375 lines
15 KiB
Python
375 lines
15 KiB
Python
import time
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import comfy.model_management
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import tempfile
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import io
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from PIL import Image
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import requests
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import numpy as np
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import yaml
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import logging
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import folder_paths
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import os
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import sys
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sys.path.append(".")
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from comfy_api.latest._input_impl.video_types import VideoFromFile
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from comfy.comfy_types import IO, FileLocator, ComfyNodeABC
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from .utils import tensor2pil, np2tensor
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logger = logging.getLogger(__name__)
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config_dir = os.path.join(folder_paths.base_path, "config")
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if not os.path.exists(config_dir):
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os.makedirs(config_dir)
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def get_config():
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try:
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config_path = os.path.join(config_dir, 'replicate_config.yml')
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with open(config_path, 'r') as f:
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config = yaml.load(f, Loader=yaml.FullLoader)
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return config
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except:
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return {}
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def save_config(config):
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config_path = os.path.join(config_dir, 'replicate_config.yml')
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with open(config_path, 'w') as f:
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yaml.dump(config, f, indent=4)
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class ComfyUIReplicateRun:
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"""结合ComfyUI中断机制的Replicate运行器"""
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def __init__(self, timeout_seconds=300, check_interval=1.0):
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self.timeout_seconds = timeout_seconds
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self.check_interval = check_interval
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def run_with_interrupt_check(self, client, ref, input=None, **params):
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"""带中断检查的replicate运行"""
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start_time = time.time()
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# 设置wait=False，手动控制轮询
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params['wait'] = False
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try:
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# 创建预测
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if hasattr(ref, 'id'):
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prediction = client.predictions.create(
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version=ref.id, input=input or {}, **params
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)
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else:
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prediction = client.models.predictions.create(
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model=ref, input=input or {}, **params
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)
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# 手动轮询，检查中断
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while True:
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# 检查超时
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if time.time() - start_time > self.timeout_seconds:
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prediction.cancel()
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raise Exception(f"timeout ({self.timeout_seconds} seconds)")
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# 检查ComfyUI中断信号
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if comfy.model_management.processing_interrupted():
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prediction.cancel()
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raise comfy.model_management.InterruptProcessingException(
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"ComfyUI interrupted")
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# 检查预测状态
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prediction.reload()
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if prediction.status == "succeeded":
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return prediction.output
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elif prediction.status == "failed":
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raise Exception(f"prediction failed: {prediction.error}")
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elif prediction.status in ["starting", "processing"]:
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time.sleep(self.check_interval)
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else:
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prediction.cancel()
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raise Exception(f"unknown status: {prediction.status}")
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except Exception as e:
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logging.error(f"Replicate operation failed: {e}")
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raise
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class ReplicateRequstNode:
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def __init__(self, api_key=None):
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from replicate.client import Client
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config = get_config()
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self.api_key = api_key or config.get("REPLICATE_API_TOKEN")
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if self.api_key is not None:
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self.configure_replicate()
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self.client = Client(timeout=60)
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def configure_replicate(self):
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if self.api_key:
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os.environ["REPLICATE_API_TOKEN"] = self.api_key
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"default": "style of 80s cyberpunk, a portrait photo", "multiline": True}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
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"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"}),
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"steps": ("INT", {"default": 28, "min": 1, "max": 100}),
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"guidance": ("FLOAT", {"default": 3.5, "min": 0.1, "max": 100.0, "step": 0.1}),
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"go_fast": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"api_key": ("STRING", {"default": ""}),
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"lora_path": ("STRING", {"default": "", "multiline": True}),
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"lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
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"extra_lora": ("STRING", {"default": "", "multiline": True}),
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"extra_lora_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
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"model": ("STRING", {"default": "black-forest-labs/flux-dev-lora"}),
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"num_outputs": ("INT", {"default": 1, "min": 1, "max": 10}),
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"image": ("IMAGE",),
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"timeout": ("INT", {"default": 60, "min": 1, "max": 3000}),
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}
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}
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RETURN_TYPES = ("IMAGE", "INT", "INT", "STRING")
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RETURN_NAMES = ("image", "width", "height", "url")
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FUNCTION = "generate_image"
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CATEGORY = "utils/image"
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def generate_image(self, prompt, seed, aspect_ratio, steps, guidance, go_fast, lora_path="", lora_scale=1.0,
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api_key="", extra_lora="", extra_lora_scale=1.0, model="black-forest-labs/flux-dev-lora",
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num_outputs=1, image=None, timeout=60):
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# 更新API key
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if api_key.strip():
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self.api_key = api_key
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save_config({"REPLICATE_API_TOKEN": self.api_key})
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self.configure_replicate()
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if not self.api_key:
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raise ValueError(
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"API key not found in replicate_config.yml or node input")
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try:
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# 准备输入参数
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input_params = {
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"prompt": prompt,
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"lora_weights": lora_path,
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"seed": seed,
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"aspect_ratio": aspect_ratio,
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"num_inference_steps": steps,
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"guidance": guidance,
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"go_fast": go_fast,
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"lora_scale": lora_scale,
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"output_format": "png",
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"num_outputs": num_outputs
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}
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# 添加额外的LoRA参数
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if extra_lora.strip():
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input_params["extra_lora"] = extra_lora
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input_params["extra_lora_scale"] = extra_lora_scale
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# 处理输入图像
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if image is not None and len(image) > 0:
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# 将tensor转换为PIL图像，然后保存为临时文件
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pil_image = tensor2pil(image[0]) # 取第一张图片
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# 创建临时文件
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with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
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pil_image.save(temp_file.name, format='PNG')
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temp_file_path = temp_file.name
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# 使用open()创建文件对象
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input_params["input_image"] = open(temp_file_path, "rb")
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logger.debug(f"已添加输入图像文件: {temp_file_path}")
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logger.debug(f"调用Replicate API，参数: {input_params}")
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runner = ComfyUIReplicateRun(timeout_seconds=timeout, check_interval=1.0)
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# 调用Replicate API
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output = runner.run_with_interrupt_check(self.client, model, input=input_params)
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# 清理临时文件
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if image is not None and len(image) > 0:
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try:
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input_params["input_image"].close()
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os.unlink(temp_file_path)
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except:
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pass
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images = []
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urls = []
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if not isinstance(output, list):
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output = [output]
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for image_url in output:
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logger.debug(f"生成的图片URL: {image_url}")
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urls.append(str(image_url))
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response = requests.get(image_url)
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response.raise_for_status()
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image = Image.open(io.BytesIO(response.content))
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width, height = image.size
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image_array = np.array(image)
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if len(image_array.shape) == 3 and image_array.shape[2] == 4:
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image_array = image_array[:, :, :3]
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images.append(image_array)
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image_tensor = np2tensor(images)
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urls_str = ",".join(urls)
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return (image_tensor, width, height, urls_str)
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except Exception as e:
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logger.exception(f"Replicate API调用失败: {str(e)}")
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raise e
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class ReplicateVideoRequestNode:
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def __init__(self, api_key=None):
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from replicate.client import Client
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config = get_config()
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self.api_key = api_key or config.get("REPLICATE_API_TOKEN")
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if self.api_key is not None:
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self.configure_replicate()
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self.client = Client(timeout=300)
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def configure_replicate(self):
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if self.api_key:
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os.environ["REPLICATE_API_TOKEN"] = self.api_key
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"default": "", "multiline": True}),
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"model": ("STRING", {"default": "wan-video/wan-2.2-i2v-fast"}),
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"num_frames": ("INT", {"default": 81, "min": 81, "max": 121}),
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"resolution": (["480p", "720p"], {"default": "720p"}),
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"frames_per_second": ("INT", {"default": 16, "min": 5, "max": 30, "step": 1}),
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},
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"optional": {
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"image": ("IMAGE", {"default": None, "tooltip": "The image for the image to video only."}),
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"go_fast": ("BOOLEAN", {"default": True}),
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"sample_shift": ("FLOAT", {"default": 12.0, "min": 1.0, "max": 20.0, "step": 0.1}),
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"lora_weights_transformer": ("STRING", {"default": "", "multiline": True}),
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"lora_scale_transformer": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
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"lora_weights_transformer_2": ("STRING", {"default": "", "multiline": True}),
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"lora_scale_transformer_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01}),
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"api_key": ("STRING", {"default": ""}),
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"timeout": ("INT", {"default": 300, "min": 1, "max": 3000}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
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"disable_safety_checker": ("BOOLEAN", {"default": True}),
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"aspect_ratio": (["16:9", "9:16"], {"default": "16:9", "tooltip": "The aspect ratio for the text to video only."}),
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}
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}
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RETURN_TYPES = (IO.VIDEO, "INT", "INT", "FLOAT", "STRING")
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RETURN_NAMES = ("video", "width", "height", "fps", "url")
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FUNCTION = "generate_video"
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CATEGORY = "utils/video"
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def generate_video(self, prompt, model, num_frames, resolution, frames_per_second, image=None,
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go_fast=True, sample_shift=12.0, lora_weights_transformer="",
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lora_scale_transformer=1.0, lora_weights_transformer_2="",
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lora_scale_transformer_2=1.0, api_key="", timeout=300, seed=0, disable_safety_checker=True, aspect_ratio="16:9"):
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if api_key.strip():
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self.api_key = api_key
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save_config({"REPLICATE_API_TOKEN": self.api_key})
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self.configure_replicate()
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if not self.api_key:
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raise ValueError("API key not found in replicate_config.yml or node input")
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try:
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input_params = {
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"prompt": prompt,
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"num_frames": num_frames,
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"resolution": resolution,
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"frames_per_second": frames_per_second,
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"go_fast": go_fast,
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"sample_shift": sample_shift,
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"seed": seed,
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"disable_safety_checker": disable_safety_checker,
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"aspect_ratio": aspect_ratio,
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}
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temp_file_paths = []
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if image is not None and len(image) > 0:
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for i, image_tensor in enumerate(image):
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pil_image = tensor2pil(image_tensor)
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with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
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pil_image.save(temp_file.name, format='PNG')
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temp_file_paths.append(temp_file.name)
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if i == 0:
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input_params["image"] = open(temp_file_paths[-1], "rb")
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else:
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input_params["last_image"] = open(temp_file_paths[-1], "rb")
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if lora_weights_transformer.strip():
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input_params["lora_weights_transformer"] = lora_weights_transformer
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input_params["lora_scale_transformer"] = lora_scale_transformer
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if lora_weights_transformer_2.strip():
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input_params["lora_weights_transformer_2"] = lora_weights_transformer_2
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input_params["lora_scale_transformer_2"] = lora_scale_transformer_2
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logger.debug(f"调用Replicate API生成视频，参数: {input_params}")
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runner = ComfyUIReplicateRun(timeout_seconds=timeout, check_interval=1.0)
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output = runner.run_with_interrupt_check(self.client, model, input=input_params)
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for i, temp_file_path in enumerate(temp_file_paths):
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try:
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if i == 0:
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input_params["image"].close()
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else:
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input_params["last_image"].close()
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os.unlink(temp_file_path)
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except:
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pass
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if not isinstance(output, list):
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output = [output]
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video_url = output[0] if output else None
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if not video_url:
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raise Exception("No video URL returned from API")
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logger.debug(f"生成的视频URL: {video_url}")
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videos_dir = os.path.join(folder_paths.get_output_directory(), "videos_utils_nodes")
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if not os.path.exists(videos_dir):
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os.makedirs(videos_dir)
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video_filename = f"replicate_video_{int(time.time())}.mp4"
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video_path = os.path.join(videos_dir, video_filename)
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response = requests.get(video_url)
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response.raise_for_status()
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with open(video_path, 'wb') as f:
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f.write(response.content)
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logger.info(f"视频已保存到: {video_path}")
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video_input = VideoFromFile(video_path)
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width, height = video_input.get_dimensions()
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fps = float(frames_per_second)
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return (video_input, width, height, fps, video_url)
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except Exception as e:
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logger.exception(f"Replicate视频生成失败: {str(e)}")
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raise e
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NODE_CLASS_MAPPINGS = {
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"ReplicateRequstNode": ReplicateRequstNode,
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"ReplicateVideoRequestNode": ReplicateVideoRequestNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ReplicateVideoRequestNode": "Replicate Video Request",
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"ReplicateRequstNode": "Replicate Image Request",
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}
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