1617 lines
67 KiB
Python
1617 lines
67 KiB
Python
from __future__ import annotations
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import math
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import inspect
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import os
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from collections import Counter
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from collections.abc import Mapping
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from typing import Any, Dict, List
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import torch
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import comfy.sampler_helpers
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import comfy.samplers
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import comfy.model_patcher
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import comfy.patcher_extension
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import comfy.hooks
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def _to_sigma_scalar(v) -> float | None:
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"""Return scalar sigma even if passed as a batch vector (shape [B])."""
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if v is None:
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return None
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if torch.is_tensor(v):
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t = v.detach()
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if t.numel() == 0:
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return None
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t = t.reshape(-1)
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if t.numel() == 1:
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return float(t.item())
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# Usually all batch sigmas are identical; if not, be conservative.
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if not torch.allclose(t, t[0], rtol=1e-3, atol=1e-5):
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return float(t.max().item())
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return float(t[0].item())
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try:
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return float(v)
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except Exception:
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return None
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def _clone_cond_metadata(cond):
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"""
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Clone only the conditioning metadata containers, not the tensors.
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Comfy cond items are typically [tensor, {metadata...}, ...].
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"""
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if isinstance(cond, Mapping):
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return {
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k: _clone_cond_metadata(v) if isinstance(v, (Mapping, list, tuple)) else v for k, v in cond.items()
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}
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if isinstance(cond, list):
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return [_clone_cond_metadata(v) if isinstance(v, (Mapping, list, tuple)) else v for v in cond]
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if isinstance(cond, tuple):
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return tuple(_clone_cond_metadata(v) if isinstance(v, (Mapping, list, tuple)) else v for v in cond)
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return cond
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def _clone_container(x: Any) -> Any:
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if isinstance(x, dict):
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return {k: _clone_container(v) for k, v in x.items()}
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if isinstance(x, list):
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return [_clone_container(v) for v in x]
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if isinstance(x, tuple):
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return tuple(_clone_container(v) for v in x)
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return x
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def _clone_transformer_options(to: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Clone transformer_options containers so the bad-model pass can't mutate the
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good-model pass state across timesteps.
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"""
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return _clone_container(to or {})
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def _clone_model_options_for_bad(model_options: Dict[str, Any]) -> Dict[str, Any]:
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bad_opts = dict(model_options)
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to = model_options.get("transformer_options") or {}
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bad_opts["transformer_options"] = _clone_transformer_options(to)
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return bad_opts
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AG_SWAP_MODE_SHARED_SAFE = "shared_safe_low_vram"
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AG_SWAP_MODE_SHARED_FAST = "shared_fast_extra_vram"
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AG_SWAP_MODE_DUAL_MODELS = "dual_models_2x_vram"
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AG_SWAP_MODE_CHOICES = [
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AG_SWAP_MODE_SHARED_SAFE,
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AG_SWAP_MODE_SHARED_FAST,
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AG_SWAP_MODE_DUAL_MODELS,
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]
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AG_DELTA_MODE_RAW = "raw_delta"
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AG_DELTA_MODE_PROJECT_CFG = "project_cfg"
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AG_DELTA_MODE_BAD_CONDITIONAL = "bad_conditional"
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AG_DELTA_MODE_REJECT_CFG = "reject_cfg"
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AG_DELTA_MODE_CHOICES = [
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AG_DELTA_MODE_BAD_CONDITIONAL,
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AG_DELTA_MODE_RAW,
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AG_DELTA_MODE_PROJECT_CFG,
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AG_DELTA_MODE_REJECT_CFG,
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]
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AG_POST_CFG_KEEP = "keep"
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AG_POST_CFG_APPLY_AFTER = "apply_after"
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AG_POST_CFG_SKIP = "skip"
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AG_POST_CFG_MODE_CHOICES = [
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AG_POST_CFG_KEEP,
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AG_POST_CFG_APPLY_AFTER,
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AG_POST_CFG_SKIP,
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]
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AG_RAMP_FLAT = "flat"
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AG_RAMP_DETAIL_LATE = "detail_late"
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AG_RAMP_COMPOSE_EARLY = "compose_early"
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AG_RAMP_MID_PEAK = "mid_peak"
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AG_RAMP_MODE_CHOICES = [
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AG_RAMP_FLAT,
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AG_RAMP_DETAIL_LATE,
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AG_RAMP_COMPOSE_EARLY,
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AG_RAMP_MID_PEAK,
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]
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# Default cap for AG magnitude relative to the actually applied CFG update magnitude.
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# Old code used 0.10 which is often visually near-invisible.
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AG_DEFAULT_MAX_RATIO = 0.35
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def _ag_ramp_factor(prog01: float, *, mode: str, power: float) -> float:
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"""
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prog01: 0 early (high sigma) -> 1 late (low sigma)
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Returns factor in [0,1] to scale max_ratio.
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"""
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p = max(1e-6, float(power))
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x = max(0.0, min(1.0, float(prog01)))
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if mode == AG_RAMP_FLAT:
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f = 1.0
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elif mode == AG_RAMP_DETAIL_LATE:
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f = x ** p
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elif mode == AG_RAMP_COMPOSE_EARLY:
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f = (1.0 - x) ** p
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elif mode == AG_RAMP_MID_PEAK:
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bell = 4.0 * x * (1.0 - x)
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f = max(0.0, bell) ** p
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else:
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f = 1.0
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return max(0.0, min(1.0, float(f)))
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def _patcher_has_active_patches(patcher) -> bool:
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backup = getattr(patcher, "backup", None)
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if isinstance(backup, dict) and len(backup) > 0:
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return True
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obj_backup = getattr(patcher, "object_patches_backup", None)
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if isinstance(obj_backup, dict) and len(obj_backup) > 0:
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return True
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return False
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def _infer_single_active_owner(patchers):
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active_patchers = [candidate for candidate in patchers if _patcher_has_active_patches(candidate)]
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if len(active_patchers) == 1:
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return active_patchers[0], False
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if len(active_patchers) > 1:
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return None, True
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return None, False
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def _debug_print_weight_signature_once(model, patcher, *, tag: str, enabled: bool | None = None) -> None:
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if enabled is None:
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enabled = bool(getattr(model, "_ag_debug_swap", False)) or os.environ.get("AG_DEBUG_SWAP", "0") == "1"
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if not enabled:
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return
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key = f"_ag_dbg_sig_once_{tag}_{id(patcher)}"
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if hasattr(model, key):
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return
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try:
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for _, param in model.named_parameters():
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sig = float(param.detach().flatten()[:2048].float().sum().cpu())
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print("[AutoGuidance] active patcher", tag, "sig", sig)
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break
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setattr(model, key, True)
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except Exception as e:
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print(f"[AutoGuidance] signature debug failed for {tag}: {e!r}")
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def _layer_digest(model, name: str):
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try:
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for param_name, param in model.named_parameters():
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if param_name == name:
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t = param.detach().float().flatten()
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return float(t[:4096].sum().cpu()), float(t[:4096].abs().sum().cpu())
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except Exception:
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pass
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return None
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def _debug_print_layer_digest_once(
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model,
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patcher,
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*,
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enabled: bool | None = None,
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layer_name: str = "diffusion_model.input_blocks.1.0.in_layers.2.weight",
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) -> None:
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if enabled is None:
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enabled = bool(getattr(model, "_ag_debug_swap", False)) or os.environ.get("AG_DEBUG_SWAP", "0") == "1"
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if not enabled:
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return
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uuid = getattr(patcher, "patches_uuid", None)
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key = f"_ag_dbg_layer_once_{uuid}_{id(patcher)}"
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if hasattr(model, key):
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return
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digest = _layer_digest(model, layer_name)
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print("[AutoGuidance] layer_digest", {"layer": layer_name, "uuid": uuid, "digest": digest})
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setattr(model, key, True)
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def _prepare_fast_weight_swap(model, patcher, *, peer_patchers, device) -> None:
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"""
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Configure patchers for faster swapping.
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IMPORTANT:
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Do NOT share `backup` dicts across patchers. Many Comfy builds clear/replace
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`backup` inside unpatch_model(), and sharing it causes weight corruption.
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We still get speed by keeping backups on the target device and enabling
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inplace updates.
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"""
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for p in (patcher, *peer_patchers):
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if device is not None:
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try:
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p.offload_device = device
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except Exception:
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pass
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try:
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p.weight_inplace_update = True
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except Exception:
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pass
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def _call_unpatch_model_compat(patcher, *, device, prefer_device: bool = False) -> None:
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"""
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Unpatch compat across Comfy variants.
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When prefer_device=True, try device-targeted unpatch first to better preserve
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on-device backup behavior on builds that support it.
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"""
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if prefer_device and device is not None:
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try:
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patcher.unpatch_model(device_to=device, unpatch_weights=True)
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return
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except TypeError:
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pass
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except Exception:
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pass
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try:
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patcher.unpatch_model(unpatch_weights=True)
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return
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except TypeError:
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pass
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try:
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if device is not None:
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patcher.unpatch_model(device_to=device, unpatch_weights=True)
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else:
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patcher.unpatch_model(unpatch_weights=True)
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return
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except TypeError:
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pass
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try:
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if device is not None:
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patcher.unpatch_model(device, True)
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else:
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patcher.unpatch_model()
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return
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except Exception:
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patcher.unpatch_model()
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def _activate_patcher_for_forward(
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patcher: comfy.model_patcher.ModelPatcher,
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*,
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peer_patchers: tuple[comfy.model_patcher.ModelPatcher, ...] = (),
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fast_weight_swap: bool = False,
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safe_force_clean_swap: bool | None = None,
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uuid_only_noop: bool | None = None,
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debug_swap: bool | None = None,
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) -> None:
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m = getattr(patcher, "model", None)
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if m is None:
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return
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if not hasattr(m, "current_weight_patches_uuid"):
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m.current_weight_patches_uuid = None
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# Opt-in performance mode for shared-model LoRA swapping (costs extra VRAM).
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# IMPORTANT: only enabled via node/UI.
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fast_mode = bool(fast_weight_swap)
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# In shared_safe mode (fast_mode=False), default to a forced clean swap.
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# (Prevents state leak across wrapper stacks on some Comfy builds.)
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if safe_force_clean_swap is None:
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safe_force_clean_swap = (os.environ.get("AG_SAFE_FORCE_CLEAN_SWAP", "1") == "1")
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safe_force_clean = (not fast_mode) and bool(safe_force_clean_swap)
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if uuid_only_noop is None:
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uuid_only_noop = (os.environ.get("AG_UUID_ONLY_NOOP", "0") == "1")
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want_uuid = getattr(patcher, "patches_uuid", None)
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cur_uuid = getattr(m, "current_weight_patches_uuid", None)
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cur_owner = getattr(m, "current_patcher", None)
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all_patchers = (patcher, *peer_patchers)
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device = getattr(patcher, "load_device", None) or getattr(m, "device", None)
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def _debug_after_activate() -> None:
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_debug_print_weight_signature_once(m, patcher, tag=str(getattr(patcher, "patches_uuid", "none")), enabled=debug_swap)
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_debug_print_layer_digest_once(m, patcher, enabled=debug_swap)
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if safe_force_clean:
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# Avoid doing the expensive work repeatedly if we already activated this patcher last time.
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last_id = getattr(m, "_ag_last_patcher_id", None)
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if last_id == id(patcher):
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m.current_patcher = patcher
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m.current_weight_patches_uuid = getattr(patcher, "patches_uuid", None)
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_debug_after_activate()
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return
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# Force-remove any lingering peer patches, then apply the requested patcher.
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for p in (*peer_patchers, patcher):
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try:
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_call_unpatch_model_compat(p, device=device)
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except Exception:
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pass
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m.current_weight_patches_uuid = None
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m.current_patcher = None
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_call_patch_model_safe(patcher, device=device)
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m.current_patcher = patcher
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m.current_weight_patches_uuid = getattr(patcher, "patches_uuid", None)
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m._ag_last_patcher_id = id(patcher)
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_debug_after_activate()
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return
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# UUIDs are not always reliable on all Comfy builds. Only use UUID ownership
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# resolution when both sides are concrete. Otherwise infer from active backups.
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can_use_uuid_resolution = (want_uuid is not None and cur_uuid is not None)
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if can_use_uuid_resolution and (cur_owner is None or getattr(cur_owner, "patches_uuid", None) != cur_uuid):
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resolved = None
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for candidate in all_patchers:
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if getattr(candidate, "patches_uuid", None) == cur_uuid:
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resolved = candidate
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break
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if resolved is not None:
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cur_owner = resolved
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if cur_owner is None:
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inferred_owner, _ = _infer_single_active_owner(all_patchers)
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if inferred_owner is not None:
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cur_owner = inferred_owner
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real_owner = getattr(m, "current_patcher", None)
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can_noop_by_uuid = (want_uuid is not None and cur_uuid is not None)
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if can_noop_by_uuid and cur_uuid == want_uuid and (uuid_only_noop or real_owner is patcher):
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m.current_patcher = patcher
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_debug_after_activate()
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return
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# (device already computed above)
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def _unpatch_owner_fast(owner) -> None:
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if owner is None:
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return
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_call_unpatch_model_compat(owner, device=device, prefer_device=True)
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m.current_weight_patches_uuid = None
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if getattr(m, "current_patcher", None) is owner:
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m.current_patcher = None
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def _unpatch_active_peers() -> None:
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cleaned = False
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for candidate in peer_patchers:
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if _patcher_has_active_patches(candidate):
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_call_unpatch_model_compat(candidate, device=device, prefer_device=fast_mode)
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cleaned = True
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if cleaned:
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m.current_weight_patches_uuid = None
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if getattr(m, "current_patcher", None) in peer_patchers:
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m.current_patcher = None
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# Fast mode keeps backups on target device and uses inplace updates, but
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# still performs correctness-first unpatch/patch calls across Comfy variants.
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if fast_mode:
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_prepare_fast_weight_swap(m, patcher, peer_patchers=peer_patchers, device=device)
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fast_force_clean = bool(safe_force_clean_swap)
|
|
|
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if fast_force_clean:
|
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last_id = getattr(m, "_ag_last_patcher_id_fast", None)
|
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if last_id == id(patcher):
|
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m.current_patcher = patcher
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m.current_weight_patches_uuid = getattr(patcher, "patches_uuid", None)
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_debug_after_activate()
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return
|
|
|
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for p in (*peer_patchers, patcher):
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try:
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_call_unpatch_model_compat(p, device=device, prefer_device=True)
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except Exception:
|
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pass
|
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m.current_weight_patches_uuid = None
|
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m.current_patcher = None
|
|
|
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_call_patch_model_safe(patcher, device=device)
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m.current_patcher = patcher
|
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m.current_weight_patches_uuid = getattr(patcher, "patches_uuid", None)
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m._ag_last_patcher_id_fast = id(patcher)
|
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_debug_after_activate()
|
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return
|
|
|
|
# Less strict: unpatch inferred owner only, still using safe patch_model.
|
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owner = getattr(m, "current_patcher", None) or cur_owner
|
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if owner is None:
|
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inferred_owner, ambiguous = _infer_single_active_owner(all_patchers)
|
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if inferred_owner is not None:
|
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owner = inferred_owner
|
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elif ambiguous:
|
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_unpatch_active_peers()
|
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if owner is not None and owner is not patcher:
|
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_unpatch_owner_fast(owner)
|
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elif owner is patcher and cur_uuid != want_uuid:
|
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_unpatch_owner_fast(patcher)
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|
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_call_patch_model_safe(patcher, device=device)
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m.current_patcher = patcher
|
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m.current_weight_patches_uuid = getattr(patcher, "patches_uuid", None)
|
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_debug_after_activate()
|
|
return
|
|
|
|
def _unpatch_owner(owner) -> None:
|
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if owner is None:
|
|
return
|
|
try:
|
|
if device is not None:
|
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owner.unpatch_model(device_to=device, unpatch_weights=True)
|
|
else:
|
|
owner.unpatch_model(unpatch_weights=True)
|
|
except TypeError:
|
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try:
|
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if device is not None:
|
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owner.unpatch_model(device, True)
|
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else:
|
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owner.unpatch_model()
|
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except Exception:
|
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owner.unpatch_model()
|
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|
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m.current_weight_patches_uuid = None
|
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if getattr(m, "current_patcher", None) is owner:
|
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m.current_patcher = None
|
|
|
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if cur_owner is None:
|
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inferred_owner, ambiguous = _infer_single_active_owner(all_patchers)
|
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if inferred_owner is not None:
|
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cur_owner = inferred_owner
|
|
elif ambiguous:
|
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_unpatch_active_peers()
|
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|
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if cur_owner is not None and cur_owner is not patcher:
|
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_unpatch_owner(cur_owner)
|
|
elif cur_owner is patcher and cur_uuid != want_uuid:
|
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_unpatch_owner(patcher)
|
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|
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cur_uuid = getattr(m, "current_weight_patches_uuid", None)
|
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cur_owner = getattr(m, "current_patcher", None)
|
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|
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real_owner = getattr(m, "current_patcher", None)
|
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can_noop_by_uuid = (want_uuid is not None and cur_uuid is not None)
|
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if can_noop_by_uuid and cur_uuid == want_uuid and (uuid_only_noop or real_owner is patcher):
|
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m.current_patcher = patcher
|
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_debug_after_activate()
|
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return
|
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|
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_call_patch_model_safe(patcher, device=device)
|
|
|
|
m.current_patcher = patcher
|
|
m.current_weight_patches_uuid = getattr(patcher, "patches_uuid", None)
|
|
_debug_after_activate()
|
|
|
|
|
|
def _sig_accepts_kw(fn, name: str) -> bool:
|
|
try:
|
|
sig = inspect.signature(fn)
|
|
except Exception:
|
|
return True
|
|
|
|
if name in sig.parameters:
|
|
return True
|
|
|
|
return any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values())
|
|
|
|
|
|
def _call_patch_model_safe(patcher, *, device):
|
|
"""
|
|
Correctness-first patch path (low VRAM, slower).
|
|
"""
|
|
fn = patcher.patch_model
|
|
|
|
def _is_sig_typeerror(e: TypeError) -> bool:
|
|
msg = str(e)
|
|
return (
|
|
"unexpected keyword argument" in msg
|
|
or "got an unexpected keyword argument" in msg
|
|
or "positional argument" in msg
|
|
or "required positional argument" in msg
|
|
or "missing a required keyword-only argument" in msg
|
|
or "missing required keyword-only argument" in msg
|
|
or "required keyword-only argument" in msg
|
|
or "got multiple values for argument" in msg
|
|
or "multiple values for keyword argument" in msg
|
|
or ("takes" in msg and "positional" in msg)
|
|
)
|
|
|
|
def _try(load_weights: bool, force: bool, *, minimal: bool) -> bool:
|
|
kw = {}
|
|
|
|
if not minimal:
|
|
if device is not None and _sig_accepts_kw(fn, "device_to"):
|
|
kw["device_to"] = device
|
|
if _sig_accepts_kw(fn, "lowvram_model_memory"):
|
|
kw["lowvram_model_memory"] = 0
|
|
|
|
if _sig_accepts_kw(fn, "load_weights"):
|
|
kw["load_weights"] = load_weights
|
|
if force and _sig_accepts_kw(fn, "force_patch_weights"):
|
|
kw["force_patch_weights"] = True
|
|
try:
|
|
fn(**kw)
|
|
return True
|
|
except TypeError as e:
|
|
if _is_sig_typeerror(e):
|
|
return False
|
|
raise
|
|
|
|
if _try(load_weights=True, force=True, minimal=False):
|
|
return
|
|
if _try(load_weights=True, force=False, minimal=False):
|
|
return
|
|
|
|
fn()
|
|
|
|
|
|
def _find_first_y(cond: Any):
|
|
if isinstance(cond, Mapping):
|
|
y = cond.get("y", None)
|
|
if y is not None:
|
|
return y
|
|
for v in cond.values():
|
|
if isinstance(v, (Mapping, list, tuple)):
|
|
y2 = _find_first_y(v)
|
|
if y2 is not None:
|
|
return y2
|
|
return None
|
|
if isinstance(cond, (list, tuple)):
|
|
for v in cond:
|
|
if isinstance(v, (Mapping, list, tuple)):
|
|
y2 = _find_first_y(v)
|
|
if y2 is not None:
|
|
return y2
|
|
return None
|
|
|
|
|
|
def _ensure_y_everywhere(cond: Any, y_ref) -> None:
|
|
if y_ref is None:
|
|
return
|
|
if isinstance(cond, Mapping):
|
|
try:
|
|
if cond.get("y", None) is None:
|
|
cond["y"] = y_ref
|
|
except Exception:
|
|
pass
|
|
for v in cond.values():
|
|
if isinstance(v, (Mapping, list, tuple)):
|
|
_ensure_y_everywhere(v, y_ref)
|
|
return
|
|
if isinstance(cond, (list, tuple)):
|
|
for v in cond:
|
|
if isinstance(v, (Mapping, list, tuple)):
|
|
_ensure_y_everywhere(v, y_ref)
|
|
|
|
|
|
def _restore_y_per_entry(dst_cond: Any, src_pos: Any, src_neg: Any = None) -> Any:
|
|
"""
|
|
Restore SDXL class label 'y' WITHOUT collapsing multi-entry conditionings.
|
|
For list conditionings, copy per-index y from src_pos/src_neg into dst_cond.
|
|
"""
|
|
is_tuple = isinstance(dst_cond, tuple)
|
|
if isinstance(dst_cond, (list, tuple)):
|
|
dst_list = list(dst_cond)
|
|
src_pos_list = list(src_pos) if isinstance(src_pos, (list, tuple)) else None
|
|
src_neg_list = list(src_neg) if isinstance(src_neg, (list, tuple)) else None
|
|
|
|
y_default = _find_first_y(src_pos) or _find_first_y(src_neg)
|
|
|
|
for i in range(len(dst_list)):
|
|
y_ref = None
|
|
if src_pos_list is not None and i < len(src_pos_list):
|
|
y_ref = _find_first_y(src_pos_list[i])
|
|
if y_ref is None and src_neg_list is not None and i < len(src_neg_list):
|
|
y_ref = _find_first_y(src_neg_list[i])
|
|
if y_ref is None:
|
|
y_ref = y_default
|
|
|
|
if y_ref is not None:
|
|
_ensure_y_everywhere(dst_list[i], y_ref)
|
|
|
|
return tuple(dst_list) if is_tuple else dst_list
|
|
|
|
y_ref = _find_first_y(src_pos) or _find_first_y(src_neg)
|
|
if y_ref is not None:
|
|
_ensure_y_everywhere(dst_cond, y_ref)
|
|
return dst_cond
|
|
|
|
|
|
def _looks_like_tensor(v: Any) -> bool:
|
|
return torch.is_tensor(v)
|
|
|
|
|
|
def _looks_like_tensorish(v: Any) -> bool:
|
|
if _looks_like_tensor(v):
|
|
return True
|
|
if isinstance(v, (list, tuple)) and v and all(_looks_like_tensor(x) for x in v):
|
|
return True
|
|
return False
|
|
|
|
|
|
def _tensorish_width(v: Any) -> int | None:
|
|
if _looks_like_tensor(v):
|
|
return int(v.shape[-1])
|
|
if isinstance(v, (list, tuple)) and v and all(_looks_like_tensor(x) for x in v):
|
|
return sum(int(x.shape[-1]) for x in v)
|
|
return None
|
|
|
|
|
|
def _coerce_tensorish_to_tensor(v: Any):
|
|
if _looks_like_tensor(v):
|
|
return v
|
|
if isinstance(v, (list, tuple)) and v and all(_looks_like_tensor(x) for x in v):
|
|
try:
|
|
return torch.cat(list(v), dim=-1)
|
|
except Exception:
|
|
return v
|
|
return v
|
|
|
|
|
|
def _ensure_primary_text_cond(dst_cond: Any, src_cond: Any) -> Any:
|
|
"""
|
|
Ensure the primary conditioning tensor (the first element of each cond item)
|
|
exists and matches src's embedding width. This prevents SDXL cross-attn from
|
|
seeing context=None and falling back to self-attn (640-dim).
|
|
"""
|
|
is_tuple = isinstance(dst_cond, tuple)
|
|
if isinstance(dst_cond, (list, tuple)) and isinstance(src_cond, (list, tuple)):
|
|
dst_list = list(dst_cond)
|
|
src_list = list(src_cond)
|
|
n = min(len(dst_list), len(src_list))
|
|
for i in range(n):
|
|
d = dst_list[i]
|
|
s = src_list[i]
|
|
|
|
d0 = d[0] if isinstance(d, (list, tuple)) and len(d) >= 1 else None
|
|
s0 = s[0] if isinstance(s, (list, tuple)) and len(s) >= 1 else None
|
|
|
|
if not _looks_like_tensorish(s0):
|
|
continue
|
|
|
|
s0_t = _coerce_tensorish_to_tensor(s0)
|
|
if not _looks_like_tensor(s0_t):
|
|
continue
|
|
d0_t = _coerce_tensorish_to_tensor(d0)
|
|
|
|
dw = _tensorish_width(d0_t)
|
|
sw = _tensorish_width(s0_t)
|
|
|
|
if sw is None:
|
|
continue
|
|
|
|
if isinstance(d, tuple):
|
|
needs_replace = (dw is None) or (dw != sw) or (not _looks_like_tensor(d0_t))
|
|
if needs_replace:
|
|
dst_list[i] = (s0_t, *d[1:])
|
|
elif d0 is not d0_t:
|
|
dst_list[i] = (d0_t, *d[1:])
|
|
elif isinstance(d, list):
|
|
if len(d) >= 1:
|
|
needs_replace = (dw is None) or (dw != sw) or (not _looks_like_tensor(d0_t))
|
|
if needs_replace:
|
|
d[0] = s0_t
|
|
elif d0 is not d0_t:
|
|
d[0] = d0_t
|
|
else:
|
|
dst_list[i] = [s0_t]
|
|
return tuple(dst_list) if is_tuple else dst_list
|
|
|
|
return dst_cond
|
|
|
|
|
|
class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
|
|
"""
|
|
AutoGuidance + CFG guider.
|
|
|
|
- "good" model: used for CFG (positive + negative)
|
|
- "bad" model: used as the autoguide reference (positive only)
|
|
|
|
Output matches what ComfyUI guiders return from predict_noise(): "denoised" prediction
|
|
(same convention used by other custom guiders that subclass CFGGuider).
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
good_model,
|
|
bad_model,
|
|
*,
|
|
swap_mode: str = AG_SWAP_MODE_SHARED_SAFE,
|
|
ag_delta_mode: str = AG_DELTA_MODE_BAD_CONDITIONAL,
|
|
ag_max_ratio: float = AG_DEFAULT_MAX_RATIO,
|
|
ag_allow_negative: bool = True,
|
|
ag_ramp_mode: str = AG_RAMP_FLAT,
|
|
ag_ramp_power: float = 2.0,
|
|
ag_ramp_floor: float = 0.0,
|
|
ag_post_cfg_mode: str = AG_POST_CFG_KEEP,
|
|
safe_force_clean_swap: bool = True,
|
|
uuid_only_noop: bool = False,
|
|
debug_swap: bool = False,
|
|
debug_metrics: bool = False,
|
|
):
|
|
super().__init__(good_model)
|
|
self.bad_model_patcher = bad_model
|
|
self.inner_bad_model = None
|
|
self.bad_conds = None
|
|
self.w_ag: float = 1.0 # paper-style weight; (w_ag - 1) is the delta scale
|
|
self.swap_mode: str = str(swap_mode)
|
|
self.ag_delta_mode: str = str(ag_delta_mode)
|
|
self.ag_max_ratio: float = float(ag_max_ratio)
|
|
self.ag_allow_negative: bool = bool(ag_allow_negative)
|
|
self.ag_ramp_mode: str = str(ag_ramp_mode)
|
|
self.ag_ramp_power: float = float(ag_ramp_power)
|
|
self.ag_ramp_floor: float = float(ag_ramp_floor)
|
|
self.ag_post_cfg_mode: str = str(ag_post_cfg_mode)
|
|
self.safe_force_clean_swap: bool = bool(safe_force_clean_swap)
|
|
self.uuid_only_noop: bool = bool(uuid_only_noop)
|
|
self.debug_swap: bool = bool(debug_swap)
|
|
self.debug_metrics: bool = bool(debug_metrics)
|
|
|
|
def set_conds(self, positive, negative) -> None:
|
|
# Keep the same conditioning dict style used by CFGGuider-based guiders.
|
|
self.inner_set_conds({"positive": positive, "negative": negative})
|
|
|
|
def set_scales(self, cfg: float, w_ag: float) -> None:
|
|
self.cfg = float(cfg)
|
|
self.w_ag = float(w_ag)
|
|
|
|
def outer_sample(
|
|
self,
|
|
noise,
|
|
latent_image,
|
|
sampler,
|
|
sigmas,
|
|
denoise_mask=None,
|
|
callback=None,
|
|
disable_pbar=False,
|
|
seed=None,
|
|
):
|
|
self.inner_model, self.conds, loaded_good = comfy.sampler_helpers.prepare_sampling(
|
|
self.model_patcher, noise.shape, self.conds, self.model_options
|
|
)
|
|
self.inner_bad_model, self.bad_conds, loaded_bad = comfy.sampler_helpers.prepare_sampling(
|
|
self.bad_model_patcher, noise.shape, self.bad_conds, self.model_options
|
|
)
|
|
# SDXL class-conditional models require 'y'. Some hook/filter stacks drop it for the bad model.
|
|
# Restore 'y' per conditioning entry (do NOT merge other tensors like embeddings).
|
|
if self.bad_conds and self.conds:
|
|
bad_positive = self.bad_conds.get("positive", None)
|
|
good_positive = self.conds.get("positive", None)
|
|
good_negative = self.conds.get("negative", None)
|
|
|
|
if bad_positive is not None and (good_positive is not None or good_negative is not None):
|
|
bad_positive = _clone_cond_metadata(bad_positive)
|
|
bad_positive = _restore_y_per_entry(bad_positive, good_positive, good_negative)
|
|
self.bad_conds["positive"] = bad_positive
|
|
|
|
seen = set()
|
|
loaded_all = []
|
|
for model in (loaded_good + loaded_bad):
|
|
model_id = id(model)
|
|
if model_id in seen:
|
|
continue
|
|
seen.add(model_id)
|
|
loaded_all.append(model)
|
|
self.loaded_models = loaded_all
|
|
|
|
device = self.model_patcher.load_device
|
|
|
|
if denoise_mask is not None:
|
|
denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
|
|
|
|
noise = noise.to(device)
|
|
latent_image = latent_image.to(device)
|
|
try:
|
|
v = sigmas[0] if sigmas is not None and len(sigmas) > 0 else None
|
|
if v is None:
|
|
self._ag_sigma_max = None
|
|
elif torch.is_tensor(v):
|
|
self._ag_sigma_max = float(v.detach().cpu().item())
|
|
else:
|
|
self._ag_sigma_max = float(v)
|
|
except Exception:
|
|
self._ag_sigma_max = None
|
|
sigmas = sigmas.to(device)
|
|
self._ag_step = 0
|
|
try:
|
|
self._ag_steps_total = int(sigmas.shape[0])
|
|
except Exception:
|
|
self._ag_steps_total = None
|
|
|
|
comfy.samplers.cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
|
|
|
|
try:
|
|
self.model_patcher.pre_run()
|
|
self.bad_model_patcher.pre_run()
|
|
|
|
# Dual-model sanity: confirm good/bad model weights differ when swap activation is not used.
|
|
if getattr(self, "swap_mode", None) == AG_SWAP_MODE_DUAL_MODELS:
|
|
dbg = bool(getattr(self, "debug_swap", False)) or (os.environ.get("AG_DEBUG_SWAP", "0") == "1")
|
|
if dbg and not hasattr(self, "_ag_dbg_dual_digest_once"):
|
|
layer = "diffusion_model.input_blocks.1.0.in_layers.2.weight"
|
|
gm = getattr(self.model_patcher, "model", None)
|
|
bm = getattr(self.bad_model_patcher, "model", None)
|
|
print("[AutoGuidance] dual_layer_digest", {"good": _layer_digest(gm, layer), "bad": _layer_digest(bm, layer)})
|
|
self._ag_dbg_dual_digest_once = True
|
|
|
|
output = self.inner_sample(
|
|
noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed
|
|
)
|
|
finally:
|
|
comfy.samplers.cast_to_load_options(self.model_options, device=self.model_patcher.offload_device)
|
|
self.bad_model_patcher.cleanup()
|
|
self.model_patcher.cleanup()
|
|
|
|
cleanup_conds = {}
|
|
keys = set(self.conds.keys()) | (set(self.bad_conds.keys()) if self.bad_conds else set())
|
|
for k in keys:
|
|
cleanup_conds[k] = (self.conds.get(k, []) or []) + (
|
|
(self.bad_conds.get(k, []) or []) if self.bad_conds else []
|
|
)
|
|
comfy.sampler_helpers.cleanup_models(cleanup_conds, self.loaded_models)
|
|
|
|
del self.inner_model
|
|
del self.inner_bad_model
|
|
del self.loaded_models
|
|
|
|
return output
|
|
|
|
def sample(
|
|
self,
|
|
noise,
|
|
latent_image,
|
|
sampler,
|
|
sigmas,
|
|
denoise_mask=None,
|
|
callback=None,
|
|
disable_pbar=False,
|
|
seed=None,
|
|
):
|
|
"""
|
|
Override CFGGuider.sample to mirror upstream CFGGuider sample/outer_sample lifecycle,
|
|
extended to prepare BOTH the good and bad models.
|
|
"""
|
|
if sigmas.shape[-1] == 0:
|
|
return latent_image
|
|
|
|
self.conds = {}
|
|
self.bad_conds = {}
|
|
for k in self.original_conds:
|
|
self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
|
|
self.bad_conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
|
|
|
|
comfy.samplers.preprocess_conds_hooks(self.conds)
|
|
comfy.samplers.preprocess_conds_hooks(self.bad_conds)
|
|
|
|
try:
|
|
orig_model_options = self.model_options
|
|
if not isinstance(self.model_options, dict):
|
|
self.model_options = {}
|
|
self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
|
|
self.model_options.setdefault("transformer_options", {})
|
|
|
|
orig_hook_mode_good = self.model_patcher.hook_mode
|
|
orig_hook_mode_bad = self.bad_model_patcher.hook_mode
|
|
if comfy.samplers.get_total_hook_groups_in_conds(self.conds) <= 1:
|
|
self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
|
|
self.bad_model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
|
|
|
|
comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options)
|
|
comfy.sampler_helpers.prepare_model_patcher(self.bad_model_patcher, self.bad_conds, self.model_options)
|
|
|
|
comfy.samplers.filter_registered_hooks_on_conds(self.conds, self.model_options)
|
|
comfy.samplers.filter_registered_hooks_on_conds(self.bad_conds, self.model_options)
|
|
|
|
executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(
|
|
self.outer_sample,
|
|
self,
|
|
comfy.patcher_extension.get_all_wrappers(
|
|
comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True
|
|
),
|
|
)
|
|
output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
|
|
finally:
|
|
comfy.samplers.cast_to_load_options(self.model_options, device=self.model_patcher.offload_device)
|
|
|
|
self.model_options = orig_model_options
|
|
self.model_patcher.hook_mode = orig_hook_mode_good
|
|
self.bad_model_patcher.hook_mode = orig_hook_mode_bad
|
|
self.model_patcher.restore_hook_patches()
|
|
self.bad_model_patcher.restore_hook_patches()
|
|
|
|
del self.conds
|
|
del self.bad_conds
|
|
|
|
return output
|
|
|
|
def predict_noise(self, x, timestep, model_options: Dict[str, Any] | None = None, seed=None, **kwargs):
|
|
"""
|
|
Mirrors comfy.samplers.sampling_function + cfg_function, but injects AutoGuidance.
|
|
NOTE: Comfy may also apply sampler_post_cfg_function outside the guider. If we handle
|
|
post-CFG here, we strip the list in-place to avoid double-application.
|
|
"""
|
|
if model_options is None:
|
|
model_options = {}
|
|
|
|
# IMPORTANT: model_options may be reused by wrappers/caches across calls.
|
|
# Copy before mutating (we strip post-cfg hooks below).
|
|
model_options = dict(model_options)
|
|
|
|
# Ensure wrapper-related options always exist
|
|
if model_options.get("transformer_options", None) is None:
|
|
model_options["transformer_options"] = {}
|
|
|
|
step = int(getattr(self, "_ag_step", 0))
|
|
self._ag_step = step + 1
|
|
total_steps = getattr(self, "_ag_steps_total", None)
|
|
last = (total_steps is not None and step == total_steps - 1)
|
|
|
|
post_cfg_mode = str(getattr(self, "ag_post_cfg_mode", AG_POST_CFG_KEEP))
|
|
# Copy the hook list for our internal use, but IMPORTANT: strip it from model_options
|
|
# so Comfy doesn't apply it again outside this guider.
|
|
orig_post_cfg_fns = model_options.get("sampler_post_cfg_function", []) or []
|
|
post_cfg_fns = list(orig_post_cfg_fns)
|
|
if post_cfg_mode == AG_POST_CFG_SKIP:
|
|
post_cfg_fns = []
|
|
# If there were any hooks, strip them in-place to prevent double application downstream.
|
|
# This is intentional in all modes (including keep) to enforce exactly-once semantics.
|
|
# (We either run them ourselves, or intentionally skip them.)
|
|
if orig_post_cfg_fns:
|
|
try:
|
|
model_options["sampler_post_cfg_function"] = []
|
|
except Exception:
|
|
pass
|
|
|
|
# IMPORTANT: do NOT let the bad-model pass mutate shared transformer_options
|
|
# that the good-model pass relies on across timesteps.
|
|
bad_model_options = _clone_model_options_for_bad(model_options)
|
|
shared_model = (getattr(self.model_patcher, "model", None) is getattr(self.bad_model_patcher, "model", None))
|
|
|
|
def _patch_info(p):
|
|
patches = getattr(p, "patches", None)
|
|
if not isinstance(patches, dict):
|
|
return {"uuid": getattr(p, "patches_uuid", None), "count": None, "top": []}
|
|
c = Counter(k.split(".", 1)[0] for k in patches.keys())
|
|
keys = list(patches.keys())
|
|
return {
|
|
"uuid": getattr(p, "patches_uuid", None),
|
|
"count": len(patches),
|
|
"top": c.most_common(10),
|
|
"sample_keys": keys[:8],
|
|
}
|
|
|
|
dbg = bool(getattr(self, "debug_swap", False)) or (os.environ.get("AG_DEBUG_SWAP", "0") == "1")
|
|
patch_dbg_key = f"_ag_dbg_patches_once_{getattr(self.bad_model_patcher, 'patches_uuid', None)}"
|
|
if dbg and not hasattr(self, patch_dbg_key):
|
|
print(
|
|
"[AutoGuidance] patch_info",
|
|
{
|
|
"good": _patch_info(self.model_patcher),
|
|
"bad": _patch_info(self.bad_model_patcher),
|
|
"shared": shared_model,
|
|
},
|
|
)
|
|
setattr(self, patch_dbg_key, True)
|
|
|
|
# 3-mode policy:
|
|
# - shared_safe_low_vram: always use slow correctness-first patching
|
|
# - shared_fast_extra_vram: use fast weightsafe swap (extra VRAM)
|
|
# - dual_models_2x_vram: requires truly distinct model objects; otherwise fall back safely
|
|
swap_mode = getattr(self, "swap_mode", AG_SWAP_MODE_SHARED_SAFE)
|
|
if swap_mode == AG_SWAP_MODE_DUAL_MODELS and shared_model:
|
|
if not hasattr(self, "_ag_warn_dual_shared_once"):
|
|
print(
|
|
"[AutoGuidance] swap_mode=dual_models_2x_vram requested but good/bad share the same base model object; "
|
|
"falling back to shared_safe_low_vram to avoid washed-out output. "
|
|
"To actually use dual models, load the checkpoint twice as two distinct instances (e.g., via a copied/symlinked file path)."
|
|
)
|
|
self._ag_warn_dual_shared_once = True
|
|
swap_mode = AG_SWAP_MODE_SHARED_SAFE
|
|
|
|
fast_swap = (swap_mode == AG_SWAP_MODE_SHARED_FAST)
|
|
|
|
|
|
def _activate(p, peers: tuple[comfy.model_patcher.ModelPatcher, ...]) -> None:
|
|
if not shared_model:
|
|
return
|
|
|
|
# Allow environment variables to force-enable/disable debug/safety even when the node
|
|
# is used with default UI values.
|
|
safe_force_clean = bool(getattr(self, "safe_force_clean_swap", True))
|
|
env_safe = os.environ.get("AG_SAFE_FORCE_CLEAN_SWAP", None)
|
|
if env_safe in ("0", "1"):
|
|
safe_force_clean = (env_safe == "1")
|
|
|
|
uuid_noop = bool(getattr(self, "uuid_only_noop", False)) or (os.environ.get("AG_UUID_ONLY_NOOP", "0") == "1")
|
|
dbg_swap = bool(getattr(self, "debug_swap", False)) or (os.environ.get("AG_DEBUG_SWAP", "0") == "1")
|
|
|
|
_activate_patcher_for_forward(
|
|
p,
|
|
peer_patchers=peers,
|
|
fast_weight_swap=fast_swap,
|
|
safe_force_clean_swap=safe_force_clean,
|
|
uuid_only_noop=uuid_noop,
|
|
debug_swap=dbg_swap,
|
|
)
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
|
|
if not hasattr(self, "_ag_dbg_once"):
|
|
try:
|
|
if shared_model:
|
|
print(
|
|
f"[AutoGuidance] shared_model={shared_model} "
|
|
f"good_uuid={getattr(self.model_patcher, 'patches_uuid', None)} "
|
|
f"bad_uuid={getattr(self.bad_model_patcher, 'patches_uuid', None)} "
|
|
f"current_uuid={getattr(getattr(self.model_patcher, 'model', None), 'current_weight_patches_uuid', None)}"
|
|
)
|
|
except Exception as e:
|
|
print(f"[AutoGuidance] dbg failed: {e!r}")
|
|
self._ag_dbg_once = True
|
|
|
|
try:
|
|
positive_cond = self.conds.get("positive", None)
|
|
negative_cond = self.conds.get("negative", None)
|
|
|
|
# SDXL needs the extra y/adm inputs; do not use None-uncond optimization.
|
|
uncond_for_good = negative_cond
|
|
|
|
conds_good: List[Any] = [positive_cond, uncond_for_good]
|
|
|
|
def _run_good(conds_list: List[Any]) -> List[Any]:
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
if not hasattr(self, "_ag_dbg_flip_good_once"):
|
|
m = getattr(self.model_patcher, "model", None)
|
|
print(
|
|
"[AutoGuidance] good_active?",
|
|
getattr(m, "current_weight_patches_uuid", None),
|
|
"want",
|
|
getattr(self.model_patcher, "patches_uuid", None),
|
|
)
|
|
self._ag_dbg_flip_good_once = True
|
|
if shared_model and not hasattr(self, "_ag_uuid_check_good_once"):
|
|
m = getattr(self.model_patcher, "model", None)
|
|
print("[AutoGuidance] after_good_activate current_uuid=", getattr(m, "current_weight_patches_uuid", None))
|
|
self._ag_uuid_check_good_once = True
|
|
# Keep hooks/wrappers that consult current_patcher aligned.
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
return comfy.samplers.calc_cond_batch(
|
|
self.inner_model,
|
|
conds_list,
|
|
x,
|
|
timestep,
|
|
model_options,
|
|
)
|
|
|
|
out_good = _run_good(conds_good)
|
|
|
|
for fn in model_options.get("sampler_pre_cfg_function", []):
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
args = {
|
|
"conds": conds_good,
|
|
"conds_out": out_good,
|
|
"cond_scale": self.cfg,
|
|
"timestep": timestep,
|
|
"input": x,
|
|
"sigma": timestep,
|
|
"model": self.inner_model,
|
|
"model_options": model_options,
|
|
}
|
|
out_good = fn(args)
|
|
|
|
cond_pred = out_good[0]
|
|
uncond_pred = out_good[1]
|
|
|
|
# IMPORTANT: protect good outputs from being overwritten by the later bad-model pass.
|
|
if torch.is_tensor(cond_pred):
|
|
cond_pred_good = cond_pred.clone()
|
|
else:
|
|
cond_pred_good = cond_pred
|
|
if torch.is_tensor(uncond_pred):
|
|
uncond_pred_good = uncond_pred.clone()
|
|
else:
|
|
uncond_pred_good = uncond_pred
|
|
|
|
if "sampler_cfg_function" in model_options:
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
args = {
|
|
"cond": x - cond_pred,
|
|
"uncond": x - uncond_pred,
|
|
"cond_scale": self.cfg,
|
|
"timestep": timestep,
|
|
"input": x,
|
|
"sigma": timestep,
|
|
"cond_denoised": cond_pred_good,
|
|
"uncond_denoised": uncond_pred_good,
|
|
"model": self.inner_model,
|
|
"model_options": model_options,
|
|
}
|
|
cfg_out = x - model_options["sampler_cfg_function"](args)
|
|
else:
|
|
cfg_out = uncond_pred + (cond_pred - uncond_pred) * self.cfg
|
|
|
|
if self.w_ag <= 1.0 + 1e-6:
|
|
denoised = cfg_out
|
|
for fn in model_options.get("sampler_post_cfg_function", []):
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
denoised = fn(
|
|
{
|
|
"denoised": denoised,
|
|
"cond": positive_cond,
|
|
"uncond": uncond_for_good,
|
|
"model": self.inner_model,
|
|
"uncond_denoised": uncond_pred_good,
|
|
"cond_denoised": cond_pred_good,
|
|
"sigma": timestep,
|
|
"model_options": model_options,
|
|
"input": x,
|
|
"cond_scale": self.cfg,
|
|
"bad_model": self.inner_bad_model,
|
|
"bad_cond_denoised": None,
|
|
"autoguidance_w": self.w_ag,
|
|
}
|
|
)
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
return denoised
|
|
|
|
# Prefer bad-model filtered conds for hook compatibility, but restore 'y' if required.
|
|
pos_bad_cond = self.bad_conds["positive"] if self.bad_conds and "positive" in self.bad_conds else positive_cond
|
|
pos_bad_cond = _clone_cond_metadata(pos_bad_cond)
|
|
|
|
pos_bad_cond = _restore_y_per_entry(pos_bad_cond, positive_cond, negative_cond)
|
|
pos_bad_cond = _ensure_primary_text_cond(pos_bad_cond, positive_cond)
|
|
if self.bad_conds is not None:
|
|
self.bad_conds["positive"] = pos_bad_cond
|
|
|
|
# Prefer running full bad-model CFG so AG compares guided predictions
|
|
# (cfg_good - cfg_bad), which is significantly more stable for LCM/DMD2.
|
|
neg_src = None
|
|
if self.bad_conds is not None:
|
|
neg_src = self.bad_conds.get("negative", None)
|
|
neg_bad = _clone_cond_metadata(neg_src if neg_src is not None else uncond_for_good)
|
|
neg_bad = _restore_y_per_entry(neg_bad, positive_cond, negative_cond)
|
|
neg_bad = _ensure_primary_text_cond(neg_bad, uncond_for_good)
|
|
if self.bad_conds is not None:
|
|
self.bad_conds["negative"] = neg_bad
|
|
conds_bad: List[Any] = [pos_bad_cond, neg_bad]
|
|
|
|
def _run_bad(conds_list: List[Any]) -> List[Any]:
|
|
if shared_model:
|
|
_activate(self.bad_model_patcher, (self.model_patcher,))
|
|
if not hasattr(self, "_ag_dbg_flip_bad_once"):
|
|
m = getattr(self.bad_model_patcher, "model", None)
|
|
print(
|
|
"[AutoGuidance] bad_active?",
|
|
getattr(m, "current_weight_patches_uuid", None),
|
|
"want",
|
|
getattr(self.bad_model_patcher, "patches_uuid", None),
|
|
)
|
|
self._ag_dbg_flip_bad_once = True
|
|
if shared_model and not hasattr(self, "_ag_uuid_check_bad_once"):
|
|
m = getattr(self.bad_model_patcher, "model", None)
|
|
print("[AutoGuidance] after_bad_activate current_uuid=", getattr(m, "current_weight_patches_uuid", None))
|
|
self._ag_uuid_check_bad_once = True
|
|
self.inner_bad_model.current_patcher = self.bad_model_patcher
|
|
return comfy.samplers.calc_cond_batch(
|
|
self.inner_bad_model,
|
|
conds_list,
|
|
x,
|
|
timestep,
|
|
bad_model_options,
|
|
)
|
|
|
|
try:
|
|
out_bad = _run_bad(conds_bad)
|
|
except AssertionError as e:
|
|
if "must specify y if and only if the model is class-conditional" not in str(e):
|
|
raise
|
|
pos_bad_cond = _clone_cond_metadata(pos_bad_cond)
|
|
pos_bad_cond = _restore_y_per_entry(pos_bad_cond, positive_cond, negative_cond)
|
|
pos_bad_cond = _ensure_primary_text_cond(pos_bad_cond, positive_cond)
|
|
neg_bad = _clone_cond_metadata(uncond_for_good)
|
|
neg_bad = _restore_y_per_entry(neg_bad, positive_cond, negative_cond)
|
|
neg_bad = _ensure_primary_text_cond(neg_bad, uncond_for_good)
|
|
if self.bad_conds is not None:
|
|
self.bad_conds["positive"] = pos_bad_cond
|
|
self.bad_conds["negative"] = neg_bad
|
|
conds_bad = [pos_bad_cond, neg_bad]
|
|
try:
|
|
out_bad = _run_bad(conds_bad)
|
|
except RuntimeError as e2:
|
|
if "mat1 and mat2 shapes cannot be multiplied" not in str(e2):
|
|
raise
|
|
pos_bad_cond = _clone_cond_metadata(positive_cond)
|
|
pos_bad_cond = _restore_y_per_entry(pos_bad_cond, positive_cond, negative_cond)
|
|
pos_bad_cond = _ensure_primary_text_cond(pos_bad_cond, positive_cond)
|
|
neg_bad = _clone_cond_metadata(uncond_for_good)
|
|
neg_bad = _restore_y_per_entry(neg_bad, positive_cond, negative_cond)
|
|
neg_bad = _ensure_primary_text_cond(neg_bad, uncond_for_good)
|
|
if self.bad_conds is not None:
|
|
self.bad_conds["positive"] = pos_bad_cond
|
|
self.bad_conds["negative"] = neg_bad
|
|
conds_bad = [pos_bad_cond, neg_bad]
|
|
out_bad = _run_bad(conds_bad)
|
|
except RuntimeError as e:
|
|
if "mat1 and mat2 shapes cannot be multiplied" not in str(e):
|
|
raise
|
|
pos_bad_cond = _clone_cond_metadata(positive_cond)
|
|
pos_bad_cond = _restore_y_per_entry(pos_bad_cond, positive_cond, negative_cond)
|
|
pos_bad_cond = _ensure_primary_text_cond(pos_bad_cond, positive_cond)
|
|
neg_bad = _clone_cond_metadata(uncond_for_good)
|
|
neg_bad = _restore_y_per_entry(neg_bad, positive_cond, negative_cond)
|
|
neg_bad = _ensure_primary_text_cond(neg_bad, uncond_for_good)
|
|
if self.bad_conds is not None:
|
|
self.bad_conds["positive"] = pos_bad_cond
|
|
self.bad_conds["negative"] = neg_bad
|
|
conds_bad = [pos_bad_cond, neg_bad]
|
|
out_bad = _run_bad(conds_bad)
|
|
bad_cond_pred = out_bad[0]
|
|
bad_uncond_pred = out_bad[1]
|
|
|
|
dbg = bool(getattr(self, "debug_metrics", False)) or (os.environ.get("AG_DEBUG_METRICS", "0") == "1")
|
|
if dbg and step == 0 and not hasattr(self, "_ag_dbg_post_cfg_once"):
|
|
try:
|
|
print("[AutoGuidance] post_cfg_functions", [getattr(fn, "__name__", type(fn).__name__) for fn in post_cfg_fns])
|
|
print("[AutoGuidance] post_cfg_mode", post_cfg_mode)
|
|
except Exception as e:
|
|
print("[AutoGuidance] post_cfg_functions failed", repr(e))
|
|
self._ag_dbg_post_cfg_once = True
|
|
bad_sig_key = f"_ag_dbg_badcond_sig_once_{getattr(self.bad_model_patcher, 'patches_uuid', None)}"
|
|
if dbg and not hasattr(self, bad_sig_key):
|
|
try:
|
|
sig = float(bad_cond_pred.detach().float().flatten()[:8192].sum().cpu())
|
|
print("[AutoGuidance] bad_cond_pred_sig", sig)
|
|
except Exception as e:
|
|
print("[AutoGuidance] bad_cond_pred_sig failed", repr(e))
|
|
setattr(self, bad_sig_key, True)
|
|
|
|
# Keep bad CFG computation consistent with the good path.
|
|
# Some workflows install a sampler_cfg_function (e.g. cfg-rescale, custom thresholding).
|
|
if "sampler_cfg_function" in model_options:
|
|
if shared_model:
|
|
_activate(self.bad_model_patcher, (self.model_patcher,))
|
|
self.inner_bad_model.current_patcher = self.bad_model_patcher
|
|
args_bad = {
|
|
"cond": x - bad_cond_pred,
|
|
"uncond": x - bad_uncond_pred,
|
|
"cond_scale": self.cfg,
|
|
"timestep": timestep,
|
|
"input": x,
|
|
"sigma": timestep,
|
|
"cond_denoised": bad_cond_pred,
|
|
"uncond_denoised": bad_uncond_pred,
|
|
"model": self.inner_bad_model,
|
|
"model_options": bad_model_options,
|
|
}
|
|
bad_cfg_out = x - model_options["sampler_cfg_function"](args_bad)
|
|
else:
|
|
bad_cfg_out = bad_uncond_pred + (bad_cond_pred - bad_uncond_pred) * self.cfg
|
|
|
|
# Leave both models in a known patcher state to reduce cross-talk across wrappers/cached objects.
|
|
self.inner_bad_model.current_patcher = self.bad_model_patcher
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
|
|
d_cfg = cond_pred_good - uncond_pred_good
|
|
cfg_update_dir = cfg_out - uncond_pred_good # actual applied CFG update direction
|
|
# Two useful AG deltas:
|
|
# - d_ag_cond: conditional good-vs-bad direction (paper-style push-away-from-bad)
|
|
# - d_ag_cfg: CFG output good-vs-bad direction (optional/raw mode)
|
|
d_ag_cond = cond_pred_good - bad_cond_pred
|
|
d_ag_cfg = cfg_out - bad_cfg_out
|
|
w = max(float(self.w_ag - 1.0), 0.0)
|
|
if w <= 0.0:
|
|
ag_delta = None
|
|
denoised = cfg_out
|
|
else:
|
|
mode = getattr(self, "ag_delta_mode", AG_DELTA_MODE_BAD_CONDITIONAL)
|
|
allow_neg = bool(getattr(self, "ag_allow_negative", True))
|
|
|
|
# Choose AG direction.
|
|
if mode == AG_DELTA_MODE_PROJECT_CFG:
|
|
cfg_denom = (cfg_update_dir.float() * cfg_update_dir.float()).sum() + 1e-8
|
|
# Project the conditional push-away-from-bad direction onto the actual CFG update direction.
|
|
alpha = (d_ag_cond.float() * cfg_update_dir.float()).sum() / cfg_denom
|
|
if not allow_neg:
|
|
alpha = torch.clamp(alpha, min=0.0)
|
|
d_ag_dir = cfg_update_dir * alpha.to(cfg_update_dir.dtype)
|
|
elif mode == AG_DELTA_MODE_REJECT_CFG:
|
|
# Remove CFG-parallel component (w.r.t. actual CFG update) so AG can drive non-CFG structure changes.
|
|
cfg_denom = (cfg_update_dir.float() * cfg_update_dir.float()).sum() + 1e-8
|
|
alpha = (d_ag_cond.float() * cfg_update_dir.float()).sum() / cfg_denom
|
|
if not allow_neg:
|
|
alpha = torch.clamp(alpha, min=0.0)
|
|
d_ag_dir = d_ag_cond - cfg_update_dir * alpha.to(cfg_update_dir.dtype)
|
|
elif mode == AG_DELTA_MODE_RAW:
|
|
# Delta between good- and bad-guided outputs.
|
|
d_ag_dir = d_ag_cfg
|
|
else:
|
|
# Default: conditional good-vs-bad direction.
|
|
d_ag_dir = d_ag_cond
|
|
|
|
ag_delta = w * d_ag_dir
|
|
|
|
# Cap magnitude relative to the actual applied CFG update.
|
|
max_ratio = float(getattr(self, "ag_max_ratio", AG_DEFAULT_MAX_RATIO))
|
|
if max_ratio > 0.0:
|
|
n_cfg = cfg_update_dir.float().pow(2).sum().sqrt() + 1e-8
|
|
n_delta = ag_delta.float().pow(2).sum().sqrt() + 1e-8
|
|
ratio = max_ratio
|
|
sigma_max = getattr(self, "_ag_sigma_max", None)
|
|
sigma_cur = None
|
|
prog = None
|
|
ramp_factor = 1.0
|
|
total_steps = getattr(self, "_ag_steps_total", None)
|
|
if total_steps is not None and int(total_steps) > 1:
|
|
prog = float(step) / float(int(total_steps) - 1) # 0 early -> 1 late
|
|
prog = max(0.0, min(1.0, prog))
|
|
ramp_mode = str(getattr(self, "ag_ramp_mode", AG_RAMP_FLAT))
|
|
ramp_power = float(getattr(self, "ag_ramp_power", 2.0))
|
|
ramp_floor = float(getattr(self, "ag_ramp_floor", 0.0))
|
|
ramp_floor = max(0.0, min(1.0, ramp_floor))
|
|
|
|
ramp_factor = _ag_ramp_factor(prog, mode=ramp_mode, power=ramp_power)
|
|
ratio = max_ratio * (ramp_floor + (1.0 - ramp_floor) * ramp_factor)
|
|
|
|
limit = ratio * n_cfg
|
|
scale = torch.clamp(limit / (n_delta + 1e-8), max=1.0)
|
|
ag_delta = ag_delta * scale.to(ag_delta.dtype)
|
|
n_applied = ag_delta.float().pow(2).sum().sqrt() + 1e-8
|
|
|
|
debug_metrics = bool(getattr(self, "debug_metrics", False)) or (os.environ.get("AG_DEBUG_METRICS", "0") == "1")
|
|
if debug_metrics and not hasattr(self, "_ag_dbg_dir_once"):
|
|
d1 = d_ag_dir.detach().float()
|
|
d2 = cfg_update_dir.detach().float()
|
|
n1 = d1.pow(2).sum().sqrt() + 1e-8
|
|
n2 = d2.pow(2).sum().sqrt() + 1e-8
|
|
cos = float((d1 * d2).sum().cpu() / (n1 * n2))
|
|
sig = float(d1.flatten()[:8192].sum().cpu())
|
|
print("[AutoGuidance] dir", {"cos_ag_vs_cfg_update": cos, "ag_dir_sig": sig})
|
|
self._ag_dbg_dir_once = True
|
|
|
|
if debug_metrics and (step == 0 or last):
|
|
sigma_cur = _to_sigma_scalar(timestep)
|
|
# Useful sanity checks: if these are ~0, your bad model path isn't actually different.
|
|
d_cond = (cond_pred_good - bad_cond_pred).float()
|
|
n_cond = d_cond.pow(2).sum().sqrt() + 1e-8
|
|
print(
|
|
"[AutoGuidance] metrics_step",
|
|
step,
|
|
{
|
|
"mode": mode,
|
|
"w_ag": float(self.w_ag),
|
|
"w": float(w),
|
|
"cfg": float(self.cfg),
|
|
"ag_max_ratio": float(max_ratio),
|
|
"ag_ramp_mode": str(getattr(self, "ag_ramp_mode", AG_RAMP_FLAT)),
|
|
"ag_ramp_power": float(getattr(self, "ag_ramp_power", 2.0)),
|
|
"ag_ramp_floor": float(getattr(self, "ag_ramp_floor", 0.0)),
|
|
"sigma_max": float(sigma_max) if sigma_max is not None else None,
|
|
"sigma_cur": float(sigma_cur) if sigma_cur is not None else None,
|
|
"prog": float(prog) if prog is not None else None,
|
|
"ramp_factor": float(ramp_factor),
|
|
"ratio_eff": float(ratio),
|
|
"limit": float(limit.detach().cpu()) if torch.is_tensor(limit) else float(limit),
|
|
"n_cfg": float(n_cfg.detach().cpu()),
|
|
"n_delta": float(n_delta.detach().cpu()),
|
|
"n_delta_applied": float(n_applied.detach().cpu()),
|
|
"scale": float(scale.detach().cpu()),
|
|
"n_good_minus_bad_cond": float(n_cond.detach().cpu()),
|
|
},
|
|
)
|
|
|
|
denoised = cfg_out + ag_delta
|
|
|
|
# If requested, postpone applying AG until after sampler_post_cfg_function hooks.
|
|
if ag_delta is not None and post_cfg_mode == AG_POST_CFG_APPLY_AFTER:
|
|
denoised = cfg_out
|
|
|
|
denoised_before_post = denoised
|
|
|
|
for fn in post_cfg_fns:
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
args = {
|
|
"denoised": denoised,
|
|
"cond": positive_cond,
|
|
"uncond": uncond_for_good,
|
|
"model": self.inner_model,
|
|
"uncond_denoised": uncond_pred_good,
|
|
"cond_denoised": cond_pred_good,
|
|
"sigma": timestep,
|
|
"model_options": model_options,
|
|
"input": x,
|
|
"cond_scale": self.cfg,
|
|
"bad_model": self.inner_bad_model,
|
|
"bad_cond_denoised": bad_cond_pred,
|
|
"autoguidance_w": self.w_ag,
|
|
}
|
|
denoised = fn(args)
|
|
|
|
denoised_after_post = denoised
|
|
|
|
if ag_delta is not None and post_cfg_mode == AG_POST_CFG_APPLY_AFTER:
|
|
denoised = denoised + ag_delta
|
|
|
|
# Debug how much post_cfg altered the result (and what we finally return).
|
|
debug_metrics = bool(getattr(self, "debug_metrics", False)) or (os.environ.get("AG_DEBUG_METRICS", "0") == "1")
|
|
if debug_metrics and (step == 0 or last):
|
|
try:
|
|
def _l2(t):
|
|
return float(t.detach().float().pow(2).sum().sqrt().cpu())
|
|
|
|
n_pre = _l2(denoised_before_post - cfg_out)
|
|
n_post = _l2(denoised_after_post - cfg_out)
|
|
n_change = _l2(denoised_after_post - denoised_before_post)
|
|
n_final = _l2(denoised - cfg_out)
|
|
print(
|
|
"[AutoGuidance] post_cfg_effect_step",
|
|
step,
|
|
{
|
|
"n_pre_minus_cfg": n_pre,
|
|
"n_post_minus_cfg": n_post,
|
|
"n_post_change": n_change,
|
|
"n_final_minus_cfg": n_final,
|
|
},
|
|
)
|
|
except Exception as e:
|
|
print("[AutoGuidance] post_cfg_effect failed", repr(e))
|
|
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
return denoised
|
|
finally:
|
|
try:
|
|
if shared_model:
|
|
_activate(self.model_patcher, (self.bad_model_patcher,))
|
|
except Exception:
|
|
pass
|
|
if self.inner_bad_model is not None:
|
|
self.inner_bad_model.current_patcher = self.bad_model_patcher
|
|
if self.inner_model is not None:
|
|
self.inner_model.current_patcher = self.model_patcher
|
|
|
|
|
|
class AutoGuidanceCFGGuider:
|
|
"""
|
|
Node that constructs the GUIDER object.
|
|
Use with SamplerCustomAdvanced (guider input).
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"good_model": ("MODEL",),
|
|
"bad_model": ("MODEL",),
|
|
"positive": ("CONDITIONING",),
|
|
"negative": ("CONDITIONING",),
|
|
"cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "round": 0.01}),
|
|
# Paper-style parameterization:
|
|
# w_ag = 1.0 => off, 2.0 => moderate, 3.0 => strong
|
|
"w_autoguide": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 5.0, "step": 0.05, "round": 0.01}),
|
|
# 3-mode behavior selection:
|
|
# - shared_safe_low_vram: correctness-first, slow, minimal extra VRAM (default)
|
|
# - shared_fast_extra_vram: faster shared-model swapping, costs extra VRAM
|
|
# - dual_models_2x_vram: fastest, but requires truly distinct model instances (~2x VRAM)
|
|
"swap_mode": (AG_SWAP_MODE_CHOICES, {"default": AG_SWAP_MODE_SHARED_SAFE}),
|
|
},
|
|
"optional": {
|
|
# If AG looks too subtle, increase ag_max_ratio first (0.35 -> 0.75).
|
|
"ag_delta_mode": (AG_DELTA_MODE_CHOICES, {"default": AG_DELTA_MODE_BAD_CONDITIONAL}),
|
|
"ag_max_ratio": (
|
|
"FLOAT",
|
|
{"default": AG_DEFAULT_MAX_RATIO, "min": 0.0, "max": 2.0, "step": 0.05, "round": 0.01},
|
|
),
|
|
# Only affects project_cfg mode; when False, opposite-direction AG becomes 0.
|
|
"ag_allow_negative": ("BOOLEAN", {"default": True}),
|
|
# Ramp for AG cap over denoise progress:
|
|
# - flat: constant cap (default, avoids zero AG at early steps)
|
|
# - detail_late: stronger late (detail emphasis)
|
|
# - compose_early: stronger early (composition emphasis)
|
|
# - mid_peak: strongest at mid-steps
|
|
"ag_ramp_mode": (AG_RAMP_MODE_CHOICES, {"default": AG_RAMP_FLAT}),
|
|
"ag_ramp_power": ("FLOAT", {"default": 2.0, "min": 0.5, "max": 8.0, "step": 0.5, "round": 0.1}),
|
|
# Minimum always-on fraction of ag_max_ratio.
|
|
"ag_ramp_floor": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.05, "round": 0.01}),
|
|
|
|
# Post-CFG hook handling (useful if a hook normalizes/clamps away AG).
|
|
"ag_post_cfg_mode": (AG_POST_CFG_MODE_CHOICES, {"default": AG_POST_CFG_KEEP}),
|
|
|
|
# === Swap/ownership safety tuning ===
|
|
# In shared_safe_low_vram mode, force a clean swap every time we switch between good/bad.
|
|
# This is slower but fixes "washed out" or "same output" issues on some ComfyUI builds.
|
|
"safe_force_clean_swap": ("BOOLEAN", {"default": True}),
|
|
# If enabled, treat "same UUID" as a no-op without further ownership validation.
|
|
# Useful for debugging, but can hide broken swaps.
|
|
"uuid_only_noop": ("BOOLEAN", {"default": False}),
|
|
|
|
# === Debug ===
|
|
# Print a one-time lightweight weight signature per active patcher swap.
|
|
"debug_swap": ("BOOLEAN", {"default": True}),
|
|
# Print one-time AG magnitude metrics (including good-vs-bad-cond distance).
|
|
"debug_metrics": ("BOOLEAN", {"default": True}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("GUIDER",)
|
|
FUNCTION = "get_guider"
|
|
CATEGORY = "sampling/guiders"
|
|
|
|
def get_guider(
|
|
self,
|
|
good_model,
|
|
bad_model,
|
|
positive,
|
|
negative,
|
|
cfg: float,
|
|
w_autoguide: float,
|
|
swap_mode=AG_SWAP_MODE_SHARED_SAFE,
|
|
ag_delta_mode=AG_DELTA_MODE_BAD_CONDITIONAL,
|
|
ag_max_ratio=AG_DEFAULT_MAX_RATIO,
|
|
ag_allow_negative=True,
|
|
ag_ramp_mode=AG_RAMP_FLAT,
|
|
ag_ramp_power=2.0,
|
|
ag_ramp_floor=0.15,
|
|
ag_post_cfg_mode=AG_POST_CFG_KEEP,
|
|
safe_force_clean_swap=True,
|
|
uuid_only_noop=False,
|
|
debug_swap=True,
|
|
debug_metrics=True,
|
|
):
|
|
guider = Guider_AutoGuidanceCFG(
|
|
good_model,
|
|
bad_model,
|
|
swap_mode=swap_mode,
|
|
ag_delta_mode=ag_delta_mode,
|
|
ag_max_ratio=ag_max_ratio,
|
|
ag_allow_negative=ag_allow_negative,
|
|
ag_ramp_mode=ag_ramp_mode,
|
|
ag_ramp_power=ag_ramp_power,
|
|
ag_ramp_floor=ag_ramp_floor,
|
|
ag_post_cfg_mode=ag_post_cfg_mode,
|
|
safe_force_clean_swap=safe_force_clean_swap,
|
|
uuid_only_noop=uuid_only_noop,
|
|
debug_swap=debug_swap,
|
|
debug_metrics=debug_metrics,
|
|
)
|
|
# Make swap debugging available inside patcher-level helpers.
|
|
for _p in (good_model, bad_model):
|
|
_m = getattr(_p, "model", None)
|
|
if _m is not None:
|
|
try:
|
|
setattr(_m, "_ag_debug_swap", bool(debug_swap))
|
|
except Exception:
|
|
pass
|
|
guider.set_conds(positive, negative)
|
|
guider.set_scales(cfg=cfg, w_ag=w_autoguide)
|
|
return (guider,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"AutoGuidanceCFGGuider": AutoGuidanceCFGGuider,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"AutoGuidanceCFGGuider": "AutoGuidance CFG Guider (good+bad)",
|
|
}
|