scripts/sigma_core.py

"""sigma_core.py — SigMa positional-encoding core, EXTRACTED VERBATIM from
github.com/bxuanz/SigMa flux/transformer_flux.py (ICML 2026 paper 47JZSOkw5C).

Only the diffusers-independent RoPE/SigMa math is copied here so it runs without
the repo's heavy diffusers/transformers imports (which clash with the local
huggingface-hub version). No logic is modified. Line provenance in transformer_flux.py:
  get_adaptive_scale        L89-111
  find_correction_factor    L534-535
  find_correction_range     L538-544
  linear_ramp_mask          L547-553
  find_newbase_ntk          L556-560
  get_1d_rotary_pos_embed   L568-685
  FluxPosEmbed              L687-776
"""
import math
import torch
import torch.nn as nn
import numpy as np
from typing import List, Union

def get_adaptive_scale(t: float, scale_factor: float) -> float:
    """
    Logit-space SigMa scheduler:
    mu_d(t) = sigmoid(gamma_d * (logit(t) - logit(t_c,d))).
    """
    t_center = 1.0 / scale_factor
    gamma_d = math.sqrt(scale_factor)

    # logit(t) is defined on (0, 1); Flux can pass t=1 at the first step.
    eps = 1e-6
    t = min(max(t, eps), 1.0 - eps)
    t_center = min(max(t_center, eps), 1.0 - eps)

    def logit(value: float) -> float:
        return math.log(value / (1.0 - value))

    # 注意：Flux 中 t=1 是噪声，t=0 是图。
    # 当 t > t_center (早期)，x > 0 -> alpha -> 1 (使用 NTK/YaRN)
    # 当 t < t_center (晚期)，x < 0 -> alpha -> 0 (回归 Base 以获得锐利纹理)
    x = gamma_d * (logit(t) - logit(t_center))
    alpha = 1 / (1 + math.exp(-x))

    return alpha

def find_correction_factor(num_rotations, dim, base, max_position_embeddings):
    return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base)) #Inverse dim formula to find number of rotations

def find_correction_range(low_ratio, high_ratio, dim, base, ori_max_pe_len):
    """
    Find the correction range for NTK-by-parts interpolation.
    """
    low = np.floor(find_correction_factor(low_ratio, dim, base, ori_max_pe_len))
    high = np.ceil(find_correction_factor(high_ratio, dim, base, ori_max_pe_len))
    return max(low, 0), min(high, dim-1) #Clamp values just in case

def linear_ramp_mask(min, max, dim):
    if min == max:
        max += 0.001 #Prevent singularity

    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func

def find_newbase_ntk(dim, base, scale):
    """
    Calculate the new base for NTK-aware scaling.
    """
    return base * (scale ** (dim / (dim - 2)))

def get_1d_rotary_pos_embed(
    dim: int,
    pos: Union[np.ndarray, int],
    theta: float = 10000.0,
    use_real=False,
    linear_factor=1.0,
    ntk_factor=1.0,
    repeat_interleave_real=True,
    freqs_dtype=torch.float32,
    yarn=False,
    max_pe_len=None,
    ori_max_pe_len=64, # [重要] 听你的，保持 64 不动！这是画质的基石。
    sigma=False,
    current_timestep=1.0,
    gamma_factor=1.0,
):
    assert dim % 2 == 0

    if isinstance(pos, int):
        pos = torch.arange(pos)
    if isinstance(pos, np.ndarray):
        pos = torch.from_numpy(pos)

    device = pos.device

    # 这里的 scale 用于计算 RoPE 频率，必须基于 ori_max_pe_len=64
    if yarn and max_pe_len is not None and max_pe_len > ori_max_pe_len:
        if not isinstance(max_pe_len, torch.Tensor):
            max_pe_len = torch.tensor(max_pe_len, dtype=freqs_dtype, device=device)

        # [Track 1: 几何缩放] 
        # 保持 64 基准，scale 约为 64.0 (4096/64)
        # 这一步保证了图像质量不下降
        scale = torch.clamp_min(max_pe_len / ori_max_pe_len, 1.0)
        scale_val = scale.item() 

        # YaRN 默认参数
        beta_0 = 1.25
        beta_1 = 0.75
        gamma_0 = 16
        gamma_1 = 2

        freqs_base = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim))

        # 这里的 freqs_linear 使用 Base-64 的 scale，保证坐标系正确
        freqs_linear = 1.0 / torch.einsum(
            '..., f -> ... f',
            scale,
            (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim))
        )

        new_base = find_newbase_ntk(dim, theta, scale)
        if new_base.dim() > 0:
            new_base = new_base.view(-1, 1)
        freqs_ntk = 1.0 / torch.pow(
            new_base,
            (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim)
        )
        if freqs_ntk.dim() > 1:
            freqs_ntk = freqs_ntk.squeeze()

        # -----------------------------------------------------------
        # [SigMa core logic]
        # -----------------------------------------------------------
        if sigma:
            adaptive_alpha = get_adaptive_scale(current_timestep, scale_val)
            beta_0 = beta_0 * adaptive_alpha
            beta_1 = beta_1 * adaptive_alpha
        
        low, high = find_correction_range(beta_0, beta_1, dim, theta, ori_max_pe_len)
        low = max(0, low)
        high = min(dim // 2, high)

        freqs_mask = (1 - linear_ramp_mask(low, high, dim // 2).to(device).to(freqs_dtype))
        freqs = freqs_linear * (1 - freqs_mask) + freqs_ntk * freqs_mask

        if sigma:
            gamma_0 = gamma_0 * adaptive_alpha
            gamma_1 = gamma_1 * adaptive_alpha

        low, high = find_correction_range(gamma_0, gamma_1, dim, theta, ori_max_pe_len)
        low = max(0, low)
        high = min(dim // 2, high)

        freqs_mask = (1 - linear_ramp_mask(low, high, dim // 2).to(device).to(freqs_dtype))
        freqs = freqs * (1 - freqs_mask) + freqs_base * freqs_mask

    else:
        theta_ntk = theta * ntk_factor
        freqs = 1.0 / (theta_ntk ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim)) / linear_factor

    freqs = torch.outer(pos, freqs)

    is_npu = freqs.device.type == "npu"
    if is_npu:
        freqs = freqs.float()
    if use_real and repeat_interleave_real:
        freqs_cos = freqs.cos().repeat_interleave(2, dim=1, output_size=freqs.shape[1] * 2).float()
        freqs_sin = freqs.sin().repeat_interleave(2, dim=1, output_size=freqs.shape[1] * 2).float()

        # MScale 逻辑
        if yarn and max_pe_len is not None and max_pe_len > ori_max_pe_len:
            scale_factor_tensor = scale if isinstance(scale, torch.Tensor) else torch.tensor(scale)

            # MScale 这里的公式 0.1 * ln(scale)
            target_mscale = 0.1 * torch.log(scale_factor_tensor) + 1.0

            if sigma:
                adaptive_alpha = get_adaptive_scale(current_timestep, scale_val)
                mscale = (target_mscale - 1.0) * adaptive_alpha + 1.0
            else:
                mscale = target_mscale

            mscale = mscale.to(freqs_cos.device)
            freqs_cos = freqs_cos * mscale
            freqs_sin = freqs_sin * mscale

        return freqs_cos, freqs_sin

class FluxPosEmbed(nn.Module):
    def __init__(
            self,
            theta: int,
            axes_dim: List[int],
            method: str = 'yarn',
            sigma: bool = True,
            gamma_factor: float = 1,
    ):
        super().__init__()
        self.theta = theta
        self.axes_dim = axes_dim
        self.base_resolution = 1024
        self.patch_size = 16
        self.base_patches = self.base_resolution // self.patch_size
        self.method = method
        self.sigma = sigma if method != 'base' else False
        self.current_timestep = 1.0
        self.gamma_factor = gamma_factor

    def set_timestep(self, timestep: float):
        """Set current timestep for SigMa."""
        self.current_timestep = timestep

    def forward(self, ids: torch.Tensor) -> torch.Tensor:
        n_axes = ids.shape[-1]
        cos_out = []
        sin_out = []
        pos = ids.float()
        is_mps = ids.device.type == "mps"
        is_npu = ids.device.type == "npu"
        freqs_dtype = torch.float32 if (is_mps or is_npu) else torch.float64

        for i in range(n_axes):
            common_kwargs = {
                'dim': self.axes_dim[i],
                'pos': pos[:, i],
                'theta': self.theta,
                'repeat_interleave_real': True,
                'use_real': True,
                'freqs_dtype': freqs_dtype,
            }

            if i > 0:
                max_pos = pos[:, i].max().item()
                current_patches = max_pos + 1

                if self.method == 'yarn' and current_patches > self.base_patches:
                    max_pe_len = torch.tensor(current_patches, dtype=freqs_dtype, device=pos.device)
                    cos, sin = get_1d_rotary_pos_embed(
                        **common_kwargs,
                        yarn=True,
                        max_pe_len=max_pe_len,
                        ori_max_pe_len=self.base_patches,
                        sigma=self.sigma,
                        current_timestep=self.current_timestep,
                        gamma_factor=self.gamma_factor,
                    )

                elif self.method == 'ntk' and current_patches > self.base_patches:
                    # 计算基础 NTK 因子
                    scale_s = current_patches / self.base_patches
                    base_ntk = scale_s ** (self.axes_dim[i] / (self.axes_dim[i] - 2))
                    
                    # [SigMa core update: dynamic NTK]
                    if self.sigma:
                        # 1. 计算自适应强度 alpha
                        adaptive_alpha = get_adaptive_scale(self.current_timestep, scale_s)
                        
                        # 2. 应用强度
                        # [修改] 移除 2.0，回归 power 1.0 (adaptive_alpha)
                        ntk_factor = base_ntk ** (adaptive_alpha)
                    else:
                        ntk_factor = base_ntk
                    
                    ntk_factor = max(1.0, ntk_factor)

                    cos, sin = get_1d_rotary_pos_embed(**common_kwargs, ntk_factor=ntk_factor)

                else:
                    cos, sin = get_1d_rotary_pos_embed(**common_kwargs)
            else:
                cos, sin = get_1d_rotary_pos_embed(**common_kwargs)

            cos_out.append(cos)
            sin_out.append(sin)

        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
        return freqs_cos, freqs_sin