v1.0.0
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simple321vip
315
skills/creative/comfyui/scripts/extract_schema.py
Executable file
315
skills/creative/comfyui/scripts/extract_schema.py
Executable file
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#!/usr/bin/env python3
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"""
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extract_schema.py — Analyze a ComfyUI API-format workflow and extract
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controllable parameters.
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Improvements over v1:
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- Catalogs live in `_common.py`, shared with `check_deps.py`
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- Coverage expanded for Flux / SD3 / Wan / Hunyuan / LTX / IPAdapter / rgthree
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- Symmetric duplicate-name resolution: ALL duplicates get a node-id suffix
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(instead of "first wins, second renamed"), so callers see consistent names
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- Negative prompt detected by tracing `KSampler.negative` connections back to
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the source CLIPTextEncode (more reliable than meta-title heuristic)
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- Embedding references in prompt text are extracted as model dependencies
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- Detects Primitive nodes that drive other nodes' inputs (and surfaces them
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as the user-facing parameter)
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- Reroutes are followed when tracing connections
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Usage:
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python3 extract_schema.py workflow_api.json
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python3 extract_schema.py workflow_api.json --output schema.json
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Stdlib-only. Python 3.10+.
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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from pathlib import Path
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from typing import Any
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from _common import ( # noqa: E402
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OUTPUT_NODES, PARAM_PATTERNS, PROMPT_FIELDS,
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is_link, iter_embedding_refs, iter_model_deps, iter_nodes, unwrap_workflow,
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)
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# Sampler nodes whose `positive` / `negative` connections we trace
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SAMPLER_NODE_FAMILY = {
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"KSampler", "KSamplerAdvanced",
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"SamplerCustom", "SamplerCustomAdvanced",
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"BasicGuider", "CFGGuider", "DualCFGGuider",
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}
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def infer_type(value: Any) -> str:
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if isinstance(value, bool):
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return "bool"
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if isinstance(value, int):
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return "int"
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if isinstance(value, float):
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return "float"
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if isinstance(value, str):
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return "string"
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if isinstance(value, list):
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return "link"
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if isinstance(value, dict):
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return "object"
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return "unknown"
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def trace_to_node(workflow: dict, link: list, *, max_hops: int = 8) -> str | None:
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"""Follow a [node_id, slot] link, hopping through Reroute / Primitive nodes
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if needed, to find the *upstream* node id that holds the actual value/input.
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Bounded by both `max_hops` AND a visited-set to prevent infinite loops on
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pathological graphs.
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"""
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if not is_link(link):
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return None
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nid: str | None = link[0]
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visited: set[str] = set()
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for _ in range(max_hops):
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if nid is None or nid in visited:
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return nid
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visited.add(nid)
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node = workflow.get(nid)
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if not isinstance(node, dict):
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return None
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cls = node.get("class_type", "")
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# Reroute / Primitive / passthrough wrappers
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if cls in ("Reroute", "PrimitiveNode", "Note", "easy showAnything"):
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inputs = node.get("inputs", {}) or {}
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# Find first link-shaped input and follow it
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next_link = next((v for v in inputs.values() if is_link(v)), None)
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if next_link is None:
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return nid
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nid = next_link[0]
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continue
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return nid
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return nid
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def find_negative_prompt_node(workflow: dict) -> str | None:
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"""Trace `negative` input of a sampler back to the source text encoder."""
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for nid, node in iter_nodes(workflow):
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if node["class_type"] not in SAMPLER_NODE_FAMILY:
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continue
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inputs = node.get("inputs", {}) or {}
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neg = inputs.get("negative")
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if not is_link(neg):
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continue
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src = trace_to_node(workflow, neg)
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if src and isinstance(workflow.get(src), dict):
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cls = workflow[src].get("class_type", "")
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if cls.startswith("CLIPTextEncode") or cls in ("smZ CLIPTextEncode", "BNK_CLIPTextEncodeAdvanced"):
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return src
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return None
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def find_positive_prompt_node(workflow: dict) -> str | None:
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for nid, node in iter_nodes(workflow):
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if node["class_type"] not in SAMPLER_NODE_FAMILY:
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continue
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inputs = node.get("inputs", {}) or {}
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pos = inputs.get("positive")
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if not is_link(pos):
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continue
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src = trace_to_node(workflow, pos)
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if src and isinstance(workflow.get(src), dict):
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cls = workflow[src].get("class_type", "")
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if cls.startswith("CLIPTextEncode") or cls in ("smZ CLIPTextEncode", "BNK_CLIPTextEncodeAdvanced"):
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return src
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return None
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def extract_schema(workflow: dict) -> dict:
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"""Extract controllable parameters from a workflow.
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Returns:
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{
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"parameters": { friendly_name: {node_id, field, type, value, ...} },
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"output_nodes": [node_id, ...],
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"model_dependencies": [{node_id, class_type, field, value, folder}],
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"embedding_dependencies": [{node_id, embedding_name, found_in_field, value_excerpt}],
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"summary": {...}
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}
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"""
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output_nodes: list[str] = []
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# First pass: identify positive / negative prompt nodes via connection tracing
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pos_node = find_positive_prompt_node(workflow)
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neg_node = find_negative_prompt_node(workflow)
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# ----- collect raw parameter candidates -----
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# Each candidate = (friendly_name, node_id, field, value)
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# We resolve duplicate friendly_names AFTER the loop so dedup is symmetric.
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raw_params: list[dict] = []
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for node_id, node in iter_nodes(workflow):
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cls = node["class_type"]
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inputs = node.get("inputs", {}) or {}
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if cls in OUTPUT_NODES:
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output_nodes.append(node_id)
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# Match this node against PARAM_PATTERNS
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for p_class, p_field, friendly in PARAM_PATTERNS:
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if cls != p_class:
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continue
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if p_field not in inputs:
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continue
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value = inputs[p_field]
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t = infer_type(value)
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if t == "link":
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continue # connections aren't directly controllable
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actual_name = friendly
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# Disambiguate prompt vs negative_prompt by connection tracing
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if friendly == "prompt":
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if node_id == neg_node and pos_node != neg_node:
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actual_name = "negative_prompt"
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elif node_id == pos_node:
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actual_name = "prompt"
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else:
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# Fallback: use _meta.title hints if present
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meta_title = (node.get("_meta") or {}).get("title", "").lower()
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if any(t_ in meta_title for t_ in ("negative", "neg", "-prompt", "anti")):
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actual_name = "negative_prompt"
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raw_params.append({
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"name_hint": actual_name,
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"node_id": node_id,
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"field": p_field,
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"type": t,
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"value": value,
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"class_type": cls,
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})
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# ----- symmetric duplicate-name resolution -----
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# Group by name_hint. If a hint appears once, keep it. If multiple, suffix
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# ALL with their node_id. Always-stable, always-uniquely-addressable.
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by_name: dict[str, list[dict]] = {}
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for r in raw_params:
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by_name.setdefault(r["name_hint"], []).append(r)
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parameters: dict[str, dict] = {}
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for name, entries in by_name.items():
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if len(entries) == 1:
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r = entries[0]
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parameters[name] = {
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"node_id": r["node_id"], "field": r["field"],
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"type": r["type"], "value": r["value"],
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"class_type": r["class_type"],
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}
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else:
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# Sort by node_id (string-natural) for stability
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entries.sort(key=lambda x: (str(x["node_id"]).zfill(8), x["field"]))
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for r in entries:
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full_name = f"{name}_{r['node_id']}"
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parameters[full_name] = {
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"node_id": r["node_id"], "field": r["field"],
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"type": r["type"], "value": r["value"],
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"class_type": r["class_type"],
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"alias_of": name,
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}
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# ----- model dependencies -----
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model_deps = list(iter_model_deps(workflow))
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# ----- embedding dependencies (in prompt text) -----
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embedding_deps: list[dict] = []
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seen_emb: set[tuple[str, str]] = set()
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for nid, emb_name in iter_embedding_refs(workflow):
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key = (nid, emb_name)
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if key in seen_emb:
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continue
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seen_emb.add(key)
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# Find which field had the reference, for context
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node = workflow.get(nid, {})
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inputs = node.get("inputs", {}) or {}
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found_field = None
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excerpt = None
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for fname, fval in inputs.items():
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if isinstance(fval, str) and fname in PROMPT_FIELDS and emb_name in fval:
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found_field = fname
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excerpt = fval[:120]
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break
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embedding_deps.append({
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"node_id": nid,
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"embedding_name": emb_name,
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"field": found_field,
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"value_excerpt": excerpt,
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"folder": "embeddings",
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})
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# ----- summary -----
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summary = {
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"parameter_count": len(parameters),
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"output_node_count": len(output_nodes),
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"model_dep_count": len(model_deps),
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"embedding_dep_count": len(embedding_deps),
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"has_negative_prompt": "negative_prompt" in parameters,
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"has_seed": "seed" in parameters or any(p.startswith("seed_") for p in parameters),
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"is_video_workflow": any(
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workflow.get(n, {}).get("class_type", "") in {
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"VHS_VideoCombine", "SaveVideo", "SaveAnimatedWEBP", "SaveAnimatedPNG",
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} for n in output_nodes
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),
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}
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return {
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"parameters": parameters,
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"output_nodes": output_nodes,
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"model_dependencies": model_deps,
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"embedding_dependencies": embedding_deps,
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"summary": summary,
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}
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def main(argv: list[str] | None = None) -> int:
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p = argparse.ArgumentParser(description="Extract controllable parameters from a ComfyUI workflow")
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p.add_argument("workflow", help="Path to workflow API JSON file")
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p.add_argument("--output", "-o", help="Output file (default: stdout)")
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p.add_argument("--summary-only", action="store_true",
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help="Only print the summary block")
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args = p.parse_args(argv)
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wf_path = Path(args.workflow).expanduser()
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if not wf_path.exists():
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print(f"Error: {wf_path} not found", file=sys.stderr)
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return 1
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try:
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with wf_path.open() as f:
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payload = json.load(f)
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workflow = unwrap_workflow(payload)
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except ValueError as e:
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print(f"Error: {e}", file=sys.stderr)
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return 1
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except json.JSONDecodeError as e:
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print(f"Error: invalid JSON — {e}", file=sys.stderr)
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return 1
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schema = extract_schema(workflow)
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if args.summary_only:
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out = json.dumps(schema["summary"], indent=2)
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else:
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out = json.dumps(schema, indent=2, default=str)
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if args.output:
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Path(args.output).write_text(out)
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print(f"Schema written to {args.output}", file=sys.stderr)
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else:
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print(out)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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