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feat: add Metadata Overwrite node for manual generation params override

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This commit is contained in:
Will Miao
2026-07-26 08:50:21 +08:00
parent 077e70169d
commit 125bed3f09
8 changed files with 437 additions and 6 deletions
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5
__init__.py
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@@ -18,6 +18,7 @@ try: # pragma: no cover - import fallback for pytest collection
from .py.nodes.lora_info import LoraInfoLM
from .py.nodes.lora_syntax_to_path import LoraSyntaxToPath
from .py.nodes.create_hook_lora import CreateHookLoraLM
from .py.nodes.metadata_overwrite import MetadataOverwriteLM
from .py.metadata_collector import init as init_metadata_collector
except (
ImportError
@@ -66,6 +67,9 @@ except (
CreateHookLoraLM = importlib.import_module(
"py.nodes.create_hook_lora"
).CreateHookLoraLM
MetadataOverwriteLM = importlib.import_module(
"py.nodes.metadata_overwrite"
).MetadataOverwriteLM
init_metadata_collector = importlib.import_module("py.metadata_collector").init
NODE_CLASS_MAPPINGS = {
@@ -88,6 +92,7 @@ NODE_CLASS_MAPPINGS = {
LoraInfoLM.NAME: LoraInfoLM,
LoraSyntaxToPath.NAME: LoraSyntaxToPath,
CreateHookLoraLM.NAME: CreateHookLoraLM,
MetadataOverwriteLM.NAME: MetadataOverwriteLM,
}
WEB_DIRECTORY = "./web/comfyui"

10
py/metadata_collector/constants.py
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@@ -9,6 +9,14 @@ EMBEDDINGS = "embeddings"
SIZE = "size"
IMAGES = "images"
IS_SAMPLER = "is_sampler" # New constant to mark sampler nodes
OVERWRITE = "overwrite" # Manual metadata overwrite from MetadataOverwriteLM node
# Field names that the MetadataOverwriteLM node and its extractor share
METADATA_OVERWRITE_FIELDS = (
"prompt", "negative_prompt", "seed", "steps", "cfg_scale",
"sampler", "scheduler", "checkpoint", "loras", "size",
"clip_skip", "additional_data",
)
# Complete list of categories to track
METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES]
METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES, OVERWRITE]

14
py/metadata_collector/metadata_processor.py
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@@ -6,7 +6,7 @@ from .constants import IMAGES
# Check if running in standalone mode
standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER, OVERWRITE
from .node_extractors import NODE_EXTRACTORS
logger = logging.getLogger(__name__)
@@ -524,7 +524,8 @@ class MetadataProcessor:
"checkpoint": None,
"loras": "",
"size": None,
"clip_skip": None
"clip_skip": None,
"additional_data": "",
}
# Get the prompt object for node relationship tracing
@@ -672,7 +673,14 @@ class MetadataProcessor:
break
if params["clip_skip"] is None:
params["clip_skip"] = "1"
# ---- Apply manual metadata overwrites ----
for overwrite_info in metadata.get(OVERWRITE, {}).values():
overwrite_params = overwrite_info.get("parameters", {})
for key, value in overwrite_params.items():
if value: # truthy check — only overwrite when user provided a real value
params[key] = value
return params
@staticmethod

10
py/metadata_collector/metadata_registry.py
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@@ -1,7 +1,7 @@
import time
from nodes import NODE_CLASS_MAPPINGS # type: ignore
from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
from .constants import METADATA_CATEGORIES, IMAGES
from .constants import METADATA_CATEGORIES, IMAGES, OVERWRITE
class MetadataRegistry:
@@ -133,8 +133,16 @@ class MetadataRegistry:
if cache_key in self.node_cache:
cached_data = self.node_cache[cache_key]
# Detect bypass (mode=4) / mute (mode=2) — these nodes
# were intentionally disabled and should not contribute
# overwrite values from a previous execution's cache.
node_mode = node_data.get("mode", 0)
node_is_disabled = node_mode in (2, 4)
# Apply cached metadata to the current metadata
for category in self.metadata_categories:
if category == OVERWRITE and node_is_disabled:
continue
if category in cached_data and node_id in cached_data[category]:
if node_id not in metadata[category]:
metadata[category][node_id] = cached_data[category][

30
py/metadata_collector/node_extractors.py
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@@ -2,7 +2,7 @@ import json
import os
import re
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE, METADATA_OVERWRITE_FIELDS
def _store_checkpoint_metadata(metadata, node_id, model_name):
@@ -1221,6 +1221,32 @@ class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
class MetadataOverwriteExtractor(NodeMetadataExtractor):
"""Extract manually specified metadata from MetadataOverwriteLM node.
Stores truthy input values under the OVERWRITE category so that
extract_generation_params can merge them over the inferred params.
"""
@staticmethod
def extract(node_id, inputs, outputs, metadata):
if not inputs:
return
overwrite_params = {}
for key in METADATA_OVERWRITE_FIELDS:
value = inputs.get(key)
if value: # truthy — only overwrite when user provided a real value
overwrite_params[key] = value
if overwrite_params:
metadata.setdefault(OVERWRITE, {})
metadata[OVERWRITE][node_id] = {
"parameters": overwrite_params,
"node_id": node_id,
}
# Registry of node-specific extractors
# Keys are node class names
NODE_EXTRACTORS = {
@@ -1288,5 +1314,7 @@ NODE_EXTRACTORS = {
"CFGGuider": CFGGuiderExtractor, # Add CFGGuider
# Image
"VAEDecode": VAEDecodeExtractor, # Added VAEDecode extractor
# Metadata overwrite
"MetadataOverwriteLM": MetadataOverwriteExtractor,
# Add other nodes as needed
}

154
py/nodes/metadata_overwrite.py Normal file
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@@ -0,0 +1,154 @@
"""Metadata Overwrite node — allows users to manually specify generation parameters
that override the automatically collected/inferred metadata.
All inputs have falsy defaults: only truthy (non-empty / non-zero) values
will overwrite the corresponding field in the final metadata.
"""
from typing import Any
from ..metadata_collector.constants import METADATA_OVERWRITE_FIELDS
class MetadataOverwriteLM:
NAME = "Metadata Overwrite (LoraManager)"
CATEGORY = "Lora Manager/utils"
DESCRIPTION = (
"Manually specify generation parameters to override automatically collected "
"metadata. Only filled/connected inputs will take effect — empty defaults "
"are ignored."
)
@classmethod
def INPUT_TYPES(cls) -> dict[str, Any]:
return {
"optional": {
"prompt": (
"STRING",
{
"default": "",
"multiline": True,
"tooltip": "Positive prompt. Only overwrites when non-empty.",
},
),
"negative_prompt": (
"STRING",
{
"default": "",
"multiline": True,
"tooltip": "Negative prompt. Only overwrites when non-empty.",
},
),
"seed": (
"INT",
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": False,
"tooltip": "Seed value. Only overwrites when > 0.",
},
),
"steps": (
"INT",
{
"default": 0,
"min": 0,
"max": 10000,
"tooltip": "Number of steps. Only overwrites when > 0.",
},
),
"cfg_scale": (
"FLOAT",
{
"default": 0.0,
"min": 0.0,
"max": 100.0,
"tooltip": "CFG scale. Only overwrites when > 0.",
},
),
"sampler": (
"STRING",
{
"default": "",
"tooltip": "Sampler name. Only overwrites when non-empty.",
},
),
"scheduler": (
"STRING",
{
"default": "",
"tooltip": "Scheduler name. Only overwrites when non-empty.",
},
),
"checkpoint": (
"STRING",
{
"default": "",
"tooltip": "Checkpoint / model name. Only overwrites when non-empty.",
},
),
"loras": (
"STRING",
{
"default": "",
"multiline": True,
"tooltip": (
"LoRA syntax, e.g. <lora:name:strength> "
"or <lora:name:model_strength:clip_strength>, "
"separated by spaces. Only overwrites when non-empty."
),
},
),
"size": (
"STRING",
{
"default": "",
"tooltip": (
"Image size in WIDTHxHEIGHT format (e.g. 512x768). "
"Only overwrites when non-empty."
),
},
),
"clip_skip": (
"INT",
{
"default": 0,
"min": -24,
"max": 24,
"tooltip": "Clip skip. Only overwrites when non-zero.",
},
),
"additional_data": (
"STRING",
{
"default": "",
"multiline": True,
"tooltip": (
"Additional data to embed in the image metadata. "
"Inserted between Clip skip and Model hash in the "
"A1111-compatible parameters string. "
'Example: "Copyright": "Some license info"'
),
},
),
},
}
RETURN_TYPES = ("METADATA",)
RETURN_NAMES = ("metadata",)
FUNCTION = "collect_metadata"
OUTPUT_NODE = True
def collect_metadata(self, **kwargs: Any) -> tuple[dict[str, Any]]:
"""Collect non-falsy input values into a metadata dict.
Only values that are truthy (non-empty string, non-zero number)
are included — matching the overwrite logic in the metadata pipeline.
"""
result: dict[str, Any] = {}
for key in METADATA_OVERWRITE_FIELDS:
value = kwargs.get(key)
if value:
result[key] = value
return (result,)

3
py/nodes/save_image.py
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@@ -471,6 +471,9 @@ class SaveImageLM:
params.append(f"Clip skip: {abs(cs)}")
except (ValueError, TypeError):
pass
additional_data = metadata_dict.get("additional_data", "")
if additional_data:
params.append(additional_data)
if ckpt_hash:
params.append(f"Model hash: {ckpt_hash[:10].upper()}")
if ckpt_display_name:

217
tests/metadata_collector/test_metadata_collector.py
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@@ -820,3 +820,220 @@ def test_lora_manager_checkpoint_and_unet_loaders_extract_models(metadata_regist
"type": "checkpoint",
"node_id": "unet_node",
}
# ---------------------------------------------------------------------------
# MetadataOverwriteExtractor & overwrite merge tests
# ---------------------------------------------------------------------------
from py.metadata_collector.constants import OVERWRITE, METADATA_OVERWRITE_FIELDS
from py.metadata_collector.node_extractors import MetadataOverwriteExtractor
def test_metadata_overwrite_extractor_stores_truthy_values(metadata_registry):
"""Extractor should store truthy inputs under the OVERWRITE category."""
metadata_registry.start_collection("prompt-ow")
metadata = metadata_registry.prompt_metadata["prompt-ow"]
inputs = {
"prompt": "a beautiful landscape",
"negative_prompt": "",
"seed": 42,
"steps": 0,
"cfg_scale": 7.5,
"sampler": "",
"scheduler": "",
"checkpoint": "myModel.safetensors",
"loras": "<lora:detail:0.8>",
"size": "1024x768",
"clip_skip": 0,
"additional_data": '{"Copyright": "CC0"}',
}
MetadataOverwriteExtractor.extract("ow-1", inputs, None, metadata)
assert OVERWRITE in metadata
assert "ow-1" in metadata[OVERWRITE]
params = metadata[OVERWRITE]["ow-1"]["parameters"]
# Truthy values stored
assert params["prompt"] == "a beautiful landscape"
assert params["seed"] == 42
assert params["cfg_scale"] == 7.5
assert params["checkpoint"] == "myModel.safetensors"
assert params["loras"] == "<lora:detail:0.8>"
assert params["size"] == "1024x768"
assert params["additional_data"] == '{"Copyright": "CC0"}'
# Falsy values NOT stored
assert "negative_prompt" not in params
assert "steps" not in params
assert "sampler" not in params
assert "scheduler" not in params
assert "clip_skip" not in params
metadata_registry.clear_metadata()
def test_metadata_overwrite_extractor_empty_inputs(metadata_registry):
"""Extractor with all-falsy inputs should NOT create OVERWRITE category."""
metadata_registry.start_collection("prompt-ow2")
metadata = metadata_registry.prompt_metadata["prompt-ow2"]
inputs = {key: "" for key in METADATA_OVERWRITE_FIELDS}
inputs.update({"seed": 0, "steps": 0, "cfg_scale": 0.0, "clip_skip": 0})
MetadataOverwriteExtractor.extract("ow-2", inputs, None, metadata)
# start_collection pre-creates empty dicts for all categories,
# but no node should have populated OVERWRITE with any data
assert not metadata[OVERWRITE]
metadata_registry.clear_metadata()
def test_extract_generation_params_applies_overwrite(metadata_registry, populated_registry, monkeypatch):
"""overwrite values should replace inferred params in extract_generation_params."""
import py.metadata_collector.metadata_processor as mp
monkeypatch.setattr(mp, "standalone_mode", False)
metadata = populated_registry["metadata"]
registry_obj = populated_registry["registry"]
# Simulate the MetadataOverwriteLM node having been executed with overwrite values
registry_obj.start_collection("promptA")
# Re-populate with the same data (start_collection resets)
registry_obj.set_current_prompt(populated_registry["prompt"])
metadata2 = registry_obj.prompt_metadata["promptA"]
# Inject overwrite data into metadata
metadata2[OVERWRITE] = {
"ow-1": {
"parameters": {
"seed": 777,
"additional_data": '{"AuthorURL": "https://civitai.com/user/foo"}',
},
"node_id": "ow-1",
}
}
# Copy other categories from original populated metadata
for cat in ("models", "prompts", "sampling", "loras", "size", "images"):
if cat in metadata:
metadata2[cat] = metadata[cat]
metadata2["execution_order"] = metadata["execution_order"]
params = MetadataProcessor.extract_generation_params(metadata2, id="vae")
# Overwritten values
assert params["seed"] == 777
assert params["additional_data"] == '{"AuthorURL": "https://civitai.com/user/foo"}'
# Inferred values still present (not overwritten)
assert params["prompt"] == "A castle on a hill"
assert params["cfg_scale"] == 7.5
assert params["checkpoint"] == "model.safetensors"
registry_obj.clear_metadata()
def test_extract_generation_params_overwrite_falsy_skipped(metadata_registry, populated_registry, monkeypatch):
"""Overwrite entries with falsy values should NOT replace inferred params."""
import py.metadata_collector.metadata_processor as mp
monkeypatch.setattr(mp, "standalone_mode", False)
metadata = populated_registry["metadata"]
registry_obj = populated_registry["registry"]
registry_obj.start_collection("promptA")
registry_obj.set_current_prompt(populated_registry["prompt"])
metadata2 = registry_obj.prompt_metadata["promptA"]
# Inject overwrite with falsy values
metadata2[OVERWRITE] = {
"ow-1": {
"parameters": {
"seed": 0,
"steps": 0,
"cfg_scale": 0.0,
"prompt": "",
"clip_skip": 0,
},
"node_id": "ow-1",
}
}
for cat in ("models", "prompts", "sampling", "loras", "size", "images"):
if cat in metadata:
metadata2[cat] = metadata[cat]
metadata2["execution_order"] = metadata["execution_order"]
params = MetadataProcessor.extract_generation_params(metadata2, id="vae")
# Falsy overwrites should NOT have replaced inferred values
assert params["prompt"] == "A castle on a hill"
assert params["cfg_scale"] == 7.5
registry_obj.clear_metadata()
def test_fill_missing_metadata_skips_overwrite_for_bypassed_node(metadata_registry):
"""Bypassed (mode=4) node should not have OVERWRITE filled from cache."""
metadata_registry.start_collection("prompt-bypass")
# Simulate a previous execution that cached overwrite data
metadata_registry.record_node_execution(
"ow-1",
"MetadataOverwriteLM",
{"seed": 99, "prompt": "test", "steps": 0, "cfg_scale": 0.0,
"negative_prompt": "", "sampler": "", "scheduler": "", "checkpoint": "",
"loras": "", "size": "", "clip_skip": 0, "additional_data": ""},
None,
)
# Now start a new prompt where the node is bypassed (mode=4)
metadata_registry.start_collection("prompt-bypass-2")
original_prompt = {
"ow-1": {"class_type": "MetadataOverwriteLM", "inputs": {}, "mode": 4},
}
metadata_registry.set_current_prompt(
SimpleNamespace(original_prompt=original_prompt)
)
metadata = metadata_registry.get_metadata("prompt-bypass-2")
# The overwrite data should NOT be present (node was bypassed, not
# a cache hit — it should not inherit previous execution's overwrite)
assert "ow-1" not in metadata.get(OVERWRITE, {})
metadata_registry.clear_metadata()
def test_fill_missing_metadata_fills_overwrite_for_muted_node(metadata_registry):
"""Muted (mode=2) node should also not have OVERWRITE filled from cache."""
metadata_registry.start_collection("prompt-mute")
# Simulate a previous execution that cached overwrite data
metadata_registry.record_node_execution(
"ow-1",
"MetadataOverwriteLM",
{"seed": 88, "prompt": "test2", "steps": 0, "cfg_scale": 0.0,
"negative_prompt": "", "sampler": "", "scheduler": "", "checkpoint": "",
"loras": "", "size": "", "clip_skip": 0, "additional_data": ""},
None,
)
# Start a new prompt where the node is muted (mode=2)
metadata_registry.start_collection("prompt-mute-2")
original_prompt = {
"ow-1": {"class_type": "MetadataOverwriteLM", "inputs": {}, "mode": 2},
}
metadata_registry.set_current_prompt(
SimpleNamespace(original_prompt=original_prompt)
)
metadata = metadata_registry.get_metadata("prompt-mute-2")
assert "ow-1" not in metadata.get(OVERWRITE, {})
metadata_registry.clear_metadata()