[pull] master from comfyanonymous:master #450
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* 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
This should fix the problem with the portable updater not working with portables created from a separate branch on the repo. This does not affect any current portables who were all created on the master branch.
… Flux2) (#11069) * mp: only count the offload cost of math once This was previously bundling the combined weight storage and computation cost * ops: put all post async transfer compute on the main stream Some models have massive weights that need either complex dequantization or lora patching. Don't do these patchings on the offload stream, instead do them on the main stream to syncrhonize the potentially large vram spikes for these compute processes. This avoids having to assume a worst case scenario of multiple offload streams all spiking VRAM is parallel with whatever the main stream is doing.
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