"""AlephCondAdapter — conditioning-stream address injection (research-grade export; verbatim port of pod2/aleph_diffusion_core.py). STATUS (read before using): exp002 certified this channel as a Law-2 honest negative IN CONTEXT — appended to full text conditioning the address is marginally inert (real-vs-deranged −0.0009). But addr-only steering is real (+0.0287 after 2k steps, gates GREW) — the channel is redundant-in-context, not dead. The open redesign target is COMPLEMENTARITY: inject where text is not (reduced-text regimes, sigma-gated, non-text streams). Shipped as a component for that line of work, not as a quickstart verb. """ from __future__ import annotations import torch import torch.nn as nn class AlephCondAdapter(nn.Module): """Frozen [n_addr, addr_dim] address -> n_addr conditioning positions. Everything zero-init => tokens are EXACT zeros at init and when disabled (the position-presence offset is handled by the bed: SD15 masked-append/length-toggle, Anima write-into-padding).""" def __init__(self, cond_dim: int, addr_dim: int = 128, n_addr: int = 32): super().__init__() self.cond_dim, self.addr_dim, self.n_addr = cond_dim, addr_dim, n_addr self.addr_proj = nn.Linear(addr_dim, cond_dim) nn.init.zeros_(self.addr_proj.weight) nn.init.zeros_(self.addr_proj.bias) self.pos_table = nn.Parameter(torch.zeros(n_addr, cond_dim)) self.gate = nn.Parameter(torch.tensor(-3.0)) self.enabled = True def assert_zero_init(self): assert self.addr_proj.weight.abs().max().item() == 0.0 assert self.addr_proj.bias.abs().max().item() == 0.0 assert self.pos_table.abs().max().item() == 0.0 def tokens(self, addr: torch.Tensor) -> torch.Tensor: """addr [B, n_addr, addr_dim] -> [B, n_addr, cond_dim].""" assert addr.shape[-2:] == (self.n_addr, self.addr_dim), addr.shape if not self.enabled: return addr.new_zeros(*addr.shape[:-1], self.cond_dim) return torch.sigmoid(self.gate) * (self.addr_proj(addr) + self.pos_table) @classmethod def from_state_dict(cls, sd: dict) -> "AlephCondAdapter": cond_dim, addr_dim = sd["addr_proj.weight"].shape n_addr = sd["pos_table"].shape[0] m = cls(cond_dim, addr_dim=addr_dim, n_addr=n_addr) m.load_state_dict(sd, strict=True) return m