[Log] Info: FunPack Diffusion Model Loader | flux.safetensors loaded, attention sage
[Log] Alert: MiniMaxH3ImageToVideo node | no "first_image" input is connected
[Log] Warning: FunPack LoRA Loader | style.safetensors is empty and will not load
[Log] Critical: torch | cannot allocate 14 GB (3 GB free); the run will not start
The difference between the middle two is the one that matters. Alert is a
choice you may not have made on purpose. Warning is something that will NOT
do what its name says -- the level v4 needed and did not have, which is how
560 handlers came to swallow a failure and carry on, so a feature could be
switched on, do nothing, and never say so.
Info is not decoration either: "loaded on GPU using sage attention" is how
an attention setting that did nothing gets noticed months before the
pictures look wrong enough to investigate.
Every call site moved -- there is no note() left to be vague through -- and
the loaders now say what they actually loaded rather than only what they
were asked for. Records are kept in a bounded ring as well as printed, so a
long rental session cannot turn the log into a memory leak, and served at
/funpack/api/log so a person can read it somewhere other than a terminal
full of ComfyUI's own output. There is a page for it.
Also dropped the preflight this was traded for: min and max are already
declared and already enforced at both doors, and a second protection layer
around a check that exists is ceremony.
Two mistakes worth keeping: a log line reached into the model's shape to
describe it and could have failed the load it was only narrating; and both
new pages passed `children` to an element that takes `rows`, which the kit
accepted and silently dropped. It throws on an unknown element and not on an
unknown prop.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Derived from latent_format where that is possible, which is every
single-tensor model: SDXL, Wan21 and LTXV all come out matching the numbers
core's own nodes compute, including models that ship after this.
H3 cannot be derived and is the reason modules exist. Its latent is a
NestedTensor of a video and an audio branch, its video channel count is not
the latent_channels its format reports, and its length snaps to a frame
grid. So modules/models/minimax_h3 claims it and builds it, importing the
grid from ComfyUI rather than copying a second definition of it.
Nothing outside that folder names a model, and nothing here assumes video:
a 2-D latent format yields an image latent with no branch anywhere.
Modules reach core through modules/_core.py. ComfyUI imports a pack by file
location, so an absolute `import core` in a module resolves under pytest and
not under ComfyUI -- the node then never registers, silently. That cost the
same bug twice, so there is now a test that loads the pack the way ComfyUI
does, in an isolated subprocess whose cwd is NOT the repo. The first version
of that test passed with the bug reintroduced, because cwd was on sys.path.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The naming of a node happened outside the guard: getattr's default is
evaluated eagerly, so a class that raises in __repr__ threw before the try
and cost every other module its nodes -- which ComfyUI reports as the whole
pack failing.
`get("nodes") or []` repaired a typo into "ships no nodes". Absent is none;
0 is a mistake, and this file refuses rather than repairs. Same for the id
lists.
A probe by bare class name matches Attention or Block from any architecture,
so a dotted name now requires the defining module too.
And the fixture that claimed a duplicate id sorted BEFORE the good one, so
every test asserting "the good node was collected" was satisfied by the
lookalike. The tests now assert identity, and the duplicate is made to lose.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
ComfyUI's V3 entrypoint replaces NODE_CLASS_MAPPINGS, which must not exist
at all: nodes.py checks it first and reaches comfy_entrypoint only in an
elif, so an empty dict registers nothing and reports no error.
A module declares NODES for graph structure and SETTINGS for preferences,
and the two never mix -- a saved workflow stores class_type and input
names, so letting a new setting add a socket would rot existing graphs.
Traits now come from two places: facts core can read off any model, and
providers announced by a model's own module. Core names no model, so
supporting a new one is a folder rather than an edit here.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
