xsl
443cfa298f
包含： - hair_service_sd: 主服务（换发型/换发色/生发，端口8801） - photo_service: LoRA调度+训练（端口32678） - hair_grow_service: 调试测试页（端口8888，含4个测试页） - 批量训练脚本（batch_train_hairstyles.py） - 发际线mask自动识别（hairline_mask.py，4种方案） - 手绘mask换发型（hair_swap_manual.py） - 文档：README.md + LARGE_FILES.md + docs/ 大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md OSS/COS密钥已脱敏为环境变量，原文件备份在本地
79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
import cv2
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import numpy as np
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import base64
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import requests
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from common.logger import config
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version = config.get('default', 'version')
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if version == "local":
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webui_url = 'http://192.168.1.57:57860/'
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else:
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webui_url = 'http://0.0.0.0:57860/'
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def encode_numpy_to_base64(img):
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retval, bytes = cv2.imencode('.png', img)
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encoded_image = base64.b64encode(bytes).decode('utf-8')
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return encoded_image
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def webui_img2img(img, mask, prompt='', denoising_strength=0.35):
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url = f"{webui_url}sdapi/v1/img2img"
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request_dict = {
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"prompt": prompt,
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"negative_prompt": '(nsfw:1.5), ng_deepnegative_v1_75t, (badhandv4:1.2), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, ((monochrome)), ((grayscale)) watermark, bad_pictures,easynegative',
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"sampler_name": "DPM++ 2M Karras",
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"batch_size": 1,
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"steps": 30,
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"width": img.shape[1],
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"height": img.shape[0],
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"cfg_scale": 7.0,
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"seed": 123456789,
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"mask_blur": 5,
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"init_images": [
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encode_numpy_to_base64(img)
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],
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"inpaint_full_res": False,
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"inpainting_fill": 1,
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"inpainting_mask_invert": 0,
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"mask": encode_numpy_to_base64(mask),
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"denoising_strength": denoising_strength,
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"alwayson_scripts": {
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}
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}
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response = requests.post(url=url, json=request_dict)
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ret_json = response.json()
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result = ret_json['images'][0]
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img = cv2.imdecode(np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8), cv2.IMREAD_COLOR)
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return img
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def webui_super_res_img(img, ratio):
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url = f"{webui_url}sdapi/v1/extra-single-image"
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request_dict = {
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"resize_mode": 0,
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"show_extras_results": False,
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"gfpgan_visibility": 0,
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"codeformer_visibility": 1,
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"codeformer_weight": 1,
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"upscaling_resize": ratio,
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"upscaler_1": "8x_NMKD-Superscale_150000_G",
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"upscale_first": False,
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"image": encode_numpy_to_base64(img)
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}
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response = requests.post(url=url, json=request_dict)
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ret_json = response.json()
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result = ret_json['image']
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img = cv2.imdecode(np.frombuffer(base64.b64decode(result), np.uint8), cv2.IMREAD_COLOR)
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return img
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def webui_tag_by_clip(img):
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url = f"{webui_url}sdapi/v1/interrogate"
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request_dict = {
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"image": encode_numpy_to_base64(img),
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"model": "clip"
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}
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response = requests.post(url=url, json=request_dict)
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ret_json = response.json()
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return ret_json['caption']
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