mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-08-16 02:33:21 -03:00
feat(loaders): add random model selection by base model to checkpoint/unet loaders
Add dedicated Random Checkpoint/Unet Loader (LoraManager) nodes that pick a random model from the indexed pool on every run, optionally filtered by base_model, and expose the selected model name via a STRING output.
This commit is contained in:
Martial Michel
@@ -0,0 +1,326 @@
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
from typing import Any, List, Optional, Tuple
|
||||
import comfy.sd # pyright: ignore[reportMissingImports]
|
||||
from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _reload_gguf_unet(
|
||||
unet_path: str, weight_dtype: str, disable_dynamic: bool = False
|
||||
) -> object:
|
||||
"""Reload a GGUF diffusion model from disk (cached_patcher_init factory).
|
||||
|
||||
Mirrors the GGUF branch of RandomUNETLoaderLM.load_unet so ModelPatcher
|
||||
deepclone/dynamic machinery can rebuild GGUF models with the correct
|
||||
GGMLOps. ``disable_dynamic`` is accepted for signature compatibility
|
||||
with core ComfyUI loaders.
|
||||
"""
|
||||
loader = RandomUNETLoaderLM()
|
||||
model, _unet_name = loader._load_gguf_unet(unet_path, unet_path, weight_dtype)
|
||||
return model
|
||||
|
||||
|
||||
class RandomUNETLoaderLM:
|
||||
"""UNET Loader that can randomly pick a diffusion model from the pool
|
||||
|
||||
Loads diffusion models/UNets from both standard ComfyUI folders and LoRA
|
||||
Manager's extra folder paths. Supports both regular diffusion models and
|
||||
GGUF format models. When select_at_random is enabled, ignores unet_name
|
||||
and picks a random diffusion model (optionally filtered by base_model)
|
||||
on every run.
|
||||
"""
|
||||
|
||||
NAME = "Random Unet Loader (LoraManager)"
|
||||
CATEGORY = "Lora Manager/loaders"
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
# Get list of unet names from scanner (includes extra folder paths)
|
||||
unet_names = cls._get_unet_names()
|
||||
base_models = cls._get_available_base_models()
|
||||
return {
|
||||
"required": {
|
||||
"unet_name": (
|
||||
unet_names,
|
||||
{"tooltip": "The name of the diffusion model to load."},
|
||||
),
|
||||
"weight_dtype": (
|
||||
["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],
|
||||
{"tooltip": "The dtype to use for the model weights."},
|
||||
),
|
||||
"select_at_random": (
|
||||
"BOOLEAN",
|
||||
{
|
||||
"default": False,
|
||||
"tooltip": (
|
||||
"Ignore unet_name and pick a random diffusion model from "
|
||||
"the pool (optionally filtered by base_model) on every run."
|
||||
),
|
||||
},
|
||||
),
|
||||
"base_model": (
|
||||
base_models,
|
||||
{
|
||||
"default": "Any",
|
||||
"tooltip": "Restrict random selection to this base model. 'Any' uses the full pool.",
|
||||
},
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MODEL", "STRING")
|
||||
RETURN_NAMES = ("MODEL", "model_name")
|
||||
OUTPUT_TOOLTIPS = (
|
||||
"The model used for denoising latents.",
|
||||
"The name of the diffusion model that was loaded (useful when select_at_random is enabled).",
|
||||
)
|
||||
FUNCTION = "load_unet"
|
||||
|
||||
@classmethod
|
||||
def IS_CHANGED(
|
||||
cls, unet_name, weight_dtype, select_at_random=False, base_model="Any"
|
||||
):
|
||||
# Force re-execution on every run while randomizing, since the widget
|
||||
# values themselves don't change between queue runs.
|
||||
if select_at_random:
|
||||
return float("nan")
|
||||
return unet_name
|
||||
|
||||
@staticmethod
|
||||
def _run_async(coro_fn):
|
||||
"""Run an async fetcher, handling the case where an event loop is already running."""
|
||||
import asyncio
|
||||
|
||||
try:
|
||||
asyncio.get_running_loop()
|
||||
import concurrent.futures
|
||||
|
||||
def run_in_thread():
|
||||
new_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(new_loop)
|
||||
try:
|
||||
return new_loop.run_until_complete(coro_fn())
|
||||
finally:
|
||||
new_loop.close()
|
||||
|
||||
with concurrent.futures.ThreadPoolExecutor() as executor:
|
||||
future = executor.submit(run_in_thread)
|
||||
return future.result()
|
||||
except RuntimeError:
|
||||
return asyncio.run(coro_fn())
|
||||
|
||||
@classmethod
|
||||
def _get_unet_names(cls, base_model: Optional[str] = None) -> List[str]:
|
||||
"""Get list of diffusion model names from scanner cache in ComfyUI format (relative path with extension)
|
||||
|
||||
Args:
|
||||
base_model: If given (and not "Any"), only include models matching this base model.
|
||||
"""
|
||||
try:
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
|
||||
async def _get_names():
|
||||
scanner = await ServiceRegistry.get_checkpoint_scanner()
|
||||
cache = await scanner.get_cached_data()
|
||||
|
||||
# Get all model roots for calculating relative paths
|
||||
model_roots = scanner.get_model_roots()
|
||||
|
||||
# Filter only diffusion_model type and format names
|
||||
names = []
|
||||
for item in cache.raw_data:
|
||||
if item.get("sub_type") != "diffusion_model":
|
||||
continue
|
||||
if (
|
||||
base_model
|
||||
and base_model != "Any"
|
||||
and item.get("base_model") != base_model
|
||||
):
|
||||
continue
|
||||
file_path = item.get("file_path", "")
|
||||
# Only offer models that still exist on disk so ComfyUI
|
||||
# flags missing diffusion models at queue time via
|
||||
# "value not in list" (the scanner cache can be stale).
|
||||
if file_path and os.path.exists(file_path):
|
||||
# Format using relative path with OS-native separator
|
||||
formatted_name = _format_model_name_for_comfyui(
|
||||
file_path, model_roots
|
||||
)
|
||||
if formatted_name:
|
||||
names.append(formatted_name)
|
||||
|
||||
return sorted(names)
|
||||
|
||||
return cls._run_async(_get_names)
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting unet names: {e}")
|
||||
return []
|
||||
|
||||
@classmethod
|
||||
def _get_available_base_models(cls) -> List[str]:
|
||||
"""Get distinct base_model values present among indexed diffusion models, for the random-selection filter."""
|
||||
try:
|
||||
from ..services.service_registry import ServiceRegistry
|
||||
|
||||
async def _get_base_models():
|
||||
scanner = await ServiceRegistry.get_checkpoint_scanner()
|
||||
cache = await scanner.get_cached_data()
|
||||
|
||||
base_models = set()
|
||||
for item in cache.raw_data:
|
||||
if item.get("sub_type") != "diffusion_model":
|
||||
continue
|
||||
base_model = item.get("base_model")
|
||||
file_path = item.get("file_path", "")
|
||||
if base_model and file_path and os.path.exists(file_path):
|
||||
base_models.add(base_model)
|
||||
|
||||
return sorted(base_models)
|
||||
|
||||
return ["Any"] + cls._run_async(_get_base_models)
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting available base models: {e}")
|
||||
return ["Any"]
|
||||
|
||||
def load_unet(
|
||||
self,
|
||||
unet_name: str,
|
||||
weight_dtype: str,
|
||||
select_at_random: bool = False,
|
||||
base_model: str = "Any",
|
||||
) -> Tuple[Any, ...]:
|
||||
"""Load a diffusion model by name, supporting extra folder paths
|
||||
|
||||
Args:
|
||||
unet_name: The name of the diffusion model to load (relative path with extension)
|
||||
weight_dtype: The dtype to use for model weights
|
||||
select_at_random: If True, ignore unet_name and pick randomly from the pool
|
||||
base_model: Restricts random selection to this base model ("Any" = no filter)
|
||||
|
||||
Returns:
|
||||
Tuple of (MODEL, model_name)
|
||||
"""
|
||||
import torch
|
||||
|
||||
if select_at_random:
|
||||
pool = self._get_unet_names(base_model)
|
||||
if not pool:
|
||||
raise FileNotFoundError(
|
||||
f"No diffusion models found for base model '{base_model}'. "
|
||||
"Pick a different base model or disable 'select_at_random'."
|
||||
)
|
||||
unet_name = random.choice(pool)
|
||||
logger.info(
|
||||
f"[RandomUNETLoaderLM] Randomly selected diffusion model: {unet_name}"
|
||||
)
|
||||
|
||||
# Get absolute path from cache using ComfyUI-style name
|
||||
unet_path, metadata = get_checkpoint_info_absolute(unet_name)
|
||||
|
||||
if metadata is None:
|
||||
raise FileNotFoundError(
|
||||
f"Diffusion model '{unet_name}' not found in LoRA Manager cache. "
|
||||
"Make sure the model is indexed and try again."
|
||||
)
|
||||
|
||||
# Check if it's a GGUF model
|
||||
if unet_path.endswith(".gguf"):
|
||||
return self._load_gguf_unet(unet_path, unet_name, weight_dtype)
|
||||
|
||||
# Load regular diffusion model using ComfyUI's API
|
||||
logger.info(f"Loading diffusion model from: {unet_path}")
|
||||
|
||||
# Build model options based on weight_dtype
|
||||
model_options = {}
|
||||
if weight_dtype == "fp8_e4m3fn":
|
||||
model_options["dtype"] = torch.float8_e4m3fn
|
||||
elif weight_dtype == "fp8_e4m3fn_fast":
|
||||
model_options["dtype"] = torch.float8_e4m3fn
|
||||
model_options["fp8_optimizations"] = True
|
||||
elif weight_dtype == "fp8_e5m2":
|
||||
model_options["dtype"] = torch.float8_e5m2
|
||||
|
||||
model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
|
||||
return (model, unet_name)
|
||||
|
||||
def _load_gguf_unet(
|
||||
self, unet_path: str, unet_name: str, weight_dtype: str
|
||||
) -> Tuple[Any, ...]:
|
||||
"""Load a GGUF format diffusion model
|
||||
|
||||
Args:
|
||||
unet_path: Absolute path to the GGUF file
|
||||
unet_name: Name of the model for error messages
|
||||
weight_dtype: The dtype to use for model weights
|
||||
|
||||
Returns:
|
||||
Tuple of (MODEL, model_name)
|
||||
"""
|
||||
import torch
|
||||
from .gguf_import_helper import get_gguf_modules
|
||||
|
||||
# Get ComfyUI-GGUF modules using helper (handles various import scenarios)
|
||||
try:
|
||||
loader_module, ops_module, nodes_module = get_gguf_modules()
|
||||
gguf_sd_loader = getattr(loader_module, "gguf_sd_loader")
|
||||
GGMLOps = getattr(ops_module, "GGMLOps")
|
||||
GGUFModelPatcher = getattr(nodes_module, "GGUFModelPatcher")
|
||||
except RuntimeError as e:
|
||||
raise RuntimeError(f"Cannot load GGUF model '{unet_name}'. {str(e)}")
|
||||
|
||||
logger.info(f"Loading GGUF diffusion model from: {unet_path}")
|
||||
|
||||
try:
|
||||
# Load GGUF state dict
|
||||
sd, extra = gguf_sd_loader(unet_path)
|
||||
|
||||
# Prepare kwargs for metadata if supported
|
||||
kwargs = {}
|
||||
import inspect
|
||||
|
||||
valid_params = inspect.signature(
|
||||
comfy.sd.load_diffusion_model_state_dict
|
||||
).parameters
|
||||
if "metadata" in valid_params:
|
||||
kwargs["metadata"] = extra.get("metadata", {})
|
||||
|
||||
# Setup custom operations with GGUF support
|
||||
ops = GGMLOps()
|
||||
|
||||
# Handle weight_dtype for GGUF models
|
||||
if weight_dtype in ("default", None):
|
||||
ops.Linear.dequant_dtype = None
|
||||
elif weight_dtype in ["target"]:
|
||||
ops.Linear.dequant_dtype = weight_dtype
|
||||
else:
|
||||
ops.Linear.dequant_dtype = getattr(torch, weight_dtype, None)
|
||||
|
||||
# Load the model
|
||||
model = comfy.sd.load_diffusion_model_state_dict(
|
||||
sd, model_options={"custom_operations": ops}, **kwargs
|
||||
)
|
||||
|
||||
if model is None:
|
||||
raise RuntimeError(
|
||||
f"Could not detect model type for GGUF diffusion model: {unet_path}"
|
||||
)
|
||||
|
||||
# Wrap with GGUFModelPatcher
|
||||
model = GGUFModelPatcher.clone(model)
|
||||
|
||||
# Register a reload factory so the MODEL carries its source path
|
||||
# (cached_patcher_init) like core ComfyUI loaders do — required
|
||||
# for model-name extraction downstream and for ModelPatcher
|
||||
# deepclone/dynamic machinery.
|
||||
model.cached_patcher_init = (_reload_gguf_unet, (unet_path, weight_dtype))
|
||||
|
||||
return (model, unet_name)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading GGUF diffusion model '{unet_name}': {e}")
|
||||
raise RuntimeError(
|
||||
f"Failed to load GGUF diffusion model '{unet_name}': {str(e)}"
|
||||
)
|
||||
Reference in New Issue
Block a user