import time import comfy.model_management import tempfile import io from PIL import Image import requests import numpy as np import yaml import logging import folder_paths from replicate.client import Client import os import sys sys.path.append(".") logger = logging.getLogger(__name__) config_dir = os.path.join(folder_paths.base_path, "config") if not os.path.exists(config_dir): os.makedirs(config_dir) def get_config(): try: config_path = os.path.join(config_dir, 'replicate_config.yml') with open(config_path, 'r') as f: config = yaml.load(f, Loader=yaml.FullLoader) return config except: return {} def save_config(config): config_path = os.path.join(config_dir, 'replicate_config.yml') with open(config_path, 'w') as f: yaml.dump(config, f, indent=4) class ComfyUIReplicateRun: """结合ComfyUI中断机制的Replicate运行器""" def __init__(self, timeout_seconds=300, check_interval=1.0): self.timeout_seconds = timeout_seconds self.check_interval = check_interval def run_with_interrupt_check(self, client, ref, input=None, **params): """带中断检查的replicate运行""" start_time = time.time() # 设置wait=False，手动控制轮询 params['wait'] = False try: # 创建预测 if hasattr(ref, 'id'): prediction = client.predictions.create( version=ref.id, input=input or {}, **params ) else: prediction = client.models.predictions.create( model=ref, input=input or {}, **params ) # 手动轮询，检查中断 while True: # 检查超时 if time.time() - start_time > self.timeout_seconds: raise Exception(f"timeout ({self.timeout_seconds} seconds)") # 检查ComfyUI中断信号 if comfy.model_management.processing_interrupted(): raise comfy.model_management.InterruptProcessingException( "ComfyUI interrupted") # 检查预测状态 prediction.reload() if prediction.status == "succeeded": return prediction.output elif prediction.status == "failed": raise Exception(f"prediction failed: {prediction.error}") elif prediction.status in ["starting", "processing"]: time.sleep(self.check_interval) else: raise Exception(f"unknown status: {prediction.status}") except Exception as e: logging.error(f"Replicate operation failed: {e}") raise class ReplicateRequstNode: def __init__(self, api_key=None): config = get_config() self.api_key = api_key or config.get("REPLICATE_API_TOKEN") if self.api_key is not None: self.configure_replicate() self.client = Client(timeout=60) def configure_replicate(self): if self.api_key: os.environ["REPLICATE_API_TOKEN"] = self.api_key @classmethod def INPUT_TYPES(cls): return { "required": { "prompt": ("STRING", {"default": "style of 80s cyberpunk, a portrait photo", "multiline": True}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), "aspect_ratio": (["1:1", "16:9", "21:9", "3:2", "4:3", "5:4", "9:16", "9:21", "2:3", "3:4", "4:5"], {"default": "1:1"}), "steps": ("INT", {"default": 28, "min": 1, "max": 100}), "guidance": ("FLOAT", {"default": 3.5, "min": 0.1, "max": 100.0, "step": 0.1}), "go_fast": ("BOOLEAN", {"default": True}), }, "optional": { "api_key": ("STRING", {"default": ""}), "lora_path": ("STRING", {"default": ""}), "lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}), "extra_lora": ("STRING", {"default": ""}), "extra_lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}), "model": ("STRING", {"default": "black-forest-labs/flux-dev-lora"}), "num_outputs": ("INT", {"default": 1, "min": 1, "max": 10}), "image": ("IMAGE",), "timeout": ("INT", {"default": 60, "min": 1, "max": 3000}), } } RETURN_TYPES = ("IMAGE", "INT", "INT", "STRING") RETURN_NAMES = ("image", "width", "height", "url") FUNCTION = "generate_image" CATEGORY = "utils/image" def generate_image(self, prompt, seed, aspect_ratio, steps, guidance, go_fast, lora_path="", lora_scale=1.0, api_key="", extra_lora="", extra_lora_scale=1.0, model="black-forest-labs/flux-dev-lora", num_outputs=1, image=None, timeout=60): # 更新API key if api_key.strip(): self.api_key = api_key save_config({"REPLICATE_API_TOKEN": self.api_key}) self.configure_replicate() if not self.api_key: raise ValueError( "API key not found in replicate_config.yml or node input") try: # 准备输入参数 input_params = { "prompt": prompt, "lora_weights": lora_path, "seed": seed, "aspect_ratio": aspect_ratio, "num_inference_steps": steps, "guidance": guidance, "go_fast": go_fast, "lora_scale": lora_scale, "output_format": "png", "num_outputs": num_outputs } # 添加额外的LoRA参数 if extra_lora.strip(): input_params["extra_lora"] = extra_lora input_params["extra_lora_scale"] = extra_lora_scale # 处理输入图像 if image is not None and len(image) > 0: # 将tensor转换为PIL图像，然后保存为临时文件 from .utils import tensor2pil pil_image = tensor2pil(image[0]) # 取第一张图片 # 创建临时文件 with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file: pil_image.save(temp_file.name, format='PNG') temp_file_path = temp_file.name # 使用open()创建文件对象 input_params["input_image"] = open(temp_file_path, "rb") logger.debug(f"已添加输入图像文件: {temp_file_path}") logger.debug(f"调用Replicate API，参数: {input_params}") runner = ComfyUIReplicateRun(timeout_seconds=timeout, check_interval=1.0) # 调用Replicate API output = runner.run_with_interrupt_check(self.client, model, input=input_params) # 清理临时文件 if image is not None and len(image) > 0: try: input_params["input_image"].close() os.unlink(temp_file_path) except: pass images = [] urls = [] if not isinstance(output, list): output = [output] for image_url in output: logger.debug(f"生成的图片URL: {image_url}") urls.append(str(image_url)) response = requests.get(image_url) response.raise_for_status() image = Image.open(io.BytesIO(response.content)) width, height = image.size image_array = np.array(image) if len(image_array.shape) == 3 and image_array.shape[2] == 4: image_array = image_array[:, :, :3] images.append(image_array) from .utils import np2tensor image_tensor = np2tensor(images) urls_str = ",".join(urls) return (image_tensor, width, height, urls_str) except Exception as e: logger.exception(f"Replicate API调用失败: {str(e)}") raise e NODE_CLASS_MAPPINGS = { "ReplicateRequstNode": ReplicateRequstNode, } NODE_DISPLAY_NAME_MAPPINGS = { "ReplicateRequstNode": "Replicate Request", }