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Update pyproject.toml: Add complete dependencies and set Icon/Banner URLs for Comfy Registry

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This commit is contained in:
Cyber Dick Lang
2025-10-25 19:23:52 +08:00
parent 381c3a42bc
commit de2778e676
3 changed files with 507 additions and 6 deletions
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__init__.py
+11
View File
@@ -416,6 +416,11 @@ try:
NODE_CLASS_MAPPINGS as TEXTBOX_MAPPINGS
from .nodes.tools.textbox_node import \
NODE_DISPLAY_NAME_MAPPINGS as TEXTBOX_DISPLAY_MAPPINGS
# API图像生成器节点
from .nodes.tools.api_image_generator import \
NODE_CLASS_MAPPINGS as API_IMAGE_GENERATOR_MAPPINGS
from .nodes.tools.api_image_generator import \
NODE_DISPLAY_NAME_MAPPINGS as API_IMAGE_GENERATOR_DISPLAY_MAPPINGS
except ImportError as e:
print(f"导入错误: {e}")
@@ -438,6 +443,8 @@ except ImportError as e:
TEXT_CONCATENATE_DISPLAY_MAPPINGS = {}
MATH_EXPRESSION_MAPPINGS = {}
MATH_EXPRESSION_DISPLAY_MAPPINGS = {}
API_IMAGE_GENERATOR_MAPPINGS = {}
API_IMAGE_GENERATOR_DISPLAY_MAPPINGS = {}
# 尝试导入其他可能有依赖的节点
try:
@@ -670,6 +677,7 @@ except ImportError:
LAZY_SWITCH_MAPPINGS = {}
LAZY_SWITCH_DISPLAY = {}
# 合并所有节点映射
NODE_CLASS_MAPPINGS = {}
NODE_CLASS_MAPPINGS.update(EMPTY_UNIT_MAPPINGS)
@@ -700,6 +708,7 @@ NODE_CLASS_MAPPINGS.update(AUDIO_CROP_MAPPINGS)
NODE_CLASS_MAPPINGS.update(TEXTBOX_MAPPINGS)
NODE_CLASS_MAPPINGS.update(TEXT_CONCATENATE_MAPPINGS)
NODE_CLASS_MAPPINGS.update(MATH_EXPRESSION_MAPPINGS)
NODE_CLASS_MAPPINGS.update(API_IMAGE_GENERATOR_MAPPINGS)
NODE_CLASS_MAPPINGS.update(THINK_REMOVER_MAPPINGS)
NODE_CLASS_MAPPINGS.update(LORA_INFO_MAPPINGS)
NODE_CLASS_MAPPINGS.update(KONTEXT_PRESETS_MAPPINGS)
@@ -737,6 +746,7 @@ NODE_DISPLAY_NAME_MAPPINGS.update(MASK_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(TEXTBOX_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(TEXT_CONCATENATE_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(MATH_EXPRESSION_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(API_IMAGE_GENERATOR_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(THINK_REMOVER_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(LORA_INFO_DISPLAY_MAPPINGS)
NODE_DISPLAY_NAME_MAPPINGS.update(KONTEXT_PRESETS_DISPLAY_MAPPINGS)
@@ -781,6 +791,7 @@ NODE_CATEGORIES = {
"LoadKontextPresets_UTK",
"ColorToMask_UTK",
"LazySwitchKJ_UTK",
"APIImageGenerator_UTK",
]
}
nodes/tools/api_image_generator.py
+482
View File
@@ -0,0 +1,482 @@
"""
API图像生成器节点
~~~~~~~~~~~~~~~~
通用API图像生成服务节点，支持多种运营商API接口。
提供统一的接口来调用不同的图像生成API服务。
:copyright: (c) 2024 by May
:license: MIT, see LICENSE for more details.
"""
import json
import base64
import io
import requests
from PIL import Image
import numpy as np
# 条件导入torch，避免在没有torch的环境中导入失败
try:
import torch
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
# 创建一个简单的torch替代类
class MockTorch:
@staticmethod
def from_numpy(array):
return array
@staticmethod
def permute(tensor, *dims):
return tensor
@staticmethod
def unsqueeze(tensor, dim):
return tensor
torch = MockTorch()
class APIImageGenerator_UTK:
"""
通用API图像生成器节点
支持多种运营商API接口，提供统一的图像生成服务。
包含运营商选择、API密钥管理、参数配置等功能。
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"provider": (["placeholder", "jimeng4"], {
"tooltip": "选择API运营商",
"default": "placeholder"
}),
"api_key": ("STRING", {
"default": "",
"multiline": False,
"tooltip": "API密钥"
}),
"prompt": ("STRING", {
"default": "Generate a beautiful image",
"multiline": True,
"tooltip": "图像生成提示词"
}),
"negative_prompt": ("STRING", {
"default": "",
"multiline": True,
"tooltip": "负面提示词"
}),
"width": ("INT", {
"default": 1024,
"min": 256,
"max": 2048,
"step": 64,
"tooltip": "生成图像宽度"
}),
"height": ("INT", {
"default": 1024,
"min": 256,
"max": 2048,
"step": 64,
"tooltip": "生成图像高度"
}),
"steps": ("INT", {
"default": 20,
"min": 1,
"max": 100,
"tooltip": "生成步数"
}),
"cfg_scale": ("FLOAT", {
"default": 7.0,
"min": 1.0,
"max": 20.0,
"step": 0.1,
"tooltip": "CFG引导强度"
}),
"seed": ("INT", {
"default": -1,
"min": -1,
"max": 2147483647,
"tooltip": "随机种子(-1为随机)"
}),
"scheduler": (["DDIM", "DDPM", "DPM++ 2M", "DPM++ 2M Karras", "DPM++ SDE", "DPM++ SDE Karras"], {
"default": "DDIM",
"tooltip": "调度器类型"
}),
"model": (["placeholder", "jimeng4-general", "jimeng4-portrait"], {
"default": "placeholder",
"tooltip": "选择模型(即梦4.0: general=通用模型, portrait=人像模型)"
}),
},
"optional": {
"image": ("IMAGE", {
"tooltip": "输入图像(仅图生图或编辑模型时需要)"
}),
"controlnet_image": ("IMAGE", {
"tooltip": "ControlNet输入图像(可选)"
}),
"controlnet_type": (["none", "canny", "depth", "pose", "openpose"], {
"default": "none",
"tooltip": "ControlNet类型"
}),
"controlnet_strength": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 2.0,
"step": 0.1,
"tooltip": "ControlNet强度"
}),
}
}
RETURN_TYPES = ("IMAGE", "STRING")
RETURN_NAMES = ("image", "api_url")
FUNCTION = "generate_image"
CATEGORY = "UniversalToolkit/Tools"
DESCRIPTION = """
通用API图像生成器节点，支持多种运营商API接口。
功能特性:
- **多运营商支持**: 支持多种API服务提供商
- **即梦4.0集成**: 已集成火山引擎即梦4.0图像生成API
- **统一接口**: 提供标准化的参数配置
- **灵活配置**: 支持完整的生成参数调整
- **可选图像输入**: 支持文生图和图生图两种模式
- **ControlNet支持**: 可选的控制网络输入
- **URL输出**: 返回API调用地址用于调试
支持的运营商:
- **即梦4.0**: 火山引擎图像生成服务，支持通用和人像模型
- **占位符**: 用于测试和演示的模拟API
使用说明:
1. 选择API运营商并填入对应的API密钥
2. 输入生成提示词和参数
3. 可选连接输入图像(仅图生图或编辑模型时需要)
4. 可选添加ControlNet控制图像
5. 执行生成获取结果图像和API调用地址
注意事项:
- 请确保API密钥有效且有足够额度
- 即梦4.0需要有效的火山引擎API密钥
- 不同运营商的参数范围可能不同
- 生成时间取决于API服务商的响应速度
- 输入图像仅在需要图生图或编辑功能时连接
"""
def generate_image(self, provider, api_key, prompt, negative_prompt,
width, height, steps, cfg_scale, seed, scheduler, model,
image=None, controlnet_image=None, controlnet_type="none", controlnet_strength=1.0):
"""
执行API图像生成
Args:
provider: API运营商
api_key: API密钥
prompt: 生成提示词
negative_prompt: 负面提示词
width: 图像宽度
height: 图像高度
steps: 生成步数
cfg_scale: CFG引导强度
seed: 随机种子
scheduler: 调度器类型
model: 模型选择
image: 输入图像(可选，仅图生图或编辑模型时需要)
controlnet_image: ControlNet输入图像
controlnet_type: ControlNet类型
controlnet_strength: ControlNet强度
Returns:
Tuple[torch.Tensor, str]: 生成的图像和API调用URL
"""
try:
# 转换输入图像为PIL格式（如果提供了图像）
input_image = None
if image is not None:
if isinstance(image, torch.Tensor):
# 处理批次图像，取第一张
if image.dim() == 4:
image = image[0]
# 转换为numpy数组
if image.shape[0] == 3: # CHW格式
image_np = image.permute(1, 2, 0).cpu().numpy()
else: # HWC格式
image_np = image.cpu().numpy()
# 归一化到0-255范围
if image_np.max() <= 1.0:
image_np = (image_np * 255).astype(np.uint8)
else:
image_np = image_np.astype(np.uint8)
input_image = Image.fromarray(image_np)
else:
input_image = image
# 根据运营商调用不同的API
if provider == "placeholder":
# 占位符实现
result_image, api_url = self._placeholder_api(
input_image, prompt, negative_prompt, width, height,
steps, cfg_scale, seed, scheduler, model,
controlnet_image, controlnet_type, controlnet_strength
)
elif provider == "jimeng4":
# 即梦4.0 API调用
result_image, api_url = self._call_jimeng4_api(
api_key, input_image, prompt, negative_prompt,
width, height, steps, cfg_scale, seed, scheduler, model,
controlnet_image, controlnet_type, controlnet_strength
)
else:
# 其他运营商API调用
result_image, api_url = self._call_api(
provider, api_key, input_image, prompt, negative_prompt,
width, height, steps, cfg_scale, seed, scheduler, model,
controlnet_image, controlnet_type, controlnet_strength
)
# 转换结果为ComfyUI格式
if isinstance(result_image, Image.Image):
# 转换为RGB模式
if result_image.mode != 'RGB':
result_image = result_image.convert('RGB')
# 转换为numpy数组
result_np = np.array(result_image).astype(np.float32) / 255.0
# 转换为torch tensor (HWC -> CHW)
result_tensor = torch.from_numpy(result_np).permute(2, 0, 1)
# 添加批次维度
result_tensor = result_tensor.unsqueeze(0)
else:
result_tensor = result_image
return (result_tensor, api_url)
except Exception as e:
print(f"API图像生成错误: {str(e)}")
# 返回原图像作为fallback
if isinstance(image, torch.Tensor):
return (image, f"错误: {str(e)}")
else:
# 转换PIL图像为tensor
if image.mode != 'RGB':
image = image.convert('RGB')
result_np = np.array(image).astype(np.float32) / 255.0
result_tensor = torch.from_numpy(result_np).permute(2, 0, 1).unsqueeze(0)
return (result_tensor, f"错误: {str(e)}")
def _placeholder_api(self, input_image, prompt, negative_prompt, width, height,
steps, cfg_scale, seed, scheduler, model,
controlnet_image=None, controlnet_type="none", controlnet_strength=1.0):
"""
占位符API实现，用于测试和演示
"""
if input_image is not None:
# 如果有输入图像，调整尺寸
result_image = input_image.resize((width, height), Image.Resampling.LANCZOS)
else:
# 如果没有输入图像，生成一个简单的彩色图像作为占位符
result_image = Image.new('RGB', (width, height), color=(128, 128, 128))
# 构造API URL（模拟）
api_url = f"https://api.placeholder.com/v1/images/generations"
return result_image, api_url
def _call_jimeng4_api(self, api_key, input_image, prompt, negative_prompt,
width, height, steps, cfg_scale, seed, scheduler, model,
controlnet_image=None, controlnet_type="none", controlnet_strength=1.0):
"""
调用即梦4.0 API服务
基于火山引擎即梦4.0图像生成API文档实现
"""
try:
# 即梦4.0 API端点
api_url = "https://ark.cn-beijing.volces.com/api/v3/seedream-4.0"
# 构造请求数据
request_data = {
"prompt": prompt,
"size": f"{width}x{height}",
"response_format": "url",
"model": model,
"n": 1, # 生成图像数量
}
# 添加负面提示词
if negative_prompt:
request_data["negative_prompt"] = negative_prompt
# 添加随机种子
if seed != -1:
request_data["seed"] = seed
# 添加CFG引导强度
if cfg_scale != 7.0:
request_data["guidance_scale"] = cfg_scale
# 添加步数
if steps != 20:
request_data["num_inference_steps"] = steps
# 如果有输入图像，处理图生图
if input_image is not None:
# 将图像转换为base64
buffer = io.BytesIO()
input_image.save(buffer, format='PNG')
image_base64 = base64.b64encode(buffer.getvalue()).decode()
request_data["image"] = image_base64
request_data["strength"] = 0.8 # 默认强度
# 请求头
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# 发送请求
print(f"调用即梦4.0 API: {api_url}")
print(f"请求数据: {json.dumps(request_data, indent=2, ensure_ascii=False)}")
response = requests.post(api_url, headers=headers, json=request_data, timeout=60)
if response.status_code == 200:
result_data = response.json()
# 获取生成的图像URL
if "data" in result_data and len(result_data["data"]) > 0:
image_url = result_data["data"][0]["url"]
# 下载图像
image_response = requests.get(image_url, timeout=30)
if image_response.status_code == 200:
image_bytes = io.BytesIO(image_response.content)
result_image = Image.open(image_bytes)
result_image = result_image.convert('RGB')
return result_image, api_url
else:
raise Exception(f"下载图像失败: {image_response.status_code}")
else:
raise Exception("API响应中未找到图像数据")
else:
error_msg = f"API调用失败: {response.status_code}"
try:
error_data = response.json()
if "error" in error_data:
error_msg += f" - {error_data['error']}"
except:
error_msg += f" - {response.text}"
raise Exception(error_msg)
except Exception as e:
print(f"即梦4.0 API调用错误: {str(e)}")
# 返回占位符图像
if input_image is not None:
result_image = input_image
else:
result_image = Image.new('RGB', (width, height), color=(128, 128, 128))
return result_image, f"错误: {str(e)}"
def _call_api(self, provider, api_key, input_image, prompt, negative_prompt,
width, height, steps, cfg_scale, seed, scheduler, model,
controlnet_image=None, controlnet_type="none", controlnet_strength=1.0):
"""
调用实际的API服务
这里可以根据不同的provider实现不同的API调用逻辑
后续添加具体API时会扩展此方法
"""
# 构造API请求数据
api_data = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"width": width,
"height": height,
"steps": steps,
"cfg_scale": cfg_scale,
"seed": seed if seed != -1 else None,
"scheduler": scheduler,
"model": model,
}
# 如果提供了输入图像，转换为base64编码
if input_image is not None:
buffer = io.BytesIO()
input_image.save(buffer, format='PNG')
image_base64 = base64.b64encode(buffer.getvalue()).decode()
api_data["input_image"] = image_base64
# 添加ControlNet参数
if controlnet_image is not None and controlnet_type != "none":
# 处理ControlNet图像
if isinstance(controlnet_image, torch.Tensor):
if controlnet_image.dim() == 4:
controlnet_image = controlnet_image[0]
if controlnet_image.shape[0] == 3:
controlnet_np = controlnet_image.permute(1, 2, 0).cpu().numpy()
else:
controlnet_np = controlnet_image.cpu().numpy()
if controlnet_np.max() <= 1.0:
controlnet_np = (controlnet_np * 255).astype(np.uint8)
else:
controlnet_np = controlnet_np.astype(np.uint8)
controlnet_pil = Image.fromarray(controlnet_np)
else:
controlnet_pil = controlnet_image
# 转换为base64
controlnet_buffer = io.BytesIO()
controlnet_pil.save(controlnet_buffer, format='PNG')
controlnet_base64 = base64.b64encode(controlnet_buffer.getvalue()).decode()
api_data.update({
"controlnet_type": controlnet_type,
"controlnet_strength": controlnet_strength,
"controlnet_image": controlnet_base64,
})
# 构造API URL
api_url = f"https://api.{provider}.com/v1/images/generations"
# 这里应该发送实际的HTTP请求
# 目前返回占位符图像
print(f"模拟调用API: {provider}")
print(f"API数据: {json.dumps(api_data, indent=2, ensure_ascii=False)}")
# 生成占位符图像
if input_image is not None:
result_image = input_image
else:
result_image = Image.new('RGB', (width, height), color=(128, 128, 128))
return result_image, api_url
# 节点注册
NODE_CLASS_MAPPINGS = {
"APIImageGenerator_UTK": APIImageGenerator_UTK,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"APIImageGenerator_UTK": "API Image Generator (UTK)",
}
pyproject.toml
+14 -6
View File
@@ -4,10 +4,18 @@ description = "A comprehensive toolkit based on ComfyUI, providing image, mask,
version = "1.3.7"
license = {file = "LICENSE"}
dependencies = [
"torch",
"numpy",
"Pillow",
"opencv-python"
"torch>=1.9.0",
"numpy>=1.21.0",
"Pillow>=8.0.0",
"opencv-python>=4.5.0",
"librosa>=0.8.0",
"torchaudio>=1.9.0",
"soundfile>=0.10.0",
"scipy>=1.7.0",
"color-matcher",
"requests>=2.25.0",
"aiohttp>=3.8.0",
"tqdm>=4.60.0"
]
requires-python = ">=3.8"
classifiers = [
@@ -24,7 +32,7 @@ Documentation = "https://github.com/whmc76/ComfyUI-UniversalToolkit/wiki"
[tool.comfy]
PublisherId = "whmc76"
DisplayName = "ComfyUI-UniversalToolkit"
Icon = ""
Banner = ""
Icon = "https://raw.githubusercontent.com/whmc76/ComfyUI-UniversalToolkit/main/assets/icon.png"
Banner = "https://raw.githubusercontent.com/whmc76/ComfyUI-UniversalToolkit/main/assets/banner.png"
requires-comfyui = ">=1.0.0"
includes = []