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from typing import Any
import torch
import torch.nn.functional as F
def compute_token_change_rate(token_t: torch.Tensor, token_t_minus_1: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
"""Per-token relative change using L2 norms: (||token_t|| - ||token_{t-1}||) / ||token_t||."""
norm_t = torch.linalg.vector_norm(token_t.float(), dim=-1)
norm_t_minus_1 = torch.linalg.vector_norm(token_t_minus_1.float(), dim=-1)
return (norm_t - norm_t_minus_1) / norm_t.clamp(min=eps)
def cosine_match_tokens(history_tokens: torch.Tensor, noise_tokens: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""For each noise token, find the history token with highest cosine similarity."""
history = F.normalize(history_tokens.float(), dim=-1)
noise = F.normalize(noise_tokens.float(), dim=-1)
similarity = noise @ history.T
match_indices = similarity.argmax(dim=-1)
match_scores = similarity.gather(1, match_indices.unsqueeze(1)).squeeze(1)
return match_indices, match_scores
def extract_frame_tokens(
token_sequence: torch.Tensor,
history_context_length: int,
original_context_length: int,
grid_tokens: int,
history_frame_index: int,
noise_frame_index: int,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Extract one spatial frame from short-history and current-noise token sequences."""
short_history_frames = 2
short_len = short_history_frames * grid_tokens
history_seq_len = history_context_length
short_start = history_seq_len - short_len
history_start = short_start + history_frame_index * grid_tokens
noise_start = history_seq_len + noise_frame_index * grid_tokens
history_tokens = token_sequence[history_start : history_start + grid_tokens]
noise_tokens = token_sequence[noise_start : noise_start + grid_tokens]
return history_tokens, noise_tokens
def extract_noise_frame_tokens(
token_sequence: torch.Tensor,
history_context_length: int,
grid_tokens: int,
noise_frame_index: int,
) -> torch.Tensor:
noise_start = history_context_length + noise_frame_index * grid_tokens
return token_sequence[noise_start : noise_start + grid_tokens]
def resolve_short_history_frame_index(history_frame: int, short_history_frames: int = 2) -> int:
if history_frame < 0:
history_frame += short_history_frames
return history_frame
def resolve_history_source_frame(
chunk_index: int,
history_frame: int,
*,
keep_first_frame: bool,
num_latent_frames_per_chunk: int,
short_history_frames: int = 2,
) -> tuple[int, int]:
"""Map configured short-history frame to the chunk/latent-frame where it was denoised."""
short_index = resolve_short_history_frame_index(history_frame, short_history_frames)
if keep_first_frame:
if short_index == 0:
return 0, 0
return max(0, chunk_index - 1), num_latent_frames_per_chunk - 1
return max(0, chunk_index - 1), num_latent_frames_per_chunk - 1
def token_yx_from_index(index: int, grid_w: int) -> tuple[int, int]:
return int(index) // grid_w, int(index) % grid_w
def token_rect_on_image(
token_y: int,
token_x: int,
image_height: int,
image_width: int,
grid_h: int,
grid_w: int,
patch_h: int = 2,
patch_w: int = 2,
) -> tuple[float, float, float, float]:
"""Map token grid cell to pixel rectangle on decoded frame."""
latent_h = grid_h * patch_h
latent_w = grid_w * patch_w
ly0 = token_y * patch_h
lx0 = token_x * patch_w
ly1 = ly0 + patch_h
lx1 = lx0 + patch_w
py0 = ly0 / latent_h * image_height
px0 = lx0 / latent_w * image_width
py1 = ly1 / latent_h * image_height
px1 = lx1 / latent_w * image_width
return px0, py0, px1 - px0, py1 - py0
class TokenDynamicsDebugTracker:
def __init__(self, config: dict[str, Any] | None):
self.config = config
self.state: dict[str, Any] = {}
self._frame_rates: dict[tuple[int, int], dict[str, list]] = {}
self.reset_chunk_records()
@property
def enabled(self) -> bool:
return self.config is not None and self.config.get("enabled", True)
def reset_chunk_records(self):
self.match_indices = None
self.match_scores = None
self.step_indices: list[int] = []
self.timesteps: list[float] = []
self._prev_noise_frame_tokens: dict[int, torch.Tensor] = {}
self._matching_done = False
self.noise_latent_frame = None
self.history_latent_frame = None
self.capture_pass_name = None
def set_state(self, **state):
prev_chunk = self.state.get("chunk_index")
next_chunk = state.get("chunk_index", prev_chunk)
if prev_chunk is not None and next_chunk is not None and prev_chunk != next_chunk:
self.reset_chunk_records()
self.state.update(state)
def should_record(self) -> bool:
if not self.enabled:
return False
pass_names = self.config.get("pass_names", ["cond"])
if self.state.get("pass_name", "cond") not in pass_names:
return False
return True
def should_capture(self) -> bool:
if not self.should_record():
return False
return self.is_analysis_chunk()
def is_analysis_chunk(self) -> bool:
chunks = self.config.get("chunks")
if chunks is not None and self.state.get("chunk_index", 0) not in chunks:
return False
return True
def get_recording_targets(self) -> set[tuple[int, int]]:
"""(chunk, latent_frame) pairs to record: history source frame + noise frame only."""
targets: set[tuple[int, int]] = set()
analysis_chunks = self.config.get("chunks")
if not analysis_chunks:
return targets
noise_frame = int(self.config.get("noise_frame", 0))
for chunk_index in analysis_chunks:
targets.add((int(chunk_index), noise_frame))
hist_chunk, hist_frame = resolve_history_source_frame(
int(chunk_index),
int(self.config.get("history_frame", -1)),
keep_first_frame=bool(self.config.get("keep_first_frame", True)),
num_latent_frames_per_chunk=int(self.config.get("num_latent_frames_per_chunk", 9)),
)
targets.add((hist_chunk, hist_frame))
return targets
def _record_frame_rates(self, chunk_index: int, frame_index: int, rate: torch.Tensor):
key = (chunk_index, frame_index)
bucket = self._frame_rates.setdefault(key, {"rates": [], "step_indices": [], "timesteps": []})
bucket["rates"].append(rate.detach().cpu())
bucket["step_indices"].append(int(self.state.get("step_index", 0)))
bucket["timesteps"].append(float(self.state.get("timestep", self.state.get("step_index", 0))))
def observe(
self,
latent_tokens: torch.Tensor,
history_context_length: int,
original_context_length: int,
):
if not self.should_record():
return
if self.capture_pass_name is None:
self.capture_pass_name = self.state.get("pass_name", "cond")
grid_h, grid_w = self.config.get("grid", (24, 40))
grid_tokens = int(grid_h) * int(grid_w)
history_frame = int(self.config.get("history_frame", -1))
noise_frame = int(self.config.get("noise_frame", 0))
short_history_frame = resolve_short_history_frame_index(history_frame)
chunk_index = int(self.state.get("chunk_index", 0))
recording_targets = self.get_recording_targets()
frames_to_record = sorted(frame_index for c, frame_index in recording_targets if c == chunk_index)
for frame_index in frames_to_record:
frame_tokens = extract_noise_frame_tokens(
latent_tokens[0],
history_context_length,
grid_tokens,
frame_index,
)
prev_tokens = self._prev_noise_frame_tokens.get(frame_index)
if prev_tokens is not None:
self._record_frame_rates(
chunk_index,
frame_index,
compute_token_change_rate(frame_tokens, prev_tokens),
)
self._prev_noise_frame_tokens[frame_index] = frame_tokens.detach()
step_index = int(self.state.get("step_index", 0))
total_steps = int(self.state.get("total_steps", 1))
is_last_step = step_index == total_steps - 1
if self.should_capture() and is_last_step and not self._matching_done:
latent_history, latent_noise = extract_frame_tokens(
latent_tokens[0],
history_context_length,
original_context_length,
grid_tokens,
short_history_frame,
noise_frame,
)
self.match_indices, self.match_scores = cosine_match_tokens(latent_history, latent_noise)
self._matching_done = True
def _get_frame_rate_series(self, chunk_index: int, frame_index: int) -> dict[str, Any]:
key = (chunk_index, frame_index)
if key not in self._frame_rates or not self._frame_rates[key]["rates"]:
raise KeyError(f"No patch-latent change rates recorded for chunk={chunk_index}, frame={frame_index}")
bucket = self._frame_rates[key]
return {
"rates": torch.stack(bucket["rates"], dim=0),
"step_indices": bucket["step_indices"].copy(),
"timesteps": bucket["timesteps"].copy(),
}
def set_visualization_latent_frames(
self,
denoised_latents: torch.Tensor,
history_short_latents: torch.Tensor,
):
"""Store fully denoised latent frames for VAE visualization (after chunk sampling)."""
if not self.enabled or not self._matching_done or not self.is_analysis_chunk():
return
history_frame = int(self.config.get("history_frame", -1))
noise_frame = int(self.config.get("noise_frame", 0))
short_frames = history_short_latents.shape[2]
short_index = resolve_short_history_frame_index(history_frame, short_frames)
self.history_latent_frame = history_short_latents[0, :, short_index].detach().float().cpu()
self.noise_latent_frame = denoised_latents[0, :, noise_frame].detach().float().cpu()
def save(self):
if not self.enabled or self.match_indices is None:
return None
chunk_index = int(self.state.get("chunk_index", 0))
noise_frame = int(self.config.get("noise_frame", 0))
history_source_chunk, history_source_frame = resolve_history_source_frame(
chunk_index,
int(self.config.get("history_frame", -1)),
keep_first_frame=bool(self.config.get("keep_first_frame", True)),
num_latent_frames_per_chunk=int(self.config.get("num_latent_frames_per_chunk", 9)),
)
history_series = self._get_frame_rate_series(history_source_chunk, history_source_frame)
noise_series = self._get_frame_rate_series(chunk_index, noise_frame)
self.step_indices = noise_series["step_indices"]
self.timesteps = noise_series["timesteps"]
output_dir = self.config.get("output_dir", "token_dynamics_debug")
os.makedirs(output_dir, exist_ok=True)
filename = (
f"token_dynamics_chunk{chunk_index}_"
f"hist{self.config.get('history_frame', -1)}_"
f"noise{self.config.get('noise_frame', 0)}_"
f"{self.capture_pass_name or self.state.get('pass_name', 'cond')}.pt"
)
path = os.path.join(output_dir, filename)
artifact = {
"chunk_index": chunk_index,
"history_frame": int(self.config.get("history_frame", -1)),
"noise_frame": noise_frame,
"history_source_chunk": history_source_chunk,
"history_source_frame": history_source_frame,
"grid": tuple(self.config.get("grid", (24, 40))),
"match_indices": self.match_indices.cpu(),
"match_scores": self.match_scores.cpu(),
"history_change_rates": history_series["rates"],
"noise_change_rates": noise_series["rates"],
"step_indices": self.step_indices.copy(),
"timesteps": self.timesteps.copy(),
"total_steps": int(self.state.get("total_steps", 0)),
"noise_latent_frame": self.noise_latent_frame,
"history_latent_frame": self.history_latent_frame,
}
torch.save(artifact, path)
return path
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