fix(studio): cap the negative_erase renorm gain

A token pointing along the negative is left with rounding error after
projection; restoring its norm amplified that noise ~1000x into the
conditioning. Cap the gain and refuse a non-finite result.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

CHANGELOG.md

@@ -3,6 +3,13 @@
 ## [Unreleased]
 
 ### Fixed
+- **negative_erase could put an amplified-noise vector into the conditioning.** "Keep prompt
+  strength" restored each token's norm after the erase — but a token pointing almost exactly
+  along the negative is left with nothing but rounding error, and restoring its norm scaled
+  that residue by thousands. The gain is now capped: past it the token stays quiet, because
+  it really was mostly the thing you asked to remove. A non-finite result is refused outright
+  rather than passed on, since the sampler captures conditioning for the refinement key and
+  would have banked the bad vector.
 - **Bypass now works as an A/B switch.** Wire two alternatives at one input — MiniMax H3's
   ref-to-video and first-last-to-video both feeding the sampler's latent — and bypass the
   one you are not using. A bypassed slot no longer claims the input, no longer counts as a

negative_erase.py

@@ -22,6 +22,10 @@ Pure tensor math, no ComfyUI imports, so it is testable without a model.
 
 MODES = ("project", "subtract")
 
+# How far a token may be scaled back up after the erase. See the note in `erase`: without a
+# cap, a token that WAS the unwanted concept comes back as amplified rounding error.
+RENORM_MAX_GAIN = 2.0
+
 
 def _torch():
     import torch
@@ -107,8 +111,20 @@ def erase(pos_tensor, unit, strength, meta=None, mode="project", renorm=True):
         rows = rows - float(strength) * before * u
 
     if renorm:
-        after = rows.norm(dim=-1, keepdim=True).clamp_min(1e-6)
-        rows = rows * (before / after)
+        # The gain is CAPPED, and that cap is the whole safety of this operation. A token
+        # pointing almost exactly along the negative is left with nothing but rounding error
+        # after projection; scaling that back to its original norm multiplies noise by
+        # thousands and puts a garbage vector at full prompt strength into the conditioning.
+        # Past the cap the honest answer is that the token really was mostly the thing you
+        # asked to remove, so it stays quiet instead of being refilled with residue.
+        after = rows.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+        rows = rows * (before / after).clamp(max=RENORM_MAX_GAIN)
+
+    # Never hand a non-finite conditioning onward. It does not fail here — it fails deep in
+    # the model, and worse, a run that captures conditioning for the refinement key would
+    # bank the bad vector and keep steering toward it long after this setting was turned off.
+    if not bool(torch.isfinite(rows).all()):
+        return pos_tensor
 
     if idx is None:
         work = rows

tests/test_negative_erase.py

@@ -155,3 +155,44 @@ def test_a_dimension_mismatch_leaves_the_conditioning_alone():
     out, note = ne.apply(pos, _cond(torch.tensor([[[1.0, 0.0]]])), 1.0)
     assert "no conditioning entry could be modified" in note
     assert out is pos
+
+
+# --- the renorm gain cap ---------------------------------------------------------------
+# Restoring a token's norm after projection is fine until the projection removed almost all
+# of it. Then what is left is rounding error, and "restore the original norm" means putting
+# amplified noise into the conditioning at full prompt strength — which the sampler will
+# happily bank into the refinement key and keep steering toward afterwards.
+
+
+def test_a_token_that_WAS_the_negative_goes_quiet_instead_of_exploding():
+    u = torch.tensor([1.0, 0.0])
+    pos = torch.tensor([[[5.0, 0.001]]])          # almost exactly the negative's direction
+    out = ne.erase(pos, u, 1.0, renorm=True)
+    assert float(out.norm()) < 0.01               # not 5.0 of amplified residue
+    assert float(out[0, 0, 1]) == pytest.approx(0.002, abs=1e-4)   # capped at 2x, no more
+
+
+def test_an_ordinary_token_still_gets_its_norm_back():
+    """The cap must not break the normal case it exists to serve."""
+    u = torch.tensor([1.0, 0.0])
+    out = ne.erase(torch.tensor([[[3.0, 4.0]]]), u, 1.0, renorm=True)
+    assert float(out.norm()) == pytest.approx(5.0, abs=1e-4)
+
+
+def test_the_gain_never_exceeds_the_cap():
+    u = torch.tensor([1.0, 0.0])
+    for orth in (0.001, 0.01, 0.1, 1.0, 4.0):
+        pos = torch.tensor([[[5.0, orth]]])
+        before = float(pos.norm())
+        after = float(ne.erase(pos, u, 1.0, renorm=True).norm())
+        residue = float(torch.tensor([0.0, orth]).norm())
+        assert after <= residue * ne.RENORM_MAX_GAIN + 1e-4
+        assert after <= before + 1e-4
+
+
+def test_a_non_finite_result_is_refused_rather_than_passed_on():
+    """It would not fail here — it fails deep in the model, and a run that captures
+    conditioning for the refinement key would bank the bad vector first."""
+    u = torch.tensor([float("nan"), 0.0])
+    pos = torch.tensor([[[3.0, 4.0]]])
+    assert ne.erase(pos, u, 1.0) is pos