353 lines
12 KiB
Python
353 lines
12 KiB
Python
import json
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from collections import deque
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from ...utils.logging import debug_log
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class PromptIndex:
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"""Cache prompt metadata for faster worker/master prompt preparation."""
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def __init__(self, prompt_obj):
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self._prompt_json = json.dumps(prompt_obj)
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self.nodes_by_class = {}
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self.class_by_node = {}
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self.inputs_by_node = {}
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for node_id, node in _iter_prompt_nodes(prompt_obj):
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class_type = node.get("class_type")
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node_id_str = str(node_id)
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if class_type:
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self.nodes_by_class.setdefault(class_type, []).append(node_id_str)
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self.class_by_node[node_id_str] = class_type
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self.inputs_by_node[node_id_str] = node.get("inputs", {})
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self._upstream_cache = {}
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def copy_prompt(self):
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return json.loads(self._prompt_json)
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def nodes_for_class(self, class_name):
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return self.nodes_by_class.get(class_name, [])
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def has_upstream(self, start_node_id, target_class):
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cache_key = (str(start_node_id), target_class)
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if cache_key in self._upstream_cache:
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return self._upstream_cache[cache_key]
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visited = set()
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stack = [str(start_node_id)]
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while stack:
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node_id = stack.pop()
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if node_id in visited:
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continue
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visited.add(node_id)
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inputs = self.inputs_by_node.get(node_id, {})
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for value in inputs.values():
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if isinstance(value, list) and len(value) == 2:
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upstream_id = str(value[0])
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if self.class_by_node.get(upstream_id) == target_class:
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self._upstream_cache[cache_key] = True
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return True
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if upstream_id in self.inputs_by_node:
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stack.append(upstream_id)
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self._upstream_cache[cache_key] = False
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return False
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def _iter_prompt_nodes(prompt_obj):
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for node_id, node in prompt_obj.items():
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if isinstance(node, dict):
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yield str(node_id), node
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def find_nodes_by_class(prompt_obj, class_name):
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nodes = []
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for node_id, node in _iter_prompt_nodes(prompt_obj):
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if node.get("class_type") == class_name:
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nodes.append(node_id)
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return nodes
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def _find_downstream_nodes(prompt_obj, start_ids):
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"""Return all nodes reachable downstream from the provided IDs."""
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adjacency = {}
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for node_id, node in _iter_prompt_nodes(prompt_obj):
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inputs = node.get("inputs", {})
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for value in inputs.values():
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if isinstance(value, list) and len(value) == 2:
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source_id = str(value[0])
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adjacency.setdefault(source_id, set()).add(str(node_id))
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connected = set(start_ids)
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queue = deque(start_ids)
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while queue:
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current = queue.popleft()
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for dependent in adjacency.get(current, ()): # pragma: no branch - simple iteration
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if dependent not in connected:
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connected.add(dependent)
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queue.append(dependent)
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return connected
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def _create_numeric_id_generator(prompt_obj):
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"""Return a closure that yields new numeric string IDs."""
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max_id = 0
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for node_id in prompt_obj.keys():
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try:
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numeric = int(node_id)
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except (TypeError, ValueError):
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continue
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max_id = max(max_id, numeric)
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counter = max_id
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def _next_id():
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nonlocal counter
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counter += 1
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return str(counter)
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return _next_id
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def _find_upstream_nodes(prompt_obj, start_ids):
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"""Return all nodes reachable upstream from start_ids, including start nodes."""
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connected = set(str(node_id) for node_id in start_ids)
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queue = deque(connected)
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while queue:
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node_id = queue.popleft()
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node = prompt_obj.get(node_id) or {}
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inputs = node.get("inputs", {})
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for value in inputs.values():
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if isinstance(value, list) and len(value) == 2:
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source_id = str(value[0])
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if source_id in prompt_obj and source_id not in connected:
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connected.add(source_id)
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queue.append(source_id)
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return connected
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def prune_prompt_for_worker(prompt_obj):
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"""Prune worker prompt to distributed nodes and their upstream dependencies."""
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collector_ids = find_nodes_by_class(prompt_obj, "DistributedCollector")
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upscale_ids = find_nodes_by_class(prompt_obj, "UltimateSDUpscaleDistributed")
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distributed_ids = collector_ids + upscale_ids
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if not distributed_ids:
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return prompt_obj
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connected = _find_upstream_nodes(prompt_obj, distributed_ids)
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pruned_prompt = {}
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for node_id in connected:
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node = prompt_obj.get(node_id)
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if node is not None:
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pruned_prompt[node_id] = json.loads(json.dumps(node))
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# Generate IDs from the original prompt so we never reuse IDs from pruned downstream nodes.
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next_id = _create_numeric_id_generator(prompt_obj)
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for dist_id in distributed_ids:
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if dist_id not in pruned_prompt:
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continue
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downstream = _find_downstream_nodes(prompt_obj, [dist_id])
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has_removed_downstream = any(node_id != dist_id for node_id in downstream)
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if has_removed_downstream:
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preview_id = next_id()
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pruned_prompt[preview_id] = {
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"inputs": {
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"images": [dist_id, 0],
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},
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"class_type": "PreviewImage",
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"_meta": {
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"title": "Preview Image (auto-added)",
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},
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}
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return pruned_prompt
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def prepare_delegate_master_prompt(prompt_obj, collector_ids):
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"""Prune master prompt so it only executes post-collector nodes in delegate mode."""
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downstream = _find_downstream_nodes(prompt_obj, collector_ids)
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nodes_to_keep = set(collector_ids)
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nodes_to_keep.update(downstream)
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pruned_prompt = {}
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for node_id in nodes_to_keep:
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node = prompt_obj.get(node_id)
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if node is not None:
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pruned_prompt[node_id] = json.loads(json.dumps(node))
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pruned_ids = set(pruned_prompt.keys())
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for node_id, node in pruned_prompt.items():
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inputs = node.get("inputs")
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if not inputs:
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continue
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for input_name, input_value in list(inputs.items()):
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if isinstance(input_value, list) and len(input_value) == 2:
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source_id = str(input_value[0])
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if source_id not in pruned_ids:
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inputs.pop(input_name, None)
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debug_log(
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f"Removed upstream reference '{input_name}' from node {node_id} for delegate-only master prompt."
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)
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# Generate IDs from the original prompt to avoid ID collisions with pruned nodes.
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next_id = _create_numeric_id_generator(prompt_obj)
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for collector_id in collector_ids:
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collector_entry = pruned_prompt.get(collector_id)
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if not collector_entry:
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continue
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placeholder_id = next_id()
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pruned_prompt[placeholder_id] = {
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"class_type": "DistributedEmptyImage",
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"inputs": {
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"height": 64,
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"width": 64,
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"channels": 3,
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},
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"_meta": {
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"title": "Distributed Empty Image (auto-added)",
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},
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}
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collector_entry.setdefault("inputs", {})["images"] = [placeholder_id, 0]
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debug_log(
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f"Inserted placeholder node {placeholder_id} for collector {collector_id} in delegate-only master prompt."
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)
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return pruned_prompt
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def generate_job_id_map(prompt_index, prefix):
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"""Create stable per-node job IDs for distributed nodes."""
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job_map = {}
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distributed_nodes = prompt_index.nodes_for_class("DistributedCollector") + prompt_index.nodes_for_class(
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"UltimateSDUpscaleDistributed"
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)
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for node_id in distributed_nodes:
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job_map[node_id] = f"{prefix}_{node_id}"
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return job_map
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def _override_seed_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map):
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"""Configure DistributedSeed nodes for master or worker role."""
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for node_id in prompt_index.nodes_for_class("DistributedSeed"):
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node = prompt_copy.get(node_id)
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if not isinstance(node, dict):
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continue
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inputs = node.setdefault("inputs", {})
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inputs["is_worker"] = not is_master
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if is_master:
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inputs["worker_id"] = ""
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else:
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inputs["worker_id"] = f"worker_{worker_index_map.get(participant_id, 0)}"
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def _override_collector_nodes(
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prompt_copy,
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prompt_index,
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is_master,
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participant_id,
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job_id_map,
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master_url,
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enabled_json,
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delegate_master,
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):
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"""Configure DistributedCollector nodes for master or worker role."""
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for node_id in prompt_index.nodes_for_class("DistributedCollector"):
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node = prompt_copy.get(node_id)
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if not isinstance(node, dict):
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continue
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if prompt_index.has_upstream(node_id, "UltimateSDUpscaleDistributed"):
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node.setdefault("inputs", {})["pass_through"] = True
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continue
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inputs = node.setdefault("inputs", {})
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inputs["multi_job_id"] = job_id_map.get(node_id, node_id)
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inputs["is_worker"] = not is_master
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inputs["enabled_worker_ids"] = enabled_json
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if is_master:
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inputs["delegate_only"] = bool(delegate_master)
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inputs.pop("master_url", None)
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inputs.pop("worker_id", None)
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else:
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inputs["master_url"] = master_url
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inputs["worker_id"] = participant_id
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inputs["delegate_only"] = False
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def _override_upscale_nodes(
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prompt_copy,
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prompt_index,
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is_master,
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participant_id,
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job_id_map,
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master_url,
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enabled_json,
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):
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"""Configure UltimateSDUpscaleDistributed nodes for master or worker role."""
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for node_id in prompt_index.nodes_for_class("UltimateSDUpscaleDistributed"):
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node = prompt_copy.get(node_id)
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if not isinstance(node, dict):
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continue
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inputs = node.setdefault("inputs", {})
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inputs["multi_job_id"] = job_id_map.get(node_id, node_id)
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inputs["is_worker"] = not is_master
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inputs["enabled_worker_ids"] = enabled_json
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if is_master:
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inputs.pop("master_url", None)
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inputs.pop("worker_id", None)
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else:
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inputs["master_url"] = master_url
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inputs["worker_id"] = participant_id
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def _override_value_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map):
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"""Configure DistributedValue nodes for master or worker role."""
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for node_id in prompt_index.nodes_for_class("DistributedValue"):
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node = prompt_copy.get(node_id)
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if not isinstance(node, dict):
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continue
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inputs = node.setdefault("inputs", {})
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inputs["is_worker"] = not is_master
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if is_master:
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inputs["worker_id"] = ""
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else:
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inputs["worker_id"] = f"worker_{worker_index_map.get(participant_id, 0)}"
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def apply_participant_overrides(
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prompt_copy,
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participant_id,
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enabled_worker_ids,
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job_id_map,
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master_url,
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delegate_master,
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prompt_index,
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):
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"""Return a prompt copy with hidden inputs configured for master/worker."""
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is_master = participant_id == "master"
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worker_index_map = {wid: idx for idx, wid in enumerate(enabled_worker_ids)}
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enabled_json = json.dumps(enabled_worker_ids)
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_override_seed_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map)
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_override_value_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map)
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_override_collector_nodes(
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prompt_copy,
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prompt_index,
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is_master,
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participant_id,
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job_id_map,
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master_url,
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enabled_json,
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delegate_master,
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)
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_override_upscale_nodes(
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prompt_copy,
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prompt_index,
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is_master,
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participant_id,
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job_id_map,
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master_url,
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enabled_json,
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)
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return prompt_copy
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