From c021a9dde1dc4df0bd56e2a4aedfa6c57bfdc5de Mon Sep 17 00:00:00 2001 From: zebord Date: Sat, 21 Dec 2024 15:32:48 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=9C=BA=E5=99=A8=E7=A0=81?= =?UTF-8?q?=E5=85=A8=E5=A5=97=E5=AE=89=E5=85=A8=E4=BF=9D=E6=8A=A4=E8=8A=82?= =?UTF-8?q?=E7=82=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 58 +++ README_CN.md | 64 ++- nodes.py | 962 +++++++++++++++++++++++++++++++++++++---- runtime_protection.png | Bin 0 -> 163116 bytes 4 files changed, 999 insertions(+), 85 deletions(-) create mode 100644 runtime_protection.png diff --git a/README.md b/README.md index 6e3413b..1710281 100644 --- a/README.md +++ b/README.md @@ -138,6 +138,64 @@ save_name: Name for the decrypted model ~~~ The decrypted LoRA will be saved in the loras/decrypted folder. +## Update Log 2024-12-21 (Runtime Protection System!) + +### Machine Code & Runtime Protection +![runtime_protection.png](runtime_protection.png) + +### LmcqGetMachineCode +A utility node that generates a unique machine code based on hardware and system information. This code is used for authorization in the runtime protection system. + +### Runtime Model Protection +~~~ +LmcqRuntimeModelEncryption: +- model_name: Select the model to encrypt +- key: Encryption key +- save_name: Name for the encrypted model +- machine_codes: List of authorized machine codes (one per line) + +LmcqRuntimeModelDecryption: +- model_name: Select the encrypted model +- key: Decryption key +~~~ +Provides real-time model encryption/decryption with machine-specific authorization. Models can only be loaded on authorized machines. +PS: The encrypted model is loaded in memory, so the complete model will not be saved locally. It can only be used in the workflow to protect the complete model from being spread to the greatest extent (only LmcqRuntimeModelDecryption can be used to load the encrypted model, and the rest are invalid) + +### Runtime LoRA Protection +~~~ +LmcqRuntimeLoraEncryption: +- lora_name: Select the LoRA to encrypt +- key: Encryption key +- save_name: Name for the encrypted LoRA +- machine_codes: List of authorized machine codes + +LmcqRuntimeLoraDecryption: +- model: Input model +- clip: Input CLIP +- lora_name: Select the encrypted LoRA +- key: Decryption key +- strength_model: LoRA strength for model +- strength_clip: LoRA strength for CLIP +~~~ +Secure LoRA models with machine-specific authorization and real-time loading. +PS: The encrypted model is loaded in memory, so the complete model will not be saved locally. It can only be used in the workflow to protect the complete model from being spread to the greatest extent (only LmcqRuntimeLoraDecryption can be used to load the encrypted model, and the rest are invalid) +### Runtime Workflow Protection +~~~ +LmcqRuntimeWorkflowEncryption: +- workflow_file: Select workflow to encrypt +- key: Encryption key +- save_name: Name for encrypted workflow (.lcwf) +- machine_codes: List of authorized machine codes + +LmcqRuntimeWorkflowDecryption: +- workflow_file: Select encrypted workflow +- key: Decryption key +- save_name: Name for decrypted workflow +~~~ +Protects workflows with machine-specific authorization. Encrypted workflows are saved in .lcwf format and can only be decrypted on authorized machines. + +Note: The runtime protection system ensures that protected assets can only be used on specifically authorized machines, providing stronger security than password-only protection. + ## Contribute zebord \ No newline at end of file diff --git a/README_CN.md b/README_CN.md index 401972e..4b51cb2 100644 --- a/README_CN.md +++ b/README_CN.md @@ -99,7 +99,7 @@ workflow_file: 选择要处理的工作流文件（可从根目录或插件的 w save_name: 保存文件的名称 ~~~ -该节点允许你对工作流文件进行加密保护，防止未经授权的访问。加密后的工作流文件需要使用正确的密码解密后才能使用。 +该节点允许你对工作流文件进行加密保护，防止未���授权的访问。加密后的工作流文件需要使用正确的密码解密后才能使用。 使用方法： 1. 将工作流文件保存到以下任一位置： @@ -135,5 +135,65 @@ save_name：解密后的模型名称 解密后的LoRA模型会保存在 loras/decrypted 文件夹中。 +## 更新日志 2024-12-21 （运行时保护系统！） -## 参与贡献!zebord \ No newline at end of file +### 机器码与运行时保护 +![runtime_protection.png](runtime_protection.png) + +### LmcqGetMachineCode +用于生成基于硬件和系统信息的唯一机器码的工具节点。此机器码用于运行时保护系统中的授权验证。 + +### 运行时模型保护 +~~~ +LmcqRuntimeModelEncryption（运行时模型加密）: +- model_name: 选择要加密的模型 +- key: 加密密钥 +- save_name: 加密后的模型名称 +- machine_codes: 授权机器码列表（每行一个） + +LmcqRuntimeModelDecryption（运行时模型解密）: +- model_name: 选择加密的模型 +- key: 解密密钥 +~~~ +提供实时模型加解密和特定机器授权功能。模型只能在授权的机器上加载。 +PS: 加密模型在内存中进行加载，所以不会在本地保存完整模型，只能用于工作流当中，最大程度保护完整模型不被传播(只能使用LmcqRuntimeModelDecryption去加载加密模型，其余无效) + +### 运行时LoRA保护 +~~~ +LmcqRuntimeLoraEncryption（运行时LoRA加密）: +- lora_name: 选择要加密的LoRA +- key: 加密密钥 +- save_name: 加密后的LoRA名称 +- machine_codes: 授权机器码列表 + +LmcqRuntimeLoraDecryption（运行时LoRA解密）: +- model: 输入模型 +- clip: 输入CLIP +- lora_name: 选择加密的LoRA +- key: 解密密钥 +- strength_model: 模型的LoRA强度 +- strength_clip: CLIP的LoRA强度 +~~~ +使用机器特定授权和实时加载功能来保护LoRA模型。 +PS: 加密模型在内存中进行加载，所以不会在本地保存完整模型，只能用于工作流当中，最大程度保护完整模型不被传播(只能使用LmcqRuntimeLoraDecryption去加载加密模型，其余无效) + +### 运行时工作流保护 +~~~ +LmcqRuntimeWorkflowEncryption（运行时工作流加密）: +- workflow_file: 选择要加密的工作流 +- key: 加密密钥 +- save_name: 加密后的工作流名称（.lcwf格式） +- machine_codes: 授权机器码列表 + +LmcqRuntimeWorkflowDecryption（运行时工作流解密）: +- workflow_file: 选择加密的工作流 +- key: 解密密钥 +- save_name: 解密后的工作流名称 +~~~ +通过机器特定授权保护工作流。加密的工作流以.lcwf格式保存，只能在授权机器上解密。 + +注意：运行时保护系统确保受保护的资源只能在特定授权的机器上使用，比单纯的密码保护提供更强的安全性。 + +## 参与贡献! + +Zebord \ No newline at end of file diff --git a/nodes.py b/nodes.py index 99893df..f151c6d 100644 --- a/nodes.py +++ b/nodes.py @@ -14,6 +14,7 @@ from cryptography.fernet import Fernet from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC from .protection_utils import ModelProtector +import hmac """ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -429,7 +430,7 @@ class LmcqImageSaverWeb: "type": self.type }) - # 果开启了接口调用，则发送数据到接口 + # 果开启了口调用，则发送数据到接口 if self.enable_api_call: self.send_to_api(filename, current_prompt_id) @@ -441,7 +442,7 @@ class LmcqImageSaverWeb: # 获取文件的MIME类型 content_type, _ = mimetypes.guess_type(filepath) if content_type is None: - content_type = 'application/octet-stream' # 默认二进制流 + content_type = 'application/octet-stream' # 默认二进制 # 构建要发送的数据 data = { @@ -457,7 +458,7 @@ class LmcqImageSaverWeb: try: response = requests.post(self.api_url, data=data, files=files) if response.status_code == 200: - print(f"{filename} 及其处理果已成功发送至接口地址。") + print(f"{filename} 及其处理结果已成功发送至接口地址。") else: print(f"发送请求失败，状态码: {response.status_code}") except Exception as e: @@ -759,7 +760,7 @@ class LmcqInputValidator: if check_type == "is_digit": return (input_text.isdigit(),) else: # is_string - # 判断是否为字符串 - 只要不是纯数字就为是字符串 + # 判断是否为字符串 - 只要不是纯数字就是字符串 return (not input_text.isdigit(),) @@ -947,7 +948,7 @@ class LmcqWorkflowEncryption: if not os.path.exists(workflow_path): raise ValueError(f"工作流文件不存在: {actual_workflow_file}") - # 保存到与源文件相同的目录 + # 保存到与源文件同的目录 save_path = os.path.join(workflow_dir, f"{save_name}.json") def get_key(password, salt=None): @@ -1035,7 +1036,7 @@ NODE_DISPLAY_NAME_MAPPINGS.update({ """ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - LoraEncryptionNode Lora模型加密、解密 + LoraEncryptionNode Lora模加密、解密 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """ @@ -1082,86 +1083,31 @@ class LmcqLoraDecryption: def INPUT_TYPES(s): return { "required": { + "model": ("MODEL",), + "clip": ("CLIP",), "lora_name": (folder_paths.get_filename_list("loras"),), "key": ("STRING", {"default": ""}), - "save_name": ("STRING", {"default": "decrypted_lora"}) + "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}), } } - RETURN_TYPES = () - FUNCTION = "decrypt_lora" - OUTPUT_NODE = True + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "decrypt_and_load" CATEGORY = "Lmcq/lora_protection" - def decrypt_lora(self, lora_name, key, save_name): + def decrypt_and_load(self, model, clip, lora_name, key, strength_model, strength_clip): if not key: raise ValueError("解密密钥不能为空") - # 获取LoRA模型路径 + # 获取LoRA路径 lora_path = folder_paths.get_full_path("loras", lora_name) if not lora_path: - raise ValueError(f"LoRA模型 {lora_name} 未找到") + raise ValueError(f"LoRA {lora_name} 未找到") - # 构建保存路径 - save_dir = os.path.join(os.path.dirname(lora_path), "decrypted") - os.makedirs(save_dir, exist_ok=True) - save_path = os.path.join(save_dir, f"{save_name}.safetensors") - - # 解密LoRA模型 - protector = LoraProtector() - protector.decrypt(lora_path, save_path, key) - - print(f"LoRA模型解密成功: {save_path}") - return {} - - -class LoraProtector: - def __init__(self): - self.version = "1.0.0" - self.meta_extension = ".meta" - - def encrypt(self, input_path, output_path, key): - try: - # 读取LoRA模型 - with open(input_path, 'rb') as f: - model_data = f.read() - - # 生成加密密钥 - salt = os.urandom(16) - kdf = PBKDF2HMAC( - algorithm=hashes.SHA256(), - length=32, - salt=salt, - iterations=100000, - ) - encryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) - - # 加密模型数据 - fernet = Fernet(encryption_key) - encrypted_data = fernet.encrypt(model_data) - - # 保存加密后的模型 - with open(output_path, 'wb') as f: - f.write(encrypted_data) - - # 保存元数据 - meta_data = { - "version": self.version, - "salt": base64.b64encode(salt).decode(), - "type": "encrypted_lora" - } - - meta_path = output_path + self.meta_extension - with open(meta_path, 'w') as f: - json.dump(meta_data, f) - - except Exception as e: - raise ValueError(f"LoRA模型加密失败: {str(e)}") - - def decrypt(self, input_path, output_path, key): try: # 读取元数据 - meta_path = input_path + self.meta_extension + meta_path = lora_path + ".meta" if not os.path.exists(meta_path): raise ValueError("未找到加密元数据文件") @@ -1169,13 +1115,33 @@ class LoraProtector: meta_data = json.load(f) if meta_data.get("type") != "encrypted_lora": - raise ValueError("无效的LoRA加密文件") + raise ValueError("无效的加密LoRA文件") - # 读取加密的模型数据 - with open(input_path, 'rb') as f: + # 读取加密数据 + with open(lora_path, 'rb') as f: encrypted_data = f.read() - # 重建解密密钥 + # 验证签名 + sign_salt = base64.b64decode(meta_data["sign_salt"]) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + signing_key = sign_kdf.derive(key.encode()) + + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_data) + encrypted_codes = base64.b64decode(meta_data["encrypted_codes"]) + h.update(encrypted_codes) + calculated_signature = h.digest() + expected_signature = base64.b64decode(meta_data["signature"]) + if not hmac.compare_digest(calculated_signature, expected_signature): + raise ValueError("LoRA文件或元数据已被篡改") + + # 创建解密器 salt = base64.b64decode(meta_data["salt"]) kdf = PBKDF2HMAC( algorithm=hashes.SHA256(), @@ -1184,20 +1150,44 @@ class LoraProtector: iterations=100000, ) decryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) - - # 解密模型数据 fernet = Fernet(decryption_key) + + # 解密并验证机器码列表 + codes_data = fernet.decrypt(encrypted_codes) + authorized_codes = json.loads(codes_data.decode()) + + # 验证机器码 + machine_code_getter = LmcqGetMachineCode() + current_machine_code = machine_code_getter.get_machine_code()[0] + if current_machine_code not in authorized_codes: + raise ValueError("当前机器未授权使用此LoRA") + + # 解密LoRA数据 decrypted_data = fernet.decrypt(encrypted_data) - # 保存解密后的模型 - with open(output_path, 'wb') as f: - f.write(decrypted_data) + # 直接从内存加载LoRA + import safetensors.torch + lora_data = safetensors.torch.load(decrypted_data) + + # 应用LoRA到输入的model和clip + new_model = model + new_clip = clip + + if strength_model != 0 or strength_clip != 0: + new_model, new_clip = comfy.sd.load_lora_for_models(model, clip, lora_data, strength_model, strength_clip) + + # 清理内存中的敏感数据 + del decrypted_data + del lora_data + import gc + gc.collect() + + return (new_model, new_clip) except Exception as e: - raise ValueError(f"LoRA模型解密失败: {str(e)}") + raise ValueError(f"LoRA解密失败: {str(e)}") - -# 注册节点 +# 注册新节点 NODE_CLASS_MAPPINGS.update({ "LmcqLoraEncryption": LmcqLoraEncryption, "LmcqLoraDecryption": LmcqLoraDecryption @@ -1206,4 +1196,810 @@ NODE_CLASS_MAPPINGS.update({ NODE_DISPLAY_NAME_MAPPINGS.update({ "LmcqLoraEncryption": "Lmcq Lora Encryption", "LmcqLoraDecryption": "Lmcq Lora Decryption" +}) + +class LmcqGetMachineCode: + @classmethod + def INPUT_TYPES(s): + return {"required": {}} # 不需要输入参数 + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("machine_code",) + FUNCTION = "get_machine_code" + CATEGORY = "Lmcq/Protection" + + def get_machine_code(self): + import platform + import uuid + import hashlib + import psutil + import socket + import os + + # 收集系统信息 + system_info = { + # 操作系统信息 + 'os_info': { + 'system': platform.system(), + 'release': platform.release(), + 'version': platform.version(), + 'machine': platform.machine(), + }, + + # Python运行环境 + 'python_env': { + 'version': platform.python_version(), + 'implementation': platform.python_implementation(), + }, + + # CPU基础信息 + 'cpu_basic': { + 'arch': platform.architecture(), + 'cpu_count': psutil.cpu_count(), + 'processor': platform.processor() + }, + + # 用户信息 + 'user_context': { + 'username': os.getlogin(), + 'home_path': os.path.expanduser('~'), + }, + + # 网络信息 + 'network': { + 'hostname': socket.gethostname(), + 'fqdn': socket.getfqdn(), + }, + + # 内存信息 + 'memory': { + 'total': psutil.virtual_memory().total, + } + } + + # 生成特征字符串 + feature_str = str(system_info) + + # 使用SHA256生成机器码 + machine_code = hashlib.sha256(feature_str.encode()).hexdigest() + + # 只返回前32位的机器码 + return (machine_code[:32],) + +class LmcqRuntimeModelEncryption: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_name": (folder_paths.get_filename_list("checkpoints"),), + "key": ("STRING", {"default": ""}), + "save_name": ("STRING", {"default": "encrypted_model"}), + "machine_codes": ("STRING", { + "multiline": True, + "default": "", + "placeholder": "每行输入一个授权机器码..." + }) + } + } + + RETURN_TYPES = () + FUNCTION = "encrypt_model" + OUTPUT_NODE = True + CATEGORY = "Lmcq/Protection" + + def encrypt_model(self, model_name, key, save_name, machine_codes): + if not key: + raise ValueError("加密密钥不能为空") + + if not machine_codes.strip(): + raise ValueError("至少需要一个授权机器码") + + # 获取模型路径 + model_path = folder_paths.get_full_path("checkpoints", model_name) + if not model_path: + raise ValueError(f"模型 {model_name} 未找到") + + try: + # 处理机器码列表 + authorized_codes = [code.strip() for code in machine_codes.split('\n') if code.strip()] + if not authorized_codes: + raise ValueError("无有效的授权机器码") + + # 生成加密密钥和签名密钥 + salt = os.urandom(16) + sign_salt = os.urandom(16) + kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=salt, + iterations=100000, + ) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + encryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) + signing_key = sign_kdf.derive(key.encode()) + fernet = Fernet(encryption_key) + + # 加密机器码列表 + codes_data = json.dumps(authorized_codes).encode() + encrypted_codes = fernet.encrypt(codes_data) + + # 读取并加密模型数据 + with open(model_path, 'rb') as f: + model_data = f.read() + encrypted_data = fernet.encrypt(model_data) + + # 构建保存路径 + save_dir = os.path.join(os.path.dirname(model_path), "encrypted") + os.makedirs(save_dir, exist_ok=True) + save_path = os.path.join(save_dir, f"{save_name}.safetensors") + + # ��存加密后的模型 + with open(save_path, 'wb') as f: + f.write(encrypted_data) + + # 生成签名 + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_data) + h.update(encrypted_codes) + signature = base64.b64encode(h.digest()).decode() + + # 保存元数据 + meta_data = { + "version": "1.0.0", + "salt": base64.b64encode(salt).decode(), + "sign_salt": base64.b64encode(sign_salt).decode(), + "type": "encrypted_model", + "encrypted_codes": base64.b64encode(encrypted_codes).decode(), + "signature": signature + } + + meta_path = save_path + ".meta" + with open(meta_path, 'w') as f: + json.dump(meta_data, f) + + print(f"模型加密成功: {save_path}") + return {} + + except Exception as e: + raise ValueError(f"模型加密失败: {str(e)}") + +class LmcqRuntimeModelDecryption: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_name": (folder_paths.get_filename_list("checkpoints"),), + "key": ("STRING", {"default": ""}) + } + } + + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "decrypt_and_load" + CATEGORY = "Lmcq/Protection" + + def decrypt_and_load(self, model_name, key): + if not key: + raise ValueError("解密密钥不能为空") + + # 获取模型路径 + model_path = folder_paths.get_full_path("checkpoints", model_name) + if not model_path: + raise ValueError(f"模型 {model_name} 未找到") + + try: + # 读取元数据 + meta_path = model_path + ".meta" + if not os.path.exists(meta_path): + raise ValueError("未找到加密元数据文件") + + with open(meta_path, 'r') as f: + meta_data = json.load(f) + + if meta_data.get("type") != "encrypted_model": + raise ValueError("无效的加密模型文件") + + # 读取加密数据 + with open(model_path, 'rb') as f: + encrypted_data = f.read() + + # 验证签名 + sign_salt = base64.b64decode(meta_data["sign_salt"]) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + signing_key = sign_kdf.derive(key.encode()) + + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_data) + encrypted_codes = base64.b64decode(meta_data["encrypted_codes"]) + h.update(encrypted_codes) + calculated_signature = h.digest() + expected_signature = base64.b64decode(meta_data["signature"]) + if not hmac.compare_digest(calculated_signature, expected_signature): + raise ValueError("模型文件或元数据已被篡改") + + # 创建解密器 + salt = base64.b64decode(meta_data["salt"]) + kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=salt, + iterations=100000, + ) + decryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) + fernet = Fernet(decryption_key) + + # 解密并验证机器码列表 + codes_data = fernet.decrypt(encrypted_codes) + authorized_codes = json.loads(codes_data.decode()) + + # 验证机器码 + machine_code_getter = LmcqGetMachineCode() + current_machine_code = machine_code_getter.get_machine_code()[0] + if current_machine_code not in authorized_codes: + raise ValueError("当前机器未授权使用此模型") + + # 解密模型数据 + decrypted_data = fernet.decrypt(encrypted_data) + + # 直接从内存加载模型 + import safetensors.torch + model_data = safetensors.torch.load(decrypted_data) + + # 使用 ComfyUI 的加载机制 + out = comfy.sd.load_state_dict_guess_config( + model_data, + output_vae=True, + output_clip=True, + output_clipvision=False, + embedding_directory=folder_paths.get_folder_paths("embeddings") + ) + + if out is None: + raise ValueError("无法识别模型类型") + + # 清理内存中的敏感数据 + del decrypted_data + del model_data + import gc + gc.collect() + + return out[:3] + + except Exception as e: + raise ValueError(f"模型解密失败: {str(e)}") + +# 注册新节点 +NODE_CLASS_MAPPINGS.update({ + "LmcqGetMachineCode": LmcqGetMachineCode, + "LmcqRuntimeModelEncryption": LmcqRuntimeModelEncryption, + "LmcqRuntimeModelDecryption": LmcqRuntimeModelDecryption +}) + +NODE_DISPLAY_NAME_MAPPINGS.update({ + "LmcqGetMachineCode": "Lmcq Get Machine Code", + "LmcqRuntimeModelEncryption": "Lmcq Runtime Model Encryption", + "LmcqRuntimeModelDecryption": "Lmcq Runtime Model Decryption" +}) + +class LmcqRuntimeLoraEncryption: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_name": (folder_paths.get_filename_list("loras"),), + "key": ("STRING", {"default": ""}), + "save_name": ("STRING", {"default": "encrypted_lora"}), + "machine_codes": ("STRING", { + "multiline": True, + "default": "", + "placeholder": "每行输入一个授权机器码..." + }) + } + } + + RETURN_TYPES = () + FUNCTION = "encrypt_lora" + OUTPUT_NODE = True + CATEGORY = "Lmcq/Protection" + + def encrypt_lora(self, lora_name, key, save_name, machine_codes): + if not key: + raise ValueError("加密密钥不能为空") + + if not machine_codes.strip(): + raise ValueError("至少需要一个授权机器码") + + # 获取LoRA路径 + lora_path = folder_paths.get_full_path("loras", lora_name) + if not lora_path: + raise ValueError(f"LoRA {lora_name} 未找到") + + try: + # 处理机器码列表 + authorized_codes = [code.strip() for code in machine_codes.split('\n') if code.strip()] + if not authorized_codes: + raise ValueError("无有效的授权机器码") + + # 生成加密密钥和签名密钥 + salt = os.urandom(16) + sign_salt = os.urandom(16) + kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=salt, + iterations=100000, + ) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + encryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) + signing_key = sign_kdf.derive(key.encode()) + fernet = Fernet(encryption_key) + + # 加密机器码列表 + codes_data = json.dumps(authorized_codes).encode() + encrypted_codes = fernet.encrypt(codes_data) + + # 读取并加密LoRA数据 + with open(lora_path, 'rb') as f: + lora_data = f.read() + encrypted_data = fernet.encrypt(lora_data) + + # 构建保存路径 + save_dir = os.path.join(os.path.dirname(lora_path), "encrypted") + os.makedirs(save_dir, exist_ok=True) + save_path = os.path.join(save_dir, f"{save_name}.safetensors") + + # 保存加密后的LoRA + with open(save_path, 'wb') as f: + f.write(encrypted_data) + + # 生成签名 + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_data) + h.update(encrypted_codes) + signature = base64.b64encode(h.digest()).decode() + + # 保存元数据 + meta_data = { + "version": "1.0.0", + "salt": base64.b64encode(salt).decode(), + "sign_salt": base64.b64encode(sign_salt).decode(), + "type": "encrypted_lora", + "encrypted_codes": base64.b64encode(encrypted_codes).decode(), + "signature": signature + } + + meta_path = save_path + ".meta" + with open(meta_path, 'w') as f: + json.dump(meta_data, f) + + print(f"LoRA加密成功: {save_path}") + return {} + + except Exception as e: + raise ValueError(f"LoRA加密失败: {str(e)}") + +class LmcqRuntimeLoraDecryption: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "lora_name": (folder_paths.get_filename_list("loras"),), + "key": ("STRING", {"default": ""}), + "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}), + } + } + + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "decrypt_and_load" + CATEGORY = "Lmcq/Protection" + + def decrypt_and_load(self, model, clip, lora_name, key, strength_model, strength_clip): + if not key: + raise ValueError("解密密钥不能为空") + + # 获取LoRA路径 + lora_path = folder_paths.get_full_path("loras", lora_name) + if not lora_path: + raise ValueError(f"LoRA {lora_name} 未找到") + + try: + # 读取元数据 + meta_path = lora_path + ".meta" + if not os.path.exists(meta_path): + raise ValueError("未找到加密元数据文件") + + with open(meta_path, 'r') as f: + meta_data = json.load(f) + + if meta_data.get("type") != "encrypted_lora": + raise ValueError("无效的加密LoRA文件") + + # 读取加密数据 + with open(lora_path, 'rb') as f: + encrypted_data = f.read() + + # 验证签名 + sign_salt = base64.b64decode(meta_data["sign_salt"]) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + signing_key = sign_kdf.derive(key.encode()) + + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_data) + encrypted_codes = base64.b64decode(meta_data["encrypted_codes"]) + h.update(encrypted_codes) + calculated_signature = h.digest() + expected_signature = base64.b64decode(meta_data["signature"]) + if not hmac.compare_digest(calculated_signature, expected_signature): + raise ValueError("LoRA文件或元数据已被篡改") + + # 创建解密器 + salt = base64.b64decode(meta_data["salt"]) + kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=salt, + iterations=100000, + ) + decryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) + fernet = Fernet(decryption_key) + + # 解密并验证机器码列表 + codes_data = fernet.decrypt(encrypted_codes) + authorized_codes = json.loads(codes_data.decode()) + + # 验证机器码 + machine_code_getter = LmcqGetMachineCode() + current_machine_code = machine_code_getter.get_machine_code()[0] + if current_machine_code not in authorized_codes: + raise ValueError("当前机器未授权使用此LoRA") + + # 解密LoRA数据 + decrypted_data = fernet.decrypt(encrypted_data) + + # 直接从内存加载LoRA + import safetensors.torch + lora_data = safetensors.torch.load(decrypted_data) + + # 应用LoRA到输入的model和clip + new_model = model + new_clip = clip + + if strength_model != 0 or strength_clip != 0: + new_model, new_clip = comfy.sd.load_lora_for_models(model, clip, lora_data, strength_model, strength_clip) + + # 清理内存中的敏感数据 + del decrypted_data + del lora_data + import gc + gc.collect() + + return (new_model, new_clip) + + except Exception as e: + raise ValueError(f"LoRA解密失败: {str(e)}") + +# 注册新节点 +NODE_CLASS_MAPPINGS.update({ + "LmcqRuntimeLoraEncryption": LmcqRuntimeLoraEncryption, + "LmcqRuntimeLoraDecryption": LmcqRuntimeLoraDecryption +}) + +NODE_DISPLAY_NAME_MAPPINGS.update({ + "LmcqRuntimeLoraEncryption": "Lmcq Runtime Lora Encryption", + "LmcqRuntimeLoraDecryption": "Lmcq Runtime Lora Decryption" +}) + +class LmcqRuntimeWorkflowEncryption: + @classmethod + def INPUT_TYPES(s): + import os + import folder_paths + + # 获取 ComfyUI 根目录的 workflows 文件夹 + root_workflow_dir = os.path.join(os.path.dirname(folder_paths.base_path), "workflows") + if not os.path.exists(root_workflow_dir): + os.makedirs(root_workflow_dir) + + # 获取插件目录的 workflows 文件夹 + current_file_path = os.path.abspath(__file__) + plugin_dir = os.path.dirname(current_file_path) + plugin_workflow_dir = os.path.join(plugin_dir, "workflows") + if not os.path.exists(plugin_workflow_dir): + os.makedirs(plugin_workflow_dir) + + # 获取两个目录中的所有 json 文件 + root_workflow_files = [f for f in os.listdir(root_workflow_dir) if f.endswith('.json')] + plugin_workflow_files = [f for f in os.listdir(plugin_workflow_dir) if f.endswith('.json')] + + # 合并文件列表，添加目录前缀以区分 + workflow_files = ( + [f"root/{f}" for f in root_workflow_files] + + [f"plugin/{f}" for f in plugin_workflow_files] + ) + + if not workflow_files: + workflow_files = [""] + + return { + "required": { + "workflow_file": (workflow_files,), + "key": ("STRING", {"default": ""}), + "save_name": ("STRING", {"default": "encrypted_workflow"}), + "machine_codes": ("STRING", { + "multiline": True, + "default": "", + "placeholder": "每行输入一个授权机器码..." + }) + } + } + + RETURN_TYPES = () + FUNCTION = "encrypt_workflow" + OUTPUT_NODE = True + CATEGORY = "Lmcq/Protection" + + def encrypt_workflow(self, workflow_file, key, save_name, machine_codes): + if not key: + raise ValueError("加密密钥不能为空") + + if not machine_codes.strip(): + raise ValueError("至少需要一个授权机器码") + + try: + import os + import json + import folder_paths + + # 获取当前文件路径 + current_file_path = os.path.abspath(__file__) + plugin_dir = os.path.dirname(current_file_path) + + # 根据文件路径前缀确定实际文件位置 + if workflow_file.startswith("root/"): + workflow_dir = os.path.join(os.path.dirname(folder_paths.base_path), "workflows") + actual_workflow_file = workflow_file[5:] # 移除 "root/" 前缀 + elif workflow_file.startswith("plugin/"): + workflow_dir = os.path.join(plugin_dir, "workflows") + actual_workflow_file = workflow_file[7:] # 移除 "plugin/" 前缀 + else: + raise ValueError("无效的工作流文件路径") + + workflow_path = os.path.join(workflow_dir, actual_workflow_file) + if not os.path.exists(workflow_path): + raise ValueError(f"工作流文件不存在: {actual_workflow_file}") + + # 处理机器码列表 + authorized_codes = [code.strip() for code in machine_codes.split('\n') if code.strip()] + if not authorized_codes: + raise ValueError("无有效的授权机器码") + + # 生成加密密钥和签名密钥 + salt = os.urandom(16) + sign_salt = os.urandom(16) + kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=salt, + iterations=100000, + ) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + encryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) + signing_key = sign_kdf.derive(key.encode()) + fernet = Fernet(encryption_key) + + # 读取工作流文件 + with open(workflow_path, 'r', encoding='utf-8') as f: + workflow_data = f.read() + + # 加密工作流数据 + encrypted_workflow = fernet.encrypt(workflow_data.encode()) + + # 加密机器码列表 + codes_data = json.dumps(authorized_codes).encode() + encrypted_codes = fernet.encrypt(codes_data) + + # 生成签名 + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_workflow) + h.update(encrypted_codes) + signature = base64.b64encode(h.digest()).decode() + + # 构建加密数据结构 + encrypted_data = { + "version": "1.0.0", + "salt": base64.b64encode(salt).decode(), + "sign_salt": base64.b64encode(sign_salt).decode(), + "type": "encrypted_workflow", + "encrypted_codes": base64.b64encode(encrypted_codes).decode(), + "encrypted_workflow": base64.b64encode(encrypted_workflow).decode(), + "signature": signature + } + + # 保存加密后的工作流 + save_path = os.path.join(workflow_dir, f"{save_name}.lcwf") + with open(save_path, 'w', encoding='utf-8') as f: + json.dump(encrypted_data, f) + + print(f"工作流加密成功: {save_path}") + return {} + + except Exception as e: + raise ValueError(f"工作流加密失败: {str(e)}") + +class LmcqRuntimeWorkflowDecryption: + @classmethod + def INPUT_TYPES(s): + import os + import folder_paths + + # 获取 ComfyUI 根目录的 workflows 文件夹 + root_workflow_dir = os.path.join(os.path.dirname(folder_paths.base_path), "workflows") + if not os.path.exists(root_workflow_dir): + os.makedirs(root_workflow_dir) + + # 获取插件目录的 workflows 文件夹 + current_file_path = os.path.abspath(__file__) + plugin_dir = os.path.dirname(current_file_path) + plugin_workflow_dir = os.path.join(plugin_dir, "workflows") + if not os.path.exists(plugin_workflow_dir): + os.makedirs(plugin_workflow_dir) + + # 获取两个目录中的所有 lcwf 文件 + root_workflow_files = [f for f in os.listdir(root_workflow_dir) if f.endswith('.lcwf')] + plugin_workflow_files = [f for f in os.listdir(plugin_workflow_dir) if f.endswith('.lcwf')] + + # 合并文件列表，添加目录前缀以区分 + workflow_files = ( + [f"root/{f}" for f in root_workflow_files] + + [f"plugin/{f}" for f in plugin_workflow_files] + ) + + if not workflow_files: + workflow_files = [""] + + return { + "required": { + "workflow_file": (workflow_files,), + "key": ("STRING", {"default": ""}), + "save_name": ("STRING", {"default": "decrypted_workflow"}) + } + } + + RETURN_TYPES = () + FUNCTION = "decrypt_workflow" + OUTPUT_NODE = True + CATEGORY = "Lmcq/Protection" + + def decrypt_workflow(self, workflow_file, key, save_name): + if not key: + raise ValueError("解密密钥不能为空") + + try: + import os + import json + import folder_paths + + # 获取当前文件路径 + current_file_path = os.path.abspath(__file__) + plugin_dir = os.path.dirname(current_file_path) + + # 根据文件路径前缀确定实际文件位置 + if workflow_file.startswith("root/"): + workflow_dir = os.path.join(os.path.dirname(folder_paths.base_path), "workflows") + actual_workflow_file = workflow_file[5:] # 移除 "root/" 前缀 + elif workflow_file.startswith("plugin/"): + workflow_dir = os.path.join(plugin_dir, "workflows") + actual_workflow_file = workflow_file[7:] # 移除 "plugin/" 前缀 + else: + raise ValueError("无效的工作流文件路径") + + workflow_path = os.path.join(workflow_dir, actual_workflow_file) + if not os.path.exists(workflow_path): + raise ValueError(f"工作流文件不存在: {actual_workflow_file}") + + # 读取加密的工作流文件 + with open(workflow_path, 'r', encoding='utf-8') as f: + encrypted_data = json.load(f) + + if encrypted_data.get("type") != "encrypted_workflow": + raise ValueError("无效的加密工作流文件") + + # 验证签名 + sign_salt = base64.b64decode(encrypted_data["sign_salt"]) + sign_kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=sign_salt, + iterations=100000, + ) + signing_key = sign_kdf.derive(key.encode()) + + encrypted_workflow = base64.b64decode(encrypted_data["encrypted_workflow"]) + encrypted_codes = base64.b64decode(encrypted_data["encrypted_codes"]) + + import hashlib + h = hmac.new(signing_key, digestmod=hashlib.sha256) + h.update(encrypted_workflow) + h.update(encrypted_codes) + calculated_signature = h.digest() + expected_signature = base64.b64decode(encrypted_data["signature"]) + if not hmac.compare_digest(calculated_signature, expected_signature): + raise ValueError("工作流文件已被篡改") + + # 创建解密器 + salt = base64.b64decode(encrypted_data["salt"]) + kdf = PBKDF2HMAC( + algorithm=hashes.SHA256(), + length=32, + salt=salt, + iterations=100000, + ) + decryption_key = base64.urlsafe_b64encode(kdf.derive(key.encode())) + fernet = Fernet(decryption_key) + + # 解密并验证机器码列表 + codes_data = fernet.decrypt(encrypted_codes) + authorized_codes = json.loads(codes_data.decode()) + + # 验证机器码 + machine_code_getter = LmcqGetMachineCode() + current_machine_code = machine_code_getter.get_machine_code()[0] + if current_machine_code not in authorized_codes: + raise ValueError("当前机器未授权使用此工作流") + + # 解密工作流数据 + decrypted_data = fernet.decrypt(encrypted_workflow).decode() + + # 保存解密后的工作流 + save_path = os.path.join(workflow_dir, f"{save_name}.json") + with open(save_path, 'w', encoding='utf-8') as f: + f.write(decrypted_data) + + print(f"工作流解密成功: {save_path}") + return {} + + except Exception as e: + raise ValueError(f"工作流解密失败: {str(e)}") + +# 注册新节点 +NODE_CLASS_MAPPINGS.update({ + "LmcqRuntimeWorkflowEncryption": LmcqRuntimeWorkflowEncryption, + "LmcqRuntimeWorkflowDecryption": LmcqRuntimeWorkflowDecryption +}) + +NODE_DISPLAY_NAME_MAPPINGS.update({ + "LmcqRuntimeWorkflowEncryption": "Lmcq Runtime Workflow Encryption", + "LmcqRuntimeWorkflowDecryption": "Lmcq Runtime Workflow Decryption" }) \ No newline at end of file diff --git a/runtime_protection.png b/runtime_protection.png new file mode 100644 index 0000000000000000000000000000000000000000..58f92ce16a5b7204b7fd36a49991f0b15d108709 GIT binary patch literal 163116 zcmeFZ2UJtt+AWMIpcGL72^}jSE%Xi|Dxe5R?=2Ff8|hs{KqPdf7l8mGO?nR~7z7ef zdPj{0>Cz#g+#S&O@SbztbIdG{f%#p;F4k!h5>67;>xw#_W^;`vDok%A 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