o1keyandClaude Sonnet 4.5
9ab209b2b7
Complete rewrite/sync of comfyui_o1key custom nodes. Treat this commit as the current canonical version. Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
827 lines
32 KiB
Python
827 lines
32 KiB
Python
"""
|
||
全能生图 节点
|
||
ComfyUI 自定义节点，用于调用 Gemini 模型生成图像
|
||
"""
|
||
|
||
import time
|
||
import math
|
||
import random
|
||
import asyncio
|
||
import aiohttp
|
||
from concurrent.futures import ThreadPoolExecutor
|
||
from typing import Optional, Tuple, List
|
||
|
||
import torch
|
||
import numpy as np
|
||
from PIL import Image
|
||
|
||
from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
|
||
from ..utils.file_utils import ImageInfo, generate_timestamp_filename, save_image
|
||
from ..clients.openai_client import OpenAIAPIClient
|
||
from ..models_config import (
|
||
get_enabled_models, get_model_description,
|
||
get_model_supported_aspect_ratios, get_all_supported_aspect_ratios,
|
||
get_model_supported_resolutions, get_all_supported_resolutions
|
||
)
|
||
|
||
# 检查 folder_paths 是否可用
|
||
try:
|
||
import folder_paths
|
||
FOLDER_PATHS_AVAILABLE = True
|
||
except ImportError:
|
||
FOLDER_PATHS_AVAILABLE = False
|
||
|
||
# 导入 ComfyUI 原生进度条
|
||
try:
|
||
from comfy.utils import ProgressBar
|
||
PROGRESS_BAR_AVAILABLE = True
|
||
except ImportError:
|
||
PROGRESS_BAR_AVAILABLE = False
|
||
print("⚠️ 全能生图: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
|
||
|
||
# 内存监控（可选）
|
||
try:
|
||
import psutil
|
||
MEMORY_MONITOR_AVAILABLE = True
|
||
except ImportError:
|
||
MEMORY_MONITOR_AVAILABLE = False
|
||
print("⚠️ 全能生图: psutil 不可用，内存监控功能禁用")
|
||
|
||
# ============================================================================
|
||
# 调试日志配置
|
||
# ============================================================================
|
||
# 是否启用调试日志（打印完整的 API 响应内容）
|
||
# 设置为 True 以启用调试日志，False 以禁用
|
||
DEBUG_LOG_ENABLED = False
|
||
# 是否启用请求体日志（打印发送给 API 的请求体，base64 图片数据将自动截断）
|
||
# 设置为 True 以启用请求体日志，False 以禁用
|
||
REQUEST_LOG_ENABLED = False
|
||
# ============================================================================
|
||
|
||
|
||
class QuanNengShengTu:
|
||
"""
|
||
全能生图 节点
|
||
|
||
功能：
|
||
- 文生图：基于提示词生成图像
|
||
- 图生图：基于输入图像和提示词生成新图像
|
||
- 批量生成：支持并发生成多张图像
|
||
|
||
注意：
|
||
- 支持的模型列表从 models_config.py 动态加载
|
||
- 要添加/禁用模型，请编辑 models_config.py 文件
|
||
"""
|
||
|
||
# 支持的模型列表（从配置文件动态加载）
|
||
MODELS = None # 将在 INPUT_TYPES 中动态获取
|
||
|
||
# 支持的宽高比列表（全量：所有启用模型的并集，动态加载）
|
||
ASPECT_RATIOS = [
|
||
"1:1", "4:3", "3:4", "16:9", "9:16",
|
||
"2:3", "3:2", "4:5", "5:4", "21:9",
|
||
"1:4", "4:1", "1:8", "8:1"
|
||
]
|
||
|
||
# 支持的分辨率列表（全量兜底，实际由 get_all_supported_resolutions() 动态生成）
|
||
RESOLUTIONS = ["512", "1K", "2K", "4K"]
|
||
|
||
def __init__(self):
|
||
"""初始化节点"""
|
||
self.client = None
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
"""
|
||
定义输入参数
|
||
|
||
ComfyUI 节点规范：
|
||
- required: 必选参数
|
||
- optional: 可选参数
|
||
"""
|
||
# 从配置文件动态获取启用的模型列表
|
||
enabled_models = get_enabled_models()
|
||
|
||
# 过滤掉包含"限时特价"的模型
|
||
enabled_models = [m for m in enabled_models if "限时特价" not in m]
|
||
|
||
# 如果没有启用的模型，使用空列表（会导致节点不可用，提示用户配置）
|
||
if not enabled_models:
|
||
enabled_models = ["请在 models_config.py 中启用至少一个模型"]
|
||
|
||
# 动态获取所有启用模型支持的宽高比（去重合并）
|
||
all_aspect_ratios = get_all_supported_aspect_ratios()
|
||
if not all_aspect_ratios:
|
||
all_aspect_ratios = cls.ASPECT_RATIOS
|
||
|
||
# 动态获取所有启用模型支持的分辨率（去重合并）
|
||
all_resolutions = get_all_supported_resolutions()
|
||
if not all_resolutions:
|
||
all_resolutions = cls.RESOLUTIONS
|
||
|
||
# 创建9个独立的图像输入
|
||
optional_inputs = {}
|
||
for i in range(1, 10): # 1-9
|
||
optional_inputs[f"参考图{i}"] = ("IMAGE",)
|
||
|
||
return {
|
||
"required": {
|
||
"提示词": ("STRING", {
|
||
"default": "一个中国女子的OOTD",
|
||
"multiline": True
|
||
}),
|
||
"模型": (enabled_models, {
|
||
"default": enabled_models[0]
|
||
}),
|
||
"宽高比": (all_aspect_ratios, {
|
||
"default": "1:1"
|
||
}),
|
||
"分辨率": (all_resolutions, {
|
||
"default": "2K"
|
||
}),
|
||
"生图数量": ("INT", {
|
||
"default": 1,
|
||
"min": 1,
|
||
"max": 1000,
|
||
"step": 1
|
||
}),
|
||
"像素缩放": ("BOOLEAN", {
|
||
"default": True,
|
||
"label_on": "打开",
|
||
"label_off": "关闭"
|
||
}),
|
||
"分辨率像素": ("FLOAT", {
|
||
"default": 1.0,
|
||
"min": 0.1,
|
||
"max": 100.0,
|
||
"step": 0.1,
|
||
"display": "number"
|
||
}),
|
||
"seed": ("INT", {
|
||
"default": 0,
|
||
"min": 0,
|
||
"max": 0xffffffffffffffff
|
||
})
|
||
},
|
||
"optional": optional_inputs
|
||
}
|
||
|
||
# 返回值类型
|
||
RETURN_TYPES = ("IMAGE",)
|
||
RETURN_NAMES = ("输出图像",)
|
||
|
||
# 导入 ComfyUI 的文件夹路径管理
|
||
try:
|
||
import folder_paths
|
||
FOLDER_PATHS_AVAILABLE = True
|
||
except ImportError:
|
||
FOLDER_PATHS_AVAILABLE = False
|
||
|
||
# 执行函数名
|
||
FUNCTION = "generate"
|
||
|
||
# 节点分类
|
||
CATEGORY = "image/generation"
|
||
|
||
def resize_to_megapixels(
|
||
self,
|
||
image: Image.Image,
|
||
target_megapixels: float
|
||
) -> Image.Image:
|
||
"""
|
||
将图像缩放到指定的总像素数，保持纵横比
|
||
|
||
Args:
|
||
image: PIL Image 对象
|
||
target_megapixels: 目标像素数（百万像素）
|
||
|
||
Returns:
|
||
缩放后的 PIL Image
|
||
"""
|
||
# 计算当前像素数
|
||
current_pixels = image.width * image.height
|
||
target_pixels = int(target_megapixels * 1_000_000)
|
||
|
||
# 如果当前像素数已经接近目标，则不缩放
|
||
if abs(current_pixels - target_pixels) / target_pixels < 0.05:
|
||
return image
|
||
|
||
# 计算缩放比例
|
||
scale = (target_pixels / current_pixels) ** 0.5
|
||
|
||
# 计算新尺寸
|
||
new_width = int(image.width * scale)
|
||
new_height = int(image.height * scale)
|
||
|
||
# 确保至少为1像素
|
||
new_width = max(1, new_width)
|
||
new_height = max(1, new_height)
|
||
|
||
# 使用 Lanczos 重采样
|
||
resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||
|
||
return resized_image
|
||
|
||
def validate_inputs(
|
||
self,
|
||
images: Optional[torch.Tensor],
|
||
batch_size: int
|
||
) -> None:
|
||
"""
|
||
验证输入参数
|
||
|
||
Args:
|
||
images: 输入图像张量（可选）
|
||
batch_size: 批次大小
|
||
|
||
Raises:
|
||
ValueError: 如果输入参数不合法
|
||
"""
|
||
# 检查图像数量
|
||
if images is not None:
|
||
num_images = images.shape[0]
|
||
if num_images > 14:
|
||
raise ValueError(
|
||
f"输入图像数量 {num_images} 超过限制 14 张，请减少输入图像数量"
|
||
)
|
||
|
||
# 检查批次大小
|
||
if batch_size < 1 or batch_size > 1000:
|
||
raise ValueError(
|
||
f"批次大小 {batch_size} 超出范围 [1, 1000]"
|
||
)
|
||
|
||
async def _generate_single_task(
|
||
self,
|
||
session: aiohttp.ClientSession,
|
||
prompt: str,
|
||
model: str,
|
||
resolution: str,
|
||
aspect_ratio: str,
|
||
images: List[Image.Image],
|
||
output_folder: str,
|
||
global_task_index: int,
|
||
) -> dict:
|
||
"""执行单个生成任务，生成后立即保存到磁盘"""
|
||
result = {
|
||
"global_task_index": global_task_index,
|
||
"prompt": prompt,
|
||
"success": False,
|
||
"generated_count": 0,
|
||
"saved_files": [],
|
||
"error": None
|
||
}
|
||
|
||
try:
|
||
gen_result = await self.client.generate_single_async(
|
||
prompt=prompt,
|
||
model=model,
|
||
resolution=resolution,
|
||
aspect_ratio=aspect_ratio,
|
||
images=images if images else None,
|
||
session=session,
|
||
debug=DEBUG_LOG_ENABLED,
|
||
debug_request=REQUEST_LOG_ENABLED,
|
||
enable_grounding=False,
|
||
enable_image_search=False
|
||
)
|
||
if gen_result:
|
||
images_list, _ = gen_result
|
||
for gen_img in images_list:
|
||
output_path = generate_timestamp_filename(
|
||
output_folder=output_folder,
|
||
extension=".png"
|
||
)
|
||
save_image(gen_img, output_path)
|
||
result["saved_files"].append(output_path)
|
||
gen_img = None # 释放内存
|
||
|
||
result["success"] = True
|
||
result["generated_count"] = len(images_list)
|
||
except Exception as e:
|
||
result["error"] = str(e)
|
||
|
||
return result
|
||
|
||
async def _process_batch_async(
|
||
self,
|
||
prompts: List[str],
|
||
model: str,
|
||
resolution: str,
|
||
aspect_ratio: str,
|
||
images_per_prompt: int,
|
||
input_images: List[Image.Image],
|
||
output_folder: str,
|
||
pbar=None,
|
||
) -> List[dict]:
|
||
"""异步批量处理：每个提示词独立调用 API，生成后立即写磁盘"""
|
||
# 构建任务列表：(prompt, sub_index) 用于 images_per_prompt > 1 的情况
|
||
tasks_def = []
|
||
for p_idx, prompt in enumerate(prompts):
|
||
for sub_idx in range(images_per_prompt):
|
||
tasks_def.append((p_idx, sub_idx, prompt))
|
||
|
||
total_tasks = len(tasks_def)
|
||
num_prompts = len(prompts)
|
||
print(f"全能生图: 批量提示词模式 | {num_prompts}个提示词 × {images_per_prompt}张/提示词 | 共{total_tasks}任务")
|
||
|
||
max_concurrent = 10
|
||
num_batches = math.ceil(total_tasks / max_concurrent)
|
||
|
||
all_results = []
|
||
completed = 0
|
||
success_count = 0
|
||
fail_count = 0
|
||
|
||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||
|
||
async with aiohttp.ClientSession(connector=connector) as session:
|
||
for batch_idx in range(num_batches):
|
||
start_idx = batch_idx * max_concurrent
|
||
end_idx = min(start_idx + max_concurrent, total_tasks)
|
||
|
||
tasks = []
|
||
for i in range(start_idx, end_idx):
|
||
_, _, prompt = tasks_def[i]
|
||
task = asyncio.create_task(
|
||
self._generate_single_task(
|
||
session=session,
|
||
prompt=prompt,
|
||
model=model,
|
||
resolution=resolution,
|
||
aspect_ratio=aspect_ratio,
|
||
images=input_images,
|
||
output_folder=output_folder,
|
||
global_task_index=i,
|
||
)
|
||
)
|
||
tasks.append(task)
|
||
|
||
batch_results = []
|
||
for coro in asyncio.as_completed(tasks):
|
||
result_data = None
|
||
try:
|
||
result = await coro
|
||
if isinstance(result, Exception):
|
||
result_data = {"success": False, "error": str(result), "generated_count": 0, "saved_files": [], "prompt": ""}
|
||
else:
|
||
result_data = result
|
||
batch_results.append(result_data)
|
||
except Exception as e:
|
||
result_data = {"success": False, "error": str(e), "generated_count": 0, "saved_files": [], "prompt": ""}
|
||
batch_results.append(result_data)
|
||
|
||
completed += 1
|
||
prompt_snippet = (result_data.get("prompt", "") or "")[:30]
|
||
|
||
if result_data and result_data.get("success", False):
|
||
success_count += 1
|
||
count = result_data.get("generated_count", 1)
|
||
print(f"全能生图: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✓成功({count}张)")
|
||
else:
|
||
fail_count += 1
|
||
error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
|
||
print(f"全能生图: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✗失败: {error_msg}")
|
||
|
||
if pbar is not None:
|
||
pbar.update(1)
|
||
|
||
all_results.extend(batch_results)
|
||
|
||
import gc
|
||
gc.collect()
|
||
|
||
await asyncio.sleep(0.1)
|
||
|
||
return all_results
|
||
|
||
def generate(
|
||
self,
|
||
提示词: str,
|
||
模型: str,
|
||
宽高比: str,
|
||
分辨率: str,
|
||
生图数量: int,
|
||
像素缩放: bool,
|
||
分辨率像素: float,
|
||
seed: int,
|
||
**kwargs
|
||
) -> Tuple[torch.Tensor]:
|
||
"""
|
||
生成图像
|
||
|
||
Args:
|
||
prompt: 提示词
|
||
模型: 模型名称
|
||
宽高比: 宽高比
|
||
分辨率: 分辨率
|
||
生图数量: 批次大小
|
||
像素缩放: 是否启用像素缩放
|
||
分辨率像素: 目标像素数（百万像素）
|
||
seed: 随机种子
|
||
**kwargs: 动态参考图输入 (参考图1-9)
|
||
|
||
注意：
|
||
调试日志功能已移至文件顶部配置，通过修改 DEBUG_LOG_ENABLED 常量控制
|
||
|
||
Returns:
|
||
生成的图像张量 (IMAGE,)
|
||
"""
|
||
start_time = time.time()
|
||
|
||
# 创建 ComfyUI 原生进度条
|
||
pbar = None
|
||
if PROGRESS_BAR_AVAILABLE:
|
||
pbar = ProgressBar(生图数量)
|
||
|
||
try:
|
||
# 设置随机种子（用于本地随机操作）
|
||
random.seed(seed)
|
||
np.random.seed(seed % (2**32))
|
||
|
||
# 内存监控初始化
|
||
if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
|
||
import psutil
|
||
process = psutil.Process()
|
||
initial_memory = process.memory_info().rss / 1024 / 1024
|
||
print(f"全能生图: 初始内存使用: {initial_memory:.1f} MB")
|
||
|
||
# 初始化 API 客户端
|
||
if self.client is None:
|
||
try:
|
||
self.client = OpenAIAPIClient()
|
||
except ValueError as e:
|
||
raise ValueError(f"初始化失败: {str(e)}")
|
||
|
||
# 校验分辨率与模型的兼容性
|
||
supported_resolutions = get_model_supported_resolutions(模型)
|
||
if supported_resolutions and 分辨率 not in supported_resolutions:
|
||
raise ValueError(
|
||
f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
|
||
f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
|
||
)
|
||
|
||
# 校验宽高比与模型的兼容性
|
||
supported_ratios = get_model_supported_aspect_ratios(模型)
|
||
if supported_ratios and 宽高比 not in supported_ratios:
|
||
raise ValueError(
|
||
f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
|
||
f"该模型支持的宽高比：{', '.join(supported_ratios)}"
|
||
)
|
||
|
||
# 收集独立输入的参考图
|
||
input_images = []
|
||
for i in range(1, 10): # 1-9
|
||
key = f"参考图{i}"
|
||
if key in kwargs and kwargs[key] is not None:
|
||
pil_imgs = tensor_to_pil(kwargs[key])
|
||
input_images.extend(pil_imgs)
|
||
|
||
# 验证输入图像数量
|
||
if input_images:
|
||
if len(input_images) > 14:
|
||
raise ValueError(
|
||
f"输入图像数量 {len(input_images)} 超过限制 14 张，请减少输入图像数量"
|
||
)
|
||
|
||
# 应用像素缩放（如果启用）
|
||
if input_images and 像素缩放:
|
||
scaled_images = []
|
||
for img in input_images:
|
||
scaled = self.resize_to_megapixels(img, 分辨率像素)
|
||
scaled_images.append(scaled)
|
||
input_images = scaled_images
|
||
|
||
# 解析批量提示词
|
||
batch_prompts = parse_batch_prompts(提示词)
|
||
|
||
# 打印首行概览
|
||
if batch_prompts:
|
||
# 批量提示词模式
|
||
num_prompts = len(batch_prompts)
|
||
total_images = num_prompts * 生图数量
|
||
mode_str = f"批量提示词模式 ({num_prompts}个提示词)"
|
||
if input_images:
|
||
mode_str += f" (输入{len(input_images)}张)"
|
||
print(f"全能生图: {mode_str} | {分辨率} {宽高比} | 共{total_images}张")
|
||
|
||
# 大批量警告
|
||
if total_images > 100:
|
||
print(f"⚠️ 全能生图: 警告！批量生成 {total_images} 张图片，内存占用可能较高")
|
||
print(f"⚠️ 建议：分批执行或减少生图数量")
|
||
else:
|
||
# 单提示词模式
|
||
mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式"
|
||
print(f"全能生图: {mode_str} | {分辨率} {宽高比} | {生图数量}张")
|
||
|
||
# 大批量警告
|
||
if 生图数量 > 100:
|
||
print(f"⚠️ 全能生图: 警告！批量生成 {生图数量} 张图片，内存占用可能较高")
|
||
print(f"⚠️ 建议：分批执行或减少生图数量")
|
||
|
||
# 统计变量
|
||
success_count = 0
|
||
fail_count = 0
|
||
|
||
# 进度回调 - 打印错误信息并更新进度条，添加内存监控
|
||
def progress_callback(current, total, success, error_msg=None):
|
||
nonlocal success_count, fail_count
|
||
if success:
|
||
success_count += 1
|
||
print(f"全能生图: 任务 {current}/{total} 成功 ✓")
|
||
else:
|
||
fail_count += 1
|
||
if error_msg:
|
||
print(f"全能生图: 任务 {current}/{total} 失败 ✗")
|
||
print(f"原始错误详情:\n{error_msg}")
|
||
else:
|
||
print(f"全能生图: 任务 {current}/{total} 失败 ✗")
|
||
|
||
# 更新 ComfyUI 原生进度条
|
||
if pbar is not None:
|
||
pbar.update(1)
|
||
|
||
# 内存监控（每完成10个任务检查一次）
|
||
if MEMORY_MONITOR_AVAILABLE and total > 50 and current % 10 == 0:
|
||
import gc
|
||
gc.collect() # 强制垃圾回收
|
||
current_memory = process.memory_info().rss / 1024 / 1024
|
||
memory_increase = current_memory - initial_memory
|
||
print(f"全能生图: 内存使用: {current_memory:.1f} MB (+{memory_increase:.1f} MB)")
|
||
|
||
# 内存警告阈值（2GB）
|
||
if current_memory > 2000:
|
||
print(f"⚠️ 全能生图: 内存使用过高！建议减少生图数量或分批执行")
|
||
|
||
# 根据是否有批量提示词选择生成模式
|
||
if batch_prompts:
|
||
num_prompts = len(batch_prompts)
|
||
total_images = num_prompts * 生图数量
|
||
|
||
# ===== 批量提示词模式：异步并发+磁盘保存 =====
|
||
if pbar is not None:
|
||
pbar = ProgressBar(total_images)
|
||
|
||
# 确定保存路径
|
||
output_folder = ""
|
||
if FOLDER_PATHS_AVAILABLE:
|
||
output_folder = folder_paths.get_output_directory()
|
||
print(f"全能生图: 磁盘保存模式 → {output_folder}")
|
||
else:
|
||
raise ValueError("无法获取 ComfyUI output 目录，请检查 folder_paths 是否可用")
|
||
|
||
import os
|
||
os.makedirs(output_folder, exist_ok=True)
|
||
|
||
def run_async_in_thread():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
return loop.run_until_complete(
|
||
self._process_batch_async(
|
||
prompts=batch_prompts,
|
||
model=模型,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
images_per_prompt=生图数量,
|
||
input_images=input_images,
|
||
output_folder=output_folder,
|
||
pbar=pbar,
|
||
)
|
||
)
|
||
finally:
|
||
loop.close()
|
||
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(run_async_in_thread)
|
||
try:
|
||
results = future.result(timeout=3600)
|
||
except TimeoutError:
|
||
raise RuntimeError("任务执行超时（1小时），请减少提示词数量或检查网络连接")
|
||
|
||
# 统计结果
|
||
success_count = sum(1 for r in results if r.get("success", False))
|
||
fail_count = len(results) - success_count
|
||
total_generated = sum(r.get("generated_count", 0) for r in results)
|
||
all_saved_files = []
|
||
for r in results:
|
||
all_saved_files.extend(r.get("saved_files", []))
|
||
|
||
elapsed = time.time() - start_time
|
||
time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
|
||
|
||
print(f"完成！总耗时 {time_str} | 成功: {success_count}/{total_images} | 失败: {fail_count}")
|
||
|
||
# 失败详情
|
||
failed_results = [r for r in results if not r.get("success", False)]
|
||
if failed_results:
|
||
for fr in failed_results:
|
||
idx = fr.get("global_task_index", -1) + 1
|
||
prompt_snippet = (fr.get("prompt", "") or "")[:30]
|
||
error_msg = fr.get("error", "未知错误")
|
||
print(f" 失败 #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → {error_msg}")
|
||
|
||
# 从磁盘加载最后 10 张图片
|
||
output_images = []
|
||
max_output_images = 10
|
||
recent_files = all_saved_files[-min(max_output_images, len(all_saved_files)):]
|
||
for file_path in recent_files:
|
||
try:
|
||
img = Image.open(file_path)
|
||
output_images.append(img)
|
||
except Exception as e:
|
||
print(f"全能生图: 无法加载 {file_path} - {e}")
|
||
|
||
if not output_images:
|
||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||
output_images = [placeholder]
|
||
|
||
output_tensor = pil_to_tensor(output_images)
|
||
print(f"全能生图: 共保存 {len(all_saved_files)} 张图片到磁盘，节点输出最后 {len(output_images)} 张")
|
||
|
||
import gc
|
||
gc.collect()
|
||
return (output_tensor,)
|
||
else:
|
||
# 单提示词模式
|
||
if 生图数量 == 1:
|
||
# 单张：同步生成 + 保存到磁盘 + 输出 tensor
|
||
generated_images = self.client.generate_sync(
|
||
prompt=提示词,
|
||
model=模型,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
batch_size=1,
|
||
images=input_images,
|
||
progress_callback=progress_callback,
|
||
debug=DEBUG_LOG_ENABLED,
|
||
debug_request=REQUEST_LOG_ENABLED,
|
||
enable_grounding=False,
|
||
enable_image_search=False
|
||
)
|
||
# 单张：保存到磁盘
|
||
import os
|
||
output_folder = ""
|
||
if FOLDER_PATHS_AVAILABLE:
|
||
output_folder = folder_paths.get_output_directory()
|
||
print(f"全能生图: 磁盘保存模式 → {output_folder}")
|
||
else:
|
||
raise ValueError("无法获取 ComfyUI output 目录，请检查 folder_paths 是否可用")
|
||
os.makedirs(output_folder, exist_ok=True)
|
||
for gen_img in generated_images:
|
||
output_path = generate_timestamp_filename(output_folder=output_folder)
|
||
save_image(gen_img, output_path)
|
||
else:
|
||
# 多张：异步并发 + 磁盘保存（与批量提示词逻辑一致）
|
||
print(f"全能生图: 单提示词×{生图数量}张 → 异步并发模式")
|
||
|
||
if pbar is not None:
|
||
pbar = ProgressBar(生图数量)
|
||
|
||
output_folder = ""
|
||
if FOLDER_PATHS_AVAILABLE:
|
||
output_folder = folder_paths.get_output_directory()
|
||
print(f"全能生图: 磁盘保存模式 → {output_folder}")
|
||
else:
|
||
raise ValueError("无法获取 ComfyUI output 目录，请检查 folder_paths 是否可用")
|
||
|
||
import os
|
||
os.makedirs(output_folder, exist_ok=True)
|
||
|
||
def run_async_in_thread():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
return loop.run_until_complete(
|
||
self._process_batch_async(
|
||
prompts=[提示词],
|
||
model=模型,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
images_per_prompt=生图数量,
|
||
input_images=input_images,
|
||
output_folder=output_folder,
|
||
pbar=pbar,
|
||
)
|
||
)
|
||
finally:
|
||
loop.close()
|
||
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(run_async_in_thread)
|
||
try:
|
||
results = future.result(timeout=3600)
|
||
except TimeoutError:
|
||
raise RuntimeError("任务执行超时（1小时），请减少生图数量或检查网络连接")
|
||
|
||
success_count = sum(1 for r in results if r.get("success", False))
|
||
fail_count = len(results) - success_count
|
||
total_generated = sum(r.get("generated_count", 0) for r in results)
|
||
all_saved_files = []
|
||
for r in results:
|
||
all_saved_files.extend(r.get("saved_files", []))
|
||
|
||
elapsed = time.time() - start_time
|
||
time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
|
||
print(f"完成！总耗时 {time_str} | 成功: {success_count}/{生图数量} | 失败: {fail_count}")
|
||
|
||
# 失败详情
|
||
failed_results = [r for r in results if not r.get("success", False)]
|
||
if failed_results:
|
||
for fr in failed_results:
|
||
idx = fr.get("global_task_index", -1) + 1
|
||
error_msg = fr.get("error", "未知错误")
|
||
print(f" 失败 #{idx}: {提示词[:30]}{'...' if len(提示词) >= 30 else ''} → {error_msg}")
|
||
|
||
# 从磁盘加载最后 10 张图片
|
||
output_images = []
|
||
max_output_images = 10
|
||
recent_files = all_saved_files[-min(max_output_images, len(all_saved_files)):]
|
||
for file_path in recent_files:
|
||
try:
|
||
img = Image.open(file_path)
|
||
output_images.append(img)
|
||
except Exception as e:
|
||
print(f"全能生图: 无法加载 {file_path} - {e}")
|
||
|
||
if not output_images:
|
||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||
output_images = [placeholder]
|
||
|
||
output_tensor = pil_to_tensor(output_images)
|
||
print(f"全能生图: 共保存 {len(all_saved_files)} 张图片到磁盘，节点输出最后 {len(output_images)} 张")
|
||
|
||
import gc
|
||
gc.collect()
|
||
return (output_tensor,)
|
||
|
||
|
||
# 优化：限制输出图片数量，避免内存爆炸
|
||
max_output_images = 20 # 最多输出20张图片到ComfyUI
|
||
|
||
if len(generated_images) > max_output_images:
|
||
print(f"全能生图: 生成 {len(generated_images)} 张图片，限制输出前 {max_output_images} 张到ComfyUI")
|
||
output_images = generated_images[:max_output_images]
|
||
else:
|
||
output_images = generated_images
|
||
|
||
# 转换输出图像
|
||
output_tensor = pil_to_tensor(output_images)
|
||
|
||
# 计算耗时并打印最终统计
|
||
elapsed = time.time() - start_time
|
||
if elapsed < 1:
|
||
time_str = f"{elapsed:.3f}s"
|
||
else:
|
||
time_str = f"{elapsed:.2f}s"
|
||
|
||
# 打印最终汇总
|
||
if fail_count > 0:
|
||
print(f"[4/4] 完成！总耗时 {time_str} | 成功 {success_count}张 | 失败 {fail_count}张")
|
||
else:
|
||
print(f"[4/4] 完成！总耗时 {time_str} | 成功 {len(generated_images)}张")
|
||
|
||
# 最终内存清理
|
||
import gc
|
||
gc.collect()
|
||
if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
|
||
final_memory = process.memory_info().rss / 1024 / 1024
|
||
print(f"全能生图: 最终内存使用: {final_memory:.1f} MB")
|
||
|
||
return (output_tensor,)
|
||
|
||
except ValueError as e:
|
||
# 检测是否为授权错误
|
||
if str(e) == "未授权！":
|
||
print("请联系作者授权后方可使用！")
|
||
raise ValueError("未授权！") from None
|
||
else:
|
||
error_msg = str(e)
|
||
print(f"全能生图: ❌ {error_msg}")
|
||
raise ValueError(error_msg) from None
|
||
|
||
except RuntimeError as e:
|
||
error_full = str(e)
|
||
print(f"全能生图: ❌ {error_full}")
|
||
raise RuntimeError(error_full) from None
|
||
|
||
except Exception as e:
|
||
error_msg = str(e)
|
||
print(f"全能生图: ❌ {error_msg}")
|
||
raise type(e)(error_msg) from None
|
||
|
||
finally:
|
||
# 查询余额
|
||
if self.client is not None:
|
||
try:
|
||
balance_data = self.client.query_balance_sync()
|
||
balance_info = self.client.format_balance_info(balance_data)
|
||
print(f"全能生图: {balance_info}")
|
||
except Exception:
|
||
pass
|
||
|
||
# 最终内存清理
|
||
import gc
|
||
gc.collect()
|
||
print(f"全能生图: 最终内存清理完成")
|