nodes/olivv-cs-ComfyUI-FunPack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix self_consistency: don't inject into anchor tokens, cap max at 0.5

Browse Source 
Middle-frame tokens are the anchor source — blending them with their
own mean was pointless self-contamination and the main cause of
collapse at higher strengths. Now only the surrounding frames
(before mid_start and after mid_end) receive the injection; the
anchor zone is left untouched each step.

Max strength capped at 0.5 in the UI; tooltip warns against going high.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-29 00:46:32 +03:00
co-authored by Claude Sonnet 4.6
parent 8846bdbce2
commit 665f8f1feb
1 changed files with 8 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
samplers.py
+8 -4
View File
@@ -800,7 +800,7 @@ class FunPackLTXAVSceneChainSampler:
}),
"self_consistency_strength": ("FLOAT", {
"default": 0.05, "min": 0.01, "max": 0.5, "step": 0.01,
"tooltip": "Injection strength for self-consistency. Small values (0.02–0.1) add a soft constraint; larger values lock anatomy more aggressively.",
"tooltip": "Injection strength for self-consistency. Small values (0.02–0.1) add a soft constraint. Above 0.5 output degrades significantly — stay low.",
}),
"transition_duration": ("INT", {
"default": 16, "min": 0, "max": 128, "step": 2,
@@ -1322,10 +1322,14 @@ class FunPackLTXAVSceneChainSampler:
x, is_dict = output, False
if x is None or x.dim() < 2 or x.shape[1] < mid_end:
return
# Mean of current step's middle-frame tokens → spread to all positions.
# Anchor updates every step so high-sigma noise never persists.
# Mean of current step's middle-frame tokens → spread to surrounding frames.
# Middle-frame tokens are left untouched (they are the anchor, not targets).
mid_mean = x[:, mid_start:mid_end, :].mean(dim=1, keepdim=True)
out = (1.0 - strength) * x + strength * mid_mean.expand_as(x)
out = x.clone()
if mid_start > 0:
out[:, :mid_start, :] = (1.0 - strength) * x[:, :mid_start, :] + strength * mid_mean
if mid_end < x.shape[1]:
out[:, mid_end:, :] = (1.0 - strength) * x[:, mid_end:, :] + strength * mid_mean
if is_dict:
return {**output, "img": out}
elif isinstance(output, tuple):