AdamShuo
732936b661
- 将文本生成和图像理解模块的模型选择改为下拉式选择框 - 文本生成模块新增视觉模型选项，支持多模态文本生成 - 图像编辑模块新增降噪(denoise)参数，支持0.00-1.00范围调节 - 图像编辑模块新增图像生成模型选项，支持低重绘度洗图 - 提升API Token安全性，界面不再明码显示敏感信息 - 优化token处理逻辑，支持自动加载已保存token
540 lines
22 KiB
Python
540 lines
22 KiB
Python
import requests
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import json
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import time
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import torch
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import numpy as np
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from PIL import Image
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from io import BytesIO
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import os
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import folder_paths
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import base64
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import tempfile
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def load_config():
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config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json')
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try:
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with open(config_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except:
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return {
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"default_model": "Qwen/Qwen-Image",
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"timeout": 720,
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"image_download_timeout": 30,
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"default_prompt": "A beautiful landscape"
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}
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def save_config(config: dict) -> bool:
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config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json')
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try:
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with open(config_path, 'w', encoding='utf-8') as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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return True
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except Exception as e:
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print(f"保存配置失败: {e}")
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return False
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def save_api_token(token):
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token_path = os.path.join(os.path.dirname(__file__), '.qwen_token')
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try:
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with open(token_path, 'w', encoding='utf-8') as f:
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f.write(token)
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except Exception as e:
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print(f"保存token失败(.qwen_token): {e}")
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try:
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cfg = load_config()
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cfg["api_token"] = token
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if save_config(cfg):
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return True
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return False
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except Exception as e:
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print(f"保存token失败(config.json): {e}")
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return False
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def load_api_token():
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token_path = os.path.join(os.path.dirname(__file__), '.qwen_token')
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try:
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cfg = load_config()
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token_from_cfg = cfg.get("api_token", "").strip()
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if token_from_cfg:
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return token_from_cfg
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except Exception as e:
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print(f"读取config.json中的token失败: {e}")
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try:
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if os.path.exists(token_path):
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with open(token_path, 'r', encoding='utf-8') as f:
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token = f.read().strip()
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return token if token else ""
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return ""
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except Exception as e:
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print(f"加载token失败: {e}")
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return ""
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def tensor_to_base64_url(image_tensor):
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try:
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if len(image_tensor.shape) == 4:
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image_tensor = image_tensor.squeeze(0)
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if image_tensor.max() <= 1.0:
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image_np = (image_tensor.cpu().numpy() * 255).astype(np.uint8)
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else:
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image_np = image_tensor.cpu().numpy().astype(np.uint8)
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pil_image = Image.fromarray(image_np)
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buffer = BytesIO()
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pil_image.save(buffer, format='JPEG', quality=85)
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img_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
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return f"data:image/jpeg;base64,{img_base64}"
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except Exception as e:
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print(f"图像转换失败: {e}")
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raise Exception(f"图像格式转换失败: {str(e)}")
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class ModelScopeImageNode:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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config = load_config()
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saved_token = load_api_token()
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return {
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"required": {
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"prompt": ("STRING", {
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"multiline": True,
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"default": config.get("default_prompt", "A beautiful landscape")
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}),
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"api_token": ("STRING", {
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"default": "",
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"placeholder": "请输入您的魔搭API Token",
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"multiline": False
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}),
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},
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"optional": {
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"model": (config.get("image_models", ["Qwen/Qwen-Image"]), {
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"default": config.get("default_model", "Qwen/Qwen-Image")
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}),
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"negative_prompt": ("STRING", {
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"multiline": True,
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"default": config.get("default_negative_prompt", "")
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}),
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"width": ("INT", {
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"default": config.get("default_width", 512),
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"min": 64,
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"max": 2048,
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"step": 64
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}),
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"height": ("INT", {
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"default": config.get("default_height", 512),
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"min": 64,
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"max": 2048,
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"step": 64
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}),
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"seed": ("INT", {
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"default": config.get("default_seed", -1),
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"min": -1,
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"max": 2147483647
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}),
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"steps": ("INT", {
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"default": config.get("default_steps", 30),
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"min": 1,
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"max": 100
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}),
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"guidance": ("FLOAT", {
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"default": config.get("default_guidance", 7.5),
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"min": 1.5,
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"max": 20.0,
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"step": 0.1
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "generate_image"
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CATEGORY = "ModelScopeAPI"
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def generate_image(self, prompt, api_token, model="Qwen/Qwen-Image", negative_prompt="", width=512, height=512, seed=-1, steps=30, guidance=7.5):
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config = load_config()
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if not api_token or api_token.strip() == "":
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api_token = load_api_token()
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if not api_token or api_token.strip() == "":
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raise Exception("请输入有效的API Token或确保已保存token")
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saved_token = load_api_token()
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if api_token != saved_token:
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if save_api_token(api_token):
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print("✅ API Token已自动保存")
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else:
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print("⚠️ API Token保存失败，但不影响当前使用")
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try:
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url = 'https://api-inference.modelscope.cn/v1/images/generations'
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payload = {
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'model': model,
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'prompt': prompt,
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'size': f"{width}x{height}",
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'steps': steps,
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'guidance': guidance
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}
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if negative_prompt.strip():
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payload['negative_prompt'] = negative_prompt
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print(f"🚫 负向提示词: {negative_prompt}")
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if seed != -1:
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payload['seed'] = seed
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print(f"🎯 使用指定种子: {seed}")
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else:
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import random
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random_seed = random.randint(0, 2147483647)
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payload['seed'] = random_seed
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print(f"🎲 使用随机种子: {random_seed}")
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print(f"📐 图像尺寸: {width}x{height}")
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print(f"🔧 采样步数: {steps}")
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print(f"🎨 引导系数: {guidance}")
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headers = {
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'Authorization': f'Bearer {api_token}',
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'Content-Type': 'application/json',
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'X-ModelScope-Async-Mode': 'true'
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}
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submission_response = requests.post(
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url,
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data=json.dumps(payload, ensure_ascii=False).encode('utf-8'),
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headers=headers,
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timeout=config.get("timeout", 60)
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)
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if submission_response.status_code == 400:
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print("⚠️ 提交失败，尝试使用最小参数重试...")
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minimal_payload = {
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'model': model,
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'prompt': prompt
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}
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submission_response = requests.post(
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url,
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data=json.dumps(minimal_payload, ensure_ascii=False).encode('utf-8'),
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headers=headers,
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timeout=config.get("timeout", 60)
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)
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if submission_response.status_code != 200:
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raise Exception(f"API请求失败: {submission_response.status_code}, {submission_response.text}")
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submission_json = submission_response.json()
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image_url = None
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if 'task_id' in submission_json:
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task_id = submission_json['task_id']
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print(f"🕒 已提交任务，任务ID: {task_id}，开始轮询...")
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poll_start = time.time()
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max_wait_seconds = max(60, config.get('timeout', 720))
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while True:
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task_resp = requests.get(
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f"https://api-inference.modelscope.cn/v1/tasks/{task_id}",
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headers={
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'Authorization': f'Bearer {api_token}',
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'X-ModelScope-Task-Type': 'image_generation'
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},
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timeout=config.get("image_download_timeout", 120)
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)
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if task_resp.status_code != 200:
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raise Exception(f"任务查询失败: {task_resp.status_code}, {task_resp.text}")
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task_data = task_resp.json()
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status = task_data.get('task_status')
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if status == 'SUCCEED':
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output_images = task_data.get('output_images') or []
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if not output_images:
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raise Exception("任务成功但未返回图片URL")
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image_url = output_images[0]
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print("✅ 任务完成，开始下载图片...")
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break
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if status == 'FAILED':
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raise Exception(f"任务失败: {task_data}")
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if time.time() - poll_start > max_wait_seconds:
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raise Exception("任务轮询超时，请稍后重试或降低并发")
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time.sleep(5)
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elif 'images' in submission_json and len(submission_json['images']) > 0:
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image_url = submission_json['images'][0]['url']
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print(f"⬇️ 下载生成的图片...")
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else:
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raise Exception(f"未识别的API返回格式: {submission_json}")
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img_response = requests.get(image_url, timeout=config.get("image_download_timeout", 30))
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if img_response.status_code != 200:
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raise Exception(f"图片下载失败: {img_response.status_code}")
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pil_image = Image.open(BytesIO(img_response.content))
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if pil_image.mode != 'RGB':
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pil_image = pil_image.convert('RGB')
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image_np = np.array(pil_image).astype(np.float32) / 255.0
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image_tensor = torch.from_numpy(image_np)[None,]
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print(f"🎉 图片处理完成！")
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return (image_tensor,)
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except Exception as e:
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print(f"Qwen-Image API调用失败: {str(e)}")
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error_image = Image.new('RGB', (width, height), color='red')
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error_np = np.array(error_image).astype(np.float32) / 255.0
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error_tensor = torch.from_numpy(error_np)[None,]
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return (error_tensor,)
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class ModelScopeImageEditNode:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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config = load_config()
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saved_token = load_api_token()
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return {
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"required": {
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"image": ("IMAGE",),
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"prompt": ("STRING", {
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"multiline": True,
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"default": "修改图片中的内容"
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}),
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"api_token": ("STRING", {
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"default": "",
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"placeholder": "请输入您的魔搭API Token",
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"multiline": False
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}),
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},
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"optional": {
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"model": (config.get("image_edit_models", ["Qwen/Qwen-Image-Edit"]) + config.get("image_models", []), {
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"default": "Qwen/Qwen-Image-Edit"
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}),
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"negative_prompt": ("STRING", {
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"multiline": True,
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"default": ""
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}),
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"width": ("INT", {
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"default": 512,
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"min": 64,
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"max": 1664,
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"step": 8
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}),
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"height": ("INT", {
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"default": 512,
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"min": 64,
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"max": 1664,
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"step": 8
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}),
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"steps": ("INT", {
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"default": 30,
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"min": 1,
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"max": 100,
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"step": 1
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}),
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"guidance": ("FLOAT", {
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"default": 3.5,
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"min": 1.5,
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"max": 20.0,
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"step": 0.1
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}),
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"seed": ("INT", {
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"default": -1,
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"min": -1,
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"max": 2147483647
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}),
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"denoise": ("FLOAT", {
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"default": 0.75,
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"min": 0.00,
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"max": 1.00,
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"step": 0.01
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("edited_image",)
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FUNCTION = "edit_image"
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CATEGORY = "ModelScopeAPI"
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def edit_image(self, image, prompt, api_token, model="Qwen/Qwen-Image-Edit", negative_prompt="",
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width=512, height=512, steps=30, guidance=3.5, seed=-1, denoise=0.75):
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config = load_config()
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if not api_token or api_token.strip() == "":
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api_token = load_api_token()
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if not api_token or api_token.strip() == "":
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raise Exception("请输入有效的API Token或确保已保存token")
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else:
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saved_token = load_api_token()
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if api_token != saved_token:
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if save_api_token(api_token):
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print("✅ API Token已自动保存")
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else:
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print("⚠️ API Token保存失败，但不影响当前使用")
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try:
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# 将图像转换为临时文件并上传获取URL
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temp_img_path = None
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image_url = None
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try:
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# 保存图像到临时文件
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temp_img_path = os.path.join(tempfile.gettempdir(), f"qwen_edit_temp_{int(time.time())}.jpg")
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if len(image.shape) == 4:
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img = image[0]
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else:
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img = image
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i = 255. * img.cpu().numpy()
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img_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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img_pil.save(temp_img_path)
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print(f"✅ 图像已保存到临时文件: {temp_img_path}")
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# 上传图像到kefan.cn获取URL
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upload_url = 'https://ai.kefan.cn/api/upload/local'
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with open(temp_img_path, 'rb') as img_file:
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files = {'file': img_file}
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upload_response = requests.post(
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upload_url,
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files=files,
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timeout=30
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)
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if upload_response.status_code == 200:
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upload_data = upload_response.json()
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# 修复这里的判断逻辑，kefan.cn返回code=200表示成功
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if upload_data.get('success') == True and 'data' in upload_data:
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image_url = upload_data['data']
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print(f"✅ 图像已上传成功，获取URL: {image_url}")
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else:
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print(f"⚠️ 图像上传返回错误: {upload_response.text}")
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else:
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print(f"⚠️ 图像上传失败: {upload_response.status_code}, {upload_response.text}")
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except Exception as e:
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print(f"⚠️ 图像上传异常: {str(e)}")
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# 如果上传失败，回退到base64
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if not image_url:
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print("⚠️ 图像URL获取失败，回退到使用base64")
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image_data = tensor_to_base64_url(image)
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payload = {
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'model': model,
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'prompt': prompt,
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'image': image_data
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}
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else:
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payload = {
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'model': model,
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'prompt': prompt,
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'image_url': image_url
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}
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if negative_prompt.strip():
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payload['negative_prompt'] = negative_prompt
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print(f"🚫 负向提示词: {negative_prompt}")
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# 添加新参数
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if width != 512 or height != 512:
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size = f"{width}x{height}"
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payload['size'] = size
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print(f"📏 图像尺寸: {size}")
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if steps != 30:
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payload['steps'] = steps
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print(f"🔄 采样步数: {steps}")
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if guidance != 3.5:
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payload['guidance'] = guidance
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print(f"🧭 引导系数: {guidance}")
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if seed != -1:
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payload['seed'] = seed
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print(f"🎲 随机种子: {seed}")
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if denoise != 0.75:
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payload['denoise'] = denoise
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print(f"🎚️ 降噪强度: {denoise}")
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headers = {
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'Authorization': f'Bearer {api_token}',
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'Content-Type': 'application/json',
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'X-ModelScope-Async-Mode': 'true'
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}
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print(f"🖼️ 开始编辑图片...")
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print(f"✏️ 编辑提示: {prompt}")
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url = 'https://api-inference.modelscope.cn/v1/images/generations'
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submission_response = requests.post(
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url,
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data=json.dumps(payload, ensure_ascii=False).encode('utf-8'),
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headers=headers,
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timeout=config.get("timeout", 60)
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)
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if submission_response.status_code != 200:
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raise Exception(f"API请求失败: {submission_response.status_code}, {submission_response.text}")
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submission_json = submission_response.json()
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result_image_url = None
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if 'task_id' in submission_json:
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task_id = submission_json['task_id']
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print(f"🕒 已提交任务，任务ID: {task_id}，开始轮询...")
|
|
poll_start = time.time()
|
|
max_wait_seconds = max(60, config.get('timeout', 720))
|
|
|
|
while True:
|
|
task_resp = requests.get(
|
|
f"https://api-inference.modelscope.cn/v1/tasks/{task_id}",
|
|
headers={
|
|
'Authorization': f'Bearer {api_token}',
|
|
'X-ModelScope-Task-Type': 'image_generation'
|
|
},
|
|
timeout=config.get("image_download_timeout", 120)
|
|
)
|
|
|
|
if task_resp.status_code != 200:
|
|
raise Exception(f"任务查询失败: {task_resp.status_code}, {task_resp.text}")
|
|
|
|
task_data = task_resp.json()
|
|
status = task_data.get('task_status')
|
|
|
|
if status == 'SUCCEED':
|
|
output_images = task_data.get('output_images') or []
|
|
if not output_images:
|
|
raise Exception("任务成功但未返回图片URL")
|
|
result_image_url = output_images[0]
|
|
print("✅ 任务完成，开始下载编辑后的图片...")
|
|
break
|
|
|
|
if status == 'FAILED':
|
|
error_message = task_data.get('errors', {}).get('message', '未知错误')
|
|
error_code = task_data.get('errors', {}).get('code', '未知错误码')
|
|
raise Exception(f"任务失败: 错误码 {error_code}, 错误信息: {error_message}")
|
|
|
|
if time.time() - poll_start > max_wait_seconds:
|
|
raise Exception("任务轮询超时，请稍后重试或降低并发")
|
|
|
|
time.sleep(5)
|
|
else:
|
|
raise Exception(f"未识别的API返回格式: {submission_json}")
|
|
|
|
img_response = requests.get(result_image_url, timeout=config.get("image_download_timeout", 30))
|
|
if img_response.status_code != 200:
|
|
raise Exception(f"图片下载失败: {img_response.status_code}")
|
|
|
|
pil_image = Image.open(BytesIO(img_response.content))
|
|
if pil_image.mode != 'RGB':
|
|
pil_image = pil_image.convert('RGB')
|
|
|
|
image_np = np.array(pil_image).astype(np.float32) / 255.0
|
|
image_tensor = torch.from_numpy(image_np)[None,]
|
|
|
|
# 清理临时文件
|
|
if temp_img_path and os.path.exists(temp_img_path):
|
|
try:
|
|
os.remove(temp_img_path)
|
|
except:
|
|
pass
|
|
|
|
print(f"🎉 图片编辑完成！")
|
|
return (image_tensor,)
|
|
|
|
except Exception as e:
|
|
print(f"Qwen-Image-Edit API调用失败: {str(e)}")
|
|
# 返回原图像作为错误回退
|
|
return (image.unsqueeze(0),)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ModelScopeImageNode": ModelScopeImageNode,
|
|
"ModelScopeImageEditNode": ModelScopeImageEditNode
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ModelScopeImageNode": "ModelScope-Image 生图节点",
|
|
"ModelScopeImageEditNode": "ModelScope-Image 图像编辑节点"
|
|
}
|