fix: H3 shadow negative never ran; now it does, i2v scenes too

It read the prompt length from transformer_options['c_crossattn'], which
ComfyUI never sets, so every block ran the stock forward. A model wrapper now
records it per call. Per-token mod rows (any pinned frame) no longer disable
it, and the step window reads the real schedule.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

h3_shadow_negative.py

@@ -83,7 +83,7 @@ class ShadowState:
     same fact), rebuilt fresh from `negative_context` on first use afterwards.
     """
     __slots__ = ("dm", "negative_context", "video_scale", "audio_scale", "tau", "alpha",
-                 "start_percent", "end_percent", "neg_h", "pos_text_len",
+                 "start_percent", "end_percent", "neg_h", "pos_text_len", "text_len",
                  "warned_layout", "warned_fallback", "hard_disabled")
 
     def __init__(self, dm, negative_context, video_scale, audio_scale, tau, alpha,
@@ -98,6 +98,12 @@ class ShadowState:
         self.end_percent = float(end_percent)
         self.neg_h = None
         self.pos_text_len = None
+        # The POSITIVE prompt's token count for the model call in progress. Set by the
+        # model_function_wrapper _install_h3_shadow_negative puts on the model, which is the
+        # one place the conditioning tensor is visible -- ComfyUI never copies it into
+        # transformer_options, so reading it from there (as this first shipped) found None on
+        # every block and the whole feature silently ran the stock forward.
+        self.text_len = None
         self.warned_layout = False
         self.warned_fallback = False
         self.hard_disabled = False
@@ -118,6 +124,25 @@ def _prepare_negative(state, x, transformer_options):
     return neg
 
 
+def _tag_is(row, tag):
+    """A mod row is an int, or -- once a pinned frame or mask gives rows their own strength
+    (any i2v scene) -- a per-token LongTensor. int() on that tensor raised, which disabled
+    shadow negative for the rest of the run on every i2v scene."""
+    if isinstance(row, torch.Tensor):
+        return bool(((row % 3) == tag).all())
+    return int(row) % 3 == tag
+
+
+def step_progress(transformer_options):
+    """0 at the first denoise step, 1 at the last, from the schedule core puts in
+    transformer_options (sample_sigmas + this call's sigmas). None if unresolvable."""
+    to = transformer_options or {}
+    sched, cur = to.get("sample_sigmas"), to.get("sigmas")
+    if not isinstance(sched, torch.Tensor) or cur is None:
+        return None
+    return sigma_progress(cur, sched)
+
+
 def _mod_one(h, shift, scale, row):
     return h.mul_(1.0 + scale[row].to(h.dtype)).add_(shift[row].to(h.dtype))
 
@@ -142,7 +167,7 @@ def _presentation_plan(mod_segments, pos_text_len):
         if b > pos_text_len:
             return None, None
         runs.append(seg)
-    text_runs = [r for r in runs if (r[2] % 3) == 1]
+    text_runs = [r for r in runs if _tag_is(r[2], 1)]
     if not runs or not text_runs:
         return None, None
     return runs, text_runs[-1]
@@ -183,7 +208,7 @@ def _build_shadow_attention_input(state, block, neg_h, h_pos, rope, presentation
     h_parts, r_parts, cursor = [], [], 0
     neg_a = neg_b = None
     for a, b, row in presentation_runs:
-        if (a, b, row) == user_run:
+        if (a, b) == tuple(user_run[:2]):
             neg_a, neg_b = cursor, cursor + neg_norm.shape[0]
             h_parts.append(neg_norm)
             r_parts.append(neg_rope)
@@ -233,26 +258,26 @@ def _shadow_forward(state, block, args, extra_options):
     rope = args["rope_freqs"]
     to = args["transformer_options"]
 
-    sigmas = to.get("sample_sigmas", to.get("sigmas"))
-    progress = sigma_progress(args.get("timestep", to.get("timestep")), sigmas)
+    progress = step_progress(to)
     active = state.alpha > 0 and (state.video_scale != 1.0 or state.audio_scale != 1.0)
     if progress is not None:
         active = active and state.start_percent <= progress <= state.end_percent
     if not active or len(mod_segments) < 3:
         return extra_options["original_block"](args)
 
-    state.pos_text_len = int(x.shape[0])  # placeholder, corrected below once c_crossattn known
-    cross_attn = to.get("c_crossattn")
-    pos_text_len = (int(cross_attn.shape[1])
-                    if isinstance(cross_attn, torch.Tensor) and cross_attn.ndim >= 3
-                    else None)
+    pos_text_len = state.text_len
     if pos_text_len is None:
+        if not state.warned_layout:
+            state.warned_layout = True
+            print("[FunPackSceneChain] H3 shadow negative: the prompt length was not seen for "
+                  "this model call (the wrapper that records it is missing) -- bypassing "
+                  "shadow guidance.")
         return extra_options["original_block"](args)
     state.pos_text_len = pos_text_len
 
     audio_a, audio_b, audio_row = mod_segments[-2]
     video_a, video_b, video_row = mod_segments[-1]
-    if (int(audio_row) % 3) != 2 or (int(video_row) % 3) != 0 or audio_b != video_a \
+    if not _tag_is(audio_row, 2) or not _tag_is(video_row, 0) or audio_b != video_a \
             or video_b != x.shape[0]:
         if not state.warned_layout:
             state.warned_layout = True

samplers.py

@@ -7991,6 +7991,24 @@ class FunPackLTXAVSceneChainSampler:
                 dm=dm, negative_context=negative[0][0].detach(),
                 video_scale=video_scale, audio_scale=audio_scale,
                 tau=tau, alpha=alpha, start_percent=_lo, end_percent=_hi)
+
+            # The positive prompt's length is only visible here, on the model call's own
+            # conditioning -- core never copies it into transformer_options. Recorded per
+            # call, and the shadow stream reset per call, so each denoise evaluation starts
+            # the negative fresh at block 0 whatever blocks are hooked.
+            _old_wrapper = patched.model_options.get("model_function_wrapper")
+
+            def _shadow_wrapper(apply_fn, args):
+                ctx = (args.get("c") or {}).get("c_crossattn")
+                state.text_len = (int(ctx.shape[1]) if isinstance(ctx, torch.Tensor)
+                                  and ctx.ndim >= 3 else None)
+                state.neg_h = None
+                if _old_wrapper is not None:
+                    return _old_wrapper(apply_fn, args)
+                return apply_fn(args["input"], args["timestep"], **args.get("c", {}))
+
+            patched.model_options["model_function_wrapper"] = _tag_scene_wrapper(
+                _shadow_wrapper, _old_wrapper)
             _skipped = []
             for i, block in enumerate(dm.blocks):
                 if compose and i in _claimed:

tests/test_h3_shadow_negative.py

@@ -141,3 +141,42 @@ def test_empty_negative_context_yields_nothing():
                            audio_scale=1.0, tau=2.5, alpha=0.35, start_percent=0.0,
                            end_percent=0.6)
     assert sn._prepare_negative(state, torch.zeros(1, 4), {}) is None
+
+
+# --- 2026-09-27: the fix for "never ran" --------------------------------------------
+# The prompt length used to be read from transformer_options["c_crossattn"], which ComfyUI
+# never sets, so every block ran the stock forward. Proven against a real tiny H3 forward
+# outside this (stubbed) suite; these pin the pieces the fix rests on.
+
+def test_a_per_token_mod_row_is_accepted_not_int_cast():
+    rows = torch.tensor([3, 6, 9])            # all tag 0 (video), per-token strengths
+    assert sn._tag_is(rows, 0) and not sn._tag_is(rows, 2)
+    assert sn._tag_is(5, 2) and not sn._tag_is(5, 0)
+
+
+def test_the_user_prompt_run_is_found_when_rows_are_tensors():
+    segs = [(0, 3, 1), (3, 5, torch.tensor([0, 0])), (5, 9, 1), (9, 12, 2)]
+    runs, user = sn._presentation_plan(segs, 9)
+    assert user == (5, 9, 1) and len(runs) == 3
+
+
+def test_progress_comes_from_the_schedule_core_publishes():
+    to = {"sample_sigmas": torch.tensor([1.0, 0.7, 0.3, 0.0]), "sigmas": torch.tensor([0.3])}
+    assert sn.step_progress(to) == 1.0
+    to["sigmas"] = torch.tensor([1.0])
+    assert sn.step_progress(to) == 0.0
+    assert sn.step_progress({}) is None
+
+
+def test_without_a_recorded_prompt_length_it_bypasses_and_says_so(capsys):
+    state = sn.ShadowState(dm=None, negative_context=torch.randn(1, 3, 16), video_scale=3.0,
+                           audio_scale=1.0, tau=2.5, alpha=1.0, start_percent=0.0,
+                           end_percent=1.0)
+    ran = []
+    hook = sn.make_block_hook(object(), state, 1)
+    args = {"img": torch.zeros(4, 16), "t_emb": None,
+            "mod_segments": [(0, 1, 1), (1, 2, 2), (2, 4, 0)], "rope_freqs": None,
+            "transformer_options": {}}
+    hook(args, {"original_block": lambda a: ran.append(1) or {"img": a["img"]}})
+    assert ran == [1]
+    assert "prompt length was not seen" in capsys.readouterr().out

tests/test_h3_shadow_negative_install.py

@@ -278,3 +278,31 @@ if __name__ == "__main__":
     test_both_scales_at_one_is_a_true_noop()
     test_enabled_clones_and_hooks_every_block()
     print("ok (run via pytest for the rest)")
+
+
+def test_the_model_wrapper_records_the_prompt_length_and_chains_the_old_one():
+    """The prompt length is only visible on the model call's conditioning, so the installer
+    puts a wrapper there; it must chain whatever wrapper was already installed."""
+    import h3_shadow_negative as sn
+    model = _FakeModel()
+    seen = []
+    model.model_options["model_function_wrapper"] = lambda fn, a: seen.append("old") or "out"
+    patched = S()._install_h3_shadow_negative(model, _negative(), 3.0, 1.0, 2.5, 0.35, 0.0, 0.6)
+    wrapper = patched.model_options["model_function_wrapper"]
+    captured = {}
+    orig_forward = sn._shadow_forward
+
+    def spy(state, block, args, extra):
+        captured["len"] = state.text_len
+        return extra["original_block"](args)
+
+    sn._shadow_forward = spy
+    try:
+        out = wrapper(None, {"input": None, "timestep": None,
+                             "c": {"c_crossattn": torch.zeros(1, 7, 16)}})
+        hook = patched.model_options["transformer_options"]["patches_replace"]["dit"][("double_block", 0)]
+        hook({"img": torch.zeros(2, 16)}, {"original_block": lambda a: {"img": a["img"]}})
+    finally:
+        sn._shadow_forward = orig_forward
+    assert out == "out" and seen == ["old"]
+    assert captured["len"] == 7