Pytorch only filters for OOMs in its own allocators however there are
paths that can OOM on allocators made outside the pytorch allocators.
These manifest as an AllocatorError as pytorch does not have universal
error translation to its OOM type on exception. Handle it. A log I have
for this also shows a double report of the error async, so call the
async discarder to cleanup and make these OOMs look like OOMs.
* 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>
Allows explicit control over the causal_fix flag passed to
latent_to_pixel_coords. Defaults to frame_idx == 0 when not
specified, fixing the previous heuristic.
* sd: add support for clip model reconstruction
* nodes: SetClipHooks: Demote the dynamic model patcher
* mp: Make dynamic_disable more robust
The backup need to not be cloned. In addition add a delegate object
to ModelPatcherDynamic so that non-cloning code can do
ModelPatcherDynamic demotion
* sampler_helpers: Demote to non-dynamic model patcher when hooking
* code rabbit review comments
Move essentials_category from deprecated/incorrect nodes to their replacements:
- ImageBatch → BatchImagesNode (ImageBatch is deprecated)
- Blur → removed (should use subgraph blueprint)
- GetVideoComponents → Video Slice
Amp-Thread-ID: https://ampcode.com/threads/T-019c8340-4da2-723b-a09f-83895c5bbda5
Implements per-guide attention attenuation via log-space additive bias
in self-attention. Each guide reference tracks its own strength and
optional spatial mask in conditioning metadata (guide_attention_entries).
* mp: attach re-construction arguments to model patcher
When making a model-patcher from a unet or ckpt, attach a callable
function that can be called to replay the model construction. This
can be used to deep clone model patcher WRT the actual model.
Originally written by Kosinkadink
f4b99bc623
* mp: Add disable_dynamic clone argument
Add a clone argument that lets a caller clone a ModelPatcher but disable
dynamic to demote the clone to regular MP. This is useful for legacy
features where dynamic_vram support is missing or TBD.
* torch_compile: disable dynamic_vram
This is a bigger feature. Disable for the interim to preserve
functionality.
Add 24 non-cloud essential blueprints from comfyui-wiki/Subgraph-Blueprints.
These cover common workflows: text/image/video generation, editing,
inpainting, outpainting, upscaling, depth maps, pose, captioning, and more.
Cloud-only blueprints (5) are excluded and will be added once
client-side distribution filtering lands.
Amp-Thread-ID: https://ampcode.com/threads/T-019c6f43-6212-7308-bea6-bfc35a486cbf
* lora_extract: Add a trange
If you bite off more than your GPU can chew, this kinda just hangs.
Give a rough indication of progress counting the weights in a trange.
* lora_extract: Support on-the-fly patching
Use the on-the-fly approach from the regular model saving logic for
lora extraction too. Switch off force_cast_weights accordingly.
This gets extraction working in dynamic vram while also supporting
extraction on GPU offloaded.
* Fix bypass dtype/device moving
* Force offloading mode for training
* training context var
* offloading implementation in training node
* fix wrong input type
* Support bypass load lora model, correct adapter/offloading handling
* feat(comfy_api): add basic 3D Model file types
* update Tripo nodes to use File3DGLB
* update Rodin3D nodes to use File3DGLB
* address PR review feedback:
- Rename File3D parameter 'path' to 'source'
- Convert File3D.data property to get_data()
- Make .glb extension check case-insensitive in nodes_rodin.py
- Restrict SaveGLB node to only accept File3DGLB
* Fixed a bug in the Meshy Rig and Animation nodes
* Fix backward compatability
- Add search_aliases for discoverability: resize, scale, dimensions, etc.
- Add node description for hover tooltip
- Add tooltips to all inputs explaining their behavior
- Reorder options: most common (scale dimensions) first, most technical (scale to multiple) last
Addresses user feedback that 'resize' search returned nothing useful and
options like 'match size' and 'scale to multiple' were not self-explanatory.
- Add search_aliases for discoverability: resize, scale, dimensions, etc.
- Add node description for hover tooltip
- Add tooltips to all inputs explaining their behavior
- Reorder options: most common (scale dimensions) first, most technical (scale to multiple) last
Addresses user feedback that 'resize' search returned nothing useful and
options like 'match size' and 'scale to multiple' were not self-explanatory.
* re-init
* Update model_multitalk.py
* whitespace...
* Update model_multitalk.py
* remove print
* this is redundant
* remove import
* Restore preview functionality
* Move block_idx to transformer_options
* Remove LoopingSamplerCustomAdvanced
* Remove looping functionality, keep extension functionality
* Update model_multitalk.py
* Handle ref_attn_mask with separate patch to avoid having to always return q and k from self_attn
* Chunk attention map calculation for multiple speakers to reduce peak VRAM usage
* Update model_multitalk.py
* Add ModelPatch type back
* Fix for latest upstream
* Use DynamicCombo for cleaner node
Basically just so that single_speaker mode hides mask inputs and 2nd audio input
* Update nodes_wan.py
* Support Combo outputs in a more sane way
* Remove test validate_inputs function on test node
* Make curr_prefix be a list of strings instead of string for easier parsing as keys get added to dynamic types
* Start to account for id prefixes from frontend, need to fix bug with nested dynamics
* Ensure inputs/outputs/hidden are lists in schema finalize function, remove no longer needed 'is not None' checks
* Add raw_link and extra_dict to all relevant Inputs
* Make nested DynamicCombos work properly with prefixed keys on latest frontend; breaks old Autogrow, but is pretty much ready for upcoming Autogrow keys
* Replace ... usage with a MISSING sentinel for clarity in nodes_logic.py
* Added CustomCombo node in backend to reflect frontend node
* Prepare Autogrow's expand_schema_for_dynamic to work with upcoming frontend changes
* Prepare for look up table for dynamic input stuff
* More progress towards dynamic input lookup function stuff
* Finished converting _expand_schema_for_dynamic to be done via lookup instead of OOP to guarantee working with process isolation, did refactoring to remove old implementation + cleaning INPUT_TYPES definition including v3 hidden definition
* Change order of functions
* Removed some unneeded functions after dynamic refactor
* Make MatchType's output default displayname "MATCHTYPE"
* Fix DynamicSlot get_all
* Removed redundant code - dynamic stuff no longer happens in OOP way
* Natively support AnyType (*) without __ne__ hacks
* Remove stray code that made it in
* Remove expand_schema_for_dynamic left over on DynamicInput class
* get_dynamic() on DynamicInput/Output was not doing anything anymore, so removed it
* Make validate_inputs validate combo input correctly
* Temporarily comment out conversion to 'new' (9 month old) COMBO format in get_input_info
* Remove refrences to resources feature scrapped from V3
* Expose DynamicCombo in public API
* satisfy ruff after some code got commented out
* Make missing input error prettier for dynamic types
* Created a Switch2 node as a side-by-side test, will likely go with Switch2 as the initial switch node
* Figured out Switch situation
* Pass in v3_data in IsChangedCache.get function's fingerprint_inputs, add a from_v3_data helper method to HiddenHolder
* Switch order of Switch and Soft Switch nodes in file
* Temp test node for MatchType
* Fix missing v3_data for v1 nodes in validation
* For now, remove chacking duplicate id's for dynamic types
* Add Resize Image/Mask node that thanks to MatchType+DynamicCombo is 16-nodes-in-1
* Made DynamicCombo references in DCTestNode use public interface
* Add an AnyTypeTestNode
* Make lazy status for specific inputs on DynamicInputs work by having the values of the dictionary for check_lazy_status be a tuple, where the second element is the key of the input that can be returned
* Comment out test logic nodes
* Make primitive float's step make more sense
* Add (and leave commented out) some potential logic nodes
* Change default crop option to "center" on Resize Image/Mask node
* Changed copy.copy(d) to d.copy()
* Autogrow is available in stable frontend, so exposing it in public API
* Use outputs id as display_name if no display_name present, remove v3 outputs id restriction that made them have to have unique IDs from the inputs
* Enable Custom Combo node as stable frontend now supports it
* Make id properly act like display_name on outputs
* Add Batch Images/Masks/Latents node
* Comment out Batch Images/Masks/Latents node for now, as Autogrow has a bug with MatchType where top connection is disconnected upon refresh
* Removed code for a couple test nodes in nodes_logic.py
* Add Batch Images, Batch Masks, and Batch Latents nodes with Autogrow, deprecate old Batch Images + LatentBatch nodes
Can be used to manually set the sigmas for a model.
This node accepts a list of integer and floating point numbers separated
with any non numeric character.
This operation trades in latents which in --gpu-only may be out of the GPU
The two VAE results will follow the --gpu-only defined behaviour so follow
the inpaint image device when calculating the mask in this path.
index_timestep_zero can be selected in the
FluxKontextMultiReferenceLatentMethod now with the display name set to the
more generic "Edit Model Reference Method" node.
The inpaint part is currently missing and will be implemented later.
I think they messed up this model pretty bad. They added some
control_noise_refiner blocks but don't actually use them. There is a typo
in their code so instead of doing control_noise_refiner -> control_layers
it runs the whole control_layers twice.
Unfortunately they trained with this typo so the model works but is kind
of slow and would probably perform a lot better if they corrected their
code and trained it again.
* Add Kandinsky5 model support
lite and pro T2V tested to work
* Update kandinsky5.py
* Fix fp8
* Fix fp8_scaled text encoder
* Add transformer_options for attention
* Code cleanup, optimizations, use fp32 for all layers originally at fp32
* ImageToVideo -node
* Fix I2V, add necessary latent post process nodes
* Support text to image model
* Support block replace patches (SLG mostly)
* Support official LoRAs
* Don't scale RoPE for lite model as that just doesn't work...
* Update supported_models.py
* Rever RoPE scaling to simpler one
* Fix typo
* Handle latent dim difference for image model in the VAE instead
* Add node to use different prompts for clip_l and qwen25_7b
* Reduce peak VRAM usage a bit
* Further reduce peak VRAM consumption by chunking ffn
* Update chunking
* Update memory_usage_factor
* Code cleanup, don't force the fp32 layers as it has minimal effect
* Allow for stronger changes with first frames normalization
Default values are too weak for any meaningful changes, these should probably be exposed as advanced node options when that's available.
* Add image model's own chat template, remove unused image2video template
* Remove hard error in ReplaceVideoLatentFrames -node
* Update kandinsky5.py
* Update supported_models.py
* Fix typos in prompt template
They were now fixed in the original repository as well
* Update ReplaceVideoLatentFrames
Add tooltips
Make source optional
Better handle negative index
* Rename NormalizeVideoLatentFrames -node
For bit better clarity what it does
* Fix NormalizeVideoLatentStart node out on non-op
* Apply cond slice fix
* Add FreeNoise
* Update context_windows.py
* Add option to retain condition by indexes for each window
This allows for example Wan/HunyuanVideo image to video to "work" by using the initial start frame for each window, otherwise windows beyond first will be pure T2V generations.
* Update context_windows.py
* Allow splitting multiple conds into different windows
* Add handling for audio_embed
* whitespace
* Allow freenoise to work on other dims, handle 4D batch timestep
Refactor Freenoise function. And fix batch handling as timesteps seem to be expanded to batch size now.
* Disable experimental options for now
So that the Freenoise and bugfixes can be merged first
---------
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: ozbayb <17261091+ozbayb@users.noreply.github.com>
* Added output_matchtypes to generated json for v3, initial backend support for MatchType, created nodes_logic.py and added SwitchNode
* Fixed providing list of allowed_types
* Add workaround in validation.py for V3 Combo outputs not working as Combo inputs
* Make match type receive_type pass validation
* Also add MatchType check to input_type in validation - will likely trigger when connecting to non-lazy stuff
* Make sure this PR only has MatchType stuff
* Initial work on DynamicCombo
* Add get_dynamic function, not yet filled out correctly
* Mark Switch node as Beta
* Make sure other unfinished dynamic types are not accidentally used
* Send DynamicCombo.Option inputs in the same format as normal v1 inputs
* add dynamic combo test node
* Support validation of inputs and outputs
* Add missing input params to DynamicCombo.Input
* Add get_all function to inputs for id validation purposes
* Fix imports for v3 returning everything when doing io/ui/IO/UI instead of what is in __all__ of _io.py and _ui.py
* Modifying behavior of get_dynamic in V3 + serialization so can be used in execution code
* Fix v3 schema validation code after changes
* Refactor hidden_values for v3 in execution.py to be more general v3_data, add helper functions for dynamic behavior, preparing for restructuring dynamic type into object (not finished yet)
* Add nesting of inputs on DynamicCombo during execution
* Work with latest frontend commits
* Fix cringe arrows
* frontend will no longer namespace dynamic inputs widgets so reflect that in code, refactor build_nested_inputs
* Prepare Autogrow support for the love of the game
* satisfy ruff
* Create test nodes for Autogrow to collab with frontend development
* Add nested combo to DCTestNode
* Remove array support from build_nested_inputs, properly handle missing expected values
* Make execution.validate_inputs properly validate required dynamic inputs, renamed dynamic_data to dynamic_paths for clarity
* MatchType does not need any DynamicInput/Output features on backend; will increase compatibility with dynamic types
* Probably need this for ruff check
* Change MatchType to have template be the first and only required param; output id's do nothing right now, so no need
* Fix merge regression with LatentUpscaleModel type not being put in __all__ for _io.py, fix invalid type hint for validate_inputs
* Make Switch node inputs optional, disallow both inputs from being missing, and still work properly with lazy; when one input is missing, use the other no matter what the switch is set to
* Satisfy ruff
* Move MatchType code above the types that inherit from DynamicInput
* Add DynamicSlot type, awaiting frontend support
* Make curr_prefix creation happen in Autogrow, move curr_prefix in DynamicCombo to only be created if input exists in live_inputs
* I was confused, fixing accidentally redundant curr_prefix addition in Autogrow
* Make sure Autogrow inputs are force_input = True when WidgetInput, fix runtime validation by removing original input from expected inputs, fix min/max bounds, change test nodes slightly
* Remove unnecessary id usage in Autogrow test node outputs
* Commented out Switch node + test nodes
* Remove commented out code from Autogrow
* Make TemplatePrefix max more clear, allow max == 1
* Replace all dict[str] with dict[str, Any]
* Renamed add_to_dict_live_inputs to expand_schema_for_dynamic
* Fixed typo in DynamicSlot input code
* note about live_inputs not being present soon in get_v1_info (internal function anyway)
* For now, hide DynamicCombo and Autogrow from public interface
* Removed comment
These are not actual controlnets so put it in the models/model_patches
folder and use the ModelPatchLoader + QwenImageDiffsynthControlnet node to
use it.
* Create nodes_dataset.py
* Add encoded dataset caching mechanism
* make training node to work with our dataset system
* allow trainer node to get different resolution dataset
* move all dataset related implementation to nodes_dataset
* Rewrite dataset system with new io schema
* Rewrite training system with new io schema
* add ui pbar
* Add outputs' id/name
* Fix bad id/naming
* use single process instead of input list when no need
* fix wrong output_list flag
* use torch.load/save and fix bad behaviors
* init
* update
* Update model.py
* Update model.py
* remove print
* Fix text encoding
* Prevent empty negative prompt
Really doesn't work otherwise
* fp16 works
* I2V
* Update model_base.py
* Update nodes_hunyuan.py
* Better latent rgb factors
* Use the correct sigclip output...
* Support HunyuanVideo1.5 SR model
* whitespaces...
* Proper latent channel count
* SR model fixes
This also still needs timesteps scheduling based on the noise scale, can be used with two samplers too already
* vae_refiner: roll the convolution through temporal
Work in progress.
Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.
* Support HunyuanVideo15 latent resampler
* fix
* Some cleanup
Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
* Proper hyvid15 I2V channels
Co-Authored-By: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
* Fix TokenRefiner for fp16
Otherwise x.sum has infs, just in case only casting if input is fp16, I don't know if necessary.
* Bugfix for the HunyuanVideo15 SR model
* vae_refiner: roll the convolution through temporal II
Roll the convolution through time using 2-latent-frame chunks and a
FIFO queue for the convolution seams.
Added support for encoder, lowered to 1 latent frame to save more
VRAM, made work for Hunyuan Image 3.0 (as code shared).
Fixed names, cleaned up code.
* Allow any number of input frames in VAE.
* Better VAE encode mem estimation.
* Lowvram fix.
* Fix hunyuan image 2.1 refiner.
* Fix mistake.
* Name changes.
* Rename.
* Whitespace.
* Fix.
* Fix.
---------
Co-authored-by: kijai <40791699+kijai@users.noreply.github.com>
Co-authored-by: Rattus <rattus128@gmail.com>
