
| NOTE: It takes longer to process high-resolution videos (>384 px on the shorter side). | |
| Doing so does not improve results. | |
| """, | |
| inputs=[ | |
| gr.Video(), | |
| gr.Text(label='Prompt'), | |
| gr.Text(label='Negative prompt', value='music'), | |
| gr.Number(label='Seed (-1: random)', value=-1, precision=0, minimum=-1), | |
| gr.Number(label='Num steps', value=25, precision=0, minimum=1), | |
| gr.Number(label='Guidance Strength', value=4.5, minimum=1), | |
| gr.Number(label='Duration (sec)', value=8, minimum=1), | |
| ], | |
| outputs='playable_video', | |
| cache_examples=False, | |
| title='Sonisphere - Sonic Branding Tool', | |
| ) | |
| text_to_audio_tab = gr.Interface( | |
| fn=text_to_audio, | |
| description=""" Text-to-Audio | |
| """, | |
| inputs=[ | |
| gr.Text(label='Prompt'), | |
| gr.Text(label='Negative prompt'), | |
| gr.Number(label='Seed (-1: random)', value=-1, precision=0, minimum=-1), | |
| gr.Number(label='Num steps', value=25, precision=0, minimum=1), | |
| gr.Number(label='Guidance Strength', value=4.5, minimum=1), | |
| gr.Number(label='Duration (sec)', value=8, minimum=1), | |
| ], | |
| outputs='audio', | |
| cache_examples=False, | |
| title='Sonisphere - Sonic Branding Tool', | |
| ) | |
| image_to_audio_tab = gr.Interface( | |
| fn=image_to_audio, | |
| description=""" | |
| Image-to-Audio | |
| NOTE: It takes longer to process high-resolution images (>384 px on the shorter side). | |
| Doing so does not improve results. | |
| """, | |
| inputs=[ | |
| gr.Image(type='filepath'), | |
| gr.Text(label='Prompt'), | |
| gr.Text(label='Negative prompt'), | |
| gr.Number(label='Seed (-1: random)', value=-1, precision=0, minimum=-1), | |
| gr.Number(label='Num steps', value=25, precision=0, minimum=1), | |
| gr.Number(label='Guidance Strength', value=4.5, minimum=1), | |
| gr.Number(label='Duration (sec)', value=8, minimum=1), | |
| ], | |
| outputs='playable_video', | |
| cache_examples=False, | |
| title='Image-to-Audio Synthesis (experimental)', | |
| ) | |
| if __name__ == "__main__": | |
| parser = ArgumentParser() | |
| parser.add_argument('--port', type=int, default=7860) | |
| args = parser.parse_args() | |
| gr.TabbedInterface([video_to_audio_tab, text_to_audio_tab, image_to_audio_tab], | |
| ['Video-to-Audio', 'Text-to-Audio', 'Image-to-Audio (experimental)']).launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| auth=("admin", "sonisphere"), | |
| allowed_paths=[output_dir]) |