Alisson AnjosandClaude Opus 5.5
04d8012020
Describe refs / Analyse / Auto prompt / target / mask preview ran the VLM and SAM in a server thread outside torch.inference_mode(). After a workflow run those models hold inference tensors, so the buttons failed with "Inference tensors do not track version counter". Fixes #14. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
1778 lines
101 KiB
Python
1778 lines
101 KiB
Python
from __future__ import annotations
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import importlib.util
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import os
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import sys
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import tempfile
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import types
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import unittest
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from pathlib import Path
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from unittest.mock import patch
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import torch
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ROOT = Path(__file__).resolve().parents[1]
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_TMP = tempfile.mkdtemp(prefix="bfs_shotloop_test_")
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# The module only needs a few folder_paths calls; stub them so the tests run without ComfyUI.
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_fp = types.ModuleType("folder_paths")
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_fp.get_temp_directory = lambda: _TMP
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_fp.get_input_directory = lambda: _TMP
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_fp.get_annotated_filepath = lambda name: str(Path(_TMP) / name)
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sys.modules.setdefault("folder_paths", _fp)
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class _Blocker:
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def __init__(self, message):
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self.message = message
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_ce = types.ModuleType("comfy_execution")
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_gu = types.ModuleType("comfy_execution.graph_utils")
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_gu.ExecutionBlocker = _Blocker
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sys.modules.setdefault("comfy_execution", _ce)
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sys.modules.setdefault("comfy_execution.graph_utils", _gu)
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SPEC = importlib.util.spec_from_file_location("bfs_shot_loop", ROOT / "bfs_shot_loop.py")
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assert SPEC is not None and SPEC.loader is not None
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SL = importlib.util.module_from_spec(SPEC)
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SPEC.loader.exec_module(SL)
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H3 = "H3 (17n+5)"
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def _shot(i, start, end, cut=False, gen=None, count=1, queue=False, run_id=""):
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length = end - start
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return {"index": i, "count": count, "start": start, "end": end, "length": length,
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"gen_length": gen or SL.snap_up(length, H3), "fps": 24.0, "cut_before": cut,
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"queue": queue, "run_id": run_id}
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class GridTest(unittest.TestCase):
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def test_h3_grid(self):
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self.assertEqual([SL.snap_up(x, H3) for x in (1, 5, 6, 22, 23, 107, 108)], [5, 5, 22, 22, 39, 107, 124])
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self.assertEqual([SL.snap_down(x, H3) for x in (4, 5, 21, 22, 106, 107, 110)], [5, 5, 5, 22, 90, 107, 107])
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def test_other_grids(self):
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self.assertEqual(SL.snap_up(50, "LTX / Wan (8n+1)"), 57)
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self.assertEqual(SL.snap_down(50, "LTX / Wan (8n+1)"), 49)
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self.assertEqual(SL.snap_up(6, "Wan (4n+1)"), 9)
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self.assertEqual(SL.snap_up(37, "any"), 37)
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def test_generation_size_is_on_the_multiple(self):
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w, h = SL.generation_size(1280, 720, 0.15, 32)
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self.assertEqual((w % 32, h % 32), (0, 0))
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self.assertGreater(w, h)
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def test_planner_defaults_to_half_megapixel(self):
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self.assertEqual(SL.DEFAULT_PLAN["megapixels"], 0.5)
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class TextGenerateCompatibilityTest(unittest.TestCase):
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def _generate(self, execute):
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calls = []
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def wrapped(*args, **kwargs):
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result = execute(*args, **kwargs)
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calls.append(kwargs)
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return result
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textgen = types.ModuleType("comfy_extras.nodes_textgen")
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textgen.TextGenerate = type("TextGenerate", (), {"execute": staticmethod(wrapped)})
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comfy_extras = types.ModuleType("comfy_extras")
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comfy_extras.__path__ = []
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modules = {"comfy_extras": comfy_extras, "comfy_extras.nodes_textgen": textgen}
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with patch.dict(sys.modules, modules):
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result = SL.vlm_generate(object(), "describe this", torch.zeros(1, 2, 2, 3), 32)
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self.assertEqual(result, "generated")
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return calls[0]
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def test_old_textgenerate_signature_does_not_receive_mtp(self):
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def execute(clip, prompt, max_length, sampling_mode, image=None):
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return types.SimpleNamespace(args=("generated",))
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kwargs = self._generate(execute)
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self.assertNotIn("mtp", kwargs)
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def test_new_textgenerate_signature_keeps_mtp_disabled(self):
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def execute(clip, prompt, max_length, sampling_mode, image=None, mtp="auto"):
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return types.SimpleNamespace(args=("generated",))
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kwargs = self._generate(execute)
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self.assertEqual(kwargs["mtp"], "off")
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class PlanTest(unittest.TestCase):
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def test_fixed_mode_splits_evenly_under_the_limit(self):
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segs = SL.plan_segments(300, [], "fixed", 107, 24)
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self.assertEqual([s["end"] - s["start"] for s in segs], [100, 100, 100])
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def test_camera_cut_mode_splits_a_continuous_range_at_max_length(self):
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segs = SL.plan_segments(360, [], "shots", 107, 1)
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self.assertEqual(len(segs), 4)
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self.assertTrue(all(s["end"] - s["start"] <= 107 for s in segs))
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self.assertEqual(segs[-1]["end"], 360)
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def test_shots_split_at_cuts_and_long_shots_are_divided(self):
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segs = SL.plan_segments(300, [50, 80], "shots", 107, 10)
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bounds = [(s["start"], s["end"]) for s in segs]
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self.assertEqual(bounds[:2], [(0, 50), (50, 80)])
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self.assertTrue(all(e - s <= 107 for s, e in bounds))
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self.assertEqual(bounds[-1][1], 300)
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self.assertTrue(segs[1]["cut_before"])
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def test_short_shots_merge_into_a_neighbour_that_still_fits(self):
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segs = SL.plan_segments(200, [10, 100], "shots", 107, 24)
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self.assertEqual([(s["start"], s["end"]) for s in segs], [(0, 100), (100, 200)])
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def test_limits_and_manual_bounds(self):
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self.assertEqual(len(SL.plan_segments(1000, [], "fixed", 100, 10, max_parts=3)), 3)
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self.assertEqual(SL.plan_segments(1000, [], "fixed", 100, 10, max_total=250)[-1]["end"], 250)
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manual = SL.plan_segments(100, [], "manual", 107, 10, manual=[30, 70])
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self.assertEqual([(s["start"], s["end"]) for s in manual], [(0, 30), (30, 70), (70, 100)])
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def test_builtin_detector_finds_spikes_and_respects_the_gap(self):
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score = [1.0] * 100
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raw = [0.01] * 100
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for i in (20, 22, 60):
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score[i], raw[i] = 50.0, 0.5
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self.assertEqual(SL.detect_cuts(score, raw, 0.5, 24.0), [20, 60])
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class JoinTest(unittest.TestCase):
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def _frames(self, n, value):
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return torch.full((n, 8, 8, 3), float(value))
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def test_trims_to_true_lengths_in_order(self):
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shots = [_shot(1, 30, 50, cut=True, gen=22), _shot(0, 0, 30, gen=39)]
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imgs = [self._frames(22, 0.8), self._frames(39, 0.2)]
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video, audio, fps = SL.BFSShotJoin()._join_all(imgs, shots, [0])
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self.assertEqual(video.shape[0], 50)
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self.assertAlmostEqual(float(video[0, 0, 0, 0]), 0.2)
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self.assertAlmostEqual(float(video[-1, 0, 0, 0]), 0.8)
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self.assertEqual(fps, 24.0)
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def test_crossfade_only_across_soft_joins(self):
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imgs = [self._frames(39, 0.0), self._frames(39, 1.0)]
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soft = SL.BFSShotJoin()._join_all(imgs, [_shot(0, 0, 30, gen=39), _shot(1, 30, 60, gen=39)], [4])[0]
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hard = SL.BFSShotJoin()._join_all(imgs, [_shot(0, 0, 30, gen=39), _shot(1, 30, 60, cut=True, gen=39)], [4])[0]
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self.assertGreater(float(soft[31, 0, 0, 0]), 0.0)
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self.assertLess(float(soft[31, 0, 0, 0]), 1.0)
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self.assertEqual(float(hard[31, 0, 0, 0]), 1.0)
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def test_audio_is_trimmed_to_the_video(self):
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audio = {"waveform": torch.zeros(1, 2, 48000 * 10), "sample_rate": 48000}
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imgs = [self._frames(39, 0.5)]
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_, a, _ = SL.BFSShotJoin()._join_all(imgs, [_shot(0, 0, 24, gen=39)], [0], [audio])
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self.assertEqual(a["waveform"].shape[-1], 48000)
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def test_queue_mode_blocks_until_every_shot_is_stored(self):
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join = SL.BFSShotJoin()
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shots = [_shot(0, 0, 20, count=2, queue=True, run_id="t1"), _shot(1, 20, 45, cut=True, count=2, queue=True, run_id="t1")]
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first = join.join([self._frames(22, 0.1)], [shots[0]], [0])
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self.assertIsInstance(first[0], _Blocker)
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video, _, _, _, _, _ = join.join([self._frames(39, 0.9)], [shots[1]], [0])
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self.assertEqual(video.shape[0], 45)
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self.assertAlmostEqual(float(video[0, 0, 0, 0]), 0.1, places=2)
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def test_run_again_with_every_shot_stored_generates_nothing(self):
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join = SL.BFSShotJoin()
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shots = [_shot(0, 0, 20, count=2, queue=True, run_id="t2"), _shot(1, 20, 45, cut=True, count=2, queue=True, run_id="t2")]
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join.join([self._frames(22, 0.1)], [shots[0]], [0])
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join.join([self._frames(39, 0.9)], [shots[1]], [0])
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again = dict(shots[1], assemble_only=True) # what the planner hands out when nothing is left to run
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self.assertEqual(join.check_lazy_status(images=None, shots=[again]), []) # the sampler is not pulled
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self.assertEqual(join.check_lazy_status(images=None, shots=[shots[1]]), ["images"])
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video = SL.BFSShotJoin().join(shots=[again], crossfade=[0])[0]
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self.assertEqual(video.shape[0], 45)
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self.assertAlmostEqual(float(video[0, 0, 0, 0]), 0.1, places=2)
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self.assertAlmostEqual(float(video[-1, 0, 0, 0]), 0.9, places=2)
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def test_comparison_turned_on_later_reads_the_originals_from_the_source(self):
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import cv2
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import numpy as np
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src = os.path.join(_TMP, "cmp_src.mp4")
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vw = cv2.VideoWriter(src, cv2.VideoWriter_fourcc(*"mp4v"), 24, (32, 32))
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for _ in range(50):
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vw.write(np.full((32, 32, 3), 200, np.uint8))
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vw.release()
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tl = {"path": src, "fps": 24.0, "width": 32, "height": 32, "fill": "original", "audio": None,
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"segs": [{"start": 0, "end": 20, "run": True, "cut_before": False, "run_index": 0},
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{"start": 20, "end": 45, "run": True, "cut_before": True, "run_index": 1}],
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"src": list(range(50)), "n": 50}
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shots = [_shot(0, 0, 20, count=2, queue=True, run_id="t3"), _shot(1, 20, 45, cut=True, count=2, queue=True, run_id="t3")]
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join = SL.BFSShotJoin()
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frames = lambda n, v: torch.full((n, 32, 32, 3), float(v))
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join.join([frames(22, 0.1)], [shots[0]], [0], timeline=[tl]) # stored with the comparison off
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join.join([frames(39, 0.9)], [shots[1]], [0], timeline=[tl])
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out = SL.BFSShotJoin().join(shots=[dict(shots[1], assemble_only=True)], crossfade=[0], timeline=[tl],
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comparison=[True], comparison_height=[32])
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comp, guide = out[3], out[5]
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self.assertEqual(guide.shape[0], 45) # the guide output is there again
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self.assertAlmostEqual(float(guide[10].mean()), 200 / 255, places=1) # and it is the source video
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self.assertGreater((comp - 200 / 255).abs().lt(0.05).float().mean().item(), 0.05) # original column shown
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class FilterTest(unittest.TestCase):
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STATS = {"persons": 1, "person_area": 0.2, "person_frames": 6, "faces": 1, "brightness": 0.4, "motion": 0.02, "sampled": 6}
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def test_no_filter_keeps_everything(self):
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self.assertEqual(SL.skip_reason(self.STATS, 50, dict(SL.DEFAULT_FILTERS)), "")
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self.assertFalse(SL.filters_active(dict(SL.DEFAULT_FILTERS)))
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def test_each_filter(self):
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f = dict(SL.DEFAULT_FILTERS)
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self.assertEqual(SL.skip_reason(dict(self.STATS, persons=0), 50, dict(f, person=True)), "no person")
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self.assertIn("smaller", SL.skip_reason(dict(self.STATS, person_area=0.01), 50, dict(f, person=True, min_person_area=0.03)))
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self.assertIn("more than", SL.skip_reason(dict(self.STATS, persons=5), 50, dict(f, max_persons=2)))
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self.assertEqual(SL.skip_reason(dict(self.STATS, faces=0), 50, dict(f, face=True)), "no face")
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self.assertEqual(SL.skip_reason(dict(self.STATS, brightness=0.02), 50, dict(f, skip_dark=True)), "dark / fade")
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self.assertEqual(SL.skip_reason(dict(self.STATS, motion=0.0), 50, dict(f, skip_static=True)), "static")
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self.assertIn("shorter", SL.skip_reason(self.STATS, 5, dict(f, min_frames=10)))
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class TimelineJoinTest(unittest.TestCase):
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def _tl(self, fill):
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return {"path": "", "fps": 24.0, "width": 8, "height": 8, "fill": fill, "audio": None, "n": 60,
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"src": list(range(60)),
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"segs": [{"start": 0, "end": 20, "run": True, "cut_before": False, "run_index": 0},
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{"start": 20, "end": 40, "run": False, "cut_before": True, "run_index": -1},
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{"start": 40, "end": 60, "run": True, "cut_before": True, "run_index": 1}]}
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def test_drop_removes_skipped_shots(self):
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shots = [_shot(0, 0, 20, gen=22), _shot(1, 40, 60, cut=True, gen=22)]
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imgs = [torch.zeros(22, 8, 8, 3), torch.ones(22, 8, 8, 3)]
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video = SL.BFSShotJoin()._join_all(imgs, shots, [0], None, self._tl("drop"))[0]
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self.assertEqual(video.shape[0], 40)
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self.assertEqual(float(video[25, 0, 0, 0]), 1.0)
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def test_original_fill_reads_the_source_frames(self):
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shots = [_shot(0, 0, 20, gen=22), _shot(1, 40, 60, cut=True, gen=22)]
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imgs = [torch.zeros(22, 8, 8, 3), torch.ones(22, 8, 8, 3)]
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orig = SL._read_frames
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SL._read_frames = lambda path, idx, size: [__import__("numpy").full((8, 8, 3), 128, "uint8") for _ in idx]
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try:
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video = SL.BFSShotJoin()._join_all(imgs, shots, [0], None, self._tl("original"))[0]
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finally:
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SL._read_frames = orig
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self.assertEqual(video.shape[0], 60)
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self.assertAlmostEqual(float(video[30, 0, 0, 0]), 128 / 255, places=3)
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def test_skipped_shot_keeps_its_sound(self):
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# 60 frames at 24 fps, sample rate 240 -> 10 samples per frame; each sample holds its timeline frame number
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sr = 240
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timeline_audio = {"waveform": torch.arange(600, dtype=torch.float32).div(10).floor().view(1, 1, 600), "sample_rate": sr}
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runs_only = {"waveform": torch.full((1, 1, 400), -1.0), "sample_rate": sr, "bfs_runs_only": True}
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tl = dict(self._tl("original"), audio=timeline_audio)
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shots = [_shot(0, 0, 20, gen=22), _shot(1, 40, 60, cut=True, gen=22)]
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imgs = [torch.zeros(22, 8, 8, 3), torch.ones(22, 8, 8, 3)]
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orig = SL._read_frames
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SL._read_frames = lambda path, idx, size: [__import__("numpy").full((8, 8, 3), 128, "uint8") for _ in idx]
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try:
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_, a, _ = SL.BFSShotJoin()._join_all(imgs, shots, [0], [runs_only], tl)
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# a soundtrack from elsewhere is still used as given
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_, other, _ = SL.BFSShotJoin()._join_all(imgs, shots, [0], [dict(runs_only, bfs_runs_only=False)], tl)
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finally:
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SL._read_frames = orig
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wf = a["waveform"][0, 0]
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self.assertEqual(wf.shape[0], 600)
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self.assertEqual([float(wf[i * 10]) for i in (0, 25, 30, 45, 59)], [0.0, 25.0, 30.0, 45.0, 59.0]) # in sync, skipped shot not silent
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self.assertNotIn("bfs_runs_only", a)
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self.assertEqual(float(other["waveform"][0, 0, 0]), -1.0)
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class CastTest(unittest.TestCase):
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# person 0 on frames 0-47 (big face), person 1 on 48-95, both small on 96-119
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CAST = {"step": 6, "people": [{"id": 0}, {"id": 1}], "samples":
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[{"f": f, "faces": [{"pid": 0, "area": 0.1}]} for f in range(0, 48, 6)]
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+ [{"f": f, "faces": [{"pid": 1, "area": 0.1}]} for f in range(48, 96, 6)]
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+ [{"f": f, "faces": []} for f in range(96, 120, 6)]}
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def test_shot_people_and_main(self):
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self.assertEqual(SL.shot_people(self.CAST, 0, 60), {"people": [0, 1], "main": 0})
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self.assertEqual(SL.shot_people(self.CAST, 96, 120), {"people": [], "main": -1})
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def test_change_points_need_a_long_enough_run(self):
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self.assertEqual(SL.person_change_points(self.CAST, 0, 120, 24), [48])
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self.assertEqual(SL.person_change_points(self.CAST, 0, 60, 24), [])
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def test_plan_splits_assigns_and_skips_by_person(self):
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path = str(Path(_TMP) / "cast.mp4"); Path(path).write_bytes(b"x")
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a = {"fps": 24.0, "n": 120, "score": [0.0] * 120, "raw": [0.0] * 120}
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SL._CAST_CACHE[SL._cast_key(path, a)] = self.CAST
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orig = SL.find_cuts
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SL.find_cuts = lambda *args, **kw: ([], "test")
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try:
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plan = dict(SL.DEFAULT_PLAN, max_s=10, min_s=0.5, cast={"0": {"ref": "a.png"}}, cast_split=True, cast_only=True)
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segs = SL.apply_filters(plan, a, path, SL.resolve_plan(plan, a, path))
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finally:
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SL.find_cuts = orig
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self.assertEqual([(s["start"], s["end"]) for s in segs], [(0, 48), (48, 120)])
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self.assertEqual(segs[0]["ref"], "a.png")
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self.assertTrue(segs[0]["run"])
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self.assertEqual(segs[1]["skip_reason"], "no linked person")
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class ComparisonTest(unittest.TestCase):
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def test_join_returns_a_labelled_side_by_side(self):
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shots = [dict(_shot(0, 0, 20, gen=22), frames=torch.zeros(22, 32, 48, 3), ref=torch.ones(1, 40, 30, 3),
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ref2=None, prompt="a test prompt"),
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dict(_shot(1, 20, 40, cut=True, gen=22), frames=torch.zeros(22, 32, 48, 3), ref=None, ref2=None,
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prompt="")]
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imgs = [torch.full((22, 32, 48, 3), 0.5), torch.full((22, 32, 48, 3), 0.7)]
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video, _, _, comp, _, guide = SL.BFSShotJoin().join(imgs, shots, [0], comparison=[True], label=["steps 20"])
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self.assertEqual(video.shape[0], 40)
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self.assertEqual(tuple(guide.shape), (40, 32, 48, 3)) # the originals, frame for frame
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self.assertEqual(float(guide.abs().max()), 0.0)
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self.assertEqual(comp.shape[0], 40)
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self.assertEqual(comp.shape[2] % 16, 0)
|
|
self.assertEqual(comp.shape[1] % 16, 0)
|
|
self.assertGreaterEqual(comp.shape[2], 48 * 2 + 32)
|
|
self.assertGreater(comp.shape[1], 32)
|
|
off = SL.BFSShotJoin().join(imgs, shots, [0])
|
|
self.assertEqual(off[3].shape[0], 1)
|
|
self.assertIsInstance(off[5], _Blocker) # no comparison: no originals kept
|
|
self.assertIn("comparison", off[5].message)
|
|
|
|
|
|
class ContinuityTest(unittest.TestCase):
|
|
def test_pick_frame_ignores_the_overlap(self):
|
|
frames = torch.arange(30, dtype=torch.float32).view(30, 1, 1, 1).expand(30, 2, 2, 3)
|
|
self.assertEqual(float(SL.pick_frame(frames, 20, "first")[0, 0, 0, 0]), 0.0)
|
|
self.assertEqual(float(SL.pick_frame(frames, 20, "middle")[0, 0, 0, 0]), 10.0)
|
|
self.assertEqual(float(SL.pick_frame(frames, 20, "last")[0, 0, 0, 0]), 19.0)
|
|
|
|
def test_chain_uses_the_previous_result_only_when_asked(self):
|
|
frames = torch.arange(30, dtype=torch.float32).view(30, 1, 1, 1).expand(30, 2, 2, 3)
|
|
SL.remember_result({"index": 0, "count": 2}, frames)
|
|
shot = {"index": 1, "count": 2, "chain": "reference", "chain_frame": "last", "prev_length": 20}
|
|
self.assertEqual(float(SL.chain_image(shot)[0, 0, 0, 0]), 19.0)
|
|
self.assertIsNone(SL.chain_image(dict(shot, chain="off")))
|
|
self.assertIsNone(SL.chain_image(dict(shot, index=0)))
|
|
self.assertIsNone(SL.chain_image(dict(shot, index=3, count=4))) # not the shot right after
|
|
stored = torch.ones(1, 2, 2, 3)
|
|
self.assertIs(SL.chain_image(dict(shot, chain_image=stored)), stored)
|
|
|
|
def test_plan_carries_the_per_shot_setting(self):
|
|
a = {"fps": 24.0, "n": 100, "score": [0.0] * 100, "raw": [0.0] * 100}
|
|
plan = dict(SL.DEFAULT_PLAN, mode="fixed", max_s=2, bounds=[50],
|
|
segs=[{}, {"chain": "first frame", "chain_frame": "middle"}])
|
|
segs = SL.resolve_plan(plan, a)
|
|
self.assertEqual((segs[0]["chain"], segs[1]["chain"], segs[1]["chain_frame"]), ("head", "first frame", "middle")) # head is the default
|
|
|
|
|
|
class MaskCropTest(unittest.TestCase):
|
|
def test_spec_takes_target_from_shot_and_settings_from_global(self):
|
|
spec = SL.mask_spec({"text": "person", "padding": 0.9}, {"padding": 0.3, "text": "ignored"})
|
|
self.assertEqual((spec["text"], spec["padding"], spec["fill_holes"]), ("person", 0.3, True))
|
|
|
|
def test_shape_fills_holes_and_holds_in_time(self):
|
|
m = torch.zeros(5, 9, 9, dtype=torch.uint8)
|
|
m[2, 2:7, 2:7] = 1
|
|
m[2, 4, 4] = 0 # a hole
|
|
out = SL.shape_mask(m, dict(SL.DEFAULT_MASK, temporal_expand=1))
|
|
self.assertEqual(int(out[2, 4, 4]), 1)
|
|
self.assertEqual(int(out[1, 3, 3]), 1) # held one frame back
|
|
self.assertEqual(int(out[0, 3, 3]), 0)
|
|
blocks = SL.shape_mask(m, dict(SL.DEFAULT_MASK, temporal_expand=0, blockify=4))
|
|
self.assertEqual(tuple(blocks.shape), (5, 9, 9))
|
|
|
|
def test_box_is_the_union_plus_padding(self):
|
|
m = torch.zeros(2, 10, 10, dtype=torch.uint8)
|
|
m[0, 2:4, 2:4] = 1
|
|
m[1, 6:8, 6:8] = 1
|
|
self.assertEqual(SL.crop_box(m, 0.0), [0.2, 0.2, 0.8, 0.8])
|
|
self.assertIsNone(SL.crop_box(torch.zeros(2, 10, 10), 0.1))
|
|
|
|
def test_uncrop_pastes_only_inside_the_mask(self):
|
|
full = torch.zeros(3, 20, 20, 3)
|
|
mask = torch.zeros(3, 20, 20)
|
|
mask[:, 5:15, 5:15] = 1
|
|
shot = {"full_frames": full, "crop": {"box": [0.25, 0.25, 0.75, 0.75], "mask": mask, "paste": "mask",
|
|
"expand": 0, "feather": 0}}
|
|
out = SL.uncrop(torch.ones(3, 8, 8, 3), shot)
|
|
self.assertEqual(tuple(out.shape), (3, 20, 20, 3))
|
|
self.assertEqual(float(out[0, 10, 10, 0]), 1.0)
|
|
self.assertEqual(float(out[0, 1, 1, 0]), 0.0)
|
|
|
|
|
|
class DuetPanelCropTest(unittest.TestCase):
|
|
def test_join_side_cuts_the_panel_off(self):
|
|
shot = {"width": 64, "height": 32, "panel": {"position": "left", "h": 2, "w": 4, "strip_h": 0, "strip_w": 4}}
|
|
canvas = torch.zeros(3, 32, 128, 3)
|
|
canvas[:, :, 64:] = 1.0
|
|
out = SL.crop_panel(canvas, shot)
|
|
self.assertEqual(tuple(out.shape), (3, 32, 64, 3))
|
|
self.assertEqual(float(out.min()), 1.0)
|
|
self.assertIs(SL.crop_panel(out, shot), out) # already the video size
|
|
self.assertIs(SL.crop_panel(canvas, {"width": 64}), canvas) # no panel
|
|
|
|
|
|
class SilentAudioTest(unittest.TestCase):
|
|
def test_join_without_audio_returns_a_silent_track(self):
|
|
imgs = [torch.full((39, 8, 8, 3), 0.5)]
|
|
video, a, fps = SL.BFSShotJoin()._join_all(imgs, [_shot(0, 0, 24, gen=39)], [0])
|
|
self.assertEqual(tuple(a["waveform"].shape[:2]), (1, 2))
|
|
self.assertEqual(a["waveform"].shape[-1], 44100) # 24 frames at 24 fps = 1 s
|
|
self.assertEqual(float(a["waveform"].abs().max()), 0.0)
|
|
self.assertFalse(SL._usable({"waveform": torch.zeros(1, 2, 0), "sample_rate": 44100}))
|
|
|
|
|
|
class RepackTest(unittest.TestCase):
|
|
def test_replaces_only_connected_pieces_and_fits_the_guide(self):
|
|
shot = dict(_shot(0, 0, 20, gen=22), width=8, height=8, frames=torch.zeros(22, 8, 8, 3),
|
|
ref=torch.zeros(1, 4, 4, 3), ref2=None, prompt="old", audio=None)
|
|
ref = torch.ones(1, 6, 6, 3)
|
|
out = SL.BFSShotRepack().repack([shot], refs=[ref])[0][0]
|
|
self.assertTrue(torch.equal(out["ref"], ref))
|
|
self.assertEqual(out["prompt"], "old")
|
|
self.assertIs(out["frames"], shot["frames"])
|
|
out = SL.BFSShotRepack().repack([shot], guide_frames=[torch.ones(30, 16, 16, 3)], prompt=["new"])[0][0]
|
|
self.assertEqual(tuple(out["frames"].shape), (22, 8, 8, 3))
|
|
self.assertEqual(out["prompt"], "new")
|
|
self.assertEqual(out["length"], 20)
|
|
|
|
def test_unpack_lists_the_pictures_and_repack_puts_them_back_per_shot(self):
|
|
a = [torch.full((1, 4, 4, 3), k / 10) for k in range(3)]
|
|
base = dict(_shot(0, 0, 20, gen=22), width=8, height=8, frames=torch.zeros(22, 8, 8, 3), prompt="",
|
|
audio=None)
|
|
s1, s2, s3 = dict(base, refs=a[:2]), dict(base, index=1, refs=[]), dict(base, index=2, refs=a[2:])
|
|
flat = []
|
|
for s in (s1, s2, s3): # what ComfyUI hands the next node: the lists of every Unpack call, in order
|
|
out = SL.BFSShotUnpack().unpack(s)
|
|
flat += out[1]
|
|
self.assertEqual(out[2], len(s["refs"]))
|
|
edited = [x + 1 for x in flat] # e.g. a background remover, once per picture
|
|
s1n, s2n, s3n = SL.BFSShotRepack().repack([s1, s2, s3], refs=edited)[0]
|
|
self.assertEqual([len(s1n["refs"]), len(s2n["refs"]), len(s3n["refs"])], [2, 0, 1])
|
|
self.assertTrue(torch.equal(s3n["refs"][0], a[2] + 1) and torch.equal(s1n["ref2"], a[1] + 1))
|
|
|
|
|
|
class ConditioningPromptTest(unittest.TestCase):
|
|
"""BFS Shot H3 Conditioning passes the planner's prompt and adds the setting picture (no models)."""
|
|
|
|
def setUp(self):
|
|
self.seen = seen = {}
|
|
|
|
class R2V:
|
|
@staticmethod
|
|
def execute(**kw):
|
|
seen.update(kw)
|
|
return types.SimpleNamespace(args=("pos", "lat"))
|
|
|
|
mod = types.ModuleType("comfy_extras.nodes_minimax_h3")
|
|
mod.MiniMaxH3ReferenceToVideo, mod.MiniMaxH3AddGuide = R2V, R2V
|
|
self._old = {k: sys.modules.get(k) for k in ("comfy_extras", "comfy_extras.nodes_minimax_h3")}
|
|
sys.modules["comfy_extras"] = types.ModuleType("comfy_extras")
|
|
sys.modules["comfy_extras.nodes_minimax_h3"] = mod
|
|
|
|
def tearDown(self):
|
|
for k, v in self._old.items():
|
|
if v is None:
|
|
sys.modules.pop(k, None)
|
|
else:
|
|
sys.modules[k] = v
|
|
|
|
def _shot(self):
|
|
return dict(prompt="subject_definitions: <Subject 1> is the man in <Picture 1>.", width=448, height=800,
|
|
gen_length=22, frames=torch.full((22, 8, 8, 3), 0.5), ref=torch.zeros(1, 4, 4, 3), ref2=None,
|
|
audio=None)
|
|
|
|
def test_the_planner_prompt_goes_as_it_is_and_the_model_passes_through(self):
|
|
out = SL.BFSShotH3Conditioning().condition(self._shot(), "clip", "vae", "none", "none", "match", model="m")
|
|
self.assertEqual(out[3], self._shot()["prompt"])
|
|
self.assertEqual(self.seen["prompt"], out[3])
|
|
self.assertEqual(out[2], "m")
|
|
self.assertNotIn("duet", SL.BFSShotH3Conditioning.INPUT_TYPES()["optional"])
|
|
|
|
def test_guide_remains_default_and_tara_is_explicitly_experimental(self):
|
|
cls = SL.BFSShotH3Conditioning
|
|
self.assertEqual(cls.GUIDE_MODES[0], "aligned guide (Add Guide)")
|
|
self.assertIn("TARA", cls.GUIDE_MODES[5])
|
|
self.assertIn("experimental", cls.GUIDE_MODES[5])
|
|
self.assertEqual(cls.INPUT_TYPES()["required"]["guide_mode"][1]["default"], cls.GUIDE_MODES[0])
|
|
|
|
def test_native_temporal_requires_model_before_encoding(self):
|
|
shot = self._shot()
|
|
with self.assertRaisesRegex(ValueError, "MODEL connected"):
|
|
SL.BFSShotH3Conditioning().condition(shot, "clip", "vae", SL.BFSShotH3Conditioning.GUIDE_MODES[5], "none", "match")
|
|
self.assertEqual(self.seen, {})
|
|
|
|
def test_native_temporal_passes_shot_as_video_and_returns_patched_model(self):
|
|
import minimax_h3_native_temporal as temporal
|
|
shot = self._shot(); shot["fps"] = 24
|
|
with patch.object(temporal, "patch_temporal_model", return_value="patched"):
|
|
out = SL.BFSShotH3Conditioning().condition(shot, "clip", "vae", SL.BFSShotH3Conditioning.GUIDE_MODES[5], "none", "match", model="model")
|
|
self.assertEqual(out[2], "patched")
|
|
self.assertIs(self.seen["ref_videos"]["ref_video_1"], shot["frames"])
|
|
|
|
def test_native_temporal_allows_dormant_first_shot_chain_modes(self):
|
|
import minimax_h3_native_temporal as temporal
|
|
for mode in ("head", "history", "reference", "first frame"):
|
|
with self.subTest(mode=mode), patch.object(temporal, "patch_temporal_model", return_value="patched"):
|
|
shot = self._shot()
|
|
shot.update(fps=24, index=0, count=1, chain=mode, head=0, history=None, chain_image=None)
|
|
out = SL.BFSShotH3Conditioning().condition(shot, "clip", "vae", SL.BFSShotH3Conditioning.GUIDE_MODES[5], "none", "match", model="model")
|
|
self.assertEqual(out[2], "patched")
|
|
|
|
def test_native_temporal_rejects_real_continuity_inputs(self):
|
|
cases = [dict(index=1, chain="head", head=5),
|
|
dict(index=1, chain="history", history=torch.zeros(5, 8, 8, 3)),
|
|
dict(index=1, chain="first frame", chain_image=torch.zeros(1, 8, 8, 3))]
|
|
for fields in cases:
|
|
with self.subTest(fields=list(fields)):
|
|
shot = self._shot(); shot.update(fps=24, count=2, **fields)
|
|
with self.assertRaisesRegex(ValueError, "active shot continuity"):
|
|
SL.BFSShotH3Conditioning().condition(shot, "clip", "vae", SL.BFSShotH3Conditioning.GUIDE_MODES[5], "none", "match", model="model")
|
|
|
|
def test_native_guide_scale_keeps_output_and_frames(self):
|
|
import minimax_h3_native_temporal as temporal
|
|
shot = self._shot(); shot.update(fps=24, frames=torch.zeros(22,64,128,3))
|
|
with patch.object(temporal, "patch_temporal_model", return_value="patched"):
|
|
SL.BFSShotH3Conditioning().condition(shot,"clip","vae",SL.BFSShotH3Conditioning.GUIDE_MODES[5],"none","match",model="model",native_guide_scale=0.5)
|
|
self.assertEqual(tuple(self.seen["ref_videos"]["ref_video_1"].shape),(22,32,64,3))
|
|
self.assertEqual((self.seen["width"],self.seen["height"],self.seen["length"]),(448,800,22))
|
|
self.assertEqual(tuple(shot["frames"].shape),(22,64,128,3))
|
|
|
|
def test_setting_picture_is_the_last_reference_with_static(self):
|
|
mask = torch.zeros(22, 8, 8)
|
|
mask[:, 2:4, 2:4] = 1
|
|
out = SL.BFSShotH3Conditioning().condition(self._shot(), "clip", "vae", "none", "none", "match",
|
|
setting_ref="on (generation size)", setting_mask=mask)
|
|
refs = list(self.seen["ref_images"].values())
|
|
self.assertEqual(len(refs), 2)
|
|
pic = refs[-1][0]
|
|
self.assertTrue(((pic[3, 3] == 0) | (pic[3, 3] == 1)).all()) # static inside the (grown) mask
|
|
self.assertTrue(torch.allclose(pic[7, 7], torch.tensor(0.5))) # the place outside it
|
|
self.assertIn("<Picture 2> shows the setting", out[3])
|
|
self.assertEqual(SL.add_setting("a {setting} b", 3, True), "a <Picture 3> b")
|
|
|
|
|
|
class SimpleConditioningTest(unittest.TestCase):
|
|
def test_exposes_only_the_common_controls(self):
|
|
inputs = SL.BFSShotH3ConditioningSimple.INPUT_TYPES()
|
|
names = set(inputs["required"]) | set(inputs["optional"])
|
|
self.assertIn("with_audio", names)
|
|
self.assertIn("inpaint", names)
|
|
self.assertNotIn("duet", names)
|
|
self.assertIn("model", names)
|
|
self.assertNotIn("rope_gap", names)
|
|
self.assertNotIn("task", names)
|
|
|
|
def test_forwards_safe_advanced_defaults_and_appends_model_output(self):
|
|
seen = {}
|
|
original = SL.BFSShotH3Conditioning.condition
|
|
|
|
def fake(_self, *args, **kwargs):
|
|
seen["args"], seen["kwargs"] = args, kwargs
|
|
return "positive", "latent", "unchanged model", "prompt"
|
|
|
|
SL.BFSShotH3Conditioning.condition = fake
|
|
try:
|
|
out = SL.BFSShotH3ConditioningSimple().condition(
|
|
"shot", "clip", "vae", SL.BFSShotH3ConditioningSimple.GUIDE_MODES[0], "match",
|
|
audio_vae="audio vae", with_audio=True)
|
|
finally:
|
|
SL.BFSShotH3Conditioning.condition = original
|
|
|
|
self.assertEqual(out, ("positive", "latent", "prompt", "unchanged model"))
|
|
self.assertEqual(seen["args"][4], "none")
|
|
self.assertTrue(seen["kwargs"]["with_audio"])
|
|
self.assertEqual(seen["kwargs"]["inpaint"], SL.INPAINT_MODES[0])
|
|
|
|
|
|
class TargetTest(unittest.TestCase):
|
|
def test_target_placeholder(self):
|
|
self.assertEqual(SL.fill_target("Replace {target} with <Subject 1>", "the man in a red coat"),
|
|
"Replace the man in a red coat with <Subject 1>")
|
|
self.assertEqual(SL.fill_target("Replace {target} with <Subject 1>", ""), "Replace the person with <Subject 1>")
|
|
self.assertEqual(SL.fill_target("no placeholder", "x"), "no placeholder")
|
|
|
|
|
|
class MaskOverlayTest(unittest.TestCase):
|
|
def test_overlay_marks_mask_and_box(self):
|
|
o = torch.zeros(3, 32, 32, 3)
|
|
m = torch.zeros(3, 32, 32); m[:, 8:16, 8:16] = 1
|
|
out = SL._mask_overlay(o, {"mask": m, "box": [0.1, 0.1, 0.9, 0.9]}, 3)
|
|
self.assertGreater(float(out[0, 12, 12, 0]), 0.3) # red inside the mask
|
|
self.assertEqual(float(out[0, 20, 20].sum()), 0.0) # untouched outside mask and box
|
|
self.assertGreater(float(out[0, 3, 16, 1]), 0.5) # yellow box edge
|
|
|
|
|
|
class StitchFinishTest(unittest.TestCase):
|
|
def _shot(self):
|
|
full = torch.full((4, 64, 64, 3), 0.5)
|
|
mask = torch.zeros(4, 64, 64); mask[:, 20:44, 24:40] = 1
|
|
return {"full_frames": full, "crop": {"box": [0.125, 0.125, 0.875, 0.875], "mask": mask, "paste": "mask",
|
|
"expand": 0, "feather": 2}}
|
|
|
|
def test_ring_colour_match_removes_drift_and_keeps_subject_contrast(self):
|
|
shot = self._shot()
|
|
gen = torch.full((4, 48, 48, 3), 0.62) # the model brightened the whole crop (+0.12)
|
|
gen[:, 12:36, 16:32] = 0.2 # a darker new subject
|
|
plain = SL.uncrop(gen, shot)
|
|
fixed = SL.uncrop(gen, shot, match_colors=1.0)
|
|
# around the subject the drift is gone, inside it the new subject stays darker than the scene
|
|
self.assertGreater(abs(float(plain[0, 21, 23].mean()) - 0.5), abs(float(fixed[0, 21, 23].mean()) - 0.5))
|
|
self.assertLess(float(fixed[0, 30, 30].mean()), 0.45)
|
|
|
|
def test_edge_hardness(self):
|
|
a = torch.tensor([[[0.1, 0.5, 0.9]]])
|
|
h = SL._alpha_hardness(a, 1.0)
|
|
self.assertEqual([round(float(x), 2) for x in h[0, 0]], [0.0, 0.5, 1.0])
|
|
self.assertTrue(torch.equal(SL._alpha_hardness(a, 0.0), a))
|
|
|
|
|
|
class InpaintInCropTest(unittest.TestCase):
|
|
def test_latent_starts_from_the_shot_and_only_the_mask_is_generated(self):
|
|
sys.path.insert(0, str(ROOT))
|
|
nested = sys.modules.get("comfy.nested_tensor")
|
|
if nested is None:
|
|
class _N:
|
|
is_nested = True
|
|
|
|
def __init__(self, t):
|
|
self.tensors = list(t)
|
|
comfy = sys.modules.setdefault("comfy", types.ModuleType("comfy"))
|
|
nested = types.ModuleType("comfy.nested_tensor"); nested.NestedTensor = _N
|
|
comfy.nested_tensor = nested
|
|
sys.modules["comfy.nested_tensor"] = nested
|
|
sys.modules.setdefault("comfy.utils", types.ModuleType("comfy.utils"))
|
|
|
|
class VAE:
|
|
def encode(self, px):
|
|
t = ((px.shape[0] - 5) // 17) * 5 + 2
|
|
x = px.mean(-1)[None, None]
|
|
return torch.nn.functional.interpolate(x, size=(t, px.shape[1] // 16, px.shape[2] // 16)).repeat(1, 24, 1, 1, 1)
|
|
|
|
lat = {"samples": nested.NestedTensor((torch.zeros(1, 24, 7, 8, 8), torch.zeros(1, 32, 2, 37)))}
|
|
frames = torch.full((22, 128, 128, 3), 0.4)
|
|
mask = torch.zeros(22, 128, 128); mask[:, 48:80, 48:80] = 1
|
|
pos, out = SL.inpaint_latent(lat, [], VAE(), frames, mask)
|
|
video = out["samples"].tensors[0]
|
|
self.assertAlmostEqual(float(video.mean()), 0.4, places=4) # starts from the shot's own frames
|
|
vm, am = out["noise_mask"].tensors
|
|
self.assertEqual(tuple(vm.shape), (1, 1, 7, 8, 8))
|
|
self.assertEqual(float(vm[0, 0, 3, 4, 4]), 1.0) # the person: generated
|
|
self.assertEqual(float(vm[0, 0, 3, 0, 0]), 0.0) # the rest of the crop: kept
|
|
self.assertEqual(float(vm[0, 0, 3, 2, 4]), 1.0) # grown by one latent cell
|
|
self.assertTrue(bool((am == 1).all())) # audio fully generated
|
|
_, out2 = SL.inpaint_latent(lat, [], VAE(), frames, mask, strength=0.8)
|
|
vm2 = out2["noise_mask"].tensors[0]
|
|
self.assertAlmostEqual(float(vm2[0, 0, 3, 4, 4]), 0.8, places=5) # partly regenerated inside the mask
|
|
self.assertEqual(float(vm2[0, 0, 3, 0, 0]), 0.0) # still kept outside
|
|
|
|
|
|
def test_generation_mask_with_and_without_crop(self):
|
|
m = torch.zeros(5, 64, 64); m[:, 20:40, 20:40] = 1
|
|
cropped = SL.generation_mask({"crop": {"crop_mask": m, "expand": 4}, "frames": torch.zeros(5, 64, 64, 3)})
|
|
self.assertEqual(float(cropped[0, 17, 30]), 1.0) # grown by expand
|
|
self.assertEqual(float(cropped[0, 10, 30]), 0.0)
|
|
with self.assertRaises(ValueError):
|
|
SL.generation_mask({"crop": None, "mask_src": None, "frames": torch.zeros(5, 64, 64, 3)})
|
|
orig = SL.shot_mask
|
|
SL.shot_mask = lambda *a: {"masks": torch.nn.functional.interpolate(m[:, None], size=(32, 32))[:, 0].to(torch.uint8)}
|
|
try:
|
|
full = SL.generation_mask({"crop": None, "frames": torch.zeros(5, 64, 64, 3),
|
|
"mask_src": {"path": "", "analysis": {}, "start": 0, "length": 5,
|
|
"spec": {"expand": 0}}})
|
|
finally:
|
|
SL.shot_mask = orig
|
|
self.assertEqual(tuple(full.shape), (5, 64, 64))
|
|
self.assertTrue(torch.equal(full, m))
|
|
|
|
|
|
def test_comparison_overlay_for_mask_only_shots(self):
|
|
m = torch.zeros(5, 32, 32); m[:, 8:24, 8:24] = 1
|
|
ms = {"path": __file__, "analysis": {"fps": 24.0}, "start": 0, "length": 5, "spec": dict(SL.DEFAULT_MASK, expand=0)}
|
|
shot = {"crop": None, "frames": torch.zeros(5, 64, 64, 3), "mask_src": ms, "inpaint": True}
|
|
orig = SL.shot_mask
|
|
SL.shot_mask = lambda *a: {"masks": m.to(torch.uint8)}
|
|
try:
|
|
ov = SL.overlay_of(shot)
|
|
self.assertEqual(tuple(ov["mask"].shape), (5, 64, 64))
|
|
self.assertIsNone(SL.overlay_of(dict(shot, inpaint=False))) # not used and not segmented yet: no SAM 3
|
|
self.assertIsNone(SL.overlay_of({"crop": None, "frames": shot["frames"]}))
|
|
finally:
|
|
SL.shot_mask = orig
|
|
o = SL._mask_overlay(torch.full((5, 32, 32, 3), 0.5), ov, 5)
|
|
self.assertGreater(float(o[0, 16, 16, 0]), float(o[0, 16, 16, 1])) # red inside the mask
|
|
self.assertAlmostEqual(float(o[0, 2, 2, 0]), 0.5) # untouched outside
|
|
|
|
|
|
class ExternalMaskTest(unittest.TestCase):
|
|
def _video(self, name, n=48, fps=24.0, w=64, h=36, box=(10, 5, 30, 25)):
|
|
import cv2
|
|
import numpy as np
|
|
path = str(Path(_TMP) / name)
|
|
vw = cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
|
|
for i in range(n):
|
|
f = np.zeros((h, w, 3), np.uint8)
|
|
x0, y0, x1, y1 = box
|
|
f[y0:y1, x0 + i % 4:x1 + i % 4] = 255
|
|
vw.write(f)
|
|
vw.release()
|
|
return path
|
|
|
|
def setUp(self):
|
|
self.an = {"fps": 24.0, "n": 48, "n_src": 48, "fps_src": 24.0, "width": 64, "height": 36}
|
|
|
|
def test_precedence(self):
|
|
p = {"mask_video": "global.mp4"}
|
|
self.assertEqual(SL.plan_spec(p, {"video": "shot.mp4", "text": "man"})["ext"]["scope"], "shot")
|
|
self.assertNotIn("ext", SL.plan_spec(p, {"text": "man"})) # the shot's own SAM 3 text wins
|
|
self.assertEqual(SL.plan_spec(p, {})["ext"], {"file": "global.mp4", "scope": "video"})
|
|
SL._EXT_MASK["v.mp4"] = torch.ones(48, 36, 64)
|
|
try:
|
|
self.assertEqual(SL.plan_spec(p, {}, "v.mp4")["ext"], {"tensor": "v.mp4"}) # the node input beats the panel file
|
|
finally:
|
|
SL._EXT_MASK.pop("v.mp4")
|
|
self.assertFalse(SL.spec_has_mask(SL.plan_spec({}, {})))
|
|
|
|
def test_file_and_tensor_masks_replace_sam(self):
|
|
path = self._video("roto_whole.mp4")
|
|
self._video("roto_shot.mp4", n=12)
|
|
orig = SL.segment_frames
|
|
SL.segment_frames = lambda *a, **k: self.fail("SAM 3 must not run with an external mask")
|
|
try:
|
|
r = SL.shot_mask(path, self.an, 24, 12, SL.plan_spec({"mask_video": "roto_whole.mp4"}, {}))
|
|
self.assertEqual(r["masks"].shape[0], 12)
|
|
self.assertTrue(r["box"] is not None)
|
|
cov = float(r["masks"].float().mean())
|
|
self.assertTrue(0.1 < cov < 0.4, cov)
|
|
r2 = SL.shot_mask(path, self.an, 24, 12, SL.plan_spec({}, {"video": "roto_shot.mp4"}))
|
|
self.assertEqual(r2["masks"].shape[0], 12)
|
|
t = torch.zeros(48, 36, 64); t[:, 0:18, :] = 1 # top half
|
|
SL._EXT_MASK[path] = t
|
|
r3 = SL.shot_mask(path, self.an, 0, 10, SL.plan_spec({}, {}, path))
|
|
m = r3["masks"].float()
|
|
self.assertGreater(float(m[:, :8].mean()), 0.9)
|
|
self.assertLess(float(m[:, -8:].mean()), 0.1)
|
|
finally:
|
|
SL.segment_frames = orig
|
|
SL._EXT_MASK.pop(path, None)
|
|
|
|
|
|
class FramePasteTest(unittest.TestCase):
|
|
def test_paste_back_keeps_the_background_and_takes_the_new_outline(self):
|
|
base = torch.full((5, 64, 64, 3), 0.2) # the original frames
|
|
result = torch.full((5, 64, 64, 3), 0.9) # generated: everything changed
|
|
old = torch.zeros(5, 64, 64); old[:, 20:44, 20:36] = 1 # the original person
|
|
new = torch.zeros(5, 64, 64); new[:, 16:48, 18:44] = 1 # the new one is bigger
|
|
ms = {"path": "", "analysis": {}, "start": 0, "length": 5, "spec": dict(SL.DEFAULT_MASK, expand=0, feather=0)}
|
|
shot = {"index": 0, "frames": base, "mask_src": ms, "paste": True}
|
|
o1, o2 = SL.shot_mask, SL.segment_frames
|
|
SL.shot_mask = lambda *a: {"masks": old.to(torch.uint8)}
|
|
SL.segment_frames = lambda imgs, sp: torch.nn.functional.interpolate(new[:, None], size=imgs.shape[1:3])[:, 0]
|
|
try:
|
|
out = SL.paste_back(result, shot, True)
|
|
only_old = SL.paste_back(result, shot, False)
|
|
finally:
|
|
SL.shot_mask, SL.segment_frames = o1, o2
|
|
self.assertAlmostEqual(float(out[0, 2, 2, 0]), 0.2, places=4) # background: the original
|
|
self.assertAlmostEqual(float(out[0, 30, 28, 0]), 0.9, places=4) # the person: generated
|
|
self.assertAlmostEqual(float(out[0, 30, 41, 0]), 0.9, places=4) # the bigger new outline is pasted too
|
|
self.assertAlmostEqual(float(only_old[0, 30, 41, 0]), 0.2, places=4)
|
|
|
|
|
|
class MaskGuideTest(unittest.TestCase):
|
|
def test_masked_only_greys_everything_outside_the_mask(self):
|
|
fr = torch.full((4, 32, 32, 3), 0.9)
|
|
m = torch.zeros(4, 16, 16); m[:, 4:12, 4:12] = 1
|
|
g = SL.masked_only(fr, m)
|
|
self.assertEqual(tuple(g.shape), (4, 32, 32, 3))
|
|
self.assertAlmostEqual(float(g[0, 16, 16, 0]), 0.9) # the masked region
|
|
self.assertAlmostEqual(float(g[0, 2, 2, 0]), 0.5) # grey elsewhere
|
|
self.assertEqual(SL.MASK_GUIDES[0], "off")
|
|
fr2 = torch.rand(3, 64, 64, 3)
|
|
m2 = torch.zeros(3, 64, 64); m2[:, 10:50, 10:50] = 1
|
|
old_det = dict(SL._DET); SL._DET["pose"] = None # no YOLO pose model: a clear error
|
|
try:
|
|
with self.assertRaisesRegex(ValueError, "YOLO pose model"):
|
|
SL.masked_only(fr2, m2, look="pose (people)")
|
|
finally:
|
|
SL._DET.clear(); SL._DET.update(old_det)
|
|
for look in [x for x in SL.MASK_GUIDE_LOOKS if not x.startswith("pose")]:
|
|
g2 = SL.masked_only(fr2, m2, look=look)
|
|
self.assertEqual(tuple(g2.shape), (3, 64, 64, 3), look)
|
|
self.assertAlmostEqual(float(g2[0, 2, 2, 0]), 0.5, msg=look) # grey outside, every look
|
|
if look != "colour": # no colour inside
|
|
self.assertLess(float((g2[..., 0] - g2[..., 1]).abs().max()), 1e-5, look)
|
|
t = SL.add_mask_video("x subject_definitions: <Subject 1> is ...", 2)
|
|
self.assertIn("<Video 2> shows only the region", t)
|
|
self.assertEqual(SL.add_mask_video("follow {mask_video}", 1), "follow <Video 1>")
|
|
|
|
|
|
class ConditionFlowTest(unittest.TestCase):
|
|
"""BFS Shot H3 Conditioning with the native H3 nodes replaced by recorders."""
|
|
|
|
def setUp(self):
|
|
sys.path.insert(0, str(ROOT))
|
|
calls = self.calls = {"r2v": [], "guides": []}
|
|
|
|
class Out:
|
|
def __init__(self, *a):
|
|
self.args = a
|
|
|
|
class R2V:
|
|
@staticmethod
|
|
def execute(**kw):
|
|
calls["r2v"].append(kw)
|
|
N = sys.modules["comfy.nested_tensor"].NestedTensor
|
|
return Out([[torch.zeros(1), {}]], {"samples": N((torch.zeros(1, 24, 2, 4, 4), torch.zeros(1, 32, 2, 37)))})
|
|
|
|
class Guide:
|
|
@staticmethod
|
|
def execute(positive, latent, frame_idx, vae, image=None, **kw):
|
|
calls["guides"].append(image)
|
|
return Out(positive)
|
|
|
|
mod = types.ModuleType("comfy_extras.nodes_minimax_h3")
|
|
mod.MiniMaxH3ReferenceToVideo, mod.MiniMaxH3AddGuide = R2V, Guide
|
|
sys.modules.setdefault("comfy_extras", types.ModuleType("comfy_extras"))
|
|
self._old = sys.modules.get("comfy_extras.nodes_minimax_h3")
|
|
sys.modules["comfy_extras.nodes_minimax_h3"] = mod
|
|
if "comfy.nested_tensor" not in sys.modules:
|
|
class _N:
|
|
is_nested = True
|
|
|
|
def __init__(self, t):
|
|
self.tensors = list(t)
|
|
comfy = sys.modules.setdefault("comfy", types.ModuleType("comfy"))
|
|
nt = types.ModuleType("comfy.nested_tensor"); nt.NestedTensor = _N
|
|
comfy.nested_tensor = nt; sys.modules["comfy.nested_tensor"] = nt
|
|
|
|
def tearDown(self):
|
|
if self._old is not None:
|
|
sys.modules["comfy_extras.nodes_minimax_h3"] = self._old
|
|
else:
|
|
sys.modules.pop("comfy_extras.nodes_minimax_h3", None)
|
|
|
|
def _shot(self, **kw):
|
|
m = torch.zeros(22, 64, 64); m[:, 16:48, 16:40] = 1
|
|
shot = {"index": 0, "count": 1, "frames": torch.full((22, 64, 64, 3), 0.7), "ref": torch.zeros(1, 32, 32, 3),
|
|
"ref2": None, "prompt": "subject_definitions: <Subject 1> is ...", "width": 64, "height": 64,
|
|
"gen_length": 22, "audio": None, "crop": {"crop_mask": m, "expand": 0} if kw.get("crop") else None,
|
|
"inpaint": kw.get("inpaint", True)}
|
|
if not kw.get("crop"):
|
|
shot["mask_src"] = {"path": __file__, "analysis": {"fps": 24.0}, "start": 0, "length": 22, "spec": dict(SL.DEFAULT_MASK, expand=0)}
|
|
return shot, m
|
|
|
|
def run_cond(self, shot, m, **kw):
|
|
class VAE:
|
|
def encode(self, px):
|
|
return torch.zeros(1, 24, 2, px.shape[1] // 16, px.shape[2] // 16)
|
|
orig = SL.shot_mask
|
|
SL.shot_mask = lambda *a: {"masks": m.to(torch.uint8)}
|
|
try:
|
|
return SL.BFSShotH3Conditioning().condition(shot, None, VAE(), SL.BFSShotH3Conditioning.GUIDE_MODES[0],
|
|
"none", "match", **kw)
|
|
finally:
|
|
SL.shot_mask = orig
|
|
|
|
def test_reference_video_of_the_masked_region(self):
|
|
shot, m = self._shot()
|
|
self.run_cond(shot, m, inpaint=SL.INPAINT_MODES[0], mask_guide=SL.MASK_GUIDES[3], mask_ref_size="full",
|
|
mask_guide_look="colour")
|
|
kw = self.calls["r2v"][0]
|
|
v = kw["ref_videos"]["ref_video_1"]
|
|
self.assertAlmostEqual(float(v[0, 2, 2, 0]), 0.5) # grey outside the mask
|
|
self.assertAlmostEqual(float(v[0, 30, 30, 0]), 0.7) # the subject
|
|
self.assertIn("<Video 1> shows only the region", kw["prompt"])
|
|
self.assertEqual(len(self.calls["guides"]), 1) # the normal aligned guide is still there
|
|
self.calls["r2v"].clear()
|
|
self.run_cond(shot, m, inpaint=SL.INPAINT_MODES[0], mask_guide=SL.MASK_GUIDES[3], mask_ref_size="1/2")
|
|
self.assertEqual(tuple(self.calls["r2v"][0]["ref_videos"]["ref_video_1"].shape[1:3]), (32, 32))
|
|
|
|
def test_extra_and_instead_guides(self):
|
|
shot, m = self._shot()
|
|
self.run_cond(shot, m, inpaint=SL.INPAINT_MODES[0], mask_guide=SL.MASK_GUIDES[1])
|
|
self.assertEqual(len(self.calls["guides"]), 2)
|
|
self.calls["guides"].clear()
|
|
self.run_cond(shot, m, inpaint=SL.INPAINT_MODES[0], mask_guide=SL.MASK_GUIDES[2])
|
|
self.assertEqual(len(self.calls["guides"]), 1)
|
|
self.assertAlmostEqual(float(self.calls["guides"][0][0, 2, 2, 0]), 0.5) # it is the masked one
|
|
|
|
def test_passes_every_reference_in_order(self):
|
|
refs = [torch.full((1, 32, 32, 3), k / 12) for k in range(12)]
|
|
shot, _ = self._shot(inpaint=False)
|
|
shot.pop("mask_src")
|
|
shot.update(refs=refs, ref=refs[0], ref2=refs[1], ref_names=[f"r{k}.png" for k in range(12)],
|
|
prompt="<Picture 12> is the {details@Picture 12}.")
|
|
out = SL.BFSShotH3Conditioning().condition(shot, None, None, SL.BFSShotH3Conditioning.GUIDE_MODES[3],
|
|
"none", "match", inpaint="off")
|
|
kw = self.calls["r2v"][0]
|
|
self.assertEqual(list(kw["ref_images"]), [f"ref_image_{k}" for k in range(12)])
|
|
self.assertIs(kw["ref_images"]["ref_image_11"], refs[11])
|
|
self.assertEqual(out[3], "<Picture 12> is the character.") # no VLM and no saved description
|
|
self.assertNotIn("use_ref_2", SL.BFSShotH3Conditioning.INPUT_TYPES()["required"])
|
|
self.assertNotIn("use_ref_2", SL.BFSShotH3ConditioningSimple.INPUT_TYPES()["required"])
|
|
|
|
def test_every_marked_person_in_their_picture_color(self):
|
|
refs = [torch.zeros(1, 32, 32, 3) for _ in range(3)]
|
|
shot, m = self._shot(inpaint=False)
|
|
marks = [{"picture": 1, "color": "#00ff00"}, {"picture": 3, "color": "#ff2020", "text": "the man", "points": [], "key": 0}]
|
|
shot.update(refs=refs, color_guide=True, color_guide_marks=marks, color_guide_color="#00ff00", color_guide_opacity=0.5)
|
|
second = torch.zeros(22, 64, 64); second[:, 0:10, 0:10] = 1
|
|
orig = SL.shot_mask
|
|
SL.shot_mask = lambda path, a, start, length, spec: {"masks": second if spec.get("text") == "the man" else m.to(torch.uint8)}
|
|
try:
|
|
SL.BFSShotH3Conditioning().condition(shot, None, None, SL.BFSShotH3Conditioning.GUIDE_MODES[0],
|
|
"none", "match", inpaint="off")
|
|
guide = self.calls["guides"][0]
|
|
self.assertGreater(float(guide[0, 5, 5, 0]), 0.75) # the second person, in red
|
|
self.assertGreater(float(guide[0, 30, 30, 1]), 0.75) # the selection, in green
|
|
with self.assertRaisesRegex(ValueError, "Picture 3"):
|
|
SL.BFSShotH3Conditioning().condition(dict(shot, refs=refs[:2]), None, None,
|
|
SL.BFSShotH3Conditioning.GUIDE_MODES[0], "none", "match", inpaint="off")
|
|
finally:
|
|
SL.shot_mask = orig
|
|
|
|
def test_ignored_outside_mask_only(self):
|
|
shot, m = self._shot(inpaint=False)
|
|
self.run_cond(shot, m, inpaint=SL.INPAINT_MODES[0], mask_guide=SL.MASK_GUIDES[3])
|
|
self.assertNotIn("ref_videos", self.calls["r2v"][0])
|
|
shot, m = self._shot(crop=True)
|
|
self.calls["r2v"].clear()
|
|
self.run_cond(shot, m, inpaint=SL.INPAINT_MODES[0], mask_guide=SL.MASK_GUIDES[3])
|
|
self.assertNotIn("ref_videos", self.calls["r2v"][0])
|
|
|
|
|
|
class MultiRefTest(unittest.TestCase):
|
|
"""Up to 12 reference pictures per shot (plans before 1.113 had ref / ref2)."""
|
|
|
|
def test_old_plans_become_a_list_and_keep_ref_and_ref2(self):
|
|
p = SL._load_plan('{"global_ref": "a.png", "global_ref2": "b.png"}')
|
|
self.assertEqual(p["global_refs"], ["a.png", "b.png"])
|
|
p = SL._load_plan('{"global_refs": ["a.png", "", "c.png", ""], "global_ref": "stale.png"}')
|
|
self.assertEqual(p["global_refs"], ["a.png", "", "c.png"])
|
|
self.assertEqual((p["global_ref"], p["global_ref2"]), ("a.png", ""))
|
|
self.assertEqual(len(SL.ref_list([f"{k}.png" for k in range(20)])), min(20, SL.MAX_REFS))
|
|
|
|
def test_a_shot_slot_falls_back_to_the_global_one_and_gaps_close(self):
|
|
self.assertEqual(SL.shot_ref_names(["", "x.png"], ["a.png", "b.png", "c.png"]), ["a.png", "x.png", "c.png"])
|
|
self.assertEqual(SL.shot_ref_names(["", "", "z.png"], ["a.png"]), ["a.png", "z.png"])
|
|
|
|
def test_signature_of_a_two_reference_shot_is_unchanged(self):
|
|
seg = {"start": 0, "end": 10, "ref": "a.png", "ref2": "b.png", "prompt": ""}
|
|
plan = dict(SL.DEFAULT_PLAN, global_ref="g.png")
|
|
old = SL.shot_signature(plan, seg, 0, {})
|
|
new = SL.shot_signature(dict(plan, global_refs=["g.png"]), dict(seg, refs=["a.png", "b.png"]), 0, {})
|
|
self.assertEqual(old, new)
|
|
three = SL.shot_signature(dict(plan, global_refs=["g.png"]), dict(seg, refs=["a.png", "b.png", "c.png"]), 0, {})
|
|
self.assertNotEqual(old, three)
|
|
|
|
def test_picture_details(self):
|
|
names, imgs = ["a.png", "b.png"], [torch.zeros(1, 4, 4, 3)] * 2
|
|
text = "<Subject 1> is the {details@Picture 2} shown in <Picture 2>, {details@Picture1}."
|
|
out = SL.fill_picture_details(text, names, imgs, {"b.png": "the tall man"})
|
|
self.assertEqual(out, "<Subject 1> is the tall man shown in <Picture 2>, character.")
|
|
kept = SL.fill_picture_details(text, names, imgs, {"b.png": "tall man"}, keep_missing=True)
|
|
self.assertIn("{details@Picture1}", kept)
|
|
self.assertEqual(SL.fill_picture_details("{details@Picture 5}", names, imgs, {}), "character")
|
|
self.assertEqual(SL.prompt_slot(text, "{details@Picture 2}"),
|
|
"<Subject 1> is the ___ shown in <Picture 2>, the subject of <Picture 1>.")
|
|
self.assertEqual(SL.picture_marker(text, 1), "{details@Picture1}")
|
|
self.assertEqual(SL.expand_two_ref_prompt(text), text) # the prompt already says what <Picture 2> is
|
|
|
|
def test_details_and_details2_are_pictures_1_and_2(self):
|
|
self.assertEqual(SL.picture_markers("{details} and {details2}"), "{details@Picture 1} and {details@Picture 2}")
|
|
old = {"ref_details": {"a.png|b.png": {"details": "goblin", "details2": "robot"}, "c.png|": "cat"},
|
|
"picture_details": {"b.png": "tall robot"}}
|
|
self.assertEqual(SL.saved_picture_details(old), {"a.png": "goblin", "b.png": "tall robot", "c.png": "cat"})
|
|
|
|
def test_a_picture_can_have_its_own_preset(self):
|
|
cfg = SL.vlm_cfg({"describe_preset": "short"})
|
|
plan = {"picture_cfg": {"a.png": {"preset": "custom", "custom": "Describe the outfit only."}}}
|
|
self.assertEqual(SL.picture_cfg(cfg, plan, "a.png")["describe_preset"], "custom")
|
|
self.assertIn("outfit only", SL.describe_instruction(SL.picture_cfg(cfg, plan, "a.png")))
|
|
self.assertIs(SL.picture_cfg(cfg, plan, "b.png"), cfg)
|
|
|
|
def test_colored_mark_regions_follow_the_pictures(self):
|
|
legacy = {"global_color_guide_ref1": "#168cff", "global_color_guide_ref2": "#9b30ff"}
|
|
marks = SL.color_marks(legacy, {"color_guide_text2": "the man", "color_guide_key2": 4})
|
|
self.assertEqual([(x["picture"], x["color"]) for x in marks], [(1, "#168cff"), (2, "#9b30ff")])
|
|
self.assertEqual(marks[1]["text"], "the man")
|
|
plan = {"picture_colors": ["#00ff00"]}
|
|
marks = SL.color_marks(plan, {"color_guide_picture": 2, "color_guide_regions": [
|
|
{"picture": 5, "text": "the dog"}, {"picture": 3}]}) # a region with neither text nor points is ignored
|
|
self.assertEqual([(x["picture"], x["color"]) for x in marks], [(2, "#9b30ff"), (5, "#ffe600")])
|
|
text = SL.color_guide_prompt("A scene.", marks)
|
|
self.assertIn("marked in purple uses the character appearance from <Picture 2>", text)
|
|
self.assertIn("marked in yellow uses the character appearance from <Picture 5>", text)
|
|
one = SL.color_guide_prompt("", marks[:1])
|
|
self.assertIn("the subject marked in purple uses the character appearance from <Picture 2>", one)
|
|
|
|
def test_color_contract_is_present_even_when_reference_appearance_mentions_the_color(self):
|
|
seg = {"color_guide": True, "color_guide_marks": [{"picture": 3, "color": "#00ff00"}]}
|
|
raw = "Replace the man with the green goblin from <Picture 3>. Render the scene as anime on a rooftop."
|
|
text = SL.tidy_caption(raw, seg)
|
|
self.assertIn("the subject marked in green uses the character appearance from <Picture 3>", text)
|
|
self.assertIn("anime on a rooftop", text)
|
|
self.assertNotIn("all unmarked content unchanged", text)
|
|
self.assertEqual(SL.color_guide_prompt(text, seg["color_guide_marks"]), text)
|
|
|
|
def test_auto_prompt_asks_with_the_official_format(self):
|
|
seen = {}
|
|
|
|
def fake(clip, prompt, images, max_tokens, *a, **k):
|
|
seen.update(prompt=prompt, n=images.shape[0], tokens=max_tokens)
|
|
return "```\n**subject_definitions:**\n<Subject 1> is the robot in <Picture 2>.\nsummary:\n[video editing] x```"
|
|
old, SL.vlm_generate = SL.vlm_generate, fake
|
|
try:
|
|
seg = {"color_guide": True, "color_guide_marks": [{"picture": 2, "color": "#9b30ff"}], "mask": {"text": "the man"},
|
|
"inpaint": True}
|
|
frame, pic = torch.zeros(1, 8, 8, 3), torch.zeros(1, 4, 4, 3)
|
|
out = SL.write_h3_prompt(None, [frame] * 3, [0, 0.5, 1.0], [pic, pic], ["", "a silver robot"],
|
|
"swap the man for the robot", seg, "video", 1.5)
|
|
finally:
|
|
SL.vlm_generate = old
|
|
self.assertEqual(seen["n"], 5) # 3 frames + 2 pictures
|
|
for want in ("swap the man for the robot", "<Picture 2>: image 5, described as: a silver robot",
|
|
"marked in purple follows the appearance from <Picture 2>", "Only the selected subject is generated again",
|
|
"edited version of <Video 1>", "retention_analysis:", "non_diegetic_music:", "00:00.500",
|
|
"Never write a timestamp inside a shot", "is replaced by <Subject N>"):
|
|
self.assertIn(want, seen["prompt"])
|
|
self.assertTrue(out.startswith("motion_transfer: subject_definitions:\n<Subject 1>")) # fences and bold removed
|
|
self.assertIn("marked in purple", out) # the mapping the writer left out is added
|
|
seg2 = dict(seg, prompt="")
|
|
self.assertEqual(SL.auto_prompt_for("x", ["a.png"], seg2), SL.auto_prompt_for("x", ["a.png"], dict(seg2)))
|
|
self.assertNotEqual(SL.auto_prompt_for("x", ["a.png"], seg2), SL.auto_prompt_for("y", ["a.png"], seg2))
|
|
self.assertEqual(SL.auto_frame_count(24, 24.0), 3)
|
|
self.assertEqual(SL.auto_frame_count(240, 24.0), 8)
|
|
|
|
def test_a_template_request_keeps_its_trigger_and_wording(self):
|
|
seen = {}
|
|
|
|
def fake(clip, prompt, images, max_tokens, *a, **k):
|
|
seen["prompt"] = prompt
|
|
return "subject_definitions: <Subject 1> is the woman in Picture 1. detailed_description: [Shot 1] She walks."
|
|
old, SL.vlm_generate = SL.vlm_generate, fake
|
|
template = ("body_swap: subject_definitions: <Subject 1> is the {details@Picture 1} shown in <Picture 1>. "
|
|
"summary: [video generation + reference] Replace the guide subject. detailed_description: [Shot 1] x")
|
|
try:
|
|
out = SL.write_h3_prompt(None, [torch.zeros(1, 8, 8, 3)], [0.0], [torch.zeros(1, 4, 4, 3)], [""], template,
|
|
{}, "guide")
|
|
finally:
|
|
SL.vlm_generate = old
|
|
self.assertIn("prompt TEMPLATE", seen["prompt"])
|
|
self.assertIn("'body_swap'", seen["prompt"])
|
|
self.assertIn("stands for the description of <Picture n>", seen["prompt"])
|
|
self.assertTrue(out.startswith("body_swap: subject_definitions:")) # the trigger the writer dropped
|
|
self.assertIn("the woman in <Picture 1>", out) # brackets put back
|
|
self.assertEqual(SL.request_trigger("make it rain: now"), "")
|
|
self.assertEqual(SL.request_trigger("head_swap: subject_definitions: x"), "head_swap")
|
|
self.assertFalse(SL.is_template("replace the bottle with the can"))
|
|
|
|
def test_key_frames_cover_every_piece_and_the_cuts_reach_the_writer(self):
|
|
self.assertEqual(SL.key_frames(0, 100, [], 4), [12, 38, 62, 88])
|
|
frames = SL.key_frames(0, 300, [43, 86, 129, 172, 215, 258], 3) # a montage: 7 pieces, one frame each at least
|
|
self.assertEqual(len(frames), 7)
|
|
self.assertTrue(all(any(a <= f < b for f in frames) for a, b in zip([0, 43, 86, 129, 172, 215, 258], [43, 86, 129, 172, 215, 258, 300])))
|
|
seen = {}
|
|
|
|
def fake(clip, prompt, images, max_tokens, *a, **k):
|
|
seen["prompt"] = prompt
|
|
return ("subject_definitions:\n<Subject 1> is x.\ndetailed_description:\n[Shot 1] At 00:00.000, a woman stands. "
|
|
"At 00:01.792, she walks.")
|
|
old, SL.vlm_generate = SL.vlm_generate, fake
|
|
try:
|
|
one = SL.write_h3_prompt(None, [torch.zeros(1, 8, 8, 3)], [0.0], [], [], "Troque a mulher pela <Picture 1>", {})
|
|
two = SL.write_h3_prompt(None, [torch.zeros(1, 8, 8, 3)], [0.0], [], [], "swap", {}, cuts=[1.792],
|
|
template="body_swap: subject_definitions: a. summary: b. detailed_description: [Shot 1] c")
|
|
finally:
|
|
SL.vlm_generate = old
|
|
self.assertIn("[Shot 1] A woman stands. She walks.", one) # no timestamps for one continuous shot
|
|
self.assertIn('"[Shot 2] At 00:01.792, the shot cuts to ..."', seen["prompt"])
|
|
self.assertIn("The user's request: swap", seen["prompt"])
|
|
self.assertIn("The prompt template:\nbody_swap:", seen["prompt"])
|
|
self.assertTrue(two.startswith("body_swap: "))
|
|
self.assertIn("[Shot 1] A woman stands. At 00:01.792", two)
|
|
|
|
def test_a_looping_answer_is_cleaned_and_the_labels_are_tied(self):
|
|
loop = " The goblin looks at the cat, and the cat looks at the goblin. The goblin then looks up." * 6
|
|
raw = ("subject_definitions:\n<Subject 1> is the gaunt, emaciated goblin with pale skin in <Picture 1>.\n"
|
|
"<Subject 2> is the stout goblin with red skin in <Picture 2>.\nsummary:\n[video editing] x\n"
|
|
"retention_analysis:\n<Subject 1>: fully_preserved - x\ndetailed_description:\n[Shot 1] A goblin types."
|
|
+ loop + " The gaunt goblin")
|
|
self.assertIn("sentences are repeated in a loop", SL.h3_prompt_problems(raw, 2))
|
|
t = SL.tidy_h3_prompt(raw, {}, "", False, 2)
|
|
self.assertEqual(t.count("The goblin then looks up."), 1)
|
|
self.assertNotIn("The gaunt goblin\n", t) # the sentence cut in the middle
|
|
self.assertIn("overall_soundscape:", t)
|
|
self.assertIn("non_diegetic_music:\nN/A", t)
|
|
named = SL.name_subjects(t)
|
|
self.assertIn("[Shot 1] <Subject 1> is the gaunt. <Subject 2> is the stout goblin with red skin.", named)
|
|
self.assertEqual(SL.h3_prompt_problems(named, 2), [])
|
|
|
|
def test_caption_style_asks_for_the_training_captions_shape(self):
|
|
seen = {}
|
|
|
|
def fake(clip, prompt, images, max_tokens, *a, **k):
|
|
seen.update(prompt=prompt, tokens=max_tokens)
|
|
return ('"Replace the dog in the video with the gaunt goblin from Picture 1. Replace the cat in the video with '
|
|
'the stout goblin from <Picture 2>. Keep the same background and location as in the video, and maintain '
|
|
'the motion, camera angle, and timing."')
|
|
old, SL.vlm_generate = SL.vlm_generate, fake
|
|
try:
|
|
out = SL.write_h3_prompt(None, [torch.zeros(1, 8, 8, 3)], [0.0], [torch.zeros(1, 4, 4, 3)] * 2, ["", ""],
|
|
"Troque o cão pelo <Picture 1> e o gato pelo <Picture 2>", {}, style="captions")
|
|
finally:
|
|
SL.vlm_generate = old
|
|
self.assertIn("short style of a video-edit request", seen["prompt"])
|
|
self.assertNotIn("full-reference format", seen["prompt"])
|
|
self.assertNotIn("anime", seen["prompt"].lower()) # no sample style may leak into a request with no style edit
|
|
self.assertNotIn("Example request:", seen["prompt"])
|
|
self.assertEqual(seen["tokens"], 512)
|
|
self.assertTrue(out.startswith("motion_transfer: Replace the dog in the video with the gaunt goblin from <Picture 1>."))
|
|
self.assertEqual(SL.caption_problems(out, 2), [])
|
|
self.assertTrue(SL.caption_problems("subject_definitions: <Subject 1> is x", 1))
|
|
|
|
def test_a_written_prompt_is_part_of_the_shot_signature_only_with_auto_prompt_on(self):
|
|
seg = {"start": 0, "end": 10, "ref": "", "ref2": "", "prompt": "make it rain"}
|
|
plan = dict(SL.DEFAULT_PLAN, auto_prompts={"0-10": {"text": "A"}})
|
|
base = SL.shot_signature(plan, seg, 0, {})
|
|
self.assertEqual(base, SL.shot_signature(dict(SL.DEFAULT_PLAN), seg, 0, {}))
|
|
on = dict(plan, auto_prompt={"enabled": True})
|
|
self.assertNotEqual(SL.shot_signature(on, seg, 0, {}),
|
|
SL.shot_signature(dict(on, auto_prompts={"0-10": {"text": "B"}}), seg, 0, {}))
|
|
|
|
def test_repack_replaces_the_first_two_and_keeps_the_rest(self):
|
|
refs = [torch.zeros(1, 4, 4, 3) for _ in range(3)]
|
|
new = torch.ones(1, 4, 4, 3)
|
|
out = SL.BFSShotRepack().repack([dict(_shot(0, 0, 20), refs=list(refs), ref=refs[0], ref2=refs[1])],
|
|
ref_image_2=[new])[0][0]
|
|
self.assertTrue(torch.equal(out["refs"][1], new) and torch.equal(out["ref2"], new))
|
|
self.assertIs(out["refs"][2], refs[2])
|
|
|
|
def test_the_planner_has_no_reference_inputs(self):
|
|
self.assertNotIn("ref_image", SL.BFSShotPlanner.INPUT_TYPES()["optional"])
|
|
|
|
|
|
class DescribeShortTest(unittest.TestCase):
|
|
def test_short_details_fit_inside_the_prompt(self):
|
|
self.assertEqual(SL.tidy_details('"The green goblin in a torn leather vest."\nextra', {"describe_preset": "short"}),
|
|
"green goblin in a torn leather vest")
|
|
self.assertEqual(SL.tidy_details("A young woman.", {"describe_preset": "full body"}), "A young woman.")
|
|
self.assertIn("one English noun phrase", SL.describe_instruction({}))
|
|
|
|
def test_every_preset_removes_vlm_answer_labels(self):
|
|
for preset in SL.DESCRIBE_PRESETS:
|
|
with self.subTest(preset=preset):
|
|
self.assertEqual(SL.tidy_details("**Answer:** elderly woman with large glasses", {"describe_preset": preset}),
|
|
"elderly woman with large glasses")
|
|
|
|
def test_short_mode_honours_its_twenty_word_contract(self):
|
|
text = "Answer: The " + " ".join(f"word{i}" for i in range(30))
|
|
out = SL.tidy_details(text, {"describe_preset": "short"})
|
|
self.assertEqual(len(out.split()), 20)
|
|
self.assertFalse(out.lower().startswith(("answer", "the ")))
|
|
|
|
def test_long_modes_are_bounded_but_not_collapsed_to_short(self):
|
|
text = " ".join(f"word{i}" for i in range(170))
|
|
full = SL.tidy_details(text, {"describe_preset": "full body"})
|
|
face = SL.tidy_details(text, {"describe_preset": "head / face"})
|
|
self.assertEqual(len(full.split()), 150)
|
|
self.assertEqual(len(face.split()), 110)
|
|
self.assertTrue(full.endswith("."))
|
|
|
|
|
|
class FitPromptTest(unittest.TestCase):
|
|
P = ("body_swap: subject_definitions: <Subject 1> is the {details} shown in <Picture 1>, whose identity define the "
|
|
"character. The guide video provides the scene; only {target} is replaced: their face become <Subject 1>. "
|
|
"summary: Replace {target} in the guide video with <Subject 1>.")
|
|
|
|
def test_slot_is_the_sentence_with_a_blank(self):
|
|
d = SL.prompt_slot(self.P, "{details}")
|
|
self.assertIn("<Subject 1> is the ___ shown in <Picture 1>", d)
|
|
self.assertNotIn("{target}", d)
|
|
t = SL.prompt_slot(self.P, "{target}")
|
|
self.assertIn("only ___ is replaced", t)
|
|
self.assertEqual(SL.prompt_slot("no marker here", "{details}"), "")
|
|
|
|
def test_answer_matches_the_blank(self):
|
|
d = SL.prompt_slot(self.P, "{details}")
|
|
self.assertEqual(SL.tidy_slot("A golden retriever with a thick coat.", d), "golden retriever with a thick coat")
|
|
t = SL.prompt_slot(self.P, "{target}")
|
|
self.assertEqual(SL.tidy_slot("the young woman in a black corset.", t), "the young woman in a black corset")
|
|
self.assertIn("goes into the blank", SL.describe_instruction({"describe_preset": "short"}, d))
|
|
self.assertNotIn("goes into the blank", SL.describe_instruction({"describe_preset": "short", "fit_prompt": False}, d))
|
|
self.assertNotIn("goes into the blank", SL.describe_instruction({"describe_preset": "full body"}, d))
|
|
# the VLM echoing the sentence (the bug the user hit)
|
|
echo = ("humanoid robot is the ___ shown in <Picture 1>, whose identity define the character. The robot has a sleek "
|
|
"body with a metallic silver torso.")
|
|
self.assertEqual(SL.tidy_slot(echo, d), "humanoid robot")
|
|
self.assertEqual(SL.tidy_slot("sleek silver humanoid robot with black joints shown in <Picture 1>", d),
|
|
"sleek silver humanoid robot with black joints")
|
|
self.assertEqual(SL.tidy_slot("<Subject 1> is the green goblin in a vest", d), "green goblin in a vest")
|
|
|
|
def test_target_label_is_removed_even_without_a_prompt_slot(self):
|
|
old = SL.vlm_generate
|
|
SL.vlm_generate = lambda *args, **kwargs: "Answer: elderly woman with large tortoiseshell glasses"
|
|
try:
|
|
crop = __import__("numpy").zeros((8, 8, 3), dtype="uint8")
|
|
self.assertEqual(SL.describe_target(object(), crop),
|
|
"the elderly woman with large tortoiseshell glasses")
|
|
finally:
|
|
SL.vlm_generate = old
|
|
|
|
|
|
class RangeTest(unittest.TestCase):
|
|
def test_start_end_and_max_total_from_the_start(self):
|
|
self.assertEqual(SL.plan_range(1000, 24.0, 10, 20), (240, 480))
|
|
self.assertEqual(SL.plan_range(1000, 24.0, 10, 0), (240, 1000))
|
|
self.assertEqual(SL.plan_range(1000, 24.0, 10, 0, 5), (240, 360)) # max total counted from the start
|
|
self.assertEqual(SL.plan_range(1000, 24.0, 0, 0, 0, start_f=250, end_f=500), (250, 500)) # frames win
|
|
self.assertEqual(SL.plan_range(1000, 24.0, 0, 0, 0, start_f=250, end_f=0), (250, 1000))
|
|
segs = SL.plan_segments(480, [100, 300], "shots", 107, 10, start=240)
|
|
self.assertEqual(segs[0]["start"], 240)
|
|
self.assertEqual(segs[-1]["end"], 480)
|
|
self.assertTrue(all(240 <= s["start"] < s["end"] <= 480 for s in segs))
|
|
self.assertTrue(any(s["start"] == 300 for s in segs)) # the cut inside the range is kept
|
|
fixed = SL.plan_segments(480, [], "fixed", 107, 10, start=240)
|
|
self.assertEqual((fixed[0]["start"], fixed[-1]["end"]), (240, 480))
|
|
manual = SL.plan_segments(480, [], "manual", 107, 10, manual=[100, 350], start=240)
|
|
self.assertEqual([(s["start"], s["end"]) for s in manual], [(240, 350), (350, 480)])
|
|
|
|
|
|
class TrackModeTest(unittest.TestCase):
|
|
def test_track_is_used_by_shots_without_their_own_selection_and_sliced_per_shot(self):
|
|
p = {"track": {"points": [{"x": 0.5, "y": 0.5, "label": 1}], "frame": 120, "text": "woman"}, "fps": 24}
|
|
self.assertIn("track", SL.plan_spec(p, {}, "v.mp4")["ext"])
|
|
self.assertNotIn("ext", SL.plan_spec(p, {"text": "man"}, "v.mp4")) # the shot's own text wins
|
|
self.assertEqual(SL.plan_spec(p, {"video": "m.mp4"}, "v.mp4")["ext"]["scope"], "shot")
|
|
whole = torch.zeros(200, 36, 64, dtype=torch.uint8)
|
|
whole[100:150, 10:20, 10:20] = 1 # the subject only in frames 100-149
|
|
orig = SL.track_video
|
|
SL.track_video = lambda path, a, t: (whole, 50) # the track covers frames 50-249
|
|
try:
|
|
spec = SL.plan_spec(p, {}, "v.mp4")
|
|
an = {"n": 300, "fps": 24.0}
|
|
m = SL.ext_masks(spec["ext"], an, 140, 20, 64, 36) # shot 140-159
|
|
self.assertEqual(tuple(m.shape), (20, 36, 64))
|
|
self.assertFalse(bool(m[0].any())) # frame 140 = track index 90
|
|
self.assertTrue(bool(m[-1].any())) # frame 159 = index 109
|
|
finally:
|
|
SL.track_video = orig
|
|
|
|
|
|
class DuetPromptTest(unittest.TestCase):
|
|
def test_fields_are_found_and_the_vlm_fills_them(self):
|
|
tpl = ("subject_definitions:\n<Subject 1> is the woman from <Picture 1>: {skin, face}.\n<Subject 2> is the {room} "
|
|
"from {setting}.\n\nsummary:\nx {layout}")
|
|
self.assertEqual(SL.template_slots(tpl), ["skin, face", "room"])
|
|
asked = []
|
|
|
|
def gen(clip, q, images, max_tokens, temperature=0.0, seed=0):
|
|
asked.append(q)
|
|
if images is None:
|
|
return tpl.replace("{skin, face}", "pale skin, oval face").replace("{room}", "kitchen")
|
|
return '{"medium": "phone footage", "actions": "the person waves"}'
|
|
|
|
old = SL.vlm_generate, SL.vlm_describe
|
|
SL.vlm_generate, SL.vlm_describe = gen, lambda clip, ims, q, mt=320, temperature=0.0, seed=0: "the kitchen with white cabinets"
|
|
try:
|
|
fr = torch.rand(9, 32, 32, 3)
|
|
out = SL.duet_prompt(object(), tpl, fr, [torch.rand(1, 32, 32, 3)], "character swap", "", 512,
|
|
setting=torch.rand(1, 32, 32, 3))
|
|
self.assertIn("pale skin, oval face", out); self.assertIn("{setting}", out); self.assertIn("{layout}", out)
|
|
self.assertIn("the person waves", asked[-1])
|
|
self.assertEqual(SL.duet_prompt(None, tpl, fr, [], "character swap", ""), tpl.strip())
|
|
self.assertEqual(SL.duet_prompt(object(), "a full prompt", fr, [], "character swap", ""), "a full prompt")
|
|
w = SL.duet_prompt(object(), "", fr, [torch.rand(1, 32, 32, 3)], "character swap", "", 512,
|
|
setting=torch.rand(1, 32, 32, 3))
|
|
self.assertIn("<Subject 2> is the kitchen with white cabinets from {setting}.", w)
|
|
finally:
|
|
SL.vlm_generate, SL.vlm_describe = old
|
|
|
|
|
|
class ShotHistoryTest(unittest.TestCase):
|
|
def test_history_is_taken_out_of_the_shot(self):
|
|
h = torch.full((17, 8, 8, 3), 255, dtype=torch.uint8)
|
|
shot = {"chain": "history", "index": 1, "count": 2, "history": h, "history_src": h}
|
|
hist, src = SL.shot_history(shot)
|
|
self.assertEqual((tuple(hist.shape), float(hist.max()), "history" in shot), ((17, 8, 8, 3), 1.0, False))
|
|
self.assertEqual(SL.shot_history({"chain": "off", "index": 1}), (None, None))
|
|
|
|
def test_auto_loop_takes_the_previous_render(self):
|
|
SL._LAST_RESULT.clear()
|
|
SL._LAST_RESULT.update(index=0, count=2, frames=torch.rand(40, 8, 8, 3))
|
|
hist, _ = SL.shot_history({"chain": "history", "index": 1, "count": 2, "prev_length": 30, "chain_history": 34})
|
|
self.assertEqual(hist.shape[0], 22) # 30 real frames -> 22, the longest 17k+5 that fits
|
|
SL._LAST_RESULT.clear()
|
|
|
|
def test_history_lengths_are_whole_vae_clips(self):
|
|
# 17 frames encode to 2 tokens (the first 5 frames): only 17k+5 reaches the boundary
|
|
self.assertEqual([SL.history_frame_count(w, 200) for w in (5, 17, 22, 34, 39, 51, 56)], [5, 22, 22, 39, 39, 56, 56])
|
|
self.assertEqual([SL.history_frame_count(56, a) for a in (4, 5, 21, 22, 38, 40)], [0, 5, 5, 22, 22, 39])
|
|
|
|
|
|
class ShotHeadTest(unittest.TestCase):
|
|
"""Continuity 'head': the previous shot's end starts the next shot, pinned in the latent, and the join cuts it."""
|
|
|
|
@staticmethod
|
|
def _segs(chain="head", n=22, cut=False, gap=0):
|
|
return [{"start": 0, "end": 60, "chain": "off", "cut_before": False},
|
|
{"start": 60 + gap, "end": 120, "chain": chain, "chain_history": n, "cut_before": cut}]
|
|
|
|
def test_head_length(self):
|
|
self.assertEqual(SL.head_length(self._segs(), 1, H3), 22)
|
|
self.assertEqual(SL.head_length(self._segs(n=56), 1, H3), 39) # capped at 39
|
|
self.assertEqual(SL.head_length(self._segs(n=17), 1, H3), 22) # old plans: next valid length up
|
|
self.assertEqual(SL.head_length(self._segs(), 0, H3), 0) # first shot
|
|
self.assertEqual(SL.head_length(self._segs(cut=True), 1, H3), 0) # a camera cut
|
|
self.assertEqual(SL.head_length(self._segs(gap=5), 1, H3), 0) # a skipped shot in between
|
|
self.assertEqual(SL.head_length(self._segs(chain="history"), 1, H3), 0)
|
|
self.assertEqual(SL.head_length(self._segs(), 1, "LTX / Wan (8n+1)"), 0)
|
|
|
|
def test_join_cuts_the_head(self):
|
|
img = torch.arange(30, dtype=torch.float32).view(30, 1, 1, 1).expand(30, 2, 2, 3)
|
|
shot = {"head": 5, "gen_length": 30, "frames": img.clone(), "crop": {"mask": torch.zeros(30, 2, 2), "box": [0, 0, 1, 1]}}
|
|
out, sh = SL.drop_head(img, shot)
|
|
self.assertEqual((out.shape[0], float(out[0, 0, 0, 0]), sh["gen_length"], sh["frames"].shape[0],
|
|
sh["crop"]["mask"].shape[0], sh["head"], shot["frames"].shape[0]), (25, 5.0, 25, 25, 25, 0, 30))
|
|
self.assertIs(SL.drop_head(img, {"head": 0})[0], img)
|
|
|
|
def test_remember_result_skips_the_head(self):
|
|
SL._LAST_RESULT.clear()
|
|
SL.remember_result({"index": 0, "count": 2, "head": 5}, torch.zeros(30, 2, 2, 3))
|
|
self.assertEqual(SL._LAST_RESULT["frames"].shape[0], 25)
|
|
SL._LAST_RESULT.clear()
|
|
|
|
def test_pin_head_keeps_the_first_latent_steps(self):
|
|
class NT:
|
|
def __init__(self, t):
|
|
self.tensors, self.is_nested = list(t), True
|
|
nt = types.ModuleType("comfy.nested_tensor")
|
|
nt.NestedTensor = NT
|
|
old = {k: sys.modules.get(k) for k in ("comfy", "comfy.nested_tensor")}
|
|
comfy = types.ModuleType("comfy")
|
|
comfy.nested_tensor = nt
|
|
sys.modules["comfy"], sys.modules["comfy.nested_tensor"] = comfy, nt
|
|
try:
|
|
import bfs_h3_history as HH
|
|
|
|
class VAE:
|
|
def encode(self, px):
|
|
n = px.shape[0]
|
|
self.size = tuple(px.shape[1:3])
|
|
return torch.full((1, 24, 5 * -(-n // 17) - 3, px.shape[1] // 16, px.shape[2] // 16), 7.0)
|
|
vae = VAE()
|
|
latent = {"samples": NT((torch.zeros(1, 24, 12, 4, 4), torch.zeros(1, 8, 50)))}
|
|
out, n = HH.pin_head(latent, vae, torch.rand(30, 32, 32, 3)) # 30 frames -> the last 22 -> 7 steps
|
|
v, m = out["samples"].tensors[0], out["noise_mask"].tensors[0]
|
|
self.assertEqual((n, vae.size), (22, (64, 64)))
|
|
self.assertTrue(bool((v[:, :, :7] == 7).all()) and bool((v[:, :, 7:] == 0).all()))
|
|
self.assertTrue(bool((m[:, :, :7] == 0).all()) and bool((m[:, :, 7:] == 1).all()))
|
|
self.assertTrue(bool((out["noise_mask"].tensors[1] == 1).all()))
|
|
# an existing inpaint mask stays as it is after the head
|
|
inp = torch.full((1, 1, 12, 4, 4), 0.5)
|
|
out2, _ = HH.pin_head(dict(latent, noise_mask=NT((inp, torch.ones(1, 8, 50)))), vae, torch.rand(22, 64, 64, 3))
|
|
m2 = out2["noise_mask"].tensors[0]
|
|
self.assertTrue(bool((m2[:, :, :7] == 0).all()) and bool((m2[:, :, 7:] == 0.5).all()) and bool((inp == 0.5).all()))
|
|
finally:
|
|
for k, v in old.items():
|
|
if v is None:
|
|
sys.modules.pop(k, None)
|
|
else:
|
|
sys.modules[k] = v
|
|
|
|
|
|
class MemoryStatusTest(unittest.TestCase):
|
|
def test_shot_tokens(self):
|
|
# 107 H3 frames -> 7 clips -> 32 latent steps; 960x544 -> 60 x 34 cells
|
|
self.assertEqual(SL.shot_tokens(107, 960, 544), 32 * 34 * 60)
|
|
self.assertEqual(SL.shot_tokens(22, 32, 32), 7 * 2 * 2)
|
|
self.assertGreater(SL.shot_tokens(90, 960, 544), SL.shot_tokens(73, 960, 544)) # a head makes the shot bigger
|
|
self.assertEqual(SL.shot_tokens(81, 32, 32, "LTX / Wan (8n+1)"), 21 * 2 * 2)
|
|
|
|
def test_record_and_status(self):
|
|
import json as _json
|
|
path = Path(_TMP) / SL._VRAM_FILE
|
|
path.unlink(missing_ok=True)
|
|
cuda = types.SimpleNamespace(is_available=lambda: True, is_initialized=lambda: True, current_device=lambda: 0,
|
|
get_device_name=lambda d: "Test GPU", max_memory_allocated=lambda: 12 * 2 ** 30,
|
|
reset_peak_memory_stats=lambda: None, mem_get_info=lambda: (8 * 2 ** 30, 32 * 2 ** 30))
|
|
with patch.object(SL.torch, "cuda", cuda):
|
|
SL.vram_record(5000)
|
|
SL.vram_record(3000) # smaller: the biggest stays
|
|
st = SL.vram_status()
|
|
self.assertEqual((st["gpu"], st["max_tokens"], st["max_peak_gb"], st["runs"], st["total_gb"], st["free_gb"]),
|
|
("Test GPU", 5000, 12.0, 2, 32.0, 8.0))
|
|
self.assertIn("Test GPU", _json.loads(path.read_text()))
|
|
SL.vram_record(4000) # no CUDA in use: nothing recorded, nothing started
|
|
self.assertEqual(len(_json.loads(path.read_text())["Test GPU"]["samples"]), 2)
|
|
|
|
|
|
class ShotStoreTest(unittest.TestCase):
|
|
def test_storage_round_trip(self):
|
|
d = tempfile.mkdtemp(dir=_TMP)
|
|
y = torch.linspace(0, 1, 48).view(1, 48, 1, 1)
|
|
frames = (((torch.sin(torch.arange(12).view(12, 1, 1, 1) + 9 * y) + 1) / 2).expand(12, 48, 64, 3) * 255).round().byte()
|
|
for k, mode in enumerate(("lossless", "raw", "smaller")):
|
|
SL.store_shot(d, k, frames, {"index": k, "fps": 24.0}, mode)
|
|
back, meta = SL.load_shot(d, k)
|
|
self.assertEqual((tuple(back.shape), meta["index"]), ((12, 48, 64, 3), k))
|
|
diff = int((back.int() - frames.int()).abs().max())
|
|
if mode == "smaller":
|
|
self.assertLess(diff, 40)
|
|
else:
|
|
self.assertEqual(diff, 0, mode) # lossless and raw are exact
|
|
self.assertTrue(os.path.exists(os.path.join(d, "shot_0000.mkv")))
|
|
self.assertFalse(os.path.exists(os.path.join(d, "shot_0001.mkv"))) # raw: the frames are in the .pt
|
|
|
|
def test_status_cascades_through_continuity(self):
|
|
d = tempfile.mkdtemp(dir=_TMP)
|
|
for i in range(3):
|
|
open(os.path.join(d, f"shot_{i:04d}.pt"), "wb").close()
|
|
st = {"done": [0, 1, 2], "sig": {"0": "a", "1": "b", "2": "c"}, "gen": {"0": "g0", "1": "g1", "2": "g2"},
|
|
"from": {"1": "g0", "2": "g1"}, "cur": {"sigs": ["a", "b", "c"], "prev": [False, True, False]}}
|
|
self.assertEqual(SL.shot_status(st, d), [True, True, True])
|
|
st["gen"]["0"] = "new" # shot 1 was redone: shot 2 continues from it
|
|
self.assertEqual(SL.shot_status(st, d), [True, False, True]) # shot 3 does not continue (cut)
|
|
st["cur"]["sigs"][2] = "changed" # a setting of shot 3 changed
|
|
self.assertEqual(SL.shot_status(st, d), [True, False, False])
|
|
|
|
def test_join_can_write_an_mp4(self):
|
|
import cv2
|
|
d = tempfile.mkdtemp(dir=_TMP)
|
|
imgs = [torch.full((22, 32, 32, 3), 0.2), torch.full((39, 32, 32, 3), 0.8)]
|
|
shots = [_shot(0, 0, 20, gen=22), _shot(1, 20, 45, cut=True, gen=39)]
|
|
audio = {"waveform": torch.zeros(1, 2, 48000 * 3), "sample_rate": 48000}
|
|
with patch.object(SL, "_next_output_path", lambda prefix: os.path.join(d, prefix + "_00001.mp4")):
|
|
out = SL.BFSShotJoin().join(imgs, shots, [0], audio=[audio], save_to=[SL.SAVE_TO[1]],
|
|
filename_prefix=["test"])
|
|
res = out
|
|
self.assertEqual(res[0].shape[0], 45) # the output still carries the video (read back)
|
|
self.assertAlmostEqual(float(res[0][30].mean()), 0.8, places=1)
|
|
self.assertTrue(res[4].endswith("test_00001.mp4"))
|
|
self.assertIsInstance(out, tuple) # no preview on the node: plain outputs
|
|
cap = cv2.VideoCapture(res[4]); n = 0
|
|
while cap.read()[0]:
|
|
n += 1
|
|
self.assertEqual(n, 45)
|
|
|
|
|
|
class RenderFingerprintTest(unittest.TestCase):
|
|
"""Changing what renders the shots (LoRA, seed...) must make the stored shots stale; the join's settings must not."""
|
|
|
|
@staticmethod
|
|
def graph():
|
|
return {
|
|
"1": {"class_type": "BFSShotPlanner", "inputs": {"plan": "{\"run\": \"auto\"}"}},
|
|
"2": {"class_type": "BFSShotUnpack", "inputs": {"shot": ["1", 0]}},
|
|
"3": {"class_type": "BFSShotH3ConditioningSimple", "inputs": {"shot": ["2", 10], "clip": ["4", 0], "guide_mode": "aligned guide"}},
|
|
"4": {"class_type": "CLIPLoader", "inputs": {"clip_name": "qwen3vl.safetensors", "type": "minimax"}},
|
|
"5": {"class_type": "UNETLoader", "inputs": {"unet_name": "h3.safetensors", "weight_dtype": "default"}},
|
|
"6": {"class_type": "LoraLoaderModelOnly", "inputs": {"model": ["5", 0], "lora_name": "swap_v1.safetensors", "strength_model": 1.0}},
|
|
"7": {"class_type": "KSampler", "inputs": {"model": ["6", 0], "positive": ["3", 0], "seed": 42, "steps": 8}},
|
|
"8": {"class_type": "VAEDecode", "inputs": {"samples": ["7", 0], "vae": ["9", 0]}},
|
|
"9": {"class_type": "VAELoader", "inputs": {"vae_name": "vae.safetensors"}},
|
|
"10": {"class_type": "BFSShotJoin", "inputs": {"images": ["8", 0], "shots": ["2", 10], "crossfade": 4}},
|
|
"11": {"class_type": "BFSVideoCompare", "inputs": {"guide": ["10", 5], "target": ["10", 0], "gain": 2.0}},
|
|
}
|
|
|
|
def test_render_changes_count_and_join_settings_do_not(self):
|
|
base = SL.render_fingerprint(self.graph(), "1")
|
|
self.assertTrue(base)
|
|
self.assertEqual(SL.render_fingerprint(self.graph(), "1"), base)
|
|
for node, key, value, changes in (("6", "lora_name", "swap_v2.safetensors", True), ("6", "strength_model", 0.8, True),
|
|
("7", "seed", 43, True), ("3", "guide_mode", "reference video", True),
|
|
("10", "crossfade", 8, False), ("1", "plan", "{}", False), ("11", "gain", 4.0, False)):
|
|
g = self.graph()
|
|
g[node]["inputs"][key] = value
|
|
self.assertEqual(SL.render_fingerprint(g, "1") != base, changes, f"{node}.{key}")
|
|
g = self.graph()
|
|
del g["10"]
|
|
self.assertEqual(SL.render_fingerprint(g, "1"), "")
|
|
|
|
def test_the_fingerprint_is_part_of_the_shot_signature(self):
|
|
seg = {"start": 0, "end": 20, "prompt": "x"}
|
|
a = SL.shot_signature({}, seg, 0, {"render": "aaa"})
|
|
self.assertNotEqual(a, SL.shot_signature({}, seg, 0, {"render": "bbb"}))
|
|
self.assertEqual(SL.BFSShotPlanner.INPUT_TYPES()["hidden"], {"graph": "PROMPT", "node_id": "UNIQUE_ID"})
|
|
|
|
|
|
class SecondPassSizeTest(unittest.TestCase):
|
|
def _upscaler(self, w, h, mode, val, align=32):
|
|
# Minimax H3 Latent Upscaler (3D), from a latent of w/16 x h/16 cells
|
|
if mode == "scale": tw, th = w * val, h * val
|
|
elif mode == "dims": tw, th = val
|
|
else:
|
|
th = (val * 1024 * 1024 / (w / h)) ** 0.5; tw = th * (w / h)
|
|
tw, th = round(tw / align) * align, round(th / align) * align
|
|
return int(round(tw / 16) * 16), int(round(th / 16) * 16)
|
|
|
|
def test_matches_the_latent_upscaler(self):
|
|
for w, h in ((832, 480), (480, 832), (1024, 576), (704, 704)):
|
|
self.assertEqual(SL.second_pass_size(w, h, {"mode": "scale", "scale": 2.0}), self._upscaler(w, h, "scale", 2.0))
|
|
self.assertEqual(SL.second_pass_size(w, h, {"mode": "scale", "scale": 1.5, "align": 64}),
|
|
self._upscaler(w, h, "scale", 1.5, 64))
|
|
self.assertEqual(SL.second_pass_size(w, h, {"mode": "megapixels", "megapixels": 1.0}),
|
|
self._upscaler(w, h, "mega", 1.0))
|
|
self.assertEqual(SL.second_pass_size(832, 480, {"mode": "dims", "width": 1280, "height": 704}), (1280, 704))
|
|
self.assertEqual(SL.second_pass_size(832, 480, None), (1664, 960)) # defaults: x2, align 32
|
|
|
|
|
|
class JoinWithoutFfmpegTest(unittest.TestCase):
|
|
def test_mp4_mode_works_without_the_ffmpeg_program(self):
|
|
# issue #12: Windows portable, no ffmpeg on PATH, long video -> the RAM guard picks the mp4 mode
|
|
d = tempfile.mkdtemp(dir=_TMP)
|
|
imgs = [torch.full((22, 32, 32, 3), 0.2), torch.full((39, 32, 32, 3), 0.8)]
|
|
shots = [_shot(0, 0, 20, gen=22), _shot(1, 20, 45, cut=True, gen=39)]
|
|
audio = {"waveform": torch.zeros(1, 2, 48000 * 3), "sample_rate": 48000}
|
|
with patch.dict(SL.VIO._BIN, {"ffmpeg": None, "ffprobe": None}), \
|
|
patch.object(SL, "_ram_available", lambda: 1000), \
|
|
patch.object(SL, "_next_output_path", lambda prefix: os.path.join(d, prefix + "_00001.mp4")):
|
|
out = SL.BFSShotJoin().join(imgs, shots, [0], audio=[audio], comparison=[True])
|
|
res = out
|
|
self.assertTrue(os.path.getsize(res[4]) > 0 and os.path.exists(res[4][:-4] + "_comparison.mp4"))
|
|
self.assertEqual((res[0].shape[0], res[3].shape[0], res[5].shape[0]), (45, 45, 45))
|
|
self.assertAlmostEqual(float(res[0][30].mean()), 0.8, delta=0.05)
|
|
|
|
|
|
class JoinFileMetadataTest(unittest.TestCase):
|
|
def test_mp4_carries_prompt_and_workflow(self):
|
|
import json, subprocess
|
|
d = tempfile.mkdtemp(dir=_TMP)
|
|
wf = {"nodes": [{"id": 1, "widgets_values": ["a=b; c#d \\ e\nnew line"]}]}
|
|
pr = {"1": {"class_type": "BFSShotPlanner", "inputs": {"plan": "{}"}}}
|
|
with patch.object(SL, "_next_output_path", lambda prefix: os.path.join(d, prefix + "_00001.mp4")):
|
|
out = SL.BFSShotJoin().join([torch.rand(22, 32, 32, 3)], [_shot(0, 0, 20, gen=22)], [0], save_to=[SL.SAVE_TO[1]],
|
|
filename_prefix=["m"], prompt=[pr], extra_pnginfo=[{"workflow": wf}])
|
|
tags = json.loads(subprocess.run(["ffprobe", "-v", "error", "-show_entries", "format_tags", "-of", "json",
|
|
out[4]], capture_output=True).stdout)["format"]["tags"]
|
|
self.assertEqual((json.loads(tags["workflow"]), json.loads(tags["prompt"])), (wf, pr))
|
|
|
|
|
|
class JoinRamGuardTest(unittest.TestCase):
|
|
def test_images_join_falls_back_to_mp4_when_ram_is_short(self):
|
|
d = tempfile.mkdtemp(dir=_TMP)
|
|
imgs = [torch.full((22, 32, 32, 3), 0.2), torch.full((39, 32, 32, 3), 0.8)]
|
|
shots = [_shot(0, 0, 20, gen=22), _shot(1, 20, 45, cut=True, gen=39)]
|
|
with patch.object(SL, "_ram_available", lambda: 1000), \
|
|
patch.object(SL, "_next_output_path", lambda prefix: os.path.join(d, prefix + "_00001.mp4")):
|
|
out = SL.BFSShotJoin().join(imgs, shots, [0], comparison=[True])
|
|
res = out
|
|
self.assertTrue(os.path.exists(res[4]) and os.path.exists(res[4][:-4] + "_comparison.mp4"))
|
|
self.assertEqual(res[0].shape[0], 45) # never an empty output node
|
|
self.assertTrue(res[0].shape[1] < 32 and res[3].shape[0] == 45) # scaled down to the (tiny) free RAM
|
|
self.assertTrue(os.path.exists(res[4][:-4] + "_guide.mp4") and res[5].shape[0] == 45)
|
|
normal = SL.BFSShotJoin().join(imgs, shots, [0]) # enough RAM: images as before
|
|
self.assertEqual((normal[0].shape[0], normal[4]), (45, ""))
|
|
|
|
def test_join_bytes(self):
|
|
self.assertEqual(SL._join_bytes(10, 100, 200, False, 360), 10 * 100 * 200 * 12)
|
|
self.assertGreater(SL._join_bytes(10, 100, 200, True, 1024), SL._join_bytes(10, 100, 200, True, 50))
|
|
|
|
|
|
class StoredComparisonTest(unittest.TestCase):
|
|
def test_originals_go_to_a_video_not_the_pt(self):
|
|
rid = "cmp_" + str(id(self))
|
|
y = torch.linspace(0, 1, 64).view(1, 64, 1, 1)
|
|
src = [((torch.sin(torch.arange(n).view(n, 1, 1, 1) + 7 * y) + 1) / 2).expand(n, 64, 64, 3).clone() for n in (22, 39)]
|
|
shots = [dict(_shot(0, 0, 20, count=2, queue=True, run_id=rid, gen=22), frames=src[0]),
|
|
dict(_shot(1, 20, 45, cut=True, count=2, queue=True, run_id=rid, gen=39), frames=src[1])]
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|
SL.BFSShotJoin().join([src[0] * 0.5], [shots[0]], [0], comparison=[True], comparison_height=[64])
|
|
out = SL.BFSShotJoin().join([src[1] * 0.5], [shots[1]], [0], comparison=[True], comparison_height=[64])
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|
d = SL.run_dir(rid)
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|
self.assertLess(os.path.getsize(os.path.join(d, "shot_0000.pt")), 50_000) # info only
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|
self.assertTrue(os.path.exists(os.path.join(d, "shot_0000_orig.mkv")))
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|
video, comp = out[0], out[3]
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|
self.assertEqual(video.shape[0], 45)
|
|
self.assertEqual(comp.shape[0], 45)
|
|
# the original column (left) shows the source, not the dark filler of a missing original
|
|
self.assertGreater(float(comp[:, comp.shape[1] // 2, 5:60].mean()), 0.2)
|
|
guide = out[5] # the stored originals, aligned
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|
self.assertEqual(guide.shape[0], 45)
|
|
self.assertAlmostEqual(float(guide[:20].mean()), float(src[0][:20].mean()), delta=0.03)
|
|
self.assertAlmostEqual(float(guide[20:].mean()), float(src[1][:25].mean()), delta=0.03)
|
|
|
|
|
|
class ColorAnchorTest(unittest.TestCase):
|
|
def _shot(self, sat):
|
|
src = torch.full((12, 48, 48, 3), 0.5)
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|
res = src.clone()
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|
res[:, 12:36, 12:36] = torch.tensor([0.5 - sat, 0.5 + sat, 0.5 - sat]) # a green subject
|
|
res[:, 12:36, 12:36] += torch.linspace(-0.05, 0.05, 24).view(1, 24, 1, 1)
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|
return res, src
|
|
|
|
def test_subject_chroma_goes_back_to_the_anchor(self):
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|
r1, s1 = self._shot(0.15)
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|
anchor = SL.color_stats(r1, s1)
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|
r4, s4 = self._shot(0.30) # 4 shots later: twice as saturated
|
|
out, before = SL.anchor_colors(r4, s4, anchor)
|
|
after = SL.color_stats(out, s4)
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|
self.assertGreater(abs(before["mu"][0] - anchor["mu"][0]), 5)
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|
self.assertLess(abs(after["mu"][0] - anchor["mu"][0]), 1.5)
|
|
self.assertLess(abs(after["mu"][1] - anchor["mu"][1]), 1.5)
|
|
self.assertAlmostEqual(float(out[0, 2, 2, 0]), 0.5, places=2) # the background is untouched
|
|
self.assertIsNone(SL.color_stats(s1, s1)) # no subject: nothing to anchor
|
|
|
|
|
|
class TidyDetailsTest(unittest.TestCase):
|
|
def test_short_details_drop_the_echoed_prompt(self):
|
|
cfg = {"describe_preset": "short"}
|
|
for raw in ("is the elderly woman with glasses shown in", "elderly woman with glasses is the shown in <Picture 1>",
|
|
"<Subject 1> is the elderly woman with glasses shown in <Picture 1>.", "The elderly woman with glasses"):
|
|
self.assertEqual(SL.tidy_details(raw, cfg), "elderly woman with glasses", raw)
|
|
self.assertEqual(SL.tidy_details("man shown wearing a red cap", cfg), "man shown wearing a red cap")
|
|
# descriptions already saved in a plan are cleaned when they go into the prompt
|
|
self.assertEqual(SL.clean_slot("is the woman in red shown in <Picture 1>"), "woman in red")
|
|
|
|
|
|
class FillDetailsTest(unittest.TestCase):
|
|
def test_render_node_fills_details_with_its_vlm(self):
|
|
old = SL.vlm_describe
|
|
SL.vlm_describe = lambda clip, ims, q, mt=320, temperature=0.0, seed=0: "The elderly man with white hair in a light blue t-shirt."
|
|
try:
|
|
out = SL.fill_details_now("<Subject 1> is the {details} shown in <Picture 1>.", object(), [torch.rand(1, 8, 8, 3)])
|
|
self.assertEqual(out, "<Subject 1> is the elderly man with white hair in a light blue t-shirt shown in <Picture 1>.")
|
|
self.assertIn("character", SL.fill_details_now("is the {details} shown", None, []))
|
|
self.assertEqual(SL.fill_details_now("no slot", None, []), "no slot")
|
|
finally:
|
|
SL.vlm_describe = old
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|
|
|
|
|
|
class RedoRecoveryTest(unittest.TestCase):
|
|
def setUp(self):
|
|
self.tmp = tempfile.TemporaryDirectory()
|
|
self.addCleanup(self.tmp.cleanup)
|
|
self.directory = Path(self.tmp.name)
|
|
self.plan = {"video": "test.mp4"}
|
|
self.state = {"done": [0, 1], "count": 2, "sig": {"0": "a", "1": "b"},
|
|
"gen": {"0": "old", "1": "next"}, "from": {"1": "old"},
|
|
"cur": {"sigs": ["a", "b"], "prev": [False, True]}}
|
|
for i in range(2):
|
|
(self.directory / f"shot_{i:04d}.pt").write_bytes(b"saved take")
|
|
for name, value in (("_input_path", lambda _: "video"), ("run_key", lambda *_: "test"),
|
|
("run_dir", lambda *a, **kw: str(self.directory))):
|
|
patcher = patch.object(SL, name, value)
|
|
patcher.start()
|
|
self.addCleanup(patcher.stop)
|
|
SL.save_state("test", self.state)
|
|
|
|
def test_redo_can_be_cancelled_with_continuity_restored(self):
|
|
self.assertEqual(SL.redo_shots(self.plan, [0])["ok"], [False, False])
|
|
result = SL.redo_shots(self.plan, [0], cancel=True)
|
|
self.assertEqual(result["ok"], [True, True])
|
|
self.assertEqual(result["redo"], [])
|
|
|
|
def test_reset_preserves_files_and_state(self):
|
|
SL.redo_shots(self.plan, [0], cascade=False)
|
|
result = SL.cache_status(self.plan, reset=True)
|
|
self.assertEqual(result["ok"], [True, True])
|
|
self.assertEqual((self.directory / "shot_0000.pt").read_bytes(), b"saved take")
|
|
self.assertNotIn("0", SL.run_state("test").get("keep_next", {}))
|
|
self.assertEqual(SL.run_state("test")["gen"], self.state["gen"])
|
|
|
|
def test_repeated_selection_does_not_overwrite_undo(self):
|
|
SL.redo_shots(self.plan, [0])
|
|
SL.redo_shots(self.plan, [0])
|
|
self.assertEqual(SL.redo_shots(self.plan, [0], cancel=True)["ok"], [True, True])
|
|
|
|
def test_cancel_does_not_make_changed_settings_valid(self):
|
|
SL.redo_shots(self.plan, [0])
|
|
st = SL.run_state("test")
|
|
st["cur"]["sigs"][0] = "changed seed"
|
|
SL.save_state("test", st)
|
|
self.assertEqual(SL.redo_shots(self.plan, [0], cancel=True)["ok"], [False, False])
|
|
|
|
def test_cancel_does_not_restore_replaced_take(self):
|
|
SL.redo_shots(self.plan, [0])
|
|
st = SL.run_state("test")
|
|
st["gen"]["0"] = "new"
|
|
SL.save_state("test", st)
|
|
self.assertEqual(SL.redo_shots(self.plan, [0], cancel=True)["ok"], [False, False])
|
|
|
|
def test_reset_without_selection_preserves_completed_run(self):
|
|
self.assertEqual(SL.cache_status(self.plan, reset=True)["ok"], [True, True])
|
|
|
|
|
|
class IsolatedReferenceBindingTests(unittest.TestCase):
|
|
def test_each_reference_has_its_own_image_and_index(self):
|
|
images = [torch.zeros(1, 4, 4, 3), torch.ones(1, 4, 4, 3)]
|
|
calls = []
|
|
def describe(clip, batch, instruction, tokens, **kwargs):
|
|
self.assertEqual(len(batch), 1)
|
|
calls.append((batch[0], instruction))
|
|
return "human in dark suit" if batch[0] is images[0] else "goblin in loincloth"
|
|
with patch.object(SL, "vlm_describe", side_effect=describe):
|
|
result = SL.isolated_picture_descriptions(object(), images)
|
|
self.assertEqual(result, ["human in dark suit", "goblin in loincloth"])
|
|
self.assertIn("ONLY <Picture 1>", calls[0][1])
|
|
self.assertIn("ONLY <Picture 2>", calls[1][1])
|
|
self.assertIs(calls[0][0], images[0])
|
|
self.assertIs(calls[1][0], images[1])
|
|
|
|
def test_final_writer_only_receives_separate_text_facts(self):
|
|
captured = []
|
|
def generate(clip, prompt, images, tokens, **kwargs):
|
|
captured.append((prompt, images))
|
|
return "Replace the woman with the human from <Picture 1> and the man with the goblin from <Picture 2>."
|
|
with patch.object(SL, "vlm_generate", side_effect=generate):
|
|
SL.write_h3_prompt(object(), [], [], [object(), object()],
|
|
["human in dark suit", "pink goblin"], "Replace both fighters", {},
|
|
style="captions", source_facts="A woman and a man are fighting.")
|
|
self.assertIsNone(captured[0][1])
|
|
self.assertIn("<Picture 1>: human in dark suit", captured[0][0])
|
|
self.assertIn("<Picture 2>: pink goblin", captured[0][0])
|
|
self.assertIn("A woman and a man are fighting", captured[0][0])
|
|
|
|
def test_browser_and_backend_use_same_prompt_revision(self):
|
|
browser = (ROOT / "web/js/bfs_shot_loop.js").read_text()
|
|
start = browser.index("const autoFor =")
|
|
signature = browser[start:browser.index("const writtenOf", start)]
|
|
self.assertIn('"' + SL.AUTO_PROMPT_REVISION + '"', signature)
|
|
|
|
def test_motion_trigger_is_added_once_and_can_be_disabled(self):
|
|
self.assertEqual(SL.tidy_caption("Replace the actor.", trigger="motion_transfer"), "motion_transfer: Replace the actor.")
|
|
self.assertEqual(SL.tidy_caption("motion_transfer: Replace the actor.", trigger="motion_transfer"), "motion_transfer: Replace the actor.")
|
|
self.assertEqual(SL.tidy_caption("motion_transfer: Replace the actor.", trigger=""), "Replace the actor.")
|
|
self.assertNotEqual(SL.auto_prompt_for("edit", [], {}, prompt_trigger=""), SL.auto_prompt_for("edit", [], {}, prompt_trigger="motion_transfer"))
|
|
|
|
|
|
class PromptReferenceContractTest(unittest.TestCase):
|
|
def test_short_caption_does_not_acquire_phantom_second_subject(self):
|
|
text = "motion_transfer: Replace the dancer with the character from <Picture 1>. Use the requested setting."
|
|
self.assertEqual(SL.expand_two_ref_prompt(text), text)
|
|
|
|
def test_legacy_expansion_requires_explicit_description_template(self):
|
|
text = "subject_definitions: <Subject 1> is the {details} in <Picture 1>."
|
|
self.assertIn("<Subject 2>", SL.expand_two_ref_prompt(text))
|
|
finished = "subject_definitions: <Subject 1> is the fox in <Picture 1>."
|
|
self.assertEqual(SL.expand_two_ref_prompt(finished), finished)
|
|
|
|
def test_invalid_picture_indices_are_reported_in_both_formats(self):
|
|
text = "Replace the setting with <Picture 3>."
|
|
self.assertTrue(SL.caption_problems(text, 2))
|
|
self.assertTrue(SL.h3_prompt_problems(text, 2, template=True))
|
|
self.assertEqual(SL.picture_index_problems(text, 3), [])
|
|
|
|
def test_writer_refuses_nonexistent_reference_after_retry(self):
|
|
with patch.object(SL, "vlm_generate", return_value="Replace the setting with <Picture 3>.") as generator:
|
|
with self.assertRaisesRegex(ValueError, "Picture 3.*does not exist"):
|
|
SL.write_h3_prompt(object(), [], [], [object(), object()],
|
|
["fox wearing blue pants", "city street"], "Use Picture 2 for the setting", {},
|
|
style="captions", source_facts="A dancer is moving in a street.")
|
|
self.assertEqual(generator.call_count, 2)
|
|
|
|
|
|
class PanelInferenceModeTest(unittest.TestCase):
|
|
"""Issue #14: panel buttons reuse models a workflow run left with inference tensors; they must run like a node."""
|
|
|
|
def test_panel_work_runs_in_inference_mode(self):
|
|
import asyncio
|
|
with torch.inference_mode():
|
|
cache = torch.zeros(3) # e.g. a VLM buffer created while the workflow ran
|
|
|
|
def work():
|
|
self.assertTrue(torch.is_inference_mode_enabled())
|
|
cache.add_(1)
|
|
return cache.sum().item()
|
|
|
|
self.assertEqual(asyncio.run(SL._off_loop(work)), 3.0)
|