From 7c26a30015a8b9dda42de461a490fd7c7ec6ceb9 Mon Sep 17 00:00:00 2001
From: Claude <noreply@anthropic.com>
Date: Sun, 1 Nov 2026 13:06:56 +0000
Subject: [PATCH] Combination test: every feature alone, in every pair and all
 together, with and without Two stages

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Tw9fjU9jGrByQHYf6UYMVV
---
 modules/sampling/tests/test_combinations.py | 80 +++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 modules/sampling/tests/test_combinations.py

diff --git a/modules/sampling/tests/test_combinations.py b/modules/sampling/tests/test_combinations.py
new file mode 100644
index 00000000..51fff73e
--- /dev/null
+++ b/modules/sampling/tests/test_combinations.py
@@ -0,0 +1,80 @@
+"""Combination rule (Dex): a feature is tested alone AND with the others, in every pair and all together, on the real
+MiniMaxH3 class (tiny, CPU) through ComfyUI's own guider, with and without Two stages. A new feature goes into FEATURES.
+
+Not covered here and said so: triples (the all-on case catches what only shows with many on), and anything that needs a
+GPU or real weights."""
+
+import itertools
+
+import pytest
+import torch
+
+from modules.sampling.two_stage.tests.test_two_stage import model  # noqa: F401 -- the module-scoped tiny H3
+
+SIGMAS = (1.0, 0.9, 0.7, 0.3, 0.0)
+
+FEATURES = {
+    "reins": {"blocks": "2"},
+    "attention_bandit": {},
+    "q_steer": {"blocks": "2"},
+    "attention_temperature": {"temperature": 1.1},
+    "stas": {"block": 1},
+    "noise_merge": {"seed": 5},
+    "sharpness": {},
+    "late_branch": {"block": 2},
+    "decisiveness": {},
+    "shot_memory": {},
+    "block_map": {},
+}
+SUBSETS = [()] + [(f,) for f in FEATURES] + list(itertools.combinations(FEATURES, 2)) + [tuple(FEATURES)]
+
+
+def _name(subset):
+    return "+".join(subset) or "plain"
+
+
+def _save_a_map(tmp_path, monkeypatch, n):
+    """An uneven saved map for the tiny model, so the Block map really reshapes the features that follow it."""
+    from modules.sampling.block_map import measure
+    monkeypatch.setattr(measure, "FOLDER", tmp_path / "maps")
+    monkeypatch.setattr(measure, "SWITCH", tmp_path / "block_map.enabled")
+    grid = measure.groups(n)
+    res = {"when": "test", "blocks": n, "groups": grid, "points": list(measure.POINTS), "size": 1, "delta": measure.DELTA}
+    for kind in measure.KINDS:
+        res[kind] = {"leverage": [[0.5 + g + 0.3 * p for p in range(len(measure.POINTS))] for g in range(len(grid))],
+                     "fragility": [[0.1 * (g + 1) for p in range(len(measure.POINTS))] for g in range(len(grid))]}
+    measure.save(res)
+
+
+def _run(model, subset, staged):
+    import comfy.samplers
+    from comfy.nested_tensor import NestedTensor
+    from modules.sampling.modifiers.nodes import FunUILoadModifiers
+    settings = {"taste": {"key": "fox"}}
+    for f in subset:
+        settings[f] = {"enabled": True, **FEATURES[f]}
+    if staged:
+        settings["two_stage"] = {"enabled": True, "size": 0.5, "steps": 2, "back_up": 0.5}
+    patched, status = FunUILoadModifiers.execute(model.clone(), settings).result
+    missing = [f for f in subset if f not in status]
+    assert not missing, f"never installed (a test that installs nothing proves nothing): {missing}: {status}"
+    guider = comfy.samplers.CFGGuider(patched)
+    cond = [[torch.randn(1, 5, 48), {}]]
+    guider.set_conds(cond, cond)
+    guider.set_cfg(1.0)
+    latent = NestedTensor((torch.zeros(1, 24, 2, 8, 8), torch.zeros(1, 32, 2, 3)))
+    noise = NestedTensor((torch.randn(1, 24, 2, 8, 8), torch.randn(1, 32, 2, 3)))
+    return guider.sample(noise, latent, comfy.samplers.sampler_object("euler"), torch.tensor(SIGMAS),
+                         seed=1, disable_pbar=True)
+
+
+@pytest.mark.parametrize("staged", [False, True], ids=["one-stage", "two-stages"])
+@pytest.mark.parametrize("subset", SUBSETS, ids=_name)
+def test_every_combination_finishes_with_a_finite_picture_and_sound(model, tmp_path, monkeypatch, subset, staged):
+    from core import dit_hooks
+    from modules.system.taste import store
+    monkeypatch.setattr(store, "ROOT", tmp_path / "taste")
+    _save_a_map(tmp_path, monkeypatch, dit_hooks.block_count(model))
+    video, audio = _run(model, subset, staged).unbind()
+    assert torch.isfinite(video).all() and torch.isfinite(audio).all(), _name(subset)
+    assert video.shape == (1, 24, 2, 8, 8)