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Merge pull request #35 from xmarre/codex/add-multi-guidance-combine-mode

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Improve paper-mode compatibility and remove redundant bad forward
This commit is contained in:
xmarre
2026-02-13 02:20:04 +01:00
committed by GitHub
parent 8e6b17de05 3eaf8731b7
commit eeba523c38
2 changed files with 291 additions and 81 deletions
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README.md
+14
View File
@@ -88,6 +88,10 @@ If both loaders point to the exact same path, ComfyUI may share/collapse model s
### Optional core controls
- `ag_combine_mode`
- `sequential_delta` (default; current behavior: CFG result + AG delta)
- `multi_guidance_paper` (paper-style multi-guide extrapolation using good-cond, good-uncond, bad-cond)
- In `multi_guidance_paper`, `ag_delta_mode`, `ag_max_ratio`, ramping, and project/reject controls are not used.
- `ag_delta_mode`
- `bad_conditional` (default, common starting point)
- `raw_delta`
@@ -149,6 +153,16 @@ Core idea: bad side should be meaningfully weaker/less specialized than good sid
## Practical tuning notes
- Increase `w_autoguide` above `1.0` to strengthen effect.
- In `multi_guidance_paper` mode, weights are interpreted as:
- `w_cfg = max(cfg - 1, 0)`
- `w_ag = max(w_autoguide - 1, 0)`
- output = `(1 + w_cfg + w_ag) * C - w_cfg * U - w_ag * B`
where `C` = good conditional, `U` = good negative/uncond, `B` = bad conditional.
- To reproduce fixed-total-guidance interpolation from the paper (`g`, mix `α`):
- `cfg = 1 + (g - 1)(1 - α)`
- `w_autoguide = 1 + (g - 1)α`
- `ag_combine_mode = multi_guidance_paper`
- In `multi_guidance_paper`, `ag_post_cfg_mode` only controls whether post-CFG hooks run (`skip`); there is no separate AG "apply after" stage.
- Use `ag_max_ratio` to prevent runaway/cooked outputs.
- `compose_early` tends to affect composition/structure earlier in denoise.
- Try `detail_late` for a more late-step/detail-leaning influence.
nodes.py
+277 -81
View File
@@ -5,7 +5,7 @@ import inspect
import os
from collections import Counter
from collections.abc import Mapping
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
import torch
import comfy.sampler_helpers
@@ -139,6 +139,14 @@ AG_POST_CFG_MODE_CHOICES = [
AG_POST_CFG_SKIP,
]
AG_COMBINE_MODE_SEQUENTIAL_DELTA = "sequential_delta"
AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER = "multi_guidance_paper"
AG_COMBINE_MODE_CHOICES = [
AG_COMBINE_MODE_SEQUENTIAL_DELTA,
AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER,
]
AG_RAMP_FLAT = "flat"
AG_RAMP_DETAIL_LATE = "detail_late"
AG_RAMP_COMPOSE_EARLY = "compose_early"
@@ -875,6 +883,7 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
ag_ramp_power: float = 2.0,
ag_ramp_floor: float = 0.0,
ag_post_cfg_mode: str = AG_POST_CFG_KEEP,
ag_combine_mode: str = AG_COMBINE_MODE_SEQUENTIAL_DELTA,
safe_force_clean_swap: bool = True,
uuid_only_noop: bool = False,
debug_swap: bool = False,
@@ -893,6 +902,7 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
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.ag_combine_mode: str = str(ag_combine_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)
@@ -1204,6 +1214,11 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
uncond_for_good = negative_cond
conds_good: List[Any] = [positive_cond, uncond_for_good]
ag_combine_mode = str(getattr(self, "ag_combine_mode", AG_COMBINE_MODE_SEQUENTIAL_DELTA))
w_cfg_paper = max(float(self.cfg) - 1.0, 0.0)
w_ag_paper = max(float(self.w_ag) - 1.0, 0.0)
cfg_effective_paper = 1.0 + w_cfg_paper + w_ag_paper
cond_scale_for_pre_cfg = cfg_effective_paper if ag_combine_mode == AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER else float(self.cfg)
def _run_good(conds_list: List[Any]) -> List[Any]:
if shared_model:
@@ -1241,6 +1256,7 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
active_patcher,
other_patcher,
options: Dict[str, Any],
cond_scale_override: Optional[float] = None,
) -> List[Any]:
out_cur = out_list
for fn in options.get("sampler_pre_cfg_function", []):
@@ -1250,7 +1266,7 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
args = {
"conds": conds_list,
"conds_out": out_cur,
"cond_scale": self.cfg,
"cond_scale": cond_scale_override if cond_scale_override is not None else self.cfg,
"timestep": timestep,
"input": x,
"sigma": timestep,
@@ -1267,6 +1283,7 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
active_patcher=self.model_patcher,
other_patcher=self.bad_model_patcher,
options=model_options,
cond_scale_override=cond_scale_for_pre_cfg,
)
cond_pred = out_good[0]
@@ -1282,29 +1299,60 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
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
cfg_out = None
if ag_combine_mode != AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER:
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
# Fast path: AG disabled.
if self.w_ag <= 1.0 + 1e-6:
denoised = cfg_out
for fn in model_options.get("sampler_post_cfg_function", []):
if ag_combine_mode == AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER:
cfg_effective = 1.0 + w_cfg_paper
origin = uncond_pred_good
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_good,
"uncond": x - origin,
"cond_scale": cfg_effective,
"timestep": timestep,
"input": x,
"sigma": timestep,
"cond_denoised": cond_pred_good,
"uncond_denoised": origin,
"model": self.inner_model,
"model_options": model_options,
}
denoised = x - model_options["sampler_cfg_function"](args)
else:
denoised = origin + (cond_pred_good - origin) * cfg_effective
cond_scale_for_hooks = cfg_effective
uncond_denoised_for_hooks = origin
else:
denoised = cfg_out
cond_scale_for_hooks = self.cfg
uncond_denoised_for_hooks = uncond_pred_good
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
@@ -1314,12 +1362,12 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
"cond": positive_cond,
"uncond": uncond_for_good,
"model": self.inner_model,
"uncond_denoised": uncond_pred_good,
"uncond_denoised": uncond_denoised_for_hooks,
"cond_denoised": cond_pred_good,
"sigma": timestep,
"model_options": model_options,
"input": x,
"cond_scale": self.cfg,
"cond_scale": cond_scale_for_hooks,
"bad_model": self.inner_bad_model,
"bad_cond_denoised": None,
"autoguidance_w": self.w_ag,
@@ -1410,64 +1458,203 @@ class Guider_AutoGuidanceCFG(comfy.samplers.CFGGuider):
bad_model_options,
), bad_model_options
try:
out_bad, bad_opts_used = _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)
def _rebuild_bad_pos_from_good() -> Any:
rebuilt = _clone_cond_metadata(positive_cond)
rebuilt = _restore_y_per_entry(rebuilt, positive_cond, negative_cond)
rebuilt = _ensure_primary_text_cond(rebuilt, positive_cond)
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, bad_opts_used = _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, bad_opts_used = _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, bad_opts_used = _run_bad(conds_bad)
bad_cond_pred = out_bad[0]
bad_uncond_pred = out_bad[1]
self.bad_conds["positive"] = rebuilt
return rebuilt
out_bad = _apply_pre_cfg_hooks(
conds_list=conds_bad,
out_list=out_bad,
run_model=self.inner_bad_model,
active_patcher=self.bad_model_patcher,
other_patcher=self.model_patcher,
options=bad_opts_used,
)
bad_cond_pred = out_bad[0]
bad_uncond_pred = out_bad[1]
def _rebuild_bad_neg_from_good() -> Any:
rebuilt = _clone_cond_metadata(uncond_for_good)
rebuilt = _restore_y_per_entry(rebuilt, positive_cond, negative_cond)
rebuilt = _ensure_primary_text_cond(rebuilt, uncond_for_good)
if self.bad_conds is not None:
self.bad_conds["negative"] = rebuilt
return rebuilt
def _run_bad_full_with_fallback(pos_in: Any, neg_in: Any):
pos_local = pos_in
neg_local = neg_in
conds_local = [pos_local, neg_local]
try:
return _run_bad(conds_local), conds_local
except AssertionError as e:
if "must specify y if and only if the model is class-conditional" not in str(e):
raise
pos_local = _clone_cond_metadata(pos_local)
pos_local = _restore_y_per_entry(pos_local, positive_cond, negative_cond)
pos_local = _ensure_primary_text_cond(pos_local, positive_cond)
neg_local = _rebuild_bad_neg_from_good()
if self.bad_conds is not None:
self.bad_conds["positive"] = pos_local
conds_local = [pos_local, neg_local]
try:
return _run_bad(conds_local), conds_local
except RuntimeError as e2:
if "mat1 and mat2 shapes cannot be multiplied" not in str(e2):
raise
pos_local = _rebuild_bad_pos_from_good()
neg_local = _rebuild_bad_neg_from_good()
conds_local = [pos_local, neg_local]
return _run_bad(conds_local), conds_local
except RuntimeError as e:
if "mat1 and mat2 shapes cannot be multiplied" not in str(e):
raise
pos_local = _rebuild_bad_pos_from_good()
neg_local = _rebuild_bad_neg_from_good()
conds_local = [pos_local, neg_local]
return _run_bad(conds_local), conds_local
if ag_combine_mode == AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER:
has_pre_cfg = bool(model_options.get("sampler_pre_cfg_function", []))
if has_pre_cfg:
(out_bad, bad_opts_used), conds_bad = _run_bad_full_with_fallback(pos_bad_cond, neg_bad)
out_bad = _apply_pre_cfg_hooks(
conds_list=conds_bad,
out_list=out_bad,
run_model=self.inner_bad_model,
active_patcher=self.bad_model_patcher,
other_patcher=self.model_patcher,
options=bad_opts_used,
cond_scale_override=cond_scale_for_pre_cfg,
)
bad_cond_pred = out_bad[0]
bad_uncond_pred = out_bad[1]
else:
conds_bad = [pos_bad_cond]
try:
out_bad, bad_opts_used = _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 = _rebuild_bad_pos_from_good()
conds_bad = [pos_bad_cond]
try:
out_bad, bad_opts_used = _run_bad(conds_bad)
except RuntimeError as e2:
if "mat1 and mat2 shapes cannot be multiplied" not in str(e2):
raise
pos_bad_cond = _rebuild_bad_pos_from_good()
conds_bad = [pos_bad_cond]
out_bad, bad_opts_used = _run_bad(conds_bad)
except RuntimeError as e:
if "mat1 and mat2 shapes cannot be multiplied" not in str(e):
raise
pos_bad_cond = _rebuild_bad_pos_from_good()
conds_bad = [pos_bad_cond]
out_bad, bad_opts_used = _run_bad(conds_bad)
out_bad = _apply_pre_cfg_hooks(
conds_list=conds_bad,
out_list=out_bad,
run_model=self.inner_bad_model,
active_patcher=self.bad_model_patcher,
other_patcher=self.model_patcher,
options=bad_opts_used,
cond_scale_override=cond_scale_for_pre_cfg,
)
bad_cond_pred = out_bad[0]
bad_uncond_pred = None
else:
(out_bad, bad_opts_used), conds_bad = _run_bad_full_with_fallback(pos_bad_cond, neg_bad)
bad_cond_pred = out_bad[0]
bad_uncond_pred = out_bad[1]
out_bad = _apply_pre_cfg_hooks(
conds_list=conds_bad,
out_list=out_bad,
run_model=self.inner_bad_model,
active_patcher=self.bad_model_patcher,
other_patcher=self.model_patcher,
options=bad_opts_used,
cond_scale_override=cond_scale_for_pre_cfg,
)
bad_cond_pred = out_bad[0]
bad_uncond_pred = out_bad[1]
if ag_combine_mode == AG_COMBINE_MODE_MULTI_GUIDANCE_PAPER:
w_cfg = w_cfg_paper
w_ag = w_ag_paper
w_sum = w_cfg + w_ag
cfg_effective = 1.0 + w_sum
if w_sum <= 1e-8:
origin = uncond_pred_good
denoised = cond_pred_good
else:
origin = ((w_cfg * uncond_pred_good) + (w_ag * bad_cond_pred)) / w_sum
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_good,
"uncond": x - origin,
"cond_scale": cfg_effective,
"timestep": timestep,
"input": x,
"sigma": timestep,
"cond_denoised": cond_pred_good,
"uncond_denoised": origin,
"model": self.inner_model,
"model_options": model_options,
}
denoised = x - model_options["sampler_cfg_function"](args)
else:
denoised = origin + (cond_pred_good - origin) * cfg_effective
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": origin,
"cond_denoised": cond_pred_good,
"sigma": timestep,
"model_options": model_options,
"input": x,
"cond_scale": cfg_effective,
"bad_model": self.inner_bad_model,
"bad_cond_denoised": bad_cond_pred,
"autoguidance_w": self.w_ag,
}
denoised = fn(args)
denoised_after_post = denoised
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 - cond_pred_good)
n_post = _l2(denoised_after_post - cond_pred_good)
n_change = _l2(denoised_after_post - denoised_before_post)
print(
"[AutoGuidance] paper_multi_post_cfg_effect_step",
step,
{
"cond_scale_effective": float(cfg_effective),
"w_cfg": float(w_cfg),
"w_ag": float(w_ag),
"n_pre_minus_cond": n_pre,
"n_post_minus_cond": n_post,
"n_post_change": n_change,
},
)
except Exception as e:
print("[AutoGuidance] paper_multi_post_cfg_effect failed", repr(e))
if shared_model:
_activate(self.model_patcher, (self.bad_model_patcher,))
return denoised
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"):
@@ -1722,6 +1909,7 @@ class AutoGuidanceCFGGuider:
},
"optional": {
# If AG looks too subtle, increase ag_max_ratio first (0.35 -> 0.75).
"ag_combine_mode": (AG_COMBINE_MODE_CHOICES, {"default": AG_COMBINE_MODE_SEQUENTIAL_DELTA}),
"ag_delta_mode": (AG_DELTA_MODE_CHOICES, {"default": AG_DELTA_MODE_BAD_CONDITIONAL}),
"ag_max_ratio": (
"FLOAT",
@@ -1771,6 +1959,7 @@ class AutoGuidanceCFGGuider:
cfg: float,
w_autoguide: float,
swap_mode=AG_SWAP_MODE_SHARED_SAFE,
ag_combine_mode=AG_COMBINE_MODE_SEQUENTIAL_DELTA,
ag_delta_mode=AG_DELTA_MODE_BAD_CONDITIONAL,
ag_max_ratio=AG_DEFAULT_MAX_RATIO,
ag_allow_negative=True,
@@ -1787,6 +1976,7 @@ class AutoGuidanceCFGGuider:
good_model,
bad_model,
swap_mode=swap_mode,
ag_combine_mode=ag_combine_mode,
ag_delta_mode=ag_delta_mode,
ag_max_ratio=ag_max_ratio,
ag_allow_negative=ag_allow_negative,
@@ -1992,6 +2182,7 @@ class AutoGuidanceImpactDetailerHook(_ImpactDetailerHook):
*,
w_autoguide: float = 2.0,
swap_mode: str = AG_SWAP_MODE_SHARED_SAFE,
ag_combine_mode: str = AG_COMBINE_MODE_SEQUENTIAL_DELTA,
ag_delta_mode: str = AG_DELTA_MODE_BAD_CONDITIONAL,
ag_max_ratio: float = AG_DEFAULT_MAX_RATIO,
ag_allow_negative: bool = True,
@@ -2008,6 +2199,7 @@ class AutoGuidanceImpactDetailerHook(_ImpactDetailerHook):
self.bad_model = bad_model
self.w_autoguide = float(w_autoguide)
self.swap_mode = str(swap_mode)
self.ag_combine_mode = str(ag_combine_mode)
self.ag_delta_mode = str(ag_delta_mode)
self.ag_max_ratio = float(ag_max_ratio)
self.ag_allow_negative = bool(ag_allow_negative)
@@ -2031,6 +2223,7 @@ class AutoGuidanceImpactDetailerHook(_ImpactDetailerHook):
model,
self.bad_model,
swap_mode=self.swap_mode,
ag_combine_mode=self.ag_combine_mode,
ag_delta_mode=self.ag_delta_mode,
ag_max_ratio=self.ag_max_ratio,
ag_allow_negative=self.ag_allow_negative,
@@ -2077,6 +2270,7 @@ class AutoGuidanceImpactDetailerHookProvider:
},
"optional": {
"ag_delta_mode": (AG_DELTA_MODE_CHOICES, {"default": AG_DELTA_MODE_BAD_CONDITIONAL}),
"ag_combine_mode": (AG_COMBINE_MODE_CHOICES, {"default": AG_COMBINE_MODE_SEQUENTIAL_DELTA}),
"ag_max_ratio": ("FLOAT", {"default": AG_DEFAULT_MAX_RATIO, "min": 0.0, "max": 2.0, "step": 0.05, "round": 0.01}),
"ag_allow_negative": ("BOOLEAN", {"default": True}),
"ag_ramp_mode": (AG_RAMP_MODE_CHOICES, {"default": AG_RAMP_FLAT}),
@@ -2099,6 +2293,7 @@ class AutoGuidanceImpactDetailerHookProvider:
bad_model,
w_autoguide: float,
swap_mode=AG_SWAP_MODE_SHARED_SAFE,
ag_combine_mode=AG_COMBINE_MODE_SEQUENTIAL_DELTA,
ag_delta_mode=AG_DELTA_MODE_BAD_CONDITIONAL,
ag_max_ratio=AG_DEFAULT_MAX_RATIO,
ag_allow_negative=True,
@@ -2115,6 +2310,7 @@ class AutoGuidanceImpactDetailerHookProvider:
bad_model,
w_autoguide=w_autoguide,
swap_mode=swap_mode,
ag_combine_mode=ag_combine_mode,
ag_delta_mode=ag_delta_mode,
ag_max_ratio=ag_max_ratio,
ag_allow_negative=ag_allow_negative,