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。
141 lines
5.1 KiB
Python
Executable File
141 lines
5.1 KiB
Python
Executable File
import math
|
|
|
|
import gradio as gr
|
|
import modules.scripts as scripts
|
|
from modules import deepbooru, images, processing, shared
|
|
from modules.processing import Processed
|
|
from modules.shared import opts, state
|
|
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return "Loopback"
|
|
|
|
def show(self, is_img2img):
|
|
return is_img2img
|
|
|
|
def ui(self, is_img2img):
|
|
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
|
|
final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
|
|
denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
|
|
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
|
|
|
|
return [loops, final_denoising_strength, denoising_curve, append_interrogation]
|
|
|
|
def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
|
|
processing.fix_seed(p)
|
|
batch_count = p.n_iter
|
|
p.extra_generation_params = {
|
|
"Final denoising strength": final_denoising_strength,
|
|
"Denoising curve": denoising_curve
|
|
}
|
|
|
|
p.batch_size = 1
|
|
p.n_iter = 1
|
|
|
|
info = None
|
|
initial_seed = None
|
|
initial_info = None
|
|
initial_denoising_strength = p.denoising_strength
|
|
|
|
grids = []
|
|
all_images = []
|
|
original_init_image = p.init_images
|
|
original_prompt = p.prompt
|
|
original_inpainting_fill = p.inpainting_fill
|
|
state.job_count = loops * batch_count
|
|
|
|
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
|
|
|
|
def calculate_denoising_strength(loop):
|
|
strength = initial_denoising_strength
|
|
|
|
if loops == 1:
|
|
return strength
|
|
|
|
progress = loop / (loops - 1)
|
|
if denoising_curve == "Aggressive":
|
|
strength = math.sin((progress) * math.pi * 0.5)
|
|
elif denoising_curve == "Lazy":
|
|
strength = 1 - math.cos((progress) * math.pi * 0.5)
|
|
else:
|
|
strength = progress
|
|
|
|
change = (final_denoising_strength - initial_denoising_strength) * strength
|
|
return initial_denoising_strength + change
|
|
|
|
history = []
|
|
|
|
for n in range(batch_count):
|
|
# Reset to original init image at the start of each batch
|
|
p.init_images = original_init_image
|
|
|
|
# Reset to original denoising strength
|
|
p.denoising_strength = initial_denoising_strength
|
|
|
|
last_image = None
|
|
|
|
for i in range(loops):
|
|
p.n_iter = 1
|
|
p.batch_size = 1
|
|
p.do_not_save_grid = True
|
|
|
|
if opts.img2img_color_correction:
|
|
p.color_corrections = initial_color_corrections
|
|
|
|
if append_interrogation != "None":
|
|
p.prompt = f"{original_prompt}, " if original_prompt else ""
|
|
if append_interrogation == "CLIP":
|
|
p.prompt += shared.interrogator.interrogate(p.init_images[0])
|
|
elif append_interrogation == "DeepBooru":
|
|
p.prompt += deepbooru.model.tag(p.init_images[0])
|
|
|
|
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
|
|
|
|
processed = processing.process_images(p)
|
|
|
|
# Generation cancelled.
|
|
if state.interrupted or state.stopping_generation:
|
|
break
|
|
|
|
if initial_seed is None:
|
|
initial_seed = processed.seed
|
|
initial_info = processed.info
|
|
|
|
p.seed = processed.seed + 1
|
|
p.denoising_strength = calculate_denoising_strength(i + 1)
|
|
|
|
if state.skipped:
|
|
break
|
|
|
|
last_image = processed.images[0]
|
|
p.init_images = [last_image]
|
|
p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
|
|
|
|
if batch_count == 1:
|
|
history.append(last_image)
|
|
all_images.append(last_image)
|
|
|
|
if batch_count > 1 and not state.skipped and not state.interrupted:
|
|
history.append(last_image)
|
|
all_images.append(last_image)
|
|
|
|
p.inpainting_fill = original_inpainting_fill
|
|
|
|
if state.interrupted or state.stopping_generation:
|
|
break
|
|
|
|
if len(history) > 1:
|
|
grid = images.image_grid(history, rows=1)
|
|
if opts.grid_save:
|
|
images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
|
|
|
|
if opts.return_grid:
|
|
grids.append(grid)
|
|
|
|
all_images = grids + all_images
|
|
|
|
processed = Processed(p, all_images, initial_seed, initial_info)
|
|
|
|
return processed
|