807 lines
34 KiB
Python
807 lines
34 KiB
Python
import importlib
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import inspect
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import sys
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import types
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import unittest
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class DumasH3LongVideosHelperTests(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls._saved_modules = {
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name: sys.modules.get(name)
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for name in (
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"torch",
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"nodes",
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"comfy",
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"comfy.utils",
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"comfy.samplers",
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"comfy.nested_tensor",
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"comfy.model_management",
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"node_helpers",
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"numpy",
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"PIL",
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"PIL.Image",
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"folder_paths",
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"dumas_image_nodes",
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"dumas_h3_longvideos",
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)
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}
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fake_torch = types.SimpleNamespace(
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cuda=types.SimpleNamespace(OutOfMemoryError=RuntimeError),
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float32="float32",
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)
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fake_numpy = types.SimpleNamespace(
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clip=lambda array, _low, _high: array,
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uint8="uint8",
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)
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fake_pil_image_module = types.SimpleNamespace(fromarray=lambda _array: None)
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fake_pil_module = types.SimpleNamespace(Image=fake_pil_image_module)
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fake_folder_paths = types.SimpleNamespace(
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get_temp_directory=lambda: "/tmp",
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get_output_directory=lambda: "/tmp",
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get_save_image_path=lambda prefix, _out, _width, _height: ("/tmp", prefix, 1, "", prefix),
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)
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fake_nodes = types.SimpleNamespace(common_ksampler=lambda *args, **kwargs: ({},))
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fake_comfy_samplers = types.SimpleNamespace(
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KSampler=types.SimpleNamespace(SAMPLERS=("res_multistep",), SCHEDULERS=("simple",))
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)
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fake_comfy_utils = types.SimpleNamespace(ProgressBar=lambda total: None)
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fake_mm = types.SimpleNamespace(
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current_loaded_models=[],
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free_memory=lambda *args, **kwargs: None,
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get_torch_device=lambda: "cpu",
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soft_empty_cache=lambda *args, **kwargs: None,
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unload_all_models=lambda *args, **kwargs: None,
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get_free_memory=lambda *args, **kwargs: 0,
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get_total_memory=lambda *args, **kwargs: 0,
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)
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fake_comfy = types.SimpleNamespace(
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utils=fake_comfy_utils,
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samplers=fake_comfy_samplers,
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nested_tensor=types.SimpleNamespace(),
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model_management=fake_mm,
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)
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sys.modules["torch"] = fake_torch
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sys.modules["numpy"] = fake_numpy
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sys.modules["PIL"] = fake_pil_module
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sys.modules["PIL.Image"] = fake_pil_image_module
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sys.modules["folder_paths"] = fake_folder_paths
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sys.modules["nodes"] = fake_nodes
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sys.modules["comfy"] = fake_comfy
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sys.modules["comfy.utils"] = fake_comfy_utils
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sys.modules["comfy.samplers"] = fake_comfy_samplers
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sys.modules["comfy.nested_tensor"] = fake_comfy.nested_tensor
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sys.modules["comfy.model_management"] = fake_mm
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sys.modules["node_helpers"] = types.SimpleNamespace()
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cls.image_module = importlib.import_module("dumas_image_nodes")
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cls.module = importlib.import_module("dumas_h3_longvideos")
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@classmethod
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def tearDownClass(cls):
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for name, module in cls._saved_modules.items():
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if module is None:
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sys.modules.pop(name, None)
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else:
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sys.modules[name] = module
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def test_extract_wardrobe_is_cached(self):
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fn = self.module.extract_wardrobe
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fn.cache_clear()
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beat = "walks forward\nwardrobe: red jacket, grey shorts\nlooks back"
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self.assertEqual(fn(beat), ("walks forward\nlooks back", "red jacket, grey shorts"))
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self.assertEqual(fn(beat), ("walks forward\nlooks back", "red jacket, grey shorts"))
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self.assertGreater(fn.cache_info().hits, 0)
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def test_dialogue_helpers_keep_existing_outputs_and_cache(self):
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spans_cache = self.module._dialogue_spans_cached
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sec_fn = self.module.dialogue_seconds
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words_fn = self.module.dialogue_words
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spans_cache.cache_clear()
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sec_fn.cache_clear()
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words_fn.cache_clear()
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beat = 'Mara says, "Open it now." Jon replies, "Do it."'
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self.assertEqual(self.module.dialogue_spans(beat), [3, 2])
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self.assertEqual(words_fn(beat), 5)
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self.assertAlmostEqual(sec_fn(beat), 3.5)
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self.assertAlmostEqual(sec_fn(beat, pad=False), 2.5)
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self.module.dialogue_spans(beat)
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sec_fn(beat)
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words_fn(beat)
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self.assertGreater(spans_cache.cache_info().hits, 0)
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self.assertGreater(sec_fn.cache_info().hits, 0)
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self.assertGreater(words_fn.cache_info().hits, 0)
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def test_directive_and_estimate_helpers_are_cached(self):
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directive_fn = self.module.beat_seconds_directive
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estimate_fn = self.module.estimate_beat_seconds
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action_fn = self.module.action_clauses
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directive_fn.cache_clear()
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estimate_fn.cache_clear()
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action_fn.cache_clear()
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beat = 'seconds: 7.5\nShe opens the hatch and climbs inside.'
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self.assertEqual(directive_fn(beat), 7.5)
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self.assertEqual(action_fn(beat), 2)
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self.assertAlmostEqual(estimate_fn(beat), 7.0)
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directive_fn(beat)
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action_fn(beat)
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estimate_fn(beat)
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self.assertGreater(directive_fn.cache_info().hits, 0)
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self.assertGreater(action_fn.cache_info().hits, 0)
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self.assertGreater(estimate_fn.cache_info().hits, 0)
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def test_per_shot_directive_helpers_parse_new_controls(self):
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beat = (
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"ref_mode: every shot + handoff ref\n"
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"ref_noise_aug: 0.87\n"
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"continuity: keyframe carry\n"
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"The courier waits under the sign."
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)
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self.assertEqual(
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self.module.beat_ref_mode_directive(beat),
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"every shot + handoff ref",
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)
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self.assertEqual(self.module.beat_ref_noise_aug_directive(beat), 0.87)
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self.assertEqual(
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self.module.beat_continuity_directive(beat),
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"keyframe carry",
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)
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def test_detail_pass_refines_video_but_preserves_audio(self):
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class FakeTensor:
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def __init__(self, name):
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self.name = name
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def detach(self):
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return self
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def to(self, *args, **kwargs):
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return self
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class FakeNestedTensor:
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def __init__(self, parts):
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self._parts = tuple(parts)
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self.is_nested = True
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def unbind(self):
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return self._parts
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calls = []
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first_out = {"samples": FakeNestedTensor((FakeTensor("v1"), FakeTensor("a1")))}
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second_out = {"samples": FakeNestedTensor((FakeTensor("v2"), FakeTensor("a2")))}
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original_common_ksampler = self.module.nodes.common_ksampler
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original_build = self.module._build_shot_conditioning
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original_evict = self.module._evict_all_but
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original_decode_video = self.module._decode_video
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original_decode_audio = self.module._decode_audio
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original_cleanup = self.module._deep_cleanup
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original_nested = getattr(self.module.comfy.nested_tensor, "NestedTensor", None)
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try:
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self.module.comfy.nested_tensor.NestedTensor = FakeNestedTensor
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def common_ksampler(*args, **kwargs):
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calls.append((args, kwargs))
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return (first_out if len(calls) == 1 else second_out,)
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self.module.nodes.common_ksampler = common_ksampler
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self.module._build_shot_conditioning = lambda *_args, **_kwargs: (
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"cond",
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{"samples": FakeNestedTensor((FakeTensor("basev"), FakeTensor("basea")))},
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)
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self.module._evict_all_but = lambda *_args, **_kwargs: None
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self.module._decode_video = lambda _vae, out_latent, *_args, **_kwargs: out_latent
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self.module._decode_audio = lambda _vae, out_latent: out_latent
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self.module._deep_cleanup = lambda: None
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result = self.module.H3LongVideos()._render(
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model=object(),
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clip=types.SimpleNamespace(
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tokenize=lambda text, **kwargs: text,
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encode_from_tokens_scheduled=lambda tokens: tokens,
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),
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vae=object(),
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audio_vae=object(),
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negative="negative",
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prompt="beat",
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w=128,
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h=64,
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ln=24,
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fps=24,
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tiled=False,
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sa=(123, 20, 1.0, "res_multistep", "simple", 1.0),
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handoff=None,
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detail_pass=True,
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detail_sampler_name="euler",
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detail_scheduler="beta",
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detail_steps=5,
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detail_denoise=0.4,
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)
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self.assertEqual(len(calls), 2)
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self.assertIsNot(calls[1][0][8], first_out)
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self.assertIs(calls[1][0][8]["samples"], first_out["samples"])
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self.assertEqual(calls[1][0][4], "euler")
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self.assertEqual(calls[1][0][5], "beta")
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self.assertAlmostEqual(calls[1][1]["denoise"], 0.4)
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self.assertEqual(result[1], first_out)
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self.assertEqual(result[2][0].name, "v2")
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self.assertEqual(result[2][1].name, "a1")
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self.assertEqual(result[0]["samples"].unbind()[0].name, "v2")
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self.assertEqual(result[0]["samples"].unbind()[-1].name, "a1")
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finally:
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self.module.nodes.common_ksampler = original_common_ksampler
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self.module._build_shot_conditioning = original_build
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self.module._evict_all_but = original_evict
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self.module._decode_video = original_decode_video
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self.module._decode_audio = original_decode_audio
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self.module._deep_cleanup = original_cleanup
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if original_nested is None:
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delattr(self.module.comfy.nested_tensor, "NestedTensor")
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else:
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self.module.comfy.nested_tensor.NestedTensor = original_nested
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def test_detail_pass_treats_falsey_strings_as_disabled(self):
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calls = []
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original_common_ksampler = self.module.nodes.common_ksampler
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original_build = self.module._build_shot_conditioning
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original_evict = self.module._evict_all_but
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original_decode_video = self.module._decode_video
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original_decode_audio = self.module._decode_audio
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original_cleanup = self.module._deep_cleanup
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try:
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self.module.nodes.common_ksampler = lambda *args, **kwargs: (calls.append((args, kwargs)) or {"samples": "latent"},)
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self.module._build_shot_conditioning = lambda *_args, **_kwargs: ("cond", {"samples": "base"})
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self.module._evict_all_but = lambda *_args, **_kwargs: None
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self.module._decode_video = lambda _vae, out_latent, *_args, **_kwargs: out_latent
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self.module._decode_audio = lambda _vae, out_latent: out_latent
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self.module._deep_cleanup = lambda: None
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self.module.H3LongVideos()._render(
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model=object(),
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clip=object(),
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vae=object(),
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audio_vae=object(),
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negative="negative",
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prompt="beat",
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w=128,
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h=64,
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ln=24,
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fps=24,
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tiled=False,
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sa=(123, 20, 1.0, "res_multistep", "simple", 1.0),
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handoff=None,
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detail_pass="false",
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)
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self.assertEqual(len(calls), 1)
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finally:
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self.module.nodes.common_ksampler = original_common_ksampler
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self.module._build_shot_conditioning = original_build
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self.module._evict_all_but = original_evict
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self.module._decode_video = original_decode_video
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self.module._decode_audio = original_decode_audio
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self.module._deep_cleanup = original_cleanup
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def test_distribute_generations_canonicalizes_per_shot_audio_and_anchor_directives(self):
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generations = self.module.distribute_generations(
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"",
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[
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"anchor_add: harsh sodium spill, wet asphalt reflections\n"
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"soundscape: distant traffic hiss, loose sign rattle\n"
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"music: low pulsing synth tension\n"
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"continuity: hard cut\n"
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"ref_mode: every shot\n"
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"ref_noise_aug: 0.88\n"
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"A courier waits under the streetlight."
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],
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"global rain",
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"global score",
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)
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block = generations[0]
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self.assertIn("harsh sodium spill, wet asphalt reflections", block)
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self.assertIn("overall_soundscape: distant traffic hiss, loose sign rattle", block)
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self.assertIn("non_diegetic_music: low pulsing synth tension", block)
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self.assertNotIn("\nsoundscape:", block)
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self.assertNotIn("\nmusic:", block)
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self.assertNotIn("\ncontinuity:", block)
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self.assertNotIn("\nref_mode:", block)
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self.assertNotIn("\nref_noise_aug:", block)
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self.assertNotIn("\nanchor_add:", block)
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def test_has_speech_cache_respects_written_text_filter(self):
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fn = self.module.has_speech
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fn.cache_clear()
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written = 'She reads the sign marked "EXIT" and keeps walking.'
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spoken = 'She says, "Exit now." and points to the door.'
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self.assertFalse(fn(written))
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self.assertTrue(fn(spoken))
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fn(written)
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fn(spoken)
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self.assertGreaterEqual(fn.cache_info().hits, 2)
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def test_resolve_tagged_refs_preserves_sparse_socket_numbers(self):
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refs = [
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None,
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{"kind": "character", "image": "img2", "name": "Jon"},
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None,
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None,
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None,
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None,
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{"kind": "character", "image": "img7", "name": "Mara"},
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None,
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{"kind": "location", "image": "img9", "name": "Watchtower"},
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]
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text, references, dropped = self.module.resolve_tagged_refs(
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"Mara <Picture 7> turns toward Jon <Picture 2> while <Picture 9> watches.",
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refs,
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)
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self.assertEqual(
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text,
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"Mara <Picture 2> turns toward Jon <Picture 1> while <Picture 3> watches.",
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)
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self.assertEqual(
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[self.module._reference_image(ref) for ref in references],
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["img2", "img7", "img9"],
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)
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self.assertEqual(dropped, [])
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def test_resolve_tagged_refs_drops_unconnected_sparse_slots(self):
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refs = [
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None,
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{"kind": "character", "image": "img2", "name": "Jon"},
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None,
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None,
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None,
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None,
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{"kind": "character", "image": "img7", "name": "Mara"},
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None,
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None,
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]
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text, references, dropped = self.module.resolve_tagged_refs(
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"Use <Picture 7>, skip <Picture 4>, keep <Picture 2>.",
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refs,
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)
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self.assertEqual(text, "Use <Picture 2>, skip, keep <Picture 1>.")
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self.assertEqual(
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[self.module._reference_image(ref) for ref in references],
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["img2", "img7"],
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)
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self.assertEqual(dropped, [4])
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def test_shot_references_uses_all_connected_sparse_slots(self):
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refs = [
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None,
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{"kind": "character", "image": "img2"},
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None,
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{"kind": "character", "image": "img4"},
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None,
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None,
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{"kind": "location", "image": "img7"},
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None,
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None,
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]
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for mode, shot_index in (("auto ref2v", 0), ("first shot", 0), ("every shot", 3)):
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self.assertEqual(
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[self.module._reference_image(ref) for ref in self.module.shot_references(refs, mode, shot_index, None)],
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["img2", "img4", "img7"],
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)
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def test_input_types_expose_nine_ref_slots(self):
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optional = self.module.H3LongVideos.INPUT_TYPES()["optional"]
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for index in range(1, 10):
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self.assertIn(f"ref_{index}", optional)
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def test_input_types_keep_legacy_ref_image_aliases(self):
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optional = self.module.H3LongVideos.INPUT_TYPES()["optional"]
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for index in range(1, 10):
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self.assertIn(f"ref_image_{index}", optional)
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def test_run_defaults_match_declared_ref_widget_defaults(self):
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node = self.module.H3LongVideos()
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optional = node.INPUT_TYPES()["optional"]
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params = inspect.signature(node.run).parameters
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self.assertEqual(params["ref_mode"].default, optional["ref_mode"][1]["default"])
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self.assertEqual(params["ref_image_size"].default, optional["ref_image_size"][1]["default"])
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self.assertEqual(params["ref_noise_aug"].default, optional["ref_noise_aug"][1]["default"])
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def test_node_appends_per_beat_list_outputs_without_reordering_existing_slots(self):
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self.assertEqual(
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self.module.H3LongVideos.RETURN_NAMES[-2:],
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("beat_images", "beat_audio"),
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)
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self.assertEqual(
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self.module.H3LongVideos.OUTPUT_IS_LIST[-2:],
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(True, True),
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)
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def test_reference_context_matches_character_names_and_location_tags(self):
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refs = [
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{
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"kind": "character",
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"image": "img1",
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"name": "Mara",
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"aliases": ["Xtina"],
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"description": "silver hair",
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"wardrobe": "red jacket",
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"general": "wears a long grey coat",
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"facts": {
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"gender": "female",
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"age": "41",
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"nationality": "English",
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"occupation": "a detective",
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"height_feet": "6",
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"height_inches": "2",
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"accent": "English",
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},
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},
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{"kind": "location", "image": "img2", "name": "Hangar", "description": "wet concrete floor"},
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]
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context = self.module._reference_context_for_text(
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"[Generation 1] Mara crosses the room toward <Picture 2>.",
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refs,
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)
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self.assertIn("Character facts for Mara:", context)
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self.assertIn("also known as Xtina", context)
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self.assertIn("female", context)
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self.assertIn("41 years old", context)
|
|
self.assertIn("English", context)
|
|
self.assertIn("works as a detective", context)
|
|
self.assertIn("6 foot 2 tall", context)
|
|
self.assertIn("speaks with a English accent", context)
|
|
self.assertIn("Persistent appearance for Mara: silver hair.", context)
|
|
self.assertIn("Persistent wardrobe/style for Mara: red jacket.", context)
|
|
self.assertIn("Character notes for Mara: wears a long grey coat.", context)
|
|
|
|
def test_run_uses_legacy_ref_image_inputs_when_new_slots_are_empty(self):
|
|
calls = {}
|
|
original_parse_resolution = self.module.parse_resolution
|
|
original_connected_refs = self.module._connected_refs
|
|
original_reference_character_memory = self.module._reference_character_memory
|
|
original_vram_gb = self.module.vram_gb
|
|
original_dit_resident_gb = self.module.dit_resident_gb
|
|
original_lora_overhead_gb = self.module.lora_overhead_gb
|
|
original_resolve_shot_frames = self.module.resolve_shot_frames
|
|
original_lora_active = self.module.lora_active
|
|
original_sla_pairing = self.module.sla_pairing
|
|
original_apply_h3_model_sampling = self.module.apply_h3_model_sampling
|
|
original_split_paragraphs = self.module.split_paragraphs
|
|
original_expand_beats = self.module.expand_beats
|
|
original_anchor_warnings = self.module.anchor_warnings
|
|
original_anchor_contributes_nothing = self.module.anchor_contributes_nothing
|
|
original_anchor_is_action_beat = self.module.anchor_is_action_beat
|
|
original_distribute_generations = self.module.distribute_generations
|
|
original_continuity_warnings = self.module.continuity_warnings
|
|
original_speech_flags = self.module.speech_flags
|
|
original_annotate_script_debug = self.module.annotate_script_debug
|
|
original_empty_av_latent = self.module._empty_av_latent
|
|
original_torch_zeros = getattr(self.module.torch, "zeros", None)
|
|
try:
|
|
self.module.torch.zeros = lambda shape: shape
|
|
self.module.parse_resolution = lambda _resolution: (640, 360)
|
|
self.module._connected_refs = lambda refs: [ref for ref in refs if ref is not None]
|
|
self.module._reference_character_memory = lambda refs: (calls.setdefault("refs", tuple(refs)), "")[1]
|
|
self.module.vram_gb = lambda: (0, 0)
|
|
self.module.dit_resident_gb = lambda _model: 0
|
|
self.module.lora_overhead_gb = lambda _model: 0
|
|
self.module.resolve_shot_frames = lambda *args, **kwargs: (53, "")
|
|
self.module.lora_active = lambda _model: False
|
|
self.module.sla_pairing = lambda *_args, **_kwargs: ("", False, "")
|
|
self.module.apply_h3_model_sampling = lambda model, *_args: (model, "")
|
|
self.module.split_paragraphs = lambda _prompt, _sep: ["Anchor.", "Beat."]
|
|
self.module.expand_beats = lambda beat_paras, _split: (list(beat_paras), "")
|
|
self.module.anchor_warnings = lambda _anchor: []
|
|
self.module.anchor_contributes_nothing = lambda *_args, **_kwargs: False
|
|
self.module.anchor_is_action_beat = lambda *_args, **_kwargs: False
|
|
self.module.distribute_generations = lambda _anchor, beats, *_args, **_kwargs: list(beats)
|
|
self.module.continuity_warnings = lambda _gens: []
|
|
self.module.speech_flags = lambda _beats: []
|
|
self.module.annotate_script_debug = lambda *_args, **_kwargs: "script"
|
|
self.module._empty_av_latent = lambda *_args, **_kwargs: ({"samples": "latent"}, 5)
|
|
|
|
clip = types.SimpleNamespace(
|
|
tokenize=lambda text, **kwargs: text,
|
|
encode_from_tokens_scheduled=lambda tokens: tokens,
|
|
)
|
|
|
|
result = self.module.H3LongVideos().run(
|
|
model=object(),
|
|
clip=clip,
|
|
vae=object(),
|
|
audio_vae=object(),
|
|
prompt="Anchor only.",
|
|
resolution="16:9",
|
|
steps=6,
|
|
cfg=1,
|
|
sampler_name="res_multistep",
|
|
scheduler="simple",
|
|
seed=1,
|
|
plan_only=True,
|
|
ref_image_1={"image": "legacy-1"},
|
|
ref_image_3={"image": "legacy-3"},
|
|
)
|
|
|
|
self.assertEqual(calls["refs"][0]["image"], "legacy-1")
|
|
self.assertIsNone(calls["refs"][1])
|
|
self.assertEqual(calls["refs"][2]["image"], "legacy-3")
|
|
self.assertEqual(result[2].count("ref2va: 2 reference image(s)"), 1)
|
|
finally:
|
|
self.module.parse_resolution = original_parse_resolution
|
|
self.module._connected_refs = original_connected_refs
|
|
self.module._reference_character_memory = original_reference_character_memory
|
|
self.module.vram_gb = original_vram_gb
|
|
self.module.dit_resident_gb = original_dit_resident_gb
|
|
self.module.lora_overhead_gb = original_lora_overhead_gb
|
|
self.module.resolve_shot_frames = original_resolve_shot_frames
|
|
self.module.lora_active = original_lora_active
|
|
self.module.sla_pairing = original_sla_pairing
|
|
self.module.apply_h3_model_sampling = original_apply_h3_model_sampling
|
|
self.module.split_paragraphs = original_split_paragraphs
|
|
self.module.expand_beats = original_expand_beats
|
|
self.module.anchor_warnings = original_anchor_warnings
|
|
self.module.anchor_contributes_nothing = original_anchor_contributes_nothing
|
|
self.module.anchor_is_action_beat = original_anchor_is_action_beat
|
|
self.module.distribute_generations = original_distribute_generations
|
|
self.module.continuity_warnings = original_continuity_warnings
|
|
self.module.speech_flags = original_speech_flags
|
|
self.module.annotate_script_debug = original_annotate_script_debug
|
|
self.module._empty_av_latent = original_empty_av_latent
|
|
if original_torch_zeros is None:
|
|
delattr(self.module.torch, "zeros")
|
|
else:
|
|
self.module.torch.zeros = original_torch_zeros
|
|
|
|
def test_reference_context_matches_tagged_character_without_name_in_text(self):
|
|
refs = [
|
|
{"kind": "character", "image": "img1", "name": "Mara", "description": "silver hair", "wardrobe": "red jacket"},
|
|
]
|
|
|
|
context = self.module._reference_context_for_text(
|
|
"[Generation 1] <Picture 1> walks into the room.",
|
|
refs,
|
|
)
|
|
|
|
self.assertIn("Persistent appearance for Mara: silver hair.", context)
|
|
self.assertIn("Persistent wardrobe/style for Mara: red jacket.", context)
|
|
|
|
def test_reference_context_injects_immediately_after_generation_label(self):
|
|
block = (
|
|
"[Generation 1] Classic sitcom lighting and staging. "
|
|
"Duke walks into the room."
|
|
)
|
|
context = "Character facts for Duke: female, 25 years old."
|
|
|
|
result = self.module._inject_reference_context(block, context)
|
|
|
|
self.assertEqual(
|
|
result,
|
|
"[Generation 1] Character facts for Duke: female, 25 years old. "
|
|
"Classic sitcom lighting and staging. "
|
|
"Duke walks into the room.",
|
|
)
|
|
|
|
def test_reference_character_memory_uses_character_wardrobe_only(self):
|
|
refs = [
|
|
{"kind": "character", "image": "img1", "name": "Mara", "wardrobe": "red jacket, black boots"},
|
|
{"kind": "location", "image": "img2", "name": "Hangar", "description": "wet concrete floor", "wardrobe": "should be ignored"},
|
|
]
|
|
|
|
self.assertEqual(
|
|
self.module._reference_character_memory(refs),
|
|
"Mara = red jacket, black boots",
|
|
)
|
|
|
|
def test_ref_mode_defaults_are_ref2v_biased(self):
|
|
optional = self.module.H3LongVideos.INPUT_TYPES()["optional"]
|
|
|
|
self.assertEqual(optional["ref_mode"][1]["default"], "auto ref2v")
|
|
self.assertEqual(optional["ref_noise_aug"][1]["default"], 0.95)
|
|
|
|
def test_only_canonical_h3_long_videos_node_is_exposed(self):
|
|
self.assertEqual(
|
|
self.module.NODE_CLASS_MAPPINGS,
|
|
{"DumasH3LongVideos": self.module.H3LongVideos},
|
|
)
|
|
self.assertEqual(
|
|
self.module.NODE_DISPLAY_NAME_MAPPINGS,
|
|
{"DumasH3LongVideos": "Dumas H3 Long Videos (FL2VA + REF2VA)"},
|
|
)
|
|
|
|
def test_compose_persistent_does_not_expand_ambiguous_plural_to_full_cast(self):
|
|
active = self.module.parse_wardrobe(
|
|
"Maya = she, red jacket\n"
|
|
"Jon = he, navy overalls\n"
|
|
"Becca = she, green coat"
|
|
)
|
|
|
|
shot = self.module.compose_persistent(
|
|
"Both of them walk to the door.",
|
|
active,
|
|
"",
|
|
speaking=False,
|
|
)
|
|
|
|
self.assertEqual(shot, "Both of them walk to the door.")
|
|
|
|
def test_compose_persistent_keeps_two_person_plural_binding(self):
|
|
active = self.module.parse_wardrobe(
|
|
"Maya = she, red jacket\n"
|
|
"Jon = he, navy overalls"
|
|
)
|
|
|
|
shot = self.module.compose_persistent(
|
|
"They walk to the door.",
|
|
active,
|
|
"",
|
|
speaking=False,
|
|
)
|
|
|
|
self.assertIn("Maya (red jacket)", shot)
|
|
self.assertIn("Jon (navy overalls)", shot)
|
|
self.assertIn("They walk to the door.", shot)
|
|
|
|
def test_compose_persistent_all_three_characters_binds_full_cast(self):
|
|
active = self.module.parse_wardrobe(
|
|
"Maya = she, red jacket\n"
|
|
"Jon = he, navy overalls\n"
|
|
"Becca = she, green coat"
|
|
)
|
|
|
|
shot = self.module.compose_persistent(
|
|
"The three characters walk to the door.",
|
|
active,
|
|
"",
|
|
speaking=False,
|
|
)
|
|
|
|
self.assertIn("Maya (red jacket)", shot)
|
|
self.assertIn("Jon (navy overalls)", shot)
|
|
self.assertIn("Becca (green coat)", shot)
|
|
|
|
def test_plan_only_returns_joined_generations_on_script_socket_with_anchor_override(self):
|
|
module = self.module
|
|
node = module.H3LongVideos()
|
|
|
|
class _Clip:
|
|
def tokenize(self, text):
|
|
return text
|
|
|
|
def encode_from_tokens_scheduled(self, tokens):
|
|
return tokens
|
|
|
|
class _TorchStub:
|
|
@staticmethod
|
|
def zeros(shape):
|
|
return ("zeros", shape)
|
|
|
|
original_torch = module.torch
|
|
original_vram_gb = module.vram_gb
|
|
original_dit_resident_gb = module.dit_resident_gb
|
|
original_lora_overhead_gb = module.lora_overhead_gb
|
|
original_check_vae_wiring = module.check_vae_wiring
|
|
original_check_text_encoder = module.check_text_encoder
|
|
original_apply_h3_model_sampling = module.apply_h3_model_sampling
|
|
original_sla_pairing = module.sla_pairing
|
|
original_lora_hint_notes = module.lora_hint_notes
|
|
original_schedule_balance_note = module.schedule_balance_note
|
|
original_kernel_backend_note = module.kernel_backend_note
|
|
original_audio_scale_note = module.audio_scale_note
|
|
original_quant_accel_note = module.quant_accel_note
|
|
original_lora_active = module.lora_active
|
|
original_resolve_shot_frames = module.resolve_shot_frames
|
|
original_plan_beat_frames = module.plan_beat_frames
|
|
original_dialogue_fit_warnings = module.dialogue_fit_warnings
|
|
original_dialogue_filler_warnings = module.dialogue_filler_warnings
|
|
original_distribute_generations = module.distribute_generations
|
|
original_continuity_warnings = module.continuity_warnings
|
|
original_empty_av_latent = module._empty_av_latent
|
|
try:
|
|
module.torch = _TorchStub()
|
|
module.vram_gb = lambda: (0.0, 0.0)
|
|
module.dit_resident_gb = lambda _model: 0.0
|
|
module.lora_overhead_gb = lambda _model: 0.0
|
|
module.check_vae_wiring = lambda *_args, **_kwargs: None
|
|
module.check_text_encoder = lambda *_args, **_kwargs: None
|
|
module.apply_h3_model_sampling = lambda model, *_args, **_kwargs: (model, "")
|
|
module.sla_pairing = lambda *_args, **_kwargs: ("", False, "")
|
|
module.lora_hint_notes = lambda *_args, **_kwargs: []
|
|
module.schedule_balance_note = lambda *_args, **_kwargs: ""
|
|
module.kernel_backend_note = lambda *_args, **_kwargs: ""
|
|
module.audio_scale_note = lambda *_args, **_kwargs: ""
|
|
module.quant_accel_note = lambda *_args, **_kwargs: ""
|
|
module.lora_active = lambda _model: False
|
|
module.resolve_shot_frames = lambda *_args, **_kwargs: (73, "")
|
|
module.plan_beat_frames = lambda beats, fps, budget, per_beat=True: ([73] * len(beats), [])
|
|
module.dialogue_fit_warnings = lambda *_args, **_kwargs: []
|
|
module.dialogue_filler_warnings = lambda *_args, **_kwargs: []
|
|
module.distribute_generations = lambda anchor, beats, *_args, **_kwargs: [
|
|
f"[Generation 1] {anchor}. {beats[0]}",
|
|
f"[Generation 2] {anchor}. {beats[1]}{module.ANATOMY_STATE}",
|
|
]
|
|
module.continuity_warnings = lambda _gens: []
|
|
module._empty_av_latent = lambda *_args, **_kwargs: ({"samples": "latent"},)
|
|
|
|
result = node.run(
|
|
model=object(),
|
|
clip=_Clip(),
|
|
vae=object(),
|
|
audio_vae=object(),
|
|
prompt="Francine stands alone.\n\nFrancine and Frankie walk together.",
|
|
resolution="16:9",
|
|
steps=20,
|
|
cfg=1.0,
|
|
sampler_name="res_multistep",
|
|
scheduler="simple",
|
|
seed=1,
|
|
anchor_override="editorial room, soft practical lighting",
|
|
character_memory="Francine = white top\nFrankie = black jacket",
|
|
plan_only=True,
|
|
)
|
|
|
|
self.assertIn("# anatomy_guard: injected on shot(s) 2", result[3])
|
|
self.assertIn("# shot 1 refs: none", result[3])
|
|
self.assertIn("# shot 2 refs: none", result[3])
|
|
self.assertIn(
|
|
"[Generation 1] editorial room, soft practical lighting. Francine stands alone.",
|
|
result[3],
|
|
)
|
|
self.assertIn(
|
|
"[Generation 2] editorial room, soft practical lighting. Francine and Frankie walk together.",
|
|
result[3],
|
|
)
|
|
self.assertIn("2 beat(s)", result[2])
|
|
self.assertIn("2 shot(s)", result[2])
|
|
self.assertIn("ANATOMY -- guard injected on shot(s) 2", result[2])
|
|
self.assertEqual(result[-2:], ([], []))
|
|
finally:
|
|
module.torch = original_torch
|
|
module.vram_gb = original_vram_gb
|
|
module.dit_resident_gb = original_dit_resident_gb
|
|
module.lora_overhead_gb = original_lora_overhead_gb
|
|
module.check_vae_wiring = original_check_vae_wiring
|
|
module.check_text_encoder = original_check_text_encoder
|
|
module.apply_h3_model_sampling = original_apply_h3_model_sampling
|
|
module.sla_pairing = original_sla_pairing
|
|
module.lora_hint_notes = original_lora_hint_notes
|
|
module.schedule_balance_note = original_schedule_balance_note
|
|
module.kernel_backend_note = original_kernel_backend_note
|
|
module.audio_scale_note = original_audio_scale_note
|
|
module.quant_accel_note = original_quant_accel_note
|
|
module.lora_active = original_lora_active
|
|
module.resolve_shot_frames = original_resolve_shot_frames
|
|
module.plan_beat_frames = original_plan_beat_frames
|
|
module.dialogue_fit_warnings = original_dialogue_fit_warnings
|
|
module.dialogue_filler_warnings = original_dialogue_filler_warnings
|
|
module.distribute_generations = original_distribute_generations
|
|
module.continuity_warnings = original_continuity_warnings
|
|
module._empty_av_latent = original_empty_av_latent
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|