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Spectrum acceleration for MOSS-Image-Base in diffusers.
Spectrum: "Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration"
(Han et al., arXiv:2603.01623)
Based on hanjq17/Spectrum
Usage
-----
from spectrum_moss import apply_spectrum, SpectrumConfig
pipe = DiffusionPipeline.from_pretrained(
"OpenMOSS-Team/MOSS-Image-Base",
custom_pipeline="pipeline_moss_image",
trust_remote_code=True,
torch_dtype={"default": torch.bfloat16, "vae": torch.float32},
)
apply_spectrum(pipe) # defaults below
pipe.enable_model_cpu_offload()
image = pipe("1girl", guidance_scale=3.0).images[0]
print(pipe.transformer._spectrum_state.summary())
apply_spectrum(pipe, SpectrumConfig(max_consecutive_forecasts=1)) # safer
remove_spectrum(pipe) # restore exact behaviour
No extra forward is ever spent: error statistics are measured at refresh steps by
comparing the incoming real feature against what the forecaster had predicted for it.
"""
import math
import types
from dataclasses import dataclass, field
from typing import Any, Optional
import torch
from diffusers.models.modeling_outputs import Transformer2DModelOutput
@dataclass
class SpectrumConfig:
warmup_steps: int = 8 # always-real steps at the start
tail_actual_steps: int = 2 # always-real steps at the end
window_size: float = 2.0 # initial refresh interval
flex_window: float = 0.75 # interval growth per real step
max_consecutive_forecasts: int = 2
history_points: int = 8 # K anchors kept
chebyshev_degree: int = 4 # M
ridge_lambda: float = 0.1 # lambda
blend_weight: float = 0.5 # w: spectral vs linear
cache_device: str = "same" # "same" | "cpu"
debug: bool = False
def validate(self) -> None:
if self.warmup_steps < 0 or self.tail_actual_steps < 0:
raise ValueError("warmup_steps / tail_actual_steps must be >= 0")
if self.window_size < 1.0:
raise ValueError("window_size must be >= 1.0")
if self.flex_window < 0.0:
raise ValueError("flex_window must be >= 0.0")
if self.max_consecutive_forecasts < 0:
raise ValueError("max_consecutive_forecasts must be >= 0")
if self.history_points < self.chebyshev_degree + 1:
raise ValueError("history_points must be >= chebyshev_degree + 1")
if self.chebyshev_degree < 1:
raise ValueError("chebyshev_degree must be >= 1")
if self.ridge_lambda < 0.0:
raise ValueError("ridge_lambda must be >= 0")
if not 0.0 <= self.blend_weight <= 1.0:
raise ValueError("blend_weight must be in [0, 1]")
if self.cache_device not in ("same", "cpu"):
raise ValueError("cache_device must be 'same' or 'cpu'")
@property
def window_cap(self) -> float:
return max(1.0, self.window_size, float(self.history_points))
def cheb_basis(u, degree: int) -> torch.Tensor:
u = torch.as_tensor(u, dtype=torch.float64).reshape(-1)
cols = [torch.ones_like(u)]
if degree >= 1:
cols.append(u)
for _ in range(2, degree + 1):
cols.append(2.0 * u * cols[-1] - cols[-2])
return torch.stack(cols[: degree + 1], dim=-1) # [n, P]
class Forecaster:
"""Chebyshev ridge forecaster (prediction = weighted sum of anchors)"""
def __init__(self, cfg: SpectrumConfig):
self.cfg = cfg
self.us: list[float] = []
self.feats: list[torch.Tensor] = []
self._chol: Optional[torch.Tensor] = None
self._last: Optional[tuple[float, torch.Tensor]] = None
def reset(self) -> None:
self.us.clear()
self.feats.clear()
self._chol = None
self._last = None
def ready(self) -> bool:
return len(self.us) >= self.cfg.chebyshev_degree + 1
def update(self, u: float, feat: torch.Tensor) -> None:
if self.feats and tuple(feat.shape) != tuple(self.feats[0].shape):
self.reset()
self.us.append(float(u))
self.feats.append(feat.detach())
if len(self.us) > self.cfg.history_points:
self.us.pop(0)
self.feats.pop(0)
self._chol = None
def _design(self) -> torch.Tensor:
return cheb_basis(self.us, self.cfg.chebyshev_degree) # [K, P]
def _factor(self) -> torch.Tensor:
if self._chol is not None:
return self._chol
X = self._design()
G = X.T @ X
P = G.shape[0]
eye = torch.eye(P, dtype=torch.float64)
lhs = G + self.cfg.ridge_lambda * eye
try:
chol = torch.linalg.cholesky(lhs)
except RuntimeError:
scale = max(float(G.diagonal().abs().mean().item()), 1.0)
chol = None
for eps in (1e-8, 1e-7, 1e-6, 1e-5):
try:
chol = torch.linalg.cholesky(lhs + scale * eps * eye)
break
except RuntimeError:
pass
if chol is None:
raise
self._chol = chol
return chol
def _linear_weights(self, u: float) -> torch.Tensor:
k = len(self.us)
w = torch.zeros(k, dtype=torch.float64)
if k == 0:
return w
if k == 1:
w[0] = 1.0
return w
spacing = self.us[-1] - self.us[-2]
if abs(spacing) < 1e-12:
w[-1] = 1.0
return w
ratio = (float(u) - self.us[-1]) / spacing
w[-2] = -ratio
w[-1] = 1.0 + ratio
return w
def weights(self, u: float) -> torch.Tensor:
phi = cheb_basis([u], self.cfg.chebyshev_degree) # [1, P]
solved = torch.cholesky_solve(self._design().T, self._factor()) # [P, K]
spectral = (phi @ solved).reshape(-1)
bw = self.cfg.blend_weight
if bw >= 1.0 - 1e-9:
return spectral
if bw <= 1e-9:
return self._linear_weights(u)
return bw * spectral + (1.0 - bw) * self._linear_weights(u)
def predict(self, u: float) -> torch.Tensor:
ws = self.weights(u).tolist()
acc: Optional[torch.Tensor] = None
for w, f in zip(ws, self.feats):
if w == 0.0:
continue
term = f.to(torch.float32) * float(w)
acc = term if acc is None else acc + term
if acc is None:
acc = torch.zeros_like(self.feats[-1], dtype=torch.float32)
out = acc.to(self.feats[-1].dtype)
self._last = (float(u), out)
return out
def relative_error(self, u: float, actual: torch.Tensor) -> Optional[float]:
if self._last is None:
return None
_, pred = self._last
if tuple(pred.shape) != tuple(actual.shape):
return None
num = (pred.float() - actual.float()).norm()
den = actual.float().norm().clamp_min(1e-6)
return float((num / den).item())
class Branch:
"""Per-branch scheduler state"""
def __init__(self, cfg: SpectrumConfig):
self.cfg = cfg
self.window = float(cfg.window_size)
self.consecutive = 0
self.actual = 0
self.forecast = 0
self.forecasters: dict[int, Forecaster] = {}
self.errors: list[float] = []
def forecaster(self, sample: int) -> Forecaster:
f = self.forecasters.get(sample)
if f is None:
f = self.forecasters[sample] = Forecaster(self.cfg)
return f
def decide(self, step: int, total: int) -> bool:
"""Return True for a real forward, False for a forecast."""
cfg = self.cfg
if step < cfg.warmup_steps:
return True
if cfg.tail_actual_steps > 0 and step >= max(0, total - cfg.tail_actual_steps):
return True
if self.consecutive >= cfg.max_consecutive_forecasts:
return True
interval = max(1, int(math.floor(self.window)))
return (self.consecutive + 1) % interval == 0
def note(self, actual: bool, step: int) -> None:
if actual:
self.actual += 1
self.consecutive = 0
if step >= self.cfg.warmup_steps:
self.window = min(self.window + self.cfg.flex_window, self.cfg.window_cap)
else:
self.forecast += 1
self.consecutive += 1
class State:
def __init__(self, cfg: SpectrumConfig, scheduler: Any):
self.cfg = cfg
self.scheduler = scheduler
self.original_forward = None
self.step_index = -1
self.branches: dict[Any, Branch] = {}
self._sig_counts: dict[Any, int] = {}
self._log: list[str] = []
def branch(self, key: Any) -> Branch:
b = self.branches.get(key)
if b is None:
b = self.branches[key] = Branch(self.cfg)
return b
def reset_run(self) -> None:
self.branches.clear()
self._sig_counts.clear()
self.step_index = -1
def summary(self) -> str:
a = sum(b.actual for b in self.branches.values())
f = sum(b.forecast for b in self.branches.values())
errs = [e for b in self.branches.values() for e in b.errors]
mean_err = sum(errs) / len(errs) if errs else 0.0
speed = (a + f) / a if a else 0.0
return (
f"Spectrum: actual={a} forecast={f} transformer_calls_saved="
f"{f}/{a + f} ({speed:.2f}x) mean_forecast_rel_err={mean_err:.4f}"
)
def validate_skip_layers(layers, depth: int, name: str = "skip_layers") -> tuple:
layers = () if layers is None else tuple(layers)
if any(type(i) is not int or not 0 <= i < depth for i in layers):
raise ValueError(f"{name} must contain integer block indices in [0, {depth}).")
return layers
def resolve_step(state: State, timestep: torch.Tensor) -> tuple[int, int]:
sched = state.scheduler
timesteps = getattr(sched, "timesteps", None) if sched is not None else None
if timesteps is None or len(timesteps) == 0:
return -1, 0
ts = timesteps if torch.is_tensor(timesteps) else torch.as_tensor(timesteps)
t = float(torch.as_tensor(timestep).reshape(-1)[0])
idx = int((ts.float() - t).abs().argmin().item())
return idx, len(ts)
def spectrum_forward(
self,
hidden_states: torch.Tensor,
timestep,
encoder_hidden_states: torch.Tensor,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
skip_layers=None,
):
"""
License: Apache-2.0
https://huggingface.co/nebulette/moss-spectrum-diffusers
"""
state: Optional[State] = getattr(self, "_spectrum_state", None)
if state is None or state.scheduler is None:
return state.original_forward(
hidden_states, timestep, encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
return_dict=return_dict, skip_layers=skip_layers,
)
cfg = state.cfg
skip = validate_skip_layers(skip_layers, len(self.layers))
if hidden_states.ndim != 4 or hidden_states.shape[1] != self.config.latent_channels:
raise ValueError("hidden_states must be [B, latent_channels, H, W].")
batch, _, height, width = hidden_states.shape
if encoder_hidden_states.ndim != 3 or encoder_hidden_states.shape[0] != batch:
raise ValueError("encoder_hidden_states must be [B, S, text_dim].")
mask = encoder_attention_mask
if mask is None:
mask = torch.ones(encoder_hidden_states.shape[:2], dtype=torch.bool, device=hidden_states.device)
if mask.shape != encoder_hidden_states.shape[:2] or not torch.all((mask == 0) | (mask == 1)):
raise ValueError("encoder_attention_mask must be a binary [B, S] mask.")
# --- step bookkeeping ------------------------------------------------- #
step, total = resolve_step(state, timestep)
if step < 0: # no schedule available -> stay exact
return state.original_forward(
hidden_states, timestep, encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
return_dict=return_dict, skip_layers=skip_layers,
)
if step == 0 and state.step_index > 0:
state.reset_run()
if step != state.step_index:
state.step_index = step
state._sig_counts = {}
sig = skip
occurrence = state._sig_counts.get(sig, 0)
state._sig_counts[sig] = occurrence + 1
branch = state.branch((sig, occurrence))
actual = branch.decide(step, total)
u = 0.0 if total <= 1 else max(-1.0, min(1.0, 2.0 * step / (total - 1) - 1.0))
# shared timestep conditioning (identical to the original)
times = torch.as_tensor(timestep, device=hidden_states.device, dtype=torch.float32).reshape(-1).expand(batch)
half = self.config.freq_dim // 2
freq = 10000 ** (-torch.arange(half, device=times.device, dtype=torch.float64) / half)
phase = times.double()[:, None] * freq
embedding = torch.cat((phase.cos(), phase.sin()), -1).float()
time = self.time_embedding(embedding.to(self.time_embedding[0].weight.dtype)).float()
head = self.video_head_modulation(time.to(self.video_head_modulation.weight.dtype)).view(batch, 2, -1)
mod = (
self.shared_block_modulation(time.to(self.shared_block_modulation.weight.dtype)).view(batch, 6, -1)
if actual else None
)
image_tokens = height * width
outputs = []
for index in range(batch):
context = encoder_hidden_states[index:index + 1, mask[index].bool()]
if context.shape[1] == 0:
raise ValueError("Each sample needs at least one valid text token.")
forecaster = branch.forecaster(index)
if not actual:
# skip embeddings, text blocks and all layers
try:
feat = forecaster.predict(u)
except Exception:
# numerical trouble -> fall back to a real step
actual = True
branch.note(True, step)
else:
y = self.final_norm_video(feat.to(self.final_norm_video.weight.dtype))
y = y * (1 + head[index:index + 1, 1, None]) + head[index:index + 1, 0, None]
y = self.final_linear_video(y.to(self.final_linear_video.weight.dtype))
outputs.append(y.transpose(1, 2).reshape(1, -1, height, width))
continue
# real path
video = hidden_states[index:index + 1].flatten(2).transpose(1, 2)
video = self.video_embedder(video.to(self.video_embedder.weight.dtype).contiguous())
rope = self.rope(height, width, context.shape[1], hidden_states.device).to(video.dtype)
text = self.text_embedder(context, rope[:, image_tokens:])
x = torch.cat((video, text), 1)
for layer_index, layer in enumerate(self.layers):
if layer_index not in skip:
x = layer(x, rope, mod[index:index + 1])
# make the block compatible with tuple outputs
if isinstance(x, tuple):
x = x[0]
feature = x[:, :image_tokens]
# measure the error of the forecast that was made for this step
err = forecaster.relative_error(u, feature)
if err is not None:
branch.errors.append(err)
if cfg.debug:
state._log.append(f"step {step + 1}/{total} branch={sig} rel_err={err:.4f}")
cache = feature
if cfg.cache_device == "cpu":
cache = feature.detach().to("cpu", non_blocking=False)
forecaster.update(u, cache)
y = self.final_norm_video(feature.to(self.final_norm_video.weight.dtype))
y = y * (1 + head[index:index + 1, 1, None]) + head[index:index + 1, 0, None]
y = self.final_linear_video(y.to(self.final_linear_video.weight.dtype))
outputs.append(y.transpose(1, 2).reshape(1, -1, height, width))
branch.note(actual, step)
sample = torch.cat(outputs)
if cfg.debug and step >= total - 1:
state._log.append(state.summary())
if not return_dict:
return (sample,)
return Transformer2DModelOutput(sample=sample)
def apply_spectrum(pipe, config: Optional[SpectrumConfig] = None):
"""
Patch ``pipe`` in place so the MOSS DiT runs under Spectrum scheduling.
License: Apache-2.0
https://huggingface.co/nebulette/moss-spectrum-diffusers
"""
cfg = config or SpectrumConfig()
cfg.validate()
transformer = getattr(pipe, "transformer", None)
if transformer is None:
raise ValueError("pipe has no `transformer`.")
if getattr(transformer, "_spectrum_state", None) is not None:
remove_spectrum(pipe)
state = State(cfg, getattr(pipe, "scheduler", None))
state.original_forward = transformer.forward
transformer._spectrum_state = state
transformer.forward = types.MethodType(spectrum_forward, transformer)
return pipe
def remove_spectrum(pipe) -> None:
"""Undo :func:`apply_spectrum`, restoring the untouched forward."""
transformer = getattr(pipe, "transformer", None)
if transformer is None:
return
state = getattr(transformer, "_spectrum_state", None)
if state is None:
return
if state.original_forward is not None:
transformer.forward = state.original_forward
try:
del transformer._spectrum_state
except AttributeError:
pass
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