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import random
import hashlib
import re
import sys
import io
import logging
import warnings
from contextlib import contextmanager
import folder_paths
import comfy.utils
import comfy.lora
@contextmanager
def suppress_comfy_logs():
"""Temporarily suppress stdout, stderr, logging, and warnings to hide verbose ComfyUI LoRA loading messages"""
# Save original states
old_stdout = sys.stdout
old_stderr = sys.stderr
old_logging_level = logging.root.level
# Also save file descriptors for low-level redirection
old_stdout_fd = None
old_stderr_fd = None
devnull_fd = None
try:
# Suppress Python-level output
logging.root.setLevel(logging.CRITICAL + 1)
warnings.filterwarnings("ignore")
# Create a devnull-like object for Python-level redirection
devnull = io.StringIO()
sys.stdout = devnull
sys.stderr = devnull
# Also redirect OS-level file descriptors (for C extensions, PyTorch, etc.)
# This catches output that bypasses Python's sys.stdout/stderr
try:
sys.stdout.flush()
sys.stderr.flush()
# Duplicate the current stdout/stderr file descriptors
old_stdout_fd = os.dup(1)
old_stderr_fd = os.dup(2)
# Open devnull and redirect stdout/stderr to it
devnull_fd = os.open(os.devnull, os.O_WRONLY)
os.dup2(devnull_fd, 1) # Redirect stdout (fd 1)
os.dup2(devnull_fd, 2) # Redirect stderr (fd 2)
except (OSError, AttributeError):
# If OS-level redirection fails, continue with Python-level only
pass
yield
finally:
# Restore Python-level output
sys.stdout = old_stdout
sys.stderr = old_stderr
logging.root.setLevel(old_logging_level)
warnings.filterwarnings("default")
# Restore OS-level file descriptors
if old_stdout_fd is not None:
try:
sys.stdout.flush()
sys.stderr.flush()
os.dup2(old_stdout_fd, 1)
os.dup2(old_stderr_fd, 2)
os.close(old_stdout_fd)
os.close(old_stderr_fd)
if devnull_fd is not None:
os.close(devnull_fd)
except (OSError, AttributeError):
pass
class LoraBatchLoader:
RETURN_TYPES = ("MODEL", "CLIP", "STRING")
RETURN_NAMES = ("model", "clip", "filename")
FUNCTION = "load_batch_loras"
CATEGORY = "Batch Process"
SUPPORTED_EXTENSIONS = {".safetensors", ".ckpt", ".pt", ".bin"}
def __init__(self):
self.lora_states = {}
self.current_directory = ""
self.loras = []
self.search_states = {}
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP",),
"directory": ("STRING",),
"search_title": ("STRING", {"default": ""}),
"delimiter": ("STRING", {"default": ""}),
"mode": (
["incremental", "random"],
{"default": "incremental"},
),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"filename_option": (
[
"filename",
"prefix",
"suffix",
"prefix & suffix",
"prefix nor suffix",
],
),
"strength_model": (
"FLOAT",
{"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01},
),
"strength_clip": (
"FLOAT",
{"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01},
),
},
}
def set_directory(
self, directory, filename_option="filename", search_title="", delimiter=""
):
if (
directory != self.current_directory
or filename_option
or search_title
or delimiter
):
if not os.path.isdir(directory):
raise ValueError(
f"The provided path '{directory}' is not a valid directory."
)
all_loras = [
f
for f in os.listdir(directory)
if any(f.lower().endswith(ext) for ext in self.SUPPORTED_EXTENSIONS)
]
filtered_loras = self.filter_loras(
directory, all_loras, filename_option, search_title, delimiter
)
self.loras = sorted([os.path.join(directory, f) for f in filtered_loras])
self.current_directory = directory
search_key = (directory, filename_option, search_title, delimiter)
if search_key not in self.search_states:
self.search_states[search_key] = 0
if not self.loras:
print("No matching LoRA files found in the provided directory.")
else:
print(f"Found {len(self.loras)} LoRA files in directory.")
def load_loras(self, directory):
if not os.path.isdir(directory):
raise ValueError(f"Invalid directory: {directory}")
all_loras = [
f
for f in os.listdir(directory)
if any(f.lower().endswith(ext) for ext in self.SUPPORTED_EXTENSIONS)
]
return sorted([os.path.join(directory, f) for f in all_loras])
def filter_loras(self, directory, files, filename_option, search_title, delimiter):
def get_prefix(filename):
if delimiter:
return filename.split(delimiter)[0]
else:
return re.split(r"[^a-zA-Z0-9]", filename)[0]
def get_suffix(filename):
name_without_ext = os.path.splitext(filename)[0]
if delimiter:
return name_without_ext.split(delimiter)[-1]
else:
return re.split(r"[^a-zA-Z0-9]", name_without_ext)[-1]
filtered_files = files
if search_title:
if filename_option == "filename":
filtered_files = [f for f in filtered_files if search_title in f]
elif filename_option == "prefix":
search_prefix = get_prefix(search_title)
filtered_files = [
f for f in filtered_files if get_prefix(f) == search_prefix
]
elif filename_option == "suffix":
search_suffix = get_suffix(search_title)
filtered_files = [
f for f in filtered_files if get_suffix(f) == search_suffix
]
elif filename_option == "prefix & suffix":
search_prefix = get_prefix(search_title)
search_suffix = get_suffix(search_title)
filtered_files = [
f
for f in filtered_files
if get_prefix(f) == search_prefix or get_suffix(f) == search_suffix
]
elif filename_option == "prefix nor suffix":
search_prefix = get_prefix(search_title)
search_suffix = get_suffix(search_title)
filtered_files = [
f
for f in filtered_files
if get_prefix(f) != search_prefix and get_suffix(f) != search_suffix
]
return filtered_files
def load_batch_loras(
self,
model,
clip,
directory,
search_title="",
delimiter="",
mode="incremental",
seed=0,
filename_option="filename",
strength_model=1.0,
strength_clip=1.0,
):
self.set_directory(directory, filename_option, search_title, delimiter)
if not self.loras:
print("No LoRA files found, returning original model and clip.")
return (model, clip, "no_loras_found")
search_key = (directory, filename_option, search_title, delimiter)
if mode == "incremental":
return self.load_lora_by_index(
model, clip, search_key, strength_model, strength_clip
)
elif mode == "random":
random.seed(seed)
rnd_index = random.randint(0, len(self.loras) - 1)
print(
f"[LoRA Batch Loader] Random mode - Index: {rnd_index + 1}/{len(self.loras)}"
)
return self.load_lora_by_path(
model, clip, self.loras[rnd_index], strength_model, strength_clip
)
else:
raise ValueError(f"Unknown mode: {mode}")
def load_lora_by_index(
self, model, clip, search_key, strength_model, strength_clip
):
if not self.loras:
print("No LoRAs loaded.")
return model, clip, "no_loras"
current_index = self.search_states[search_key]
if current_index >= len(self.loras):
current_index = 0
file_path = self.loras[current_index]
self.search_states[search_key] = (current_index + 1) % len(self.loras)
# Print index info for better tracking
print(f"[LoRA Batch Loader] Index: {current_index + 1}/{len(self.loras)}")
return self.load_lora_by_path(
model, clip, file_path, strength_model, strength_clip
)
def load_lora_by_path(self, model, clip, path, strength_model, strength_clip):
try:
filename = os.path.basename(path)
# Remove file extension for cleaner filename output
filename_clean = os.path.splitext(filename)[0]
# Print LoRA name every time it's loaded
print(f"[LoRA Batch Loader] Loading LoRA: {filename_clean}")
# Suppress verbose ComfyUI LoRA loading messages
with suppress_comfy_logs():
# Load the LoRA using ComfyUI's built-in LoRA loading functionality
lora = comfy.utils.load_torch_file(path, safe_load=True)
# Create key mapping for the LoRA
model_lora_keys = comfy.lora.model_lora_keys_unet(model.model)
clip_lora_keys = comfy.lora.model_lora_keys_clip(clip.cond_stage_model)
# Combine the key mappings
key_map = {}
key_map.update(model_lora_keys)
key_map.update(clip_lora_keys)
# Load the LoRA patches (this prints all the verbose messages)
loaded = comfy.lora.load_lora(lora, key_map)
# Apply LoRA to model
new_modelpatcher = model.clone()
k = {}
for x in loaded:
k[x] = loaded[x]
new_modelpatcher.add_patches(k, strength_model)
# Apply LoRA to clip
new_clip = clip.clone()
k = {}
for x in loaded:
k[x] = loaded[x]
new_clip.add_patches(k, strength_clip)
return (new_modelpatcher, new_clip, filename_clean)
except Exception as e:
print(f"Error loading LoRA {path}: {str(e)}")
return (model, clip, "error_loading_lora")
@classmethod
def IS_CHANGED(cls, directory, **kwargs):
if not os.path.exists(directory):
return ""
try:
loader = cls()
paths = loader.load_loras(directory)
return hashlib.sha256(",".join(paths).encode()).hexdigest()
except Exception as e:
print(f"Error checking for changes: {str(e)}")
return ""
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