Files
change_hair_3090/stable-diffusion-webui/scripts/poor_mans_outpainting.py
T
colomi 0eb61f3e60 初始化换发型项目:3个微服务代码 + 部署脚本
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。
2026-07-11 18:11:49 +08:00

147 lines
5.4 KiB
Python
Executable File

import math
import modules.scripts as scripts
import gradio as gr
from PIL import Image, ImageDraw
from modules import images, devices
from modules.processing import Processed, process_images
from modules.shared import opts, state
class Script(scripts.Script):
def title(self):
return "Poor man's outpainting"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
if not is_img2img:
return None
pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur"))
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill"))
direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
return [pixels, mask_blur, inpainting_fill, direction]
def run(self, p, pixels, mask_blur, inpainting_fill, direction):
initial_seed = None
initial_info = None
p.mask_blur = mask_blur * 2
p.inpainting_fill = inpainting_fill
p.inpaint_full_res = False
left = pixels if "left" in direction else 0
right = pixels if "right" in direction else 0
up = pixels if "up" in direction else 0
down = pixels if "down" in direction else 0
init_img = p.init_images[0]
target_w = math.ceil((init_img.width + left + right) / 64) * 64
target_h = math.ceil((init_img.height + up + down) / 64) * 64
if left > 0:
left = left * (target_w - init_img.width) // (left + right)
if right > 0:
right = target_w - init_img.width - left
if up > 0:
up = up * (target_h - init_img.height) // (up + down)
if down > 0:
down = target_h - init_img.height - up
img = Image.new("RGB", (target_w, target_h))
img.paste(init_img, (left, up))
mask = Image.new("L", (img.width, img.height), "white")
draw = ImageDraw.Draw(mask)
draw.rectangle((
left + (mask_blur * 2 if left > 0 else 0),
up + (mask_blur * 2 if up > 0 else 0),
mask.width - right - (mask_blur * 2 if right > 0 else 0),
mask.height - down - (mask_blur * 2 if down > 0 else 0)
), fill="black")
latent_mask = Image.new("L", (img.width, img.height), "white")
latent_draw = ImageDraw.Draw(latent_mask)
latent_draw.rectangle((
left + (mask_blur//2 if left > 0 else 0),
up + (mask_blur//2 if up > 0 else 0),
mask.width - right - (mask_blur//2 if right > 0 else 0),
mask.height - down - (mask_blur//2 if down > 0 else 0)
), fill="black")
devices.torch_gc()
grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
p.do_not_save_samples = True
work = []
work_mask = []
work_latent_mask = []
work_results = []
for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
x, w = tiledata[0:2]
if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
continue
work.append(tiledata[2])
work_mask.append(tiledata_mask[2])
work_latent_mask.append(tiledata_latent_mask[2])
batch_count = len(work)
print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
state.job_count = batch_count
for i in range(batch_count):
p.init_images = [work[i]]
p.image_mask = work_mask[i]
p.latent_mask = work_latent_mask[i]
state.job = f"Batch {i + 1} out of {batch_count}"
processed = process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
p.seed = processed.seed + 1
work_results += processed.images
image_index = 0
for y, h, row in grid.tiles:
for tiledata in row:
x, w = tiledata[0:2]
if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
continue
tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
image_index += 1
combined_image = images.combine_grid(grid)
if opts.samples_save:
images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
processed = Processed(p, [combined_image], initial_seed, initial_info)
return processed