698 lines
25 KiB
Python
698 lines
25 KiB
Python
from __future__ import annotations
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import random
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from typing import Any
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try:
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from comfy_execution.graph import ExecutionBlocker
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from comfy_execution.graph_utils import GraphBuilder, is_link
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except Exception: # Allows local syntax/import checks outside ComfyUI.
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ExecutionBlocker = None
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GraphBuilder = None
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def is_link(value: Any) -> bool:
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return isinstance(value, list) and len(value) == 2
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try:
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from nodes import NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS
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except Exception:
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ALL_NODE_CLASS_MAPPINGS = {}
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MAX_LOOP_VALUES = 20
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MAX_CARRY_VALUES = MAX_LOOP_VALUES - 2
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COLLECTION_MODES = ["auto_batch", "list", "image_batch", "latent_batch", "string_lines"]
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ACCUMULATOR_ACTIONS = ["replace_by_entry_id", "append", "clear_then_append", "clear", "read"]
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ACCUMULATOR_IMAGE_BATCH_MODES = ["same_size_only", "resize_to_first"]
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ACCUMULATOR_IMAGE_GROUPS = 4
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_ACCUMULATOR_STORES: dict[str, list[dict[str, Any]]] = {}
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class AnyType(str):
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def __ne__(self, _other: object) -> bool:
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return False
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ANY_TYPE = AnyType("*")
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def _require_graph_builder() -> None:
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if GraphBuilder is None:
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raise RuntimeError("SxCP loop nodes require ComfyUI's comfy_execution GraphBuilder.")
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def _execution_blocker() -> Any:
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return ExecutionBlocker(None) if ExecutionBlocker is not None else None
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def _torch_cat(first: Any, second: Any) -> Any | None:
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try:
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import torch
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except Exception:
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return None
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if torch.is_tensor(first) and torch.is_tensor(second):
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return torch.cat((first, second), dim=0)
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return None
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def _latent_cat(first: Any, second: Any) -> Any | None:
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if not isinstance(first, dict) or not isinstance(second, dict):
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return None
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if "samples" not in first or "samples" not in second:
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return None
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samples = _torch_cat(first["samples"], second["samples"])
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if samples is None:
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return None
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merged = dict(second)
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merged["samples"] = samples
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return merged
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def _torch_cat_many(values: list[Any]) -> Any | None:
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if not values:
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return None
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result = values[0]
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for value in values[1:]:
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result = _torch_cat(result, value)
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if result is None:
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return None
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return result
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def _is_image_tensor(value: Any) -> bool:
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try:
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import torch
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except Exception:
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return False
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return torch.is_tensor(value) and len(value.shape) == 4
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def _image_shape(value: Any) -> tuple[int, ...] | None:
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if not _is_image_tensor(value):
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return None
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return tuple(int(part) for part in value.shape[1:])
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def _split_image_value(value: Any) -> list[Any]:
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if value is None:
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return []
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if isinstance(value, (list, tuple)):
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images: list[Any] = []
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for item in value:
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images.extend(_split_image_value(item))
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return images
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if not _is_image_tensor(value):
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return []
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if int(value.shape[0]) <= 1:
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return [value]
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return [value[index : index + 1] for index in range(int(value.shape[0]))]
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def _resize_image_to_shape(image: Any, shape: tuple[int, ...]) -> Any | None:
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if not _is_image_tensor(image):
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return None
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try:
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import comfy.utils
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except Exception:
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return None
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height, width = int(shape[0]), int(shape[1])
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return comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
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def _image_batch_from_images(images: list[Any], mode: str = "same_size_only") -> Any | None:
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if not images:
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return None
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mode = mode if mode in ACCUMULATOR_IMAGE_BATCH_MODES else "same_size_only"
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first_shape = _image_shape(images[0])
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if first_shape is None:
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return None
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normalized = []
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for image in images:
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if _image_shape(image) != first_shape:
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if mode != "resize_to_first":
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return None
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image = _resize_image_to_shape(image, first_shape)
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if image is None:
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return None
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normalized.append(image)
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return _torch_cat_many(normalized)
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def _group_image_batches(images: list[Any]) -> list[Any]:
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grouped: dict[tuple[int, ...], list[Any]] = {}
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order: list[tuple[int, ...]] = []
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for image in images:
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shape = _image_shape(image)
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if shape is None:
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continue
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if shape not in grouped:
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grouped[shape] = []
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order.append(shape)
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grouped[shape].append(image)
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batches = [_torch_cat_many(grouped[shape]) for shape in order]
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return [batch for batch in batches if batch is not None]
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def _as_list(collection: Any) -> list[Any]:
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if collection is None:
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return []
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return list(collection) if isinstance(collection, list) else [collection]
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def append_collected_value(collection: Any, value: Any, mode: str = "auto_batch", skip_none: bool = True) -> Any:
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if value is None and skip_none:
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return collection
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mode = mode if mode in COLLECTION_MODES else "auto_batch"
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if mode == "string_lines":
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value_text = "" if value is None else str(value)
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if not collection:
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return value_text
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return f"{collection}\n{value_text}"
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if mode == "list":
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return _as_list(collection) + [value]
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if collection is None:
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return value
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if mode in ("auto_batch", "image_batch"):
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tensor_batch = _torch_cat(collection, value)
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if tensor_batch is not None:
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return tensor_batch
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if mode == "image_batch":
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return _as_list(collection) + [value]
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if mode in ("auto_batch", "latent_batch"):
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latent_batch = _latent_cat(collection, value)
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if latent_batch is not None:
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return latent_batch
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if mode == "latent_batch":
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return _as_list(collection) + [value]
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return _as_list(collection) + [value]
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class SxCPWhileLoopStart:
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@classmethod
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def INPUT_TYPES(cls):
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inputs = {
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"required": {
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"condition": ("BOOLEAN", {"default": True}),
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},
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"optional": {},
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}
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for index in range(MAX_LOOP_VALUES):
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inputs["optional"][f"initial_value{index}"] = (ANY_TYPE,)
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return inputs
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RETURN_TYPES = tuple(["FLOW_CONTROL"] + [ANY_TYPE] * MAX_LOOP_VALUES)
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RETURN_NAMES = tuple(["flow"] + [f"value{index}" for index in range(MAX_LOOP_VALUES)])
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FUNCTION = "open"
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CATEGORY = "prompt_builder/loop"
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def open(self, condition, **kwargs):
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values = []
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for index in range(MAX_LOOP_VALUES):
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values.append(kwargs.get(f"initial_value{index}") if condition else _execution_blocker())
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return tuple(["stub"] + values)
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class SxCPWhileLoopEnd:
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@classmethod
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def INPUT_TYPES(cls):
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inputs = {
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"required": {
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"flow": ("FLOW_CONTROL", {"rawLink": True}),
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"condition": ("BOOLEAN", {}),
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},
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"optional": {},
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"hidden": {
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"dynprompt": "DYNPROMPT",
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"unique_id": "UNIQUE_ID",
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"extra_pnginfo": "EXTRA_PNGINFO",
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},
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}
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for index in range(MAX_LOOP_VALUES):
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inputs["optional"][f"initial_value{index}"] = (ANY_TYPE,)
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return inputs
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RETURN_TYPES = tuple([ANY_TYPE] * MAX_LOOP_VALUES)
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RETURN_NAMES = tuple([f"value{index}" for index in range(MAX_LOOP_VALUES)])
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FUNCTION = "close"
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CATEGORY = "prompt_builder/loop"
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def _explore_dependencies(self, node_id: str, dynprompt: Any, upstream: dict[str, list[str]], parent_ids: list[str]) -> None:
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node_info = dynprompt.get_node(node_id)
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if "inputs" not in node_info:
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return
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for value in node_info["inputs"].values():
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if not is_link(value):
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continue
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parent_id = value[0]
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display_id = dynprompt.get_display_node_id(parent_id)
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display_node = dynprompt.get_node(display_id)
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class_type = display_node["class_type"]
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if class_type not in ("SxCPForLoopEnd", "SxCPWhileLoopEnd"):
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parent_ids.append(display_id)
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if parent_id not in upstream:
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upstream[parent_id] = []
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self._explore_dependencies(parent_id, dynprompt, upstream, parent_ids)
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upstream[parent_id].append(node_id)
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def _explore_output_nodes(
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self,
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dynprompt: Any,
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upstream: dict[str, list[str]],
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output_nodes: dict[str, Any],
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parent_ids: list[str],
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) -> None:
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for parent_id in upstream:
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display_id = dynprompt.get_display_node_id(parent_id)
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for output_id, link in output_nodes.items():
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linked_id = link[0]
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if linked_id in parent_ids and display_id == linked_id and output_id not in upstream[parent_id]:
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if "." in parent_id:
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parts = parent_id.split(".")
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parts[-1] = output_id
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upstream[parent_id].append(".".join(parts))
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else:
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upstream[parent_id].append(output_id)
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def _collect_contained(self, node_id: str, upstream: dict[str, list[str]], contained: dict[str, bool]) -> None:
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if node_id not in upstream:
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return
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for child_id in upstream[node_id]:
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if child_id in contained:
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continue
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contained[child_id] = True
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self._collect_contained(child_id, upstream, contained)
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def close(self, flow, condition, dynprompt=None, unique_id=None, **kwargs):
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if not condition:
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return tuple(kwargs.get(f"initial_value{index}") for index in range(MAX_LOOP_VALUES))
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_require_graph_builder()
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upstream: dict[str, list[str]] = {}
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parent_ids: list[str] = []
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self._explore_dependencies(unique_id, dynprompt, upstream, parent_ids)
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parent_ids = list(set(parent_ids))
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output_nodes = {}
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for node_id, node in dynprompt.get_original_prompt().items():
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if "inputs" not in node:
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continue
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class_def = ALL_NODE_CLASS_MAPPINGS.get(node["class_type"])
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if not class_def or not getattr(class_def, "OUTPUT_NODE", False):
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continue
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for value in node["inputs"].values():
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if is_link(value):
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output_nodes[node_id] = value
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graph = GraphBuilder()
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self._explore_output_nodes(dynprompt, upstream, output_nodes, parent_ids)
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contained: dict[str, bool] = {}
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open_node = flow[0]
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self._collect_contained(open_node, upstream, contained)
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contained[unique_id] = True
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contained[open_node] = True
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for node_id in contained:
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original_node = dynprompt.get_node(node_id)
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node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id)
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node.set_override_display_id(node_id)
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for node_id in contained:
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original_node = dynprompt.get_node(node_id)
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node = graph.lookup_node("Recurse" if node_id == unique_id else node_id)
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for key, value in original_node["inputs"].items():
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if is_link(value) and value[0] in contained:
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parent = graph.lookup_node(value[0])
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node.set_input(key, parent.out(value[1]))
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else:
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node.set_input(key, value)
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new_open = graph.lookup_node(open_node)
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original_open = dynprompt.get_node(open_node)
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if original_open["class_type"] == "SxCPForLoopStart":
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new_open.set_input("initial_index", kwargs.get("initial_value0"))
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new_open.set_input("initial_collected", kwargs.get("initial_value1"))
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for carry_index in range(1, MAX_CARRY_VALUES + 1):
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new_open.set_input(f"initial_value{carry_index}", kwargs.get(f"initial_value{carry_index + 1}"))
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else:
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for index in range(MAX_LOOP_VALUES):
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new_open.set_input(f"initial_value{index}", kwargs.get(f"initial_value{index}"))
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my_clone = graph.lookup_node("Recurse")
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return {
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"result": tuple(my_clone.out(index) for index in range(MAX_LOOP_VALUES)),
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"expand": graph.finalize(),
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}
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class SxCPForLoopStart:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"total": ("INT", {"default": 2, "min": 1, "max": 100000, "step": 1}),
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"skip": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}),
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},
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"optional": {
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f"initial_value{index}": (ANY_TYPE,) for index in range(1, MAX_CARRY_VALUES + 1)
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},
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"hidden": {
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"initial_index": (ANY_TYPE,),
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"initial_collected": (ANY_TYPE,),
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO",
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"unique_id": "UNIQUE_ID",
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},
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}
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RETURN_TYPES = tuple(["FLOW_CONTROL", "INT", ANY_TYPE] + [ANY_TYPE] * MAX_CARRY_VALUES)
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RETURN_NAMES = tuple(["flow", "index", "collected"] + [f"value{index}" for index in range(1, MAX_CARRY_VALUES + 1)])
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FUNCTION = "start"
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CATEGORY = "prompt_builder/loop"
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def start(self, total, skip=0, initial_index=None, initial_collected=None, **kwargs):
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_require_graph_builder()
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total = max(1, int(total))
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skip = max(0, int(skip))
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first_index = skip + 1
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index = first_index if initial_index is None else max(int(initial_index), first_index)
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collected = initial_collected
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initial_values = {
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"initial_value0": index,
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"initial_value1": collected,
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}
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for carry_index in range(1, MAX_CARRY_VALUES + 1):
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initial_values[f"initial_value{carry_index + 1}"] = kwargs.get(f"initial_value{carry_index}")
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graph = GraphBuilder()
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graph.node("SxCPWhileLoopStart", condition=index <= total, **initial_values)
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return {
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"result": tuple(["stub", index, collected] + [kwargs.get(f"initial_value{index}") for index in range(1, MAX_CARRY_VALUES + 1)]),
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"expand": graph.finalize(),
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}
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class SxCPLoopAppend:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"mode": (COLLECTION_MODES, {"default": "auto_batch"}),
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"skip_none": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"collection": (ANY_TYPE,),
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"value": (ANY_TYPE,),
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},
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}
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RETURN_TYPES = (ANY_TYPE,)
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RETURN_NAMES = ("collected",)
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FUNCTION = "append"
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CATEGORY = "prompt_builder/loop"
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def append(self, mode, skip_none, collection=None, value=None):
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return (append_collected_value(collection, value, mode=mode, skip_none=skip_none),)
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class SxCPAccumulator:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"store_key": ("STRING", {"default": "", "multiline": False}),
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"action": (ACCUMULATOR_ACTIONS, {"default": "replace_by_entry_id"}),
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"max_items": ("INT", {"default": 32, "min": 1, "max": 10000, "step": 1}),
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"image_batch_mode": (ACCUMULATOR_IMAGE_BATCH_MODES, {"default": "same_size_only"}),
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"skip_empty": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"image": ("IMAGE",),
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"value": (ANY_TYPE,),
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"entry_id": (ANY_TYPE,),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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},
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}
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RETURN_TYPES = tuple([ANY_TYPE, "IMAGE", "IMAGE"] + ["IMAGE"] * ACCUMULATOR_IMAGE_GROUPS + ["INT", "STRING"])
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RETURN_NAMES = tuple(
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["collection", "image_batch", "image_list"]
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+ [f"image_batch_{index}" for index in range(1, ACCUMULATOR_IMAGE_GROUPS + 1)]
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+ ["count", "status"]
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)
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OUTPUT_IS_LIST = tuple([False, False, True] + [False] * ACCUMULATOR_IMAGE_GROUPS + [False, False])
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FUNCTION = "accumulate"
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CATEGORY = "prompt_builder/loop"
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@classmethod
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def IS_CHANGED(cls, *args, **kwargs):
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return random.random()
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def _store_key(self, store_key: str, unique_id: Any) -> str:
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key = str(store_key or "").strip()
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return key or f"node:{unique_id}"
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def _entry_id(self, entry_id: Any, image_index: int, image_count: int) -> str:
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if entry_id is None:
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return ""
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text = str(entry_id).strip()
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if not text:
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return ""
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if image_count <= 1:
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return text
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return f"{text}:{image_index + 1}"
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def _value_for_image(self, value: Any, image_index: int, image_count: int) -> Any:
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if image_count <= 1:
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return value
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if isinstance(value, (list, tuple)) and len(value) == image_count:
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return value[image_index]
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return value
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def _entry_records(self, image: Any, value: Any, entry_id: Any, skip_empty: bool) -> list[dict[str, Any]]:
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images = _split_image_value(image)
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if not images:
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if value is None and skip_empty:
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return []
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return [{"id": self._entry_id(entry_id, 0, 1), "image": None, "value": value}]
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image_count = len(images)
|
|
return [
|
|
{
|
|
"id": self._entry_id(entry_id, index, image_count),
|
|
"image": image_item,
|
|
"value": self._value_for_image(value, index, image_count),
|
|
}
|
|
for index, image_item in enumerate(images)
|
|
]
|
|
|
|
def _append_or_replace(self, store: list[dict[str, Any]], entries: list[dict[str, Any]], action: str) -> None:
|
|
replace = action == "replace_by_entry_id"
|
|
for entry in entries:
|
|
entry_id = entry.get("id") or ""
|
|
if replace and entry_id:
|
|
for index, existing in enumerate(store):
|
|
if existing.get("id") == entry_id:
|
|
store[index] = entry
|
|
break
|
|
else:
|
|
store.append(entry)
|
|
else:
|
|
store.append(entry)
|
|
|
|
def _collection(self, store: list[dict[str, Any]]) -> list[Any]:
|
|
collection = []
|
|
for entry in store:
|
|
value = entry.get("value")
|
|
collection.append(value if value is not None else entry.get("image"))
|
|
return collection
|
|
|
|
def _status(self, key: str, store: list[dict[str, Any]], image_batch: Any, image_batches: list[Any]) -> str:
|
|
images = [entry.get("image") for entry in store if entry.get("image") is not None]
|
|
shapes = []
|
|
for image in images:
|
|
shape = _image_shape(image)
|
|
if shape is not None and shape not in shapes:
|
|
shapes.append(shape)
|
|
shape_text = ", ".join(f"{shape[1]}x{shape[0]}" for shape in shapes) or "no images"
|
|
batch_state = "all images batched" if image_batch is not None else "mixed sizes or no image batch"
|
|
return (
|
|
f"key={key}; entries={len(store)}; image_entries={len(images)}; "
|
|
f"formats={shape_text}; grouped_batches={len(image_batches)}; {batch_state}"
|
|
)
|
|
|
|
def accumulate(
|
|
self,
|
|
store_key,
|
|
action,
|
|
max_items,
|
|
image_batch_mode,
|
|
skip_empty,
|
|
image=None,
|
|
value=None,
|
|
entry_id=None,
|
|
unique_id=None,
|
|
):
|
|
key = self._store_key(store_key, unique_id)
|
|
action = action if action in ACCUMULATOR_ACTIONS else "replace_by_entry_id"
|
|
store = _ACCUMULATOR_STORES.setdefault(key, [])
|
|
|
|
if action in ("clear", "clear_then_append"):
|
|
store.clear()
|
|
|
|
if action not in ("clear", "read"):
|
|
entries = self._entry_records(image, value, entry_id, bool(skip_empty))
|
|
self._append_or_replace(store, entries, action)
|
|
|
|
max_items = max(1, int(max_items))
|
|
if len(store) > max_items:
|
|
del store[: len(store) - max_items]
|
|
|
|
images = [entry["image"] for entry in store if entry.get("image") is not None]
|
|
image_batch = _image_batch_from_images(images, image_batch_mode)
|
|
image_batches = _group_image_batches(images)
|
|
grouped_outputs = image_batches[:ACCUMULATOR_IMAGE_GROUPS]
|
|
grouped_outputs += [None] * (ACCUMULATOR_IMAGE_GROUPS - len(grouped_outputs))
|
|
status = self._status(key, store, image_batch, image_batches)
|
|
return tuple([self._collection(store), image_batch, images] + grouped_outputs + [len(store), status])
|
|
|
|
|
|
class SxCPForLoopEnd:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"flow": ("FLOW_CONTROL", {"rawLink": True}),
|
|
"collection_mode": (COLLECTION_MODES, {"default": "auto_batch"}),
|
|
"skip_none": ("BOOLEAN", {"default": True}),
|
|
},
|
|
"optional": {
|
|
"collected": (ANY_TYPE, {"rawLink": True}),
|
|
"collect_value": (ANY_TYPE, {"rawLink": True}),
|
|
**{
|
|
f"initial_value{index}": (ANY_TYPE, {"rawLink": True})
|
|
for index in range(1, MAX_CARRY_VALUES + 1)
|
|
},
|
|
},
|
|
"hidden": {
|
|
"dynprompt": "DYNPROMPT",
|
|
"extra_pnginfo": "EXTRA_PNGINFO",
|
|
"unique_id": "UNIQUE_ID",
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = tuple([ANY_TYPE] + [ANY_TYPE] * MAX_CARRY_VALUES)
|
|
RETURN_NAMES = tuple(["collected"] + [f"value{index}" for index in range(1, MAX_CARRY_VALUES + 1)])
|
|
FUNCTION = "end"
|
|
CATEGORY = "prompt_builder/loop"
|
|
|
|
def end(self, flow, collection_mode, skip_none, dynprompt=None, **kwargs):
|
|
_require_graph_builder()
|
|
graph = GraphBuilder()
|
|
loop_start = flow[0]
|
|
start_node = dynprompt.get_node(loop_start)
|
|
if start_node["class_type"] != "SxCPForLoopStart":
|
|
raise ValueError("SxCP For Loop End must receive flow from SxCP For Loop Start.")
|
|
total = start_node["inputs"]["total"]
|
|
next_index = graph.node("SxCPLoopIntAdd", a=[loop_start, 1], b=1)
|
|
condition = graph.node("SxCPLoopLessThanOrEqual", a=next_index.out(0), b=total)
|
|
collection = kwargs.get("collected") or [loop_start, 2]
|
|
collect_value = kwargs.get("collect_value")
|
|
next_collection = graph.node(
|
|
"SxCPLoopAppend",
|
|
collection=collection,
|
|
value=collect_value,
|
|
mode=collection_mode,
|
|
skip_none=skip_none,
|
|
)
|
|
next_values = {
|
|
"initial_value0": next_index.out(0),
|
|
"initial_value1": next_collection.out(0),
|
|
}
|
|
for carry_index in range(1, MAX_CARRY_VALUES + 1):
|
|
next_values[f"initial_value{carry_index + 1}"] = kwargs.get(f"initial_value{carry_index}")
|
|
while_close = graph.node("SxCPWhileLoopEnd", flow=flow, condition=condition.out(0), **next_values)
|
|
return {
|
|
"result": tuple(while_close.out(index) for index in range(1, MAX_LOOP_VALUES)),
|
|
"expand": graph.finalize(),
|
|
}
|
|
|
|
|
|
class SxCPLoopIntAdd:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"a": ("INT", {"default": 0}),
|
|
"b": ("INT", {"default": 1}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("INT",)
|
|
RETURN_NAMES = ("int",)
|
|
FUNCTION = "add"
|
|
CATEGORY = "prompt_builder/loop/internal"
|
|
|
|
def add(self, a, b):
|
|
return (int(a) + int(b),)
|
|
|
|
|
|
class SxCPLoopLessThan:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"a": ("INT", {"default": 0}),
|
|
"b": ("INT", {"default": 1}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("BOOLEAN",)
|
|
RETURN_NAMES = ("boolean",)
|
|
FUNCTION = "compare"
|
|
CATEGORY = "prompt_builder/loop/internal"
|
|
|
|
def compare(self, a, b):
|
|
return (int(a) < int(b),)
|
|
|
|
|
|
class SxCPLoopLessThanOrEqual:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"a": ("INT", {"default": 0}),
|
|
"b": ("INT", {"default": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("BOOLEAN",)
|
|
FUNCTION = "compare"
|
|
CATEGORY = "prompt_builder/loop/internal"
|
|
|
|
def compare(self, a, b):
|
|
return (int(a) <= int(b),)
|
|
|
|
|
|
LOOP_NODE_CLASS_MAPPINGS = {
|
|
"SxCPWhileLoopStart": SxCPWhileLoopStart,
|
|
"SxCPWhileLoopEnd": SxCPWhileLoopEnd,
|
|
"SxCPForLoopStart": SxCPForLoopStart,
|
|
"SxCPForLoopEnd": SxCPForLoopEnd,
|
|
"SxCPLoopAppend": SxCPLoopAppend,
|
|
"SxCPAccumulator": SxCPAccumulator,
|
|
"SxCPLoopIntAdd": SxCPLoopIntAdd,
|
|
"SxCPLoopLessThan": SxCPLoopLessThan,
|
|
"SxCPLoopLessThanOrEqual": SxCPLoopLessThanOrEqual,
|
|
}
|
|
|
|
LOOP_NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"SxCPWhileLoopStart": "SxCP While Loop Start",
|
|
"SxCPWhileLoopEnd": "SxCP While Loop End",
|
|
"SxCPForLoopStart": "SxCP For Loop Start",
|
|
"SxCPForLoopEnd": "SxCP For Loop End",
|
|
"SxCPLoopAppend": "SxCP Loop Append",
|
|
"SxCPAccumulator": "SxCP Accumulator",
|
|
"SxCPLoopIntAdd": "SxCP Loop Int Add",
|
|
"SxCPLoopLessThan": "SxCP Loop Less Than",
|
|
"SxCPLoopLessThanOrEqual": "SxCP Loop Less Than Or Equal",
|
|
}
|