* feat: add EagerEval dataclass for frontend-side node evaluation
Add EagerEval to the V3 API schema, enabling nodes to declare
frontend-evaluated JSONata expressions. The frontend uses this to
display computation results as badges without a backend round-trip.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add Math Expression node with JSONata evaluation
Add ComfyMathExpression node that evaluates JSONata expressions against
dynamically-grown numeric inputs using Autogrow + MatchType. Sends
input context via ui output so the frontend can re-evaluate when
the expression changes without a backend round-trip.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: register nodes_math.py in extras_files loader list
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address CodeRabbit review feedback
- Harden EagerEval.validate with type checks and strip() for empty strings
- Add _positional_alias for spreadsheet-style names beyond z (aa, ab...)
- Validate JSONata result is numeric before returning
- Add jsonata to requirements.txt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: remove EagerEval, scope PR to math node only
Remove EagerEval dataclass from _io.py and eager_eval usage from
nodes_math.py. Eager execution will be designed as a general-purpose
system in a separate effort.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use TemplateNames, cap inputs at 26, improve error message
Address Kosinkadink review feedback:
- Switch from Autogrow.TemplatePrefix to Autogrow.TemplateNames so input
slots are named a-z, matching expression variables directly
- Cap max inputs at 26 (a-z) instead of 100
- Simplify execute() by removing dual-mapping hack
- Include expression and result value in error message
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add unit tests for Math Expression node
Add tests for _positional_alias (a-z mapping) and execute() covering
arithmetic operations, float inputs, $sum(values), and error cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: replace jsonata with simpleeval for math evaluation
jsonata PyPI package has critical issues: no Python 3.12/3.13 wheels,
no ARM/Apple Silicon wheels, abandoned (last commit 2023), C extension.
Replace with simpleeval (pure Python, 3.4M downloads/month, MIT,
AST-based security). Add math module functions (sqrt, ceil, floor,
log, sin, cos, tan) and variadic sum() supporting both sum(values)
and sum(a, b, c). Pin version to >=1.0,<2.0.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: update tests for simpleeval migration
Update JSONata syntax to Python syntax ($sum -> sum, $string -> str),
add tests for math functions (sqrt, ceil, floor, sin, log10) and
variadic sum(a, b, c).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: replace MatchType with MultiType inputs and dual FLOAT/INT outputs
Allow mixing INT and FLOAT connections on the same node by switching
from MatchType (which forces all inputs to the same type) to MultiType.
Output both FLOAT and INT so users can pick the type they need.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: update tests for mixed INT/FLOAT inputs and dual outputs
Add assertions for both FLOAT (result[0]) and INT (result[1]) outputs.
Add test_mixed_int_float_inputs and test_mixed_resolution_scale to
verify the primary use case of multiplying resolutions by a float factor.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: make expression input multiline and validate empty expression
- Add multiline=True to expression input for better UX with longer expressions
- Add empty expression validation with clear "Expression cannot be empty." message
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add tests for empty expression validation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address review feedback — safe pow, isfinite guard, test coverage
- Wrap pow() with _safe_pow to prevent DoS via huge exponents
(pow() bypasses simpleeval's safe_power guard on **)
- Add math.isfinite() check to catch inf/nan before int() conversion
- Add int/float converters to MATH_FUNCTIONS for explicit casting
- Add "calculator" search alias
- Replace _positional_alias helper with string.ascii_lowercase
- Narrow test assertions and add error path + function coverage tests
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update requirements.txt
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: Christian Byrne <abolkonsky.rem@gmail.com>
Comfy-aimdo 0.2.7 fixes a crash when a spurious cudaAsyncFree comes in
and would cause an infinite stack overflow (via detours hooks).
A lock is also introduced on the link list holding the free sections
to avoid any possibility of threaded miscellaneous cuda allocations
being the root cause.
* ops: dont unpin nothing
This was calling into aimdo in the none case (offloaded weight). Whats worse,
is aimdo syncs for unpinning an offloaded weight, as that is the corner case of
a weight getting evicted by its own use which does require a sync. But this
was heppening every offloaded weight causing slowdown.
* mp: fix get_free_memory policy
The ModelPatcherDynamic get_free_memory was deducting the model from
to try and estimate the conceptual free memory with doing any
offloading. This is kind of what the old memory_memory_required
was estimating in ModelPatcher load logic, however in practical
reality, between over-estimates and padding, the loader usually
underloaded models enough such that sampling could send CFG +/-
through together even when partially loaded.
So don't regress from the status quo and instead go all in on the
idea that offloading is less of an issue than debatching. Tell the
sampler it can use everything.
Comfy Aimdo 0.2.4 fixes a VRAM buffer alignment issue that happens in
someworkflows where action is able to bypass the pytorch allocator
and go straight to the cuda hook.
Comfy Aimdo 0.2.2 moves the cuda allocator hook from the cudart API to
the cuda driver API on windows. This is needed to handle Windows+cu13
where cudart is statically linked.
Integrate comfy-aimdo 0.2 which takes a different approach to
installing the memory allocator hook. Instead of using the complicated
and buggy pytorch MemPool+CudaPluggableAlloctor, cuda is directly hooked
making the process much more transparent to both comfy and pytorch. As
far as pytorch knows, aimdo doesnt exist anymore, and just operates
behind the scenes.
Remove all the mempool setup stuff for dynamic_vram and bump the
comfy-aimdo version. Remove the allocator object from memory_management
and demote its use as an enablment check to a boolean flag.
Comfy-aimdo 0.2 also support the pytorch cuda async allocator, so
remove the dynamic_vram based force disablement of cuda_malloc and
just go back to the old settings of allocators based on command line
input.
* revert threaded model loader change
This change was only needed to get around the pytorch 2.7 mempool bugs,
and should have been reverted along with #12260. This fixes a different
memory leak where pytorch gets confused about cache emptying.
* load non comfy weights
* MPDynamic: Pre-generate the tensors for vbars
Apparently this is an expensive operation that slows down things.
* bump to aimdo 1.8
New features:
watermark limit feature
logging enhancements
-O2 build on linux
