colomi
0eb61f3e60
包含： - hair_service_sd: 换发型/换发色算法服务 (端口 8801) - photo_service: LoRA 训练调度服务 (端口 32678) - stable-diffusion-webui: SD WebUI 推理服务 (端口 57860) - kohya_ss_home: 训练环境代码 - meidaojia: 监控测试脚本 - setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查) - start_all_services.sh: 启动3个服务 - configure.ini.template: 路径模板化 (BASE_DIR自动推导) - conda_envs/py310.yml: py310 环境定义 大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除， 由网盘单独上传。
102 lines
3.8 KiB
Python
Executable File
102 lines
3.8 KiB
Python
Executable File
import math
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import modules.scripts as scripts
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import gradio as gr
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from PIL import Image
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from modules import processing, shared, images, devices
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from modules.processing import Processed
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from modules.shared import opts, state
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class Script(scripts.Script):
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def title(self):
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return "SD upscale"
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
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overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id("overlap"))
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scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor"))
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upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", elem_id=self.elem_id("upscaler_index"))
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return [info, overlap, upscaler_index, scale_factor]
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def run(self, p, _, overlap, upscaler_index, scale_factor):
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if isinstance(upscaler_index, str):
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upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
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processing.fix_seed(p)
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upscaler = shared.sd_upscalers[upscaler_index]
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p.extra_generation_params["SD upscale overlap"] = overlap
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p.extra_generation_params["SD upscale upscaler"] = upscaler.name
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initial_info = None
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seed = p.seed
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init_img = p.init_images[0]
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init_img = images.flatten(init_img, opts.img2img_background_color)
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if upscaler.name != "None":
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img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
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else:
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img = init_img
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devices.torch_gc()
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grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
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batch_size = p.batch_size
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upscale_count = p.n_iter
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p.n_iter = 1
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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work = []
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for _y, _h, row in grid.tiles:
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for tiledata in row:
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work.append(tiledata[2])
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batch_count = math.ceil(len(work) / batch_size)
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state.job_count = batch_count * upscale_count
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print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
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result_images = []
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for n in range(upscale_count):
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start_seed = seed + n
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p.seed = start_seed
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work_results = []
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for i in range(batch_count):
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p.batch_size = batch_size
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p.init_images = work[i * batch_size:(i + 1) * batch_size]
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state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
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processed = processing.process_images(p)
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if initial_info is None:
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initial_info = processed.info
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p.seed = processed.seed + 1
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work_results += processed.images
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image_index = 0
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for _y, _h, row in grid.tiles:
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for tiledata in row:
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tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
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image_index += 1
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combined_image = images.combine_grid(grid)
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result_images.append(combined_image)
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if opts.samples_save:
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images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
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processed = Processed(p, result_images, seed, initial_info)
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return processed
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