132 lines
6.5 KiB
Python
132 lines
6.5 KiB
Python
import time
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import requests
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import base64
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import io
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from PIL import Image
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import torch
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import numpy as np
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import logging
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import json
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from .types import STRING, INT, API_HOST
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from .utils import logger, get_nested_value
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from .api_key_manager import load_api_key
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logger = logging.getLogger(__name__)
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class FlowyIdeogram:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"prompt": ("STRING", {"multiline": True}),
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"negative_prompt": ("STRING", {"multiline": True}),
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"version": (["ideogram-v2-turbo", "ideogram-v2"],),
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"resolution": (["None", "512x1536", "576x1408", "576x1472", "576x1536", "640x1024", "640x1344", "640x1408", "640x1472", "640x1536", "704x1152", "704x1216", "704x1280", "704x1344", "704x1408", "704x1472", "720x1280", "736x1312", "768x1024", "768x1088", "768x1152", "768x1216", "768x1232", "768x1280", "768x1344", "832x960", "832x1024", "832x1088", "832x1152", "832x1216", "832x1248", "864x1152", "896x960", "896x1024", "896x1088", "896x1120", "896x1152", "960x832", "960x896", "960x1024", "960x1088", "1024x640", "1024x768", "1024x832", "1024x896", "1024x960", "1024x1024", "1088x768", "1088x832", "1088x896", "1088x960", "1120x896", "1152x704", "1152x768", "1152x832", "1152x864", "1152x896", "1216x704", "1216x768", "1216x832", "1232x768", "1248x832", "1280x704", "1280x720", "1280x768", "1280x800", "1312x736", "1344x640", "1344x704", "1344x768", "1408x576", "1408x640", "1408x704", "1472x576", "1472x640", "1472x704", "1536x512", "1536x576", "1536x640"],),
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"style_type": (["None", "Auto", "Realistic", "Design", "Anime", "Render 3D"],),
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"aspect_ratio": (["1:1", "4:3", "3:4", "16:9", "9:16", "3:2", "2:3", "16:10", "10:16", "3:1", "1:3"],),
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"magic_prompt_option": (["On", "Off"],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 2147483647}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "generate_image_with_ideogram"
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CATEGORY = "Comflowy"
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DESCRIPTION = """
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Nodes from https://comflowy.com:
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- Description: A service to generate images using Ideogram AI.
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- How to use:
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- Provide a prompt to generate an image.
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- Choose resolution, style type, aspect ratio, and magic prompt option.
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- Resolution overrides aspect ratio.
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- Magic Prompt will interpret your prompt and optimize it to maximize variety and quality of the images generated. You can also use it to write prompts in different languages.
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- Make sure to set your API Key using the 'Comflowy Set API Key' node before using this node.
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- Output: Returns the generated image.
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"""
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def generate_image_with_ideogram(self, prompt, negative_prompt, version, resolution, style_type, aspect_ratio, magic_prompt_option, seed):
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api_key = load_api_key()
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if not api_key:
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error_msg = "API Key is not set. Please use the 'Comflowy Set API Key' node to set a global API Key before using this node."
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logger.error(error_msg)
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raise ValueError(error_msg)
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logger.info(f"开始处理 Ideogram 图像生成请求。prompt: {prompt}, negative_prompt: {negative_prompt}, resolution: {resolution}, style_type: {style_type}, aspect_ratio: {aspect_ratio}, magic_prompt_option: {magic_prompt_option}, seed: {seed}")
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try:
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response = requests.post(
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f"{API_HOST}/api/open/v0/ideogram",
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headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
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json={
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"version": version,
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"resolution": resolution if resolution != "None" else None,
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"style_type": style_type if style_type != "None" else None,
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"aspect_ratio": aspect_ratio,
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"magic_prompt_option": magic_prompt_option,
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"seed": seed,
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}
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)
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response.raise_for_status()
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result = response.json()
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logger.info(f"API 请求完成。状态码: {response.status_code}")
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logger.debug(f"API 响应内容: {json.dumps(result, indent=2)}")
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if not result.get('success'):
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logger.error(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}")
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raise Exception(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}")
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output_url = result.get('data', {}).get('output')
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if not output_url or not isinstance(output_url, str):
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logger.error(f"完整的 API 响应: {json.dumps(result, indent=2)}")
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raise Exception(f"无法获取有效的输出图像 URL。API 响应中没有预期的数据结构。完整响应: {json.dumps(result, indent=2)}")
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logger.info(f"获取到的输出 URL: {output_url}")
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# 验证 URL 是否可访问
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try:
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url_check = requests.head(output_url)
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url_check.raise_for_status()
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except requests.RequestException as e:
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logger.error(f"无法访问输出 URL: {str(e)}")
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raise Exception(f"无法访问输出 URL: {str(e)}")
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# 添加延迟,等待 Replicate 处理完成
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time.sleep(10)
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img_response = requests.get(output_url, stream=True)
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img_response.raise_for_status()
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# 将图像数据转换为 PIL Image
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img = Image.open(img_response.raw)
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# 转换为 numpy 数组
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img_np = np.array(img)
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# 确保图像是 3 通道 RGB
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if len(img_np.shape) == 2: # 灰度图像
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img_np = np.stack([img_np] * 3, axis=-1)
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elif img_np.shape[-1] == 4: # RGBA 图像
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img_np = img_np[:, :, :3]
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# 转换为 float32 并归一化到 0-1 范围
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img_np = img_np.astype(np.float32) / 255.0
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# 转换为 torch tensor，确保形状为 [B,H,W,C]
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img_tensor = torch.from_numpy(img_np).unsqueeze(0) # 添加批次维度
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logger.info(f"图像处理完成。输出张量形状: {img_tensor.shape}")
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return (img_tensor,)
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except Exception as e:
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error_msg = f"图像生成过程中出错: {str(e)}"
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logger.error(error_msg)
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logger.exception("详细错误信息:")
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# 返回一个错误标记图像，确保形状为 [B,H,W,C]
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error_image = torch.zeros((1, 100, 400, 3), dtype=torch.float32)
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return (error_image,)
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