import json import os import sys from .mz_prompt_utils import Utils from nodes import MAX_RESOLUTION import comfy.utils import shutil import comfy.samplers import folder_paths WEB_DIRECTORY = "./web" AUTHOR_NAME = u"MinusZone" CATEGORY_NAME = f"{AUTHOR_NAME} - Prompt" # sys.path.append(os.path.join(os.path.dirname(__file__))) import importlib from . import mz_prompt_webserver mz_prompt_webserver.start_server() NODE_CLASS_MAPPINGS = { } NODE_DISPLAY_NAME_MAPPINGS = { } from . import mz_llama_cpp from . import mz_llava class MZ_LLamaCPPModelConfig_ManualSelect: @classmethod def INPUT_TYPES(s): gguf_dir = Utils.get_gguf_models_path() if not os.path.exists(gguf_dir): os.makedirs(gguf_dir) gguf_files = [] # walk gguf_dir for root, dirs, files in os.walk(gguf_dir): for file in files: if file.endswith(".gguf"): gguf_files.append( os.path.relpath(os.path.join(root, file), gguf_dir)) return { "required": { "llama_cpp_model": (gguf_files,), }, "optional": { }, } RETURN_TYPES = ("LLamaCPPModelConfig",) RETURN_NAMES = ("llama_cpp_model_config",) FUNCTION = "create" CATEGORY = CATEGORY_NAME def create(self, **kwargs): kwargs = kwargs.copy() llama_cpp_model = kwargs.get("llama_cpp_model", "") if llama_cpp_model != "": llama_cpp_model = os.path.join( Utils.get_gguf_models_path(), llama_cpp_model) return ({ "type": "ManualSelect", "model_path": llama_cpp_model, },) NODE_CLASS_MAPPINGS["MZ_LLamaCPPModelConfig_ManualSelect"] = MZ_LLamaCPPModelConfig_ManualSelect NODE_DISPLAY_NAME_MAPPINGS[ "MZ_LLamaCPPModelConfig_ManualSelect"] = f"{AUTHOR_NAME} - ModelConfigManualSelect" class MZ_LLamaCPPCLIPTextEncode: @classmethod def INPUT_TYPES(s): importlib.reload(mz_llama_cpp) result = { "required": { }, "optional": { "llama_cpp_model": ("LLamaCPPModelConfig",), }, } common_input = getCommonCLIPTextEncodeInput() for key in common_input["required"]: result["required"][key] = common_input["required"][key] for key in common_input["optional"]: result["optional"][key] = common_input["optional"][key] return result RETURN_TYPES = ("STRING", "CONDITIONING",) RETURN_NAMES = ("text", "conditioning",) OUTPUT_NODE = True FUNCTION = "encode" CATEGORY = CATEGORY_NAME DESCRIPTION = """ llama_cpp_model不设置时，将使用默认模型: Meta-Llama-3-8B-Instruct.Q4_K_M.gguf """ def encode(self, **kwargs): kwargs = kwargs.copy() from . import mz_llama_core_nodes importlib.reload(mz_llama_core_nodes) return mz_llama_core_nodes.llama_cpp_node_encode(kwargs) NODE_CLASS_MAPPINGS["MZ_LLamaCPPCLIPTextEncode"] = MZ_LLamaCPPCLIPTextEncode NODE_DISPLAY_NAME_MAPPINGS[ "MZ_LLamaCPPCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(llama.cpp通用)" class MZ_LLamaCPPOptions: @classmethod def INPUT_TYPES(s): value = mz_llama_cpp.LlamaCppOptions() result = {} for key in value: if type(value[key]) == bool: result[key] = ([True, False], {"default": value[key]}) elif type(value[key]) == int: result[key] = ("INT", { "default": value[key], "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}) elif type(value[key]) == float: result[key] = ("FLOAT", { "default": value[key], "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}) elif type(value[key]) == str: result[key] = ("STRING", {"default": value[key]}) elif type(value[key]) == list: result[key] = (value[key], {"default": value[key][0]}) else: raise Exception(f"Unknown type: {type(value[key])}") return { "required": result, } RETURN_TYPES = ("LLamaCPPOptions",) RETURN_NAMES = ("llama_cpp_options",) FUNCTION = "create" CATEGORY = CATEGORY_NAME def create(self, **kwargs): kwargs = kwargs.copy() importlib.reload(mz_llama_cpp) opt = {} for key in kwargs: opt[key] = kwargs[key] return (opt,) NODE_CLASS_MAPPINGS["MZ_LLamaCPPOptions"] = MZ_LLamaCPPOptions NODE_DISPLAY_NAME_MAPPINGS["MZ_LLamaCPPOptions"] = f"{AUTHOR_NAME} - LLamaCPPOptions" class MZ_CustomizeInstruct: @classmethod def INPUT_TYPES(s): from . import mz_prompts return { "required": { "system": ("STRING", {"multiline": True, "default": mz_prompts.Long_prompt}), "instruct": ("STRING", {"multiline": True, "default": "Short: %text%"}), }, } RETURN_TYPES = ("CustomizeInstruct",) RETURN_NAMES = ("customize_instruct",) FUNCTION = "create" CATEGORY = CATEGORY_NAME def create(self, **kwargs): kwargs = kwargs.copy() return (kwargs,) NODE_CLASS_MAPPINGS["MZ_CustomizeInstruct"] = MZ_CustomizeInstruct NODE_DISPLAY_NAME_MAPPINGS["MZ_CustomizeInstruct"] = f"{AUTHOR_NAME} - CustomizeInstruct" def getCommonCLIPTextEncodeInput(): from . import mz_llama_core_nodes style_presets = mz_llama_core_nodes.get_style_presets() CommonCLIPTextEncodeInput = { "required": { "prompt_version": (["v1"], {"default": "v1"}), "style_presets": ( style_presets, {"default": style_presets[1]} ), "text": ("STRING", {"multiline": True, }), "keep_device": ([False, True], {"default": False}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "clip": ("CLIP", ), "llama_cpp_options": ("LLamaCPPOptions", ), "customize_instruct": ("CustomizeInstruct", ), # "customize_json_schema": ("STRING", ), } } return CommonCLIPTextEncodeInput class MZ_LLavaImageInterrogator: @classmethod def INPUT_TYPES(s): importlib.reload(mz_llava) m_llava_models = mz_llava.LLava_models.copy() for i in range(len(m_llava_models)): if mz_llava.get_exist_model(m_llava_models[i]) is not None: m_llava_models[i] += "[downloaded]" m_llava_mmproj_models = mz_llava.LLava_mmproj_models.copy() for i in range(len(m_llava_mmproj_models)): if mz_llava.get_exist_model(m_llava_mmproj_models[i]) is not None: m_llava_mmproj_models[i] += "[downloaded]" return { "required": { "llama_cpp_model": (m_llava_models, {"default": m_llava_models[0]}), "mmproj_model": (m_llava_mmproj_models, {"default": m_llava_mmproj_models[0]}), "download_source": ( [ "none", "modelscope", "hf-mirror.com", ], {"default": "none"} ), "resolution": ("INT", {"default": 512, "min": 128, "max": 2048}), "sd_format": (["none", "v1", ], {"default": "none"}), "keep_device": ([False, True], {"default": False}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "image": ("IMAGE",), "clip": ("CLIP", ), "llama_cpp_options": ("LLamaCPPOptions", ), "customize_instruct": ("CustomizeInstruct", ), "captioner_config": ("ImageCaptionerConfig", ), }, } RETURN_TYPES = ("STRING", "CONDITIONING",) RETURN_NAMES = ("text", "conditioning",) OUTPUT_NODE = True FUNCTION = "interrogate" CATEGORY = CATEGORY_NAME def interrogate(self, **kwargs): kwargs = kwargs.copy() importlib.reload(mz_llava) kwargs["llama_cpp_model"] = kwargs.get( "llama_cpp_model", "").replace("[downloaded]", "") kwargs["mmproj_model"] = kwargs.get( "mmproj_model", "").replace("[downloaded]", "") if kwargs.get("image", None) is not None: kwargs["image"] = Utils.tensor2pil(kwargs["image"]) else: kwargs["image"] = None text = mz_llava.image_interrogator(kwargs) conditionings = None clip = kwargs.get("clip", None) if clip is not None: conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [Utils.to_debug_prompt(text),]}, "result": (text, conditionings)} NODE_CLASS_MAPPINGS["MZ_LLavaImageInterrogator"] = MZ_LLavaImageInterrogator NODE_DISPLAY_NAME_MAPPINGS[ "MZ_LLavaImageInterrogator"] = f"{AUTHOR_NAME} - ImageInterrogator(LLava)" class MZ_BaseLLavaImageInterrogator: @classmethod def INPUT_TYPES(s): return { "required": { "llama_cpp_model": ("STRING", {"default": ""}), "mmproj_model": ("STRING", {"default": ""}), "resolution": ("INT", {"default": 512, "min": 128, "max": 2048}), "sd_format": (["none", "v1"], {"default": "none"}), "keep_device": ([False, True], {"default": False}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": { "image": ("IMAGE",), "clip": ("CLIP", ), "llama_cpp_options": ("LLamaCPPOptions", ), "customize_instruct": ("CustomizeInstruct", ), "captioner_config": ("ImageCaptionerConfig", ), }, } RETURN_TYPES = ("STRING", "CONDITIONING",) RETURN_NAMES = ("text", "conditioning",) OUTPUT_NODE = True FUNCTION = "interrogate" CATEGORY = CATEGORY_NAME def interrogate(self, **kwargs): kwargs = kwargs.copy() importlib.reload(mz_llava) if kwargs.get("image", None) is not None: kwargs["image"] = Utils.tensor2pil(kwargs["image"]) else: kwargs["image"] = None text = mz_llava.base_image_interrogator(kwargs) conditionings = None clip = kwargs.get("clip", None) if clip is not None: conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [Utils.to_debug_prompt(text),]}, "result": (text, conditionings)} NODE_CLASS_MAPPINGS["MZ_BaseLLavaImageInterrogator"] = MZ_BaseLLavaImageInterrogator NODE_DISPLAY_NAME_MAPPINGS[ "MZ_BaseLLavaImageInterrogator"] = f"{AUTHOR_NAME} - ImageInterrogator(BaseLLava)" class MZ_ImageCaptionerConfig: @classmethod def INPUT_TYPES(s): return { "required": { "directory": ("STRING", {"default": "", "placeholder": "directory"}), "caption_suffix": ("STRING", {"default": ".caption"}), "force_update": ([False, True], {"default": False}), "retry_keyword": ("STRING", {"default": "not,\",error"}), }, "optional": { }, } RETURN_TYPES = ("ImageCaptionerConfig",) RETURN_NAMES = ("captioner_config", ) FUNCTION = "interrogate_batch" CATEGORY = CATEGORY_NAME def interrogate_batch(self, **kwargs): kwargs = kwargs.copy() return (kwargs, ) NODE_CLASS_MAPPINGS["MZ_ImageCaptionerConfig"] = MZ_ImageCaptionerConfig NODE_DISPLAY_NAME_MAPPINGS["MZ_ImageCaptionerConfig"] = f"{AUTHOR_NAME} - ImageCaptionerConfig" class MZ_OpenAIApiCLIPTextEncode: @classmethod def INPUT_TYPES(s): importlib.reload(mz_llama_cpp) s.openai_config_path = os.path.join( Utils.get_models_path(), "openai_config.json", ) default_config = { "base_url": "", "api_key": "", "model_name": "gpt-3.5-turbo-1106", } if os.path.exists(s.openai_config_path): try: with open(s.openai_config_path, "r", encoding="utf-8") as f: default_config = json.load(f) except Exception as e: print(f"Failed to load openai_config.json: {e}") default_api_key = default_config.get("api_key", "") if default_api_key != "": default_api_key = default_api_key[:4] + "******" result = { "required": { "base_url": ("STRING", {"default": default_config.get("base_url", ""), "placeholder": ""}), "api_key": ("STRING", {"default": default_api_key, "placeholder": ""}), "model_name": ("STRING", {"default": default_config.get("model_name", ""), }), }, "optional": { }, } common_input = getCommonCLIPTextEncodeInput() for key in common_input["required"]: if key not in ["seed", "keep_device"]: result["required"][key] = common_input["required"][key] for key in common_input["optional"]: if key != "llama_cpp_options": result["optional"][key] = common_input["optional"][key] return result RETURN_TYPES = ("STRING", "CONDITIONING",) RETURN_NAMES = ("text", "conditioning",) OUTPUT_NODE = True FUNCTION = "encode" CATEGORY = CATEGORY_NAME def encode(self, **kwargs): kwargs = kwargs.copy() import mz_openaiapi importlib.reload(mz_openaiapi) if kwargs.get("api_key", "").endswith("******"): kwargs["api_key"] = "" try: with open(self.openai_config_path, "r", encoding="utf-8") as f: config = json.load(f) kwargs["api_key"] = config.get("api_key", "") except Exception as e: print(f"Failed to load openai_config.json: {e}") if kwargs.get("api_key", "") != "": with open(self.openai_config_path, "w", encoding="utf-8") as f: json.dump({ "base_url": kwargs.get("base_url", ""), "api_key": kwargs.get("api_key", ""), "model_name": kwargs.get("model_name", ""), }, f, indent=4) else: raise ValueError("api_key is required") text = mz_openaiapi.query_beautify_prompt_text(kwargs) conditionings = None clip = kwargs.get("clip", None) if clip is not None: conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [Utils.to_debug_prompt(text),]}, "result": (text, conditionings)} NODE_CLASS_MAPPINGS["MZ_OpenAIApiCLIPTextEncode"] = MZ_OpenAIApiCLIPTextEncode NODE_DISPLAY_NAME_MAPPINGS[ "MZ_OpenAIApiCLIPTextEncode"] = f"{AUTHOR_NAME} - CLIPTextEncode(OpenAIApi)" from . import mz_gen_translate mz_gen_translate.gen_translate(NODE_DISPLAY_NAME_MAPPINGS, NODE_CLASS_MAPPINGS) from .v1.init import NODE_CLASS_MAPPINGS as DEPRECATED_NODE_CLASS_MAPPINGS from .v1.init import NODE_DISPLAY_NAME_MAPPINGS as DEPRECATED_NODE_DISPLAY_NAME_MAPPINGS NODE_CLASS_MAPPINGS.update(DEPRECATED_NODE_CLASS_MAPPINGS) NODE_DISPLAY_NAME_MAPPINGS.update(DEPRECATED_NODE_DISPLAY_NAME_MAPPINGS)