ImageGen-Z-Image / utils /app_utils.py
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import os
import requests
import hashlib
import re
from typing import Sequence, Mapping, Any, Union, Set
from pathlib import Path
import shutil
import gradio as gr
from huggingface_hub import hf_hub_download, constants as hf_constants
import torch
import numpy as np
from PIL import Image, ImageChops
from core.settings import *
DISK_LIMIT_GB = 120
MODELS_ROOT_DIR = "ComfyUI/models"
PREPROCESSOR_MODEL_MAP = None
PREPROCESSOR_PARAMETER_MAP = None
def save_uploaded_file_with_hash(file_obj: gr.File, target_dir: str) -> str:
if not file_obj:
return ""
temp_path = file_obj.name
sha256 = hashlib.sha256()
with open(temp_path, 'rb') as f:
for block in iter(lambda: f.read(65536), b''):
sha256.update(block)
file_hash = sha256.hexdigest()
_, extension = os.path.splitext(temp_path)
hashed_filename = f"{file_hash}{extension.lower()}"
dest_path = os.path.join(target_dir, hashed_filename)
os.makedirs(target_dir, exist_ok=True)
if not os.path.exists(dest_path):
shutil.copy(temp_path, dest_path)
print(f"✅ Saved uploaded file as: {dest_path}")
else:
print(f"ℹ️ File already exists (deduplicated): {dest_path}")
return hashed_filename
def bytes_to_gb(byte_size: int) -> float:
if byte_size is None or byte_size == 0:
return 0.0
return round(byte_size / (1024 ** 3), 2)
def get_directory_size(path: str) -> int:
total_size = 0
if not os.path.exists(path):
return 0
try:
for dirpath, _, filenames in os.walk(path):
for f in filenames:
fp = os.path.join(dirpath, f)
if os.path.isfile(fp) and not os.path.islink(fp):
total_size += os.path.getsize(fp)
except OSError as e:
print(f"Warning: Could not access {path} to calculate size: {e}")
return total_size
def enforce_disk_limit():
disk_limit_bytes = DISK_LIMIT_GB * (1024 ** 3)
cache_dir = hf_constants.HF_HUB_CACHE
if not os.path.exists(cache_dir):
return
print(f"--- [Storage Manager] Checking disk usage in '{cache_dir}' (Limit: {DISK_LIMIT_GB} GB) ---")
try:
all_files = []
current_size_bytes = 0
for dirpath, _, filenames in os.walk(cache_dir):
for f in filenames:
if f.endswith(".incomplete") or f.endswith(".lock"):
continue
file_path = os.path.join(dirpath, f)
if os.path.isfile(file_path) and not os.path.islink(file_path):
try:
file_size = os.path.getsize(file_path)
creation_time = os.path.getctime(file_path)
all_files.append((creation_time, file_path, file_size))
current_size_bytes += file_size
except OSError:
continue
print(f"--- [Storage Manager] Current usage: {bytes_to_gb(current_size_bytes)} GB ---")
if current_size_bytes > disk_limit_bytes:
print(f"--- [Storage Manager] Usage exceeds limit. Starting cleanup... ---")
all_files.sort(key=lambda x: x[0])
while current_size_bytes > disk_limit_bytes and all_files:
oldest_file_time, oldest_file_path, oldest_file_size = all_files.pop(0)
try:
os.remove(oldest_file_path)
current_size_bytes -= oldest_file_size
print(f"--- [Storage Manager] Deleted oldest file: {os.path.basename(oldest_file_path)} ({bytes_to_gb(oldest_file_size)} GB freed) ---")
except OSError as e:
print(f"--- [Storage Manager] Error deleting file {oldest_file_path}: {e} ---")
print(f"--- [Storage Manager] Cleanup finished. New usage: {bytes_to_gb(current_size_bytes)} GB ---")
else:
print("--- [Storage Manager] Disk usage is within the limit. No action needed. ---")
except Exception as e:
print(f"--- [Storage Manager] An unexpected error occurred: {e} ---")
def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
try:
return obj[index]
except (KeyError, IndexError):
try:
return obj["result"][index]
except (KeyError, IndexError):
return None
def sanitize_prompt(prompt: str) -> str:
if not isinstance(prompt, str):
return ""
return "".join(char for char in prompt if char.isprintable() or char in ('\n', '\t'))
def sanitize_id(input_id: str) -> str:
if not isinstance(input_id, str):
return ""
return re.sub(r'[^0-9]', '', input_id)
def sanitize_url(url: str) -> str:
if not isinstance(url, str):
raise ValueError("URL must be a string.")
url = url.strip()
if not re.match(r'^https?://[^\s/$.?#].[^\s]*$', url):
raise ValueError("Invalid URL format or scheme. Only HTTP and HTTPS are allowed.")
return url
def sanitize_filename(filename: str) -> str:
if not isinstance(filename, str):
return ""
sanitized = filename.replace('..', '')
sanitized = re.sub(r'[^\w\.\-]', '_', sanitized)
return sanitized.lstrip('/\\')
def get_civitai_file_info(version_id: str) -> dict | None:
api_url = f"https://civitai.com/api/v1/model-versions/{version_id}"
try:
response = requests.get(api_url, timeout=10)
response.raise_for_status()
data = response.json()
for file_data in data.get('files', []):
if file_data.get('type') == 'Model' and file_data['name'].endswith(('.safetensors', '.pt', '.bin')):
return file_data
if data.get('files'):
return data['files'][0]
except Exception:
return None
def download_file(url: str, save_path: str, api_key: str = None, progress=None, desc: str = "") -> str:
enforce_disk_limit()
if os.path.exists(save_path):
return f"File already exists: {os.path.basename(save_path)}"
headers = {'Authorization': f'Bearer {api_key}'} if api_key and api_key.strip() else {}
try:
if progress:
progress(0, desc=desc)
response = requests.get(url, stream=True, headers=headers, timeout=15)
response.raise_for_status()
total_size = int(response.headers.get('content-length', 0))
with open(save_path, "wb") as f:
downloaded = 0
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
if progress and total_size > 0:
downloaded += len(chunk)
progress(downloaded / total_size, desc=desc)
return f"Successfully downloaded: {os.path.basename(save_path)}"
except Exception as e:
if os.path.exists(save_path):
os.remove(save_path)
return f"Download failed for {os.path.basename(save_path)}: {e}"
def get_lora_path(source: str, id_or_url: str, civitai_key: str, progress) -> tuple[str | None, str]:
if not id_or_url or not id_or_url.strip():
return None, "No ID/URL provided."
try:
if source == "Civitai":
version_id = sanitize_id(id_or_url)
if not version_id:
return None, "Invalid Civitai ID provided. Must be numeric."
filename = sanitize_filename(f"civitai_{version_id}.safetensors")
local_path = os.path.join(LORA_DIR, filename)
file_info = get_civitai_file_info(version_id)
api_key_to_use = civitai_key
source_name = f"Civitai ID {version_id}"
else:
return None, "Invalid source."
except ValueError as e:
return None, f"Input validation failed: {e}"
if os.path.exists(local_path):
return local_path, "File already exists."
if not file_info or not file_info.get('downloadUrl'):
return None, f"Could not get download link for {source_name}."
status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
return (local_path, status) if "Successfully" in status else (None, status)
def get_embedding_path(source: str, id_or_url: str, civitai_key: str, progress) -> tuple[str | None, str]:
if not id_or_url or not id_or_url.strip():
return None, "No ID/URL provided."
try:
file_ext = ".safetensors"
if source == "Civitai":
version_id = sanitize_id(id_or_url)
if not version_id:
return None, "Invalid Civitai ID. Must be numeric."
file_info = get_civitai_file_info(version_id)
if file_info and file_info['name'].lower().endswith(('.pt', '.bin')):
file_ext = os.path.splitext(file_info['name'])[1]
filename = sanitize_filename(f"civitai_{version_id}{file_ext}")
local_path = os.path.join(EMBEDDING_DIR, filename)
api_key_to_use = civitai_key
source_name = f"Embedding Civitai ID {version_id}"
else:
return None, "Invalid source."
except ValueError as e:
return None, f"Input validation failed: {e}"
if os.path.exists(local_path):
return local_path, "File already exists."
if not file_info or not file_info.get('downloadUrl'):
return None, f"Could not get download link for {source_name}."
status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
return (local_path, status) if "Successfully" in status else (None, status)
def get_vae_path(source: str, id_or_url: str, civitai_key: str, progress) -> tuple[str | None, str]:
if not id_or_url or not id_or_url.strip():
return None, "No ID/URL provided."
try:
file_ext = ".safetensors"
if source == "Civitai":
version_id = sanitize_id(id_or_url)
if not version_id:
return None, "Invalid Civitai ID. Must be numeric."
file_info = get_civitai_file_info(version_id)
if file_info and file_info['name'].lower().endswith(('.pt', '.bin')):
file_ext = os.path.splitext(file_info['name'])[1]
filename = sanitize_filename(f"civitai_{version_id}{file_ext}")
local_path = os.path.join(VAE_DIR, filename)
api_key_to_use = civitai_key
source_name = f"VAE Civitai ID {version_id}"
else:
return None, "Invalid source."
except ValueError as e:
return None, f"Input validation failed: {e}"
if os.path.exists(local_path):
return local_path, "File already exists."
if not file_info or not file_info.get('downloadUrl'):
return None, f"Could not get download link for {source_name}."
status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
return (local_path, status) if "Successfully" in status else (None, status)
def _ensure_model_downloaded(filename: str, progress=gr.Progress()):
download_info = ALL_FILE_DOWNLOAD_MAP.get(filename)
if not download_info:
raise gr.Error(f"Model component '{filename}' not found in file_list.yaml. Cannot download.")
category_to_dir_map = {
"diffusion_models": DIFFUSION_MODELS_DIR,
"text_encoders": TEXT_ENCODERS_DIR,
"vae": VAE_DIR,
"checkpoints": CHECKPOINT_DIR,
"loras": LORA_DIR,
"controlnet": CONTROLNET_DIR,
"model_patches": MODEL_PATCHES_DIR,
"clip_vision": os.path.join(os.path.dirname(LORA_DIR), "clip_vision")
}
category = download_info.get('category')
dest_dir = category_to_dir_map.get(category)
if not dest_dir:
raise ValueError(f"Unknown model category '{category}' for file '{filename}'.")
dest_path = os.path.join(dest_dir, filename)
if os.path.lexists(dest_path):
if not os.path.exists(dest_path):
print(f"⚠️ Found and removed broken symlink: {dest_path}")
os.remove(dest_path)
else:
return filename
source = download_info.get("source")
try:
progress(0, desc=f"Downloading: {filename}")
if source == "hf":
repo_id = download_info.get("repo_id")
hf_filename = download_info.get("repository_file_path", filename)
if not repo_id:
raise ValueError(f"repo_id is missing for HF model '{filename}'")
cached_path = hf_hub_download(repo_id=repo_id, filename=hf_filename)
os.makedirs(dest_dir, exist_ok=True)
os.symlink(cached_path, dest_path)
print(f"✅ Symlinked '{cached_path}' to '{dest_path}'")
elif source == "civitai":
model_version_id = download_info.get("model_version_id")
if not model_version_id:
raise ValueError(f"model_version_id is missing for Civitai model '{filename}'")
file_info = get_civitai_file_info(model_version_id)
if not file_info or not file_info.get('downloadUrl'):
raise ConnectionError(f"Could not get download URL for Civitai model version ID {model_version_id}")
status = download_file(
file_info['downloadUrl'], dest_path, progress=progress, desc=f"Downloading: {filename}"
)
if "Failed" in status:
raise ConnectionError(status)
else:
raise NotImplementedError(f"Download source '{source}' is not implemented for '{filename}'")
progress(1.0, desc=f"Downloaded: {filename}")
except Exception as e:
if os.path.lexists(dest_path):
try:
os.remove(dest_path)
except OSError: pass
raise gr.Error(f"Failed to download and link '{filename}': {e}")
return filename
def ensure_controlnet_model_downloaded(filename: str, progress):
if not filename or filename == "None":
return
_ensure_model_downloaded(filename, progress)
def build_preprocessor_model_map():
global PREPROCESSOR_MODEL_MAP
if PREPROCESSOR_MODEL_MAP is not None: return PREPROCESSOR_MODEL_MAP
print("--- Building ControlNet Preprocessor model map ---")
manual_map = {
"dwpose": [("yzd-v/DWPose", "yolox_l.onnx"), ("yzd-v/DWPose", "dw-ll_ucoco_384.onnx"), ("hr16/UnJIT-DWPose", "dw-ll_ucoco.onnx"), ("hr16/DWPose-TorchScript-BatchSize5", "dw-ll_ucoco_384_bs5.torchscript.pt"), ("hr16/DWPose-TorchScript-BatchSize5", "rtmpose-m_ap10k_256_bs5.torchscript.pt"), ("hr16/yolo-nas-fp16", "yolo_nas_l_fp16.onnx"), ("hr16/yolo-nas-fp16", "yolo_nas_m_fp16.onnx"), ("hr16/yolo-nas-fp16", "yolo_nas_s_fp16.onnx")],
"densepose": [("LayerNorm/DensePose-TorchScript-with-hint-image", "densepose_r50_fpn_dl.torchscript"), ("LayerNorm/DensePose-TorchScript-with-hint-image", "densepose_r101_fpn_dl.torchscript")]
}
temp_map = {}
from nodes import NODE_DISPLAY_NAME_MAPPINGS
wrappers_dir = Path("./custom_nodes/comfyui_controlnet_aux/node_wrappers/")
if not wrappers_dir.exists():
print("⚠️ ControlNet AUX wrappers directory not found. Cannot build model map.")
PREPROCESSOR_MODEL_MAP = {}; return PREPROCESSOR_MODEL_MAP
for wrapper_file in wrappers_dir.glob("*.py"):
if wrapper_file.name == "__init__.py": continue
with open(wrapper_file, 'r', encoding='utf-8') as f:
content = f.read()
display_name_matches = re.findall(r'NODE_DISPLAY_NAME_MAPPINGS\s*=\s*{(?:.|\n)*?["\'](.*?)["\']\s*:\s*["\'](.*?)["\']', content)
for _, display_name in display_name_matches:
if display_name not in temp_map: temp_map[display_name] = []
manual_key = wrapper_file.stem
if manual_key in manual_map: temp_map[display_name].extend(manual_map[manual_key])
matches = re.findall(r"from_pretrained\s*\(\s*(?:filename=)?\s*f?[\"']([^\"']+)[\"']", content)
for model_filename in matches:
repo_id = "lllyasviel/Annotators"
if "depth_anything" in model_filename and "v2" in model_filename: repo_id = "LiheYoung/Depth-Anything-V2"
elif "depth_anything" in model_filename: repo_id = "LiheYoung/Depth-Anything"
elif "diffusion_edge" in model_filename: repo_id = "hr16/Diffusion-Edge"
temp_map[display_name].append((repo_id, model_filename))
final_map = {name: sorted(list(set(models))) for name, models in temp_map.items() if models}
PREPROCESSOR_MODEL_MAP = final_map
print("✅ ControlNet Preprocessor model map built."); return PREPROCESSOR_MODEL_MAP
def build_preprocessor_parameter_map():
global PREPROCESSOR_PARAMETER_MAP
if PREPROCESSOR_PARAMETER_MAP is not None: return
print("--- Building ControlNet Preprocessor parameter map ---")
param_map = {}
from nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
for class_name, node_class in NODE_CLASS_MAPPINGS.items():
if not hasattr(node_class, "INPUT_TYPES"): continue
if hasattr(node_class, '__module__') and 'comfyui_controlnet_aux.node_wrappers' not in node_class.__module__: continue
display_name = NODE_DISPLAY_NAME_MAPPINGS.get(class_name)
if not display_name: continue
try:
input_types = node_class.INPUT_TYPES()
all_inputs = {**input_types.get('required', {}), **input_types.get('optional', {})}
params = []
for name, details in all_inputs.items():
if name in ['image', 'resolution', 'pose_kps']: continue
if not isinstance(details, (list, tuple)) or not details: continue
param_type = details[0]
param_config = details[1] if len(details) > 1 and isinstance(details[1], dict) else {}
param_info = {"name": name, "type": param_type, "config": param_config}
params.append(param_info)
if params: param_map[display_name] = params
except Exception as e:
print(f"⚠️ Could not parse parameters for {display_name}: {e}")
PREPROCESSOR_PARAMETER_MAP = param_map
print("✅ ControlNet Preprocessor parameter map built.")
def print_welcome_message():
author_name = "RioShiina"
project_url = "https://huggingface.co/RioShiina"
border = "=" * 72
message = (
f"\n{border}\n\n"
f" Thank you for using this project!\n\n"
f" **Author:** {author_name}\n"
f" **Find more from the author:** {project_url}\n\n"
f" This project is open-source under the GNU General Public License v3.0 (GPL-3.0).\n"
f" As it's built upon GPL-3.0 components (like ComfyUI), any modifications you\n"
f" distribute must also be open-sourced under the same license.\n\n"
f" Your respect for the principles of free software is greatly appreciated!\n\n"
f"{border}\n"
)
print(message)