File size: 4,121 Bytes
49235ad f90d455 49235ad f90d455 49235ad f90d455 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
from pathlib import Path
import hydra
import torch
from omegaconf import DictConfig
from slider import Beatmap
from osudiffusion import DiT_models
from osuT5.inference import Preprocessor, Pipeline, Postprocessor, DiffisionPipeline
from osuT5.tokenizer import Tokenizer
from osuT5.utils import get_model
def get_args_from_beatmap(args: DictConfig):
if args.beatmap_path is None or args.beatmap_path == "":
return
beatmap_path = Path(args.beatmap_path)
if not beatmap_path.is_file():
raise FileNotFoundError(f"Beatmap file {beatmap_path} not found.")
beatmap = Beatmap.from_path(beatmap_path)
args.audio_path = beatmap_path.parent / beatmap.audio_filename
args.output_path = beatmap_path.parent
args.bpm = beatmap.bpm_max()
args.offset = min(tp.offset.total_seconds() * 1000 for tp in beatmap.timing_points)
args.slider_multiplier = beatmap.slider_multiplier
args.title = beatmap.title
args.artist = beatmap.artist
args.beatmap_id = beatmap.beatmap_id if args.beatmap_id == -1 else args.beatmap_id
args.diffusion.style_id = beatmap.beatmap_id if args.diffusion.style_id == -1 else args.diffusion.style_id
args.difficulty = float(beatmap.stars()) if args.difficulty == -1 else args.difficulty
def find_model(ckpt_path, args: DictConfig, device):
assert Path(ckpt_path).exists(), f"Could not find DiT checkpoint at {ckpt_path}"
checkpoint = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
if "ema" in checkpoint: # supports checkpoints from train.py
checkpoint = checkpoint["ema"]
model = DiT_models[args.diffusion.model](
num_classes=args.diffusion.num_classes,
context_size=19 - 3 + 128,
).to(device)
model.load_state_dict(checkpoint)
model.eval() # important!
return model
@hydra.main(config_path="configs", config_name="inference", version_base="1.1")
def main(args: DictConfig):
get_args_from_beatmap(args)
torch.set_grad_enabled(False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ckpt_path = Path(args.model_path)
model_state = torch.load(ckpt_path / "pytorch_model.bin", map_location=device)
tokenizer_state = torch.load(ckpt_path / "custom_checkpoint_0.pkl")
tokenizer = Tokenizer()
tokenizer.load_state_dict(tokenizer_state)
model = get_model(args, tokenizer)
model.load_state_dict(model_state)
model.eval()
model.to(device)
preprocessor = Preprocessor(args)
audio = preprocessor.load(args.audio_path)
sequences = preprocessor.segment(audio)
total_duration_ms = len(audio) / 16000 * 1000
args.total_duration_ms = total_duration_ms
generated_maps = []
generated_positions = []
diffs = []
if args.full_set:
for i in range(args.set_difficulties):
diffs.append(3 + i * (7 - 3) / (args.set_difficulties - 1))
print(diffs)
for diff in diffs:
print(f"Generating difficulty {diff}")
args.difficulty = diff
pipeline = Pipeline(args, tokenizer)
events = pipeline.generate(model, sequences)
generated_maps.append(events)
else:
pipeline = Pipeline(args, tokenizer)
events = pipeline.generate(model, sequences)
generated_maps.append(events)
if args.generate_positions:
model = find_model(args.diff_ckpt, args, device)
refine_model = find_model(args.diff_refine_ckpt, args, device) if len(args.diff_refine_ckpt) > 0 else None
diffusion_pipeline = DiffisionPipeline(args.diffusion)
for events in generated_maps:
events = diffusion_pipeline.generate(model, events, refine_model)
generated_positions.append(events)
else:
generated_positions = generated_maps
postprocessor = Postprocessor(args)
postprocessor.generate(generated_positions)
if __name__ == "__main__":
main()
|