digital-garbageandClaude Opus 5
a81ac48083
Decode now takes the MODEL, so a provider answers "is this mine" by identity instead of by shape. H3 was claiming any latent that happened to have two parts -- right today because it is the only thing producing one, wrong the moment a second AV model exists, and wrong in the way that reads as an unrelated fault rather than an error. It declines when the model is not wired, and the node says that is what is missing. That completes the shape a model module should have: one folder saying how this model behaves at each stage -- what it is, how its latent is built, how its output is decoded -- instead of that knowledge spreading through the conditioning and sampling code as special cases. Added a modifier that runs on SD1.5, because a feature nobody can watch is a feature nobody can check. It declares no traits, so it applies everywhere, which is the other half of the rule ALG demonstrates by narrowing. Measured on this machine at a fixed seed: off and on differ, 0.4 gives a visibly richer picture that still reads correctly, and 1.2 blows the highlights -- so the range is -1..1, set from what was measured rather than from a round number. A slider whose top half destroys the image lies about its range. Two things found while building it. ComfyUI has a real circular import between nodes_latent and nodes_post_processing that resolves only because init_extra_nodes reaches one first; a custom node has to break it the same way. And the accumulation bug returned through a third door: stripping handled keyed wrappers but not the anonymous function lists on model_options, so chaining doubled the effect. Tagging is now generic -- it looks for our mark rather than for a list of hook names that would need keeping current. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
"""Tag and strip, for BOTH shapes of hook ComfyUI offers.
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Keyed wrappers can be removed by name. The function lists on model_options
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cannot: `set_model_sampler_pre_cfg_function` appends an anonymous callable and
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records nothing about who added it. That second shape is how the accumulation bug
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came back a third time -- a model run through the loader twice carried two copies
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and applied the effect at double strength while reporting once.
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"""
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import pytest
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@pytest.fixture(autouse=True)
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def _needs_comfy(comfyui):
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"""Imports comfy."""
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@pytest.fixture
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def patcher():
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import torch
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from comfy.model_patcher import ModelPatcher
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class Stub(torch.nn.Module):
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pass
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return ModelPatcher(Stub(), load_device=torch.device("cpu"),
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offload_device=torch.device("cpu"))
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def test_a_tagged_pre_cfg_hook_is_removed(patcher):
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from core import patching
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def hook(args):
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return args["conds_out"]
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patcher.set_model_sampler_pre_cfg_function(patching.tag(hook, "funpack.x"))
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assert len(patcher.model_options["sampler_pre_cfg_function"]) == 1
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assert patching.strip(patcher, "funpack.") == 1
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assert patcher.model_options["sampler_pre_cfg_function"] == []
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def test_an_untagged_hook_is_left_alone(patcher):
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"""Someone else's hook is not ours to remove, and removing it would be a
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worse fault than the one being fixed."""
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from core import patching
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def theirs(args):
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return args["conds_out"]
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patcher.set_model_sampler_pre_cfg_function(theirs)
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assert patching.strip(patcher, "funpack.") == 0
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assert patcher.model_options["sampler_pre_cfg_function"] == [theirs]
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def test_a_differently_namespaced_hook_is_left_alone(patcher):
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from core import patching
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def other(args):
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return args["conds_out"]
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patcher.set_model_sampler_pre_cfg_function(patching.tag(other, "otherpack.y"))
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assert patching.strip(patcher, "funpack.") == 0
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def test_something_that_cannot_be_tagged_is_refused_rather_than_installed():
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"""An untaggable hook could never be removed, so installing it would build
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in the accumulation this exists to prevent."""
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from core import patching
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with pytest.raises(TypeError, match="could never be removed"):
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patching.tag(len, "funpack.x")
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def test_chaining_the_loader_does_not_stack_a_pre_cfg_modifier(patcher):
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"""The end-to-end version, through the real node."""
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from modules.sampling.modifiers.nodes import FunPackLoadModifiers
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values = {"sharpen": {"enabled": True, "amount": 0.4, "radius": 5}}
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once = FunPackLoadModifiers.execute(patcher, settings=values).result[0]
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twice = FunPackLoadModifiers.execute(once, settings=values).result[0]
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thrice = FunPackLoadModifiers.execute(twice, settings=values).result[0]
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def count(model):
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return len(model.model_options.get("sampler_pre_cfg_function", []))
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assert count(once) == count(twice) == count(thrice) == 1
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assert count(patcher) == 0, "the shared model was modified"
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