digital-garbage
0597603980
[FunPack] K/V conditioning patch: Inactive | Non-LTX model loaded
[FunPack] Chunk cache: Active | 22 chunks, 4 steps each, consistency
`feature(tag, name, active, reason)` is the shape, and `skipped` renders
identically — a deliberate skip and an inactive feature are the same fact and
should not have two spellings. `failed` becomes `Name: Failed | <effect>`,
keeping the effect rather than the exception text: "that explains what I am
looking at" is the half worth the line, and an exception message rarely says it.
Everything funpack_log prints is now cut at the first sentence end, so the call
sites keep their reasoning without spending it on every run. SENTENCE ENDS ONLY
— an em-dash introduces the reason as often as the essay, and a renderer that
guesses wrong eats the one part of the line worth reading. A single long
sentence is hard-trimmed instead of left to run.
FUNPACK_LOG=verbose (or set_verbose) prints the full text, exception included.
Converted to the new shape: the chunk cache, region locks, interior pins, the
taste push, the token-refiner edit, AdaLN gains, rating-learned gains, phrase
emphasis, the K/V conditioning patch, the scene split, Bounded Attention,
per-block LoRA weights, LTX enhancements. The rest of the log gets the trim for
free and keeps its own phrasing until converted.
Tests: 1815 python (+9), 68 JS.
94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
"""A learned conditioning survives a prompt whose length has changed.
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`_v2_shape_compatible` checks the channel width and the batch, deliberately not the sequence
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length. On LTX that was fine — the encoder pads to a fixed length, so a stored conditioning
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always matched. Qwen does not pad (`pad_to_max_length=False`), so on H3 the length changes
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with every prompt edit: a direction learned at 499 positions met a conditioning of 492 and
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the blend threw, taking the whole learned direction down with it.
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`_resize_conditioning_sequence_like` was already in the file for exactly this, with no
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callers.
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"""
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import sys
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import types
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import pytest
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import torch
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sys.path.insert(0, ".")
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sys.modules.setdefault("folder_paths", types.SimpleNamespace(models_dir=""))
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@pytest.fixture
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def studio(monkeypatch):
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import conditioning
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monkeypatch.setattr(conditioning, "_log",
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types.SimpleNamespace(failed=lambda *a, **k: None,
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note_on_change=lambda *a, **k: None, feature=lambda *a, **k: None),
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raising=False)
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return conditioning.FunPackVideoRefinerV2.__new__(conditioning.FunPackVideoRefinerV2)
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def _payload(seq, dim=8, value=1.0):
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import conditioning
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return conditioning.tensor_to_serializable(torch.full((1, seq, dim), value))
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def test_a_payload_from_a_longer_prompt_still_blends(studio):
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"""499 -> 492 is one prompt edit. It used to throw and lose the direction entirely."""
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mixed = torch.zeros(1, 492, 8)
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out = studio._v2_apply_conditioning_payload(mixed, _payload(499), 0.5)
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assert out.shape == mixed.shape
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assert torch.allclose(out, torch.full_like(mixed, 0.5))
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def test_a_payload_from_a_shorter_prompt_still_blends(studio):
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mixed = torch.zeros(1, 528, 8)
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out = studio._v2_apply_conditioning_payload(mixed, _payload(492), 0.5)
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assert out.shape == mixed.shape
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assert torch.allclose(out, torch.full_like(mixed, 0.5))
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def test_repel_survives_the_same_mismatch(studio):
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mixed = torch.zeros(1, 492, 8)
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out = studio._v2_repel_conditioning_payload(mixed, _payload(528, value=1.0), 0.5)
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assert out.shape == mixed.shape
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assert torch.allclose(out, torch.full_like(mixed, -0.5))
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def test_an_exact_match_is_not_resampled(studio):
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"""The common case — same prompt, re-rolled — must stay bit-exact."""
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mixed = torch.zeros(1, 64, 8)
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out = studio._v2_apply_conditioning_payload(mixed, _payload(64, value=2.0), 1.0)
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assert torch.equal(out, torch.full_like(mixed, 2.0))
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def test_a_different_channel_width_is_still_refused(studio):
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"""Resampling is along the SEQUENCE. A different model's conditioning is not blendable."""
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mixed = torch.zeros(1, 64, 8)
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out = studio._v2_apply_conditioning_payload(mixed, _payload(64, dim=16), 0.5)
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assert torch.equal(out, mixed)
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def test_a_broken_payload_leaves_the_conditioning_alone(studio):
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mixed = torch.zeros(1, 64, 8)
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assert torch.equal(studio._v2_apply_conditioning_payload(mixed, {"shape": [1, 64, 8]}, 0.5),
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mixed)
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def test_the_liked_blend_survives_a_prompt_whose_length_changed(monkeypatch):
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"""The Absolute/liked pull was the one payload path still doing a raw lerp. On H3 the
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encoder does not pad, so a direction learned at 586 positions met a 494-position prompt
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and the blend threw — silently dropping the pull toward what the user rated well."""
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import torch
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from conditioning import FunPackVideoRefinerV2, tensor_to_serializable
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refiner = FunPackVideoRefinerV2()
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mixed = torch.zeros(1, 494, 16)
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liked = torch.ones(1, 586, 16)
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out = refiner._v2_payload_like(tensor_to_serializable(liked), mixed)
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assert out is not None
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assert list(out.shape) == [1, 494, 16]
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assert float(mixed.lerp(out, 0.5).mean()) > 0.0
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