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from pathlib import Path
import torch
from omegaconf import DictConfig
from tqdm import tqdm
from osudiffusion import timestep_embedding
from osudiffusion import repeat_type
from osudiffusion import create_diffusion
from osudiffusion import DiT
from osuT5.dataset.data_utils import update_event_times
from osuT5.tokenizer import Event, EventType
def get_beatmap_idx(path) -> dict[int, int]:
p = Path(path)
with p.open("rb") as f:
beatmap_idx = pickle.load(f)
return beatmap_idx
class DiffisionPipeline(object):
def __init__(self, args: DictConfig):
"""Model inference stage that generates positions for distance events."""
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.num_sampling_steps = args.num_sampling_steps
self.cfg_scale = args.cfg_scale
self.seq_len = args.seq_len
self.num_classes = args.num_classes
self.beatmap_idx = get_beatmap_idx(args.beatmap_idx)
self.style_id = args.style_id
self.refine_iters = args.refine_iters
self.use_amp = args.use_amp
if self.style_id in self.beatmap_idx:
self.class_label = self.beatmap_idx[self.style_id]
else:
print(f"Beatmap ID {self.style_id} not found in dataset, using default style.")
self.class_label = self.num_classes
def generate(self, model: DiT, events: list[Event], refine_model: DiT = None) -> list[Event]:
"""Generate position events for distance events in the Event list.
Args:
model: Trained model to use for inference.
events: List of Event objects with distance events.
refine_model: Optional model to refine the generated positions.
Returns:
events: List of Event objects with position events.
"""
seq_o, seq_c, seq_len, seq_indices = self.events_to_sequence(events)
seq_o = seq_o - seq_o[0] # Normalize to relative time
print(f"seq len {seq_len}")
diffusion = create_diffusion(
str(self.num_sampling_steps),
noise_schedule="squaredcos_cap_v2",
)
# Create banded matrix attention mask for increased sequence length
attn_mask = torch.full((seq_len, seq_len), True, dtype=torch.bool, device=self.device)
for i in range(seq_len):
attn_mask[max(0, i - self.seq_len): min(seq_len, i + self.seq_len), i] = False
class_labels = [self.class_label]
# Create sampling noise:
n = len(class_labels)
z = torch.randn(n, 2, seq_len, device=self.device)
o = seq_o.repeat(n, 1).to(self.device)
c = seq_c.repeat(n, 1, 1).to(self.device)
y = torch.tensor(class_labels, device=self.device)
# Setup classifier-free guidance:
z = torch.cat([z, z], 0)
o = torch.cat([o, o], 0)
c = torch.cat([c, c], 0)
y_null = torch.tensor([self.num_classes] * n, device=self.device)
y = torch.cat([y, y_null], 0)
model_kwargs = dict(o=o, c=c, y=y, cfg_scale=self.cfg_scale, attn_mask=attn_mask)
def to_positions(samples):
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
samples *= torch.tensor((512, 384), device=self.device).repeat(n, 1).unsqueeze(2)
return samples.cpu()
# Sample images:
samples = diffusion.p_sample_loop(
model.forward_with_cfg,
z.shape,
z,
clip_denoised=True,
model_kwargs=model_kwargs,
progress=True,
device=self.device,
)
if refine_model is not None:
# Refine result with refine model
for _ in tqdm(range(self.refine_iters)):
t = torch.tensor([0] * samples.shape[0], device=self.device)
with torch.no_grad():
out = diffusion.p_sample(
model.forward_with_cfg,
samples,
t,
clip_denoised=True,
model_kwargs=model_kwargs,
)
samples = out["sample"]
positions = to_positions(samples)
return self.events_with_pos(events, positions.squeeze(0), seq_indices)
@staticmethod
def events_to_sequence(events: list[Event]) -> tuple[torch.Tensor, torch.Tensor, int, dict[int, int]]:
# Calculate the time of every event and interpolate time for control point events
event_times = []
update_event_times(events, event_times)
# Calculate the number of repeats for each slider end event
# Convert to vectorized form for osu-diffusion
nc_types = [EventType.CIRCLE, EventType.SLIDER_HEAD]
event_index = {
EventType.CIRCLE: 0,
EventType.SPINNER: 2,
EventType.SPINNER_END: 3,
EventType.SLIDER_HEAD: 4,
EventType.BEZIER_ANCHOR: 6,
EventType.PERFECT_ANCHOR: 7,
EventType.CATMULL_ANCHOR: 8,
EventType.RED_ANCHOR: 9,
EventType.LAST_ANCHOR: 10,
EventType.SLIDER_END: 11,
}
seq_indices = {}
indices = []
data_chunks = []
distance = 0
new_combo = False
head_time = 0
last_anchor_time = 0
for i, event in enumerate(events):
indices.append(i)
if event.type == EventType.DISTANCE:
distance = event.value
elif event.type == EventType.NEW_COMBO:
new_combo = True
elif event.type in event_index:
time = event_times[i]
index = event_index[event.type]
# Handle NC index offset
if event.type in nc_types and new_combo:
index += 1
new_combo = False
# Add slider end repeats index offset
if event.type == EventType.SLIDER_END:
span_duration = last_anchor_time - head_time
total_duration = time - head_time
repeats = max(int(round(total_duration / span_duration)), 1) if span_duration > 0 else 1
index += repeat_type(repeats)
elif event.type == EventType.SLIDER_HEAD:
head_time = time
elif event.type == EventType.LAST_ANCHOR:
last_anchor_time = time
features = torch.zeros(18)
features[0] = time
features[1] = distance
features[index + 2] = 1
data_chunks.append(features)
for j in indices:
seq_indices[j] = len(data_chunks) - 1
indices = []
seq = torch.stack(data_chunks, 0)
seq = torch.swapaxes(seq, 0, 1)
seq_o = seq[0, :]
seq_d = seq[1, :]
seq_c = torch.concatenate(
[
timestep_embedding(seq_d, 128).T,
seq[2:, :],
],
0,
)
return seq_o, seq_c, seq.shape[1], seq_indices
@staticmethod
def events_with_pos(events: list[Event], sampled_seq: torch.Tensor, seq_indices: dict[int, int]) -> list[Event]:
new_events = []
for i, event in enumerate(events):
if event.type == EventType.DISTANCE:
try:
index = seq_indices[i]
pos_x = sampled_seq[0, index].item()
pos_y = sampled_seq[1, index].item()
new_events.append(Event(EventType.POS_X, int(round(pos_x))))
new_events.append(Event(EventType.POS_Y, int(round(pos_y))))
except KeyError:
print(f"Warning: Key {i} not found in seq_indices. Skipping event.")
else:
new_events.append(event)
return new_events
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