88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class TIDEConfig:
|
|
"""Runtime configuration for the ComfyUI TIDE patch.
|
|
|
|
The defaults intentionally match the paper/official-code path where feasible:
|
|
base 1024x1024 FLUX training resolution, alpha_low=0.6, alpha_high=0.2,
|
|
tau_max=1.0, and Flux-style latent patch granularity of 16 image pixels per
|
|
transformer token.
|
|
"""
|
|
|
|
width: int
|
|
height: int
|
|
base_width: int = 1024
|
|
base_height: int = 1024
|
|
token_px: int = 16
|
|
text_anchor_strength: float = 1.0
|
|
temperature_strength: float = 1.0
|
|
alpha_low: float = 0.6
|
|
alpha_high: float = 0.2
|
|
tau_max: float = 1.0
|
|
theta: float = 10000.0
|
|
axes_dim: tuple[int, int, int] = (16, 56, 56)
|
|
frequency_mode: str = "official_raw"
|
|
apply_to_double_blocks: bool = True
|
|
apply_to_single_blocks: bool = True
|
|
apply_to_native_or_smaller: bool = False
|
|
force_pytorch_attention_with_mask: bool = True
|
|
preserve_existing_wrapper: bool = True
|
|
debug: bool = False
|
|
|
|
def __post_init__(self) -> None:
|
|
for name in ("width", "height", "base_width", "base_height", "token_px"):
|
|
value = getattr(self, name)
|
|
try:
|
|
numeric_value = int(value)
|
|
except (TypeError, ValueError) as exc:
|
|
raise ValueError(f"{name} must be a positive integer-like value, got {value!r}") from exc
|
|
if numeric_value <= 0:
|
|
raise ValueError(f"{name} must be > 0, got {value!r}")
|
|
|
|
@property
|
|
def target_image_tokens(self) -> int:
|
|
return max(1, (int(self.width) // self.token_px) * (int(self.height) // self.token_px))
|
|
|
|
@property
|
|
def base_image_tokens(self) -> int:
|
|
return max(1, (int(self.base_width) // self.token_px) * (int(self.base_height) // self.token_px))
|
|
|
|
@property
|
|
def target_pixel_ratio(self) -> float:
|
|
return max(1.0e-12, (float(self.width) * float(self.height)) / (float(self.base_width) * float(self.base_height)))
|
|
|
|
@property
|
|
def scale_x(self) -> float:
|
|
return max(1.0e-12, float(self.width) / float(self.base_width))
|
|
|
|
@property
|
|
def scale_y(self) -> float:
|
|
return max(1.0e-12, float(self.height) / float(self.base_height))
|
|
|
|
@property
|
|
def is_extrapolating(self) -> bool:
|
|
return self.target_image_tokens > self.base_image_tokens
|
|
|
|
@property
|
|
def is_axis_extrapolating(self) -> bool:
|
|
return self.scale_x > 1.0 or self.scale_y > 1.0
|
|
|
|
def should_apply_text_anchor(self) -> bool:
|
|
# Text anchoring is derived from total text/image token dilution, so keep
|
|
# the area-based gate unless the user explicitly forces native/smaller use.
|
|
return self.apply_to_native_or_smaller or self.is_extrapolating
|
|
|
|
def should_apply_temperature(self) -> bool:
|
|
# Dynamic Temperature Control is RoPE-axis based. Wide or tall WAN/Flux
|
|
# generations can extrapolate on one spatial axis while total pixel count
|
|
# is <= the square base area, e.g. 832x480 vs 640x640. In that case the
|
|
# width axis still needs the DTC path.
|
|
return self.apply_to_native_or_smaller or self.is_axis_extrapolating
|
|
|
|
def should_apply(self) -> bool:
|
|
return self.should_apply_text_anchor() or self.should_apply_temperature()
|