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Stable Diffusion | Gradio界面设计及webUI API调用_sd api schedule type

作者：秋刀鱼在做梦 | 2024-07-26 23:13:54

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sd api schedule type

本文基于webUI API编写了类似于webUI的Gradio交互式界面，支持文生图/图生图（SD1.x，SD2.x，SDXL），Embedding，Lora，X/Y/Z Plot，ADetailer、ControlNet，超分放大（Extras），图片信息读取（PNG Info）。

1. 在线体验

本文代码已部署到百度飞桨AI Studio平台，以供大家在线体验Stable Diffusion ComfyUI/webUI 原版界面及自制Gradio界面。

项目链接：Stable Diffusion webUI 在线体验

2. 自制Gradio界面展示

文生图界面：

Adetailer 设置界面：

ControlNet 设置界面：

X/Y/Z Plot 设置界面：

图生图界面：

图片放大界面：

图片信息读取界面：

3. Gradio界面设计及webUI API调用

  1. import base64
  2. import datetime
  3. import io
  4. import os
  5. import re
  6. import subprocess
  7. import gradio as gr
  8. import requests
  9. from PIL import Image, PngImagePlugin
  10.  
  11. design_mode = 1
  12. save_images = "Yes"
  13. url = "http://127.0.0.1:7860"
  14. if design_mode == 0:
  15.     cmd = "netstat -tulnp"
  16.     netstat_output = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True).stdout.splitlines()
  17.     for i in netstat_output:
  18.         if "stable-diffus" in i:
  19.             port = int(re.findall(r'\d+', i)[6])
  20.             url = f"http://127.0.0.1:{port}"
  21. output_dir = os.getcwd() + "/output/" + datetime.date.today().strftime("%Y-%m-%d")
  22. os.makedirs(output_dir, exist_ok=True)
  23. os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
  24.  
  25. default = {
  26.     "prompt": "(best quality:1), (high quality:1), detailed/(extreme, highly, ultra/), realistic, 1girl/(beautiful, delicate, perfect/)",
  27.     "negative_prompt": "(worst quality:1), (low quality:1), (normal quality:1), lowres, signature, blurry, watermark, duplicate, bad link, plump, bad anatomy, extra arms, extra digits, missing finger, bad hands, bad feet, deformed, error, mutation, text",
  28.     "clip_skip": 1,
  29.     "width": 512,
  30.     "height": 768,
  31.     "size_step": 64,
  32.     "steps": 20,
  33.     "cfg": 7,
  34.     "ad_nums": 2,
  35.     "ad_model": ["face_yolov8n.pt", "hand_yolov8n.pt"],
  36.     "cn_nums": 3,
  37.     "cn_type": "Canny",
  38.     "gallery_height": 600,
  39.     "lora_weight": 0.8,
  40.     "hidden_models": ["stable_cascade_stage_c", "stable_cascade_stage_b", "svd_xt_1_1", "control_v11p_sd15_canny", "control_v11f1p_sd15_depth", "control_v11p_sd15_openpose"]
  41. }
  42.  
  43. samplers = []
  44. response = requests.get(url=f"{url}/sdapi/v1/samplers").json()
  45. for i in range(len(response)):
  46.     samplers.append(response[i]["name"])
  47.  
  48. schedulers = []
  49. response = requests.get(url=f"{url}/sdapi/v1/schedulers").json()
  50. for i in range(len(response)):
  51.     schedulers.append(response[i]["label"])
  52.  
  53. upscalers = []
  54. response = requests.get(url=f"{url}/sdapi/v1/upscalers").json()
  55. for i in range(len(response)):
  56.     upscalers.append(response[i]["name"])
  57.  
  58. sd_models = []
  59. sd_models_list = {}
  60. response = requests.get(url=f"{url}/sdapi/v1/sd-models").json()
  61. for i in range(len(response)):
  62.     path, sd_model = os.path.split(response[i]["title"])
  63.     sd_model_name, sd_model_extension = os.path.splitext(sd_model)
  64.     if not sd_model_name in default["hidden_models"]:
  65.         sd_models.append(sd_model)
  66.         sd_models_list[sd_model] = response[i]["title"]
  67. sd_models = sorted(sd_models)
  68.  
  69. sd_vaes = ["Automatic", "None"]
  70. response = requests.get(url=f"{url}/sdapi/v1/sd-vae").json()
  71. for i in range(len(response)):
  72.     sd_vaes.append(response[i]["model_name"])
  73.  
  74. embeddings = []
  75. response = requests.get(url=f"{url}/sdapi/v1/embeddings").json()
  76. for key in response["loaded"]:
  77.     embeddings.append(key)
  78.  
  79. extensions = []
  80. response = requests.get(url=f"{url}/sdapi/v1/extensions").json()
  81. for i in range(len(response)):
  82.     extensions.append(response[i]["name"])
  83.  
  84. loras = []
  85. loras_name = {}
  86. loras_activation_text = {}
  87. response = requests.get(url=f"{url}/sdapi/v1/loras").json()
  88. for i in range(len(response)):
  89.     lora_name = response[i]["name"]
  90.     lora_info = requests.get(url=f"{url}/tacapi/v1/lora-info/{lora_name}").json()
  91.     if lora_info and "sd version" in lora_info:
  92.         lora_type = lora_info["sd version"]
  93.         lora_name_type = f"{lora_name} ({lora_type})"
  94.     else:
  95.         lora_name_type = f"{lora_name}"
  96.     loras.append(lora_name_type)
  97.     loras_name[lora_name_type] = lora_name
  98.     if "activation text" in loras_activation_text:
  99.         loras_activation_text[lora_name_type] = lora_info["activation text"]
  100.  
  101. xyz_args = {}
  102. xyz_plot_types = {}
  103. last_choice = "Size"
  104. response = requests.get(url=f"{url}/sdapi/v1/script-info").json()
  105. for i in range(len(response)):
  106.     if response[i]["name"] == "x/y/z plot":
  107.         if response[i]["is_img2img"] == False:
  108.             xyz_plot_types["txt2img"] = response[i]["args"][0]["choices"]
  109.             choice_index = xyz_plot_types["txt2img"].index(last_choice) + 1
  110.             xyz_plot_types["txt2img"] = xyz_plot_types["txt2img"][:choice_index]
  111.         else:
  112.             xyz_plot_types["img2img"] = response[i]["args"][0]["choices"]
  113.             choice_index = xyz_plot_types["img2img"].index(last_choice) + 1
  114.             xyz_plot_types["img2img"] = xyz_plot_types["img2img"][:choice_index]
  115.  
  116. if "adetailer" in extensions:
  117.     ad_args = {"txt2img": {}, "img2img": {}}
  118.     ad_skip_img2img = False
  119.     ad_models = ["None"]
  120.     response = requests.get(url=f"{url}/adetailer/v1/ad_model").json()
  121.     for key in response["ad_model"]:
  122.         ad_models.append(key)
  123.  
  124. if "sd-webui-controlnet" in extensions:
  125.     cn_args = {"txt2img": {}, "img2img": {}}
  126.     cn_types = []
  127.     cn_types_list = {}
  128.     response = requests.get(url=f"{url}/controlnet/control_types").json()
  129.     for key in response["control_types"]:
  130.         cn_types.append(key)
  131.         cn_types_list[key] = response["control_types"][key]
  132.     cn_default_type = default["cn_type"]
  133.     cn_module_list = cn_types_list[cn_default_type]["module_list"]
  134.     cn_model_list = cn_types_list[cn_default_type]["model_list"]
  135.     cn_default_option = cn_types_list[cn_default_type]["default_option"]
  136.     cn_default_model = cn_types_list[cn_default_type]["default_model"]
  137.  
  138. def save_image(image, part1, part2):
  139.     counter = 1
  140.     image_name = f"{part1}-{part2}-{counter}.png"
  141.     while os.path.exists(os.path.join(output_dir, image_name)):
  142.         counter += 1
  143.         image_name = f"{part1}-{part2}-{counter}.png"
  144.     image_path = os.path.join(output_dir, image_name)
  145.     image_metadata = PngImagePlugin.PngInfo()
  146.     for key, value in image.info.items():
  147.         if isinstance(key, str) and isinstance(value, str):
  148.             image_metadata.add_text(key, value)
  149.     image.save(image_path, format="PNG", pnginfo=image_metadata)
  150.  
  151. def pil_to_base64(image_pil):
  152.     buffer = io.BytesIO()
  153.     image_pil.save(buffer, format="png")
  154.     image_buffer = buffer.getbuffer()
  155.     image_base64 = base64.b64encode(image_buffer).decode("utf-8")
  156.     return image_base64
  157.  
  158. def base64_to_pil(image_base64):
  159.     image_binary = base64.b64decode(image_base64)
  160.     image_pil = Image.open(io.BytesIO(image_binary))
  161.     return image_pil
  162.  
  163. def format_prompt(prompt):
  164.     prompt = re.sub(r"\s+,", ",", prompt)
  165.     prompt = re.sub(r"\s+", " ", prompt)
  166.     prompt = re.sub(",,+", ",", prompt)
  167.     prompt = re.sub(",", ", ", prompt)
  168.     prompt = re.sub(r"\s+", " ", prompt)
  169.     prompt = re.sub(r"^,", "", prompt)
  170.     prompt = re.sub(r"^ ", "", prompt)
  171.     prompt = re.sub(r" $", "", prompt)
  172.     prompt = re.sub(r",$", "", prompt)
  173.     prompt = re.sub(": ", ":", prompt)
  174.     return prompt
  175.  
  176. def post_interrupt():
  177.     global interrupt
  178.     interrupt = True
  179.     requests.post(url=f"{url}/sdapi/v1/interrupt").json()
  180.  
  181. def gr_update_visible(visible):
  182.     return gr.update(visible=visible)
  183.  
  184. def ordinal(n: int) -> str:
  185.     d = {1: "st", 2: "nd", 3: "rd"}
  186.     return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))
  187.  
  188. def add_lora(prompt, lora):
  189.     lora_weight = default["lora_weight"]
  190.     prompt = re.sub(r"<[^<>]+>", "", prompt)
  191.     for elem in loras_activation_text:
  192.         prompt = re.sub(loras_activation_text[elem], "", prompt)
  193.     prompt = format_prompt(prompt)
  194.     for elem in lora:
  195.         lora_name = loras_name[elem]
  196.         if elem in loras_activation_text:
  197.             lora_activation_text = loras_activation_text[elem]
  198.         else:
  199.             lora_activation_text = ""
  200.         if lora_activation_text == "":
  201.             prompt = f"{prompt}, <lora:{lora_name}:{lora_weight}>"
  202.         else:
  203.             prompt = f"{prompt}, <lora:{lora_name}:{lora_weight}> {lora_activation_text}"
  204.     return prompt
  205.  
  206. def add_embedding(negative_prompt, embedding):
  207.     for elem in embeddings:
  208.         negative_prompt = re.sub(f"{elem},", "", negative_prompt)
  209.     negative_prompt = format_prompt(negative_prompt)
  210.     for elem in embedding[::-1]:
  211.         negative_prompt = f"{elem}, {negative_prompt}"
  212.     return negative_prompt
  213.  
  214. def add_xyz_plot(payload, gen_type):
  215.     global xyz_args
  216.     if gen_type in xyz_args:
  217.         payload["script_name"] = "X/Y/Z plot"
  218.         payload["script_args"] = xyz_args[gen_type]
  219.     return payload
  220.  
  221. def xyz_update_args(*args):
  222.     gen_type, enable_xyz_plot, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode = args
  223.     global xyz_args
  224.     x_type = xyz_plot_types[gen_type].index(x_type)
  225.     y_type = xyz_plot_types[gen_type].index(y_type)
  226.     z_type = xyz_plot_types[gen_type].index(z_type)
  227.     args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode]
  228.     if enable_xyz_plot == True:
  229.         xyz_args[gen_type] = args
  230.     else:
  231.         del xyz_args[gen_type]
  232.  
  233. def xyz_update_choices(xyz_type):
  234.     choices = []
  235.     if xyz_type == "Checkpoint name":
  236.         choices = sd_models
  237.     if xyz_type == "VAE":
  238.         choices = sd_vaes
  239.     if xyz_type == "Sampler":
  240.         choices = samplers
  241.     if xyz_type == "Schedule type":
  242.         choices = schedulers
  243.     if xyz_type == "Hires sampler":
  244.         choices = samplers
  245.     if xyz_type == "Hires upscaler":
  246.         choices = upscalers
  247.     if xyz_type == "Always discard next-to-last sigma":
  248.         choices = ["False", "True"]
  249.     if xyz_type == "SGM noise multiplier":
  250.         choices = ["False", "True"]
  251.     if xyz_type == "Refiner checkpoint":
  252.         choices = sd_models
  253.     if xyz_type == "RNG source":
  254.         choices = ["GPU", "CPU", "NV"]
  255.     if xyz_type == "FP8 mode":
  256.         choices = ["Disable", "Enable for SDXL", "Enable"]
  257.     if choices == []:
  258.         return gr.update(visible=True, value=None), gr.update(visible=False)
  259.     else:
  260.         return gr.update(visible=False), gr.update(visible=True, choices=choices)
  261.  
  262. def xyz_blocks(gen_type):
  263.     with gr.Blocks() as demo:
  264.         with gr.Row():
  265.             xyz_gen_type = gr.Textbox(visible=False, value=gen_type)
  266.             enable_xyz_plot = gr.Checkbox(label="Enable")
  267.         with gr.Row():
  268.             x_type = gr.Dropdown(xyz_plot_types[gen_type], label="X type", value=xyz_plot_types[gen_type][1])
  269.             x_values = gr.Textbox(label="X values", lines=1)
  270.             x_values_dropdown = gr.Dropdown(label="X values", visible=False, multiselect=True, interactive=True)
  271.         with gr.Row():
  272.             y_type = gr.Dropdown(xyz_plot_types[gen_type], label="Y type", value=xyz_plot_types[gen_type][0])
  273.             y_values = gr.Textbox(label="Y values", lines=1)
  274.             y_values_dropdown = gr.Dropdown(label="Y values", visible=False, multiselect=True, interactive=True)
  275.         with gr.Row():
  276.             z_type = gr.Dropdown(xyz_plot_types[gen_type], label="Z type", value=xyz_plot_types[gen_type][0])
  277.             z_values = gr.Textbox(label="Z values", lines=1)
  278.             z_values_dropdown = gr.Dropdown(label="Z values", visible=False, multiselect=True, interactive=True)
  279.         with gr.Row():
  280.             with gr.Column():
  281.                 draw_legend = gr.Checkbox(label='Draw legend', value=True)
  282.                 no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
  283.                 vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False)
  284.                 vary_seeds_y = gr.Checkbox(label='Vary seeds for Y', value=False)
  285.                 vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False)
  286.             with gr.Column():
  287.                 include_lone_images = gr.Checkbox(label='Include Sub Images', value=True)
  288.                 include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False)
  289.                 csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False)
  290.                 margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2)
  291.         x_type.change(fn=xyz_update_choices, inputs=x_type, outputs=[x_values, x_values_dropdown])
  292.         y_type.change(fn=xyz_update_choices, inputs=y_type, outputs=[y_values, y_values_dropdown])
  293.         z_type.change(fn=xyz_update_choices, inputs=z_type, outputs=[z_values, z_values_dropdown])
  294.         xyz_inputs = [xyz_gen_type, enable_xyz_plot, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode]
  295.         for gr_block in xyz_inputs:
  296.             if type(gr_block) is gr.components.slider.Slider:
  297.                 gr_block.release(fn=xyz_update_args, inputs=xyz_inputs, outputs=None)
  298.             else:
  299.                 gr_block.change(fn=xyz_update_args, inputs=xyz_inputs, outputs=None)
  300.     return demo
  301.  
  302. def add_adetailer(payload, gen_type):
  303.     global ad_args, ad_skip_img2img
  304.     args = ad_args[gen_type]
  305.     args = dict(sorted(args.items(), key=lambda x: x[0]))
  306.     payload["alwayson_scripts"]["adetailer"] = {"args": []}
  307.     if args == {}:
  308.         return payload
  309.     if gen_type == "img2img":
  310.         payload["alwayson_scripts"]["adetailer"]["args"] = [True, ad_skip_img2img]
  311.     else:
  312.         payload["alwayson_scripts"]["adetailer"]["args"] = [True, False]
  313.     for i in args:
  314.         payload["alwayson_scripts"]["adetailer"]["args"].append(args[i])
  315.     return payload
  316.  
  317. def ad_update_args(*args):
  318.     if "sd-webui-controlnet" in extensions:
  319.         ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face, ad_controlnet_model, ad_controlnet_module, ad_controlnet_weight, ad_controlnet_guidance_start, ad_controlnet_guidance_end = args
  320.     else:
  321.         ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face = args
  322.     global ad_args
  323.     args = {
  324.         "ad_model": ad_model,
  325.         "ad_model_classes": "",
  326.         "ad_prompt": ad_prompt,
  327.         "ad_negative_prompt": ad_negative_prompt,
  328.         "ad_confidence": ad_confidence,
  329.         "ad_mask_k_largest": ad_mask_k_largest,
  330.         "ad_mask_min_ratio": ad_mask_min_ratio,
  331.         "ad_mask_max_ratio": ad_mask_max_ratio,
  332.         "ad_dilate_erode": ad_dilate_erode,
  333.         "ad_x_offset": ad_x_offset,
  334.         "ad_y_offset": ad_y_offset,
  335.         "ad_mask_merge_invert": ad_mask_merge_invert,
  336.         "ad_mask_blur": ad_mask_blur,
  337.         "ad_denoising_strength": ad_denoising_strength,
  338.         "ad_inpaint_only_masked": ad_inpaint_only_masked,
  339.         "ad_inpaint_only_masked_padding": ad_inpaint_only_masked_padding,
  340.         "ad_use_inpaint_width_height": ad_use_inpaint_width_height,
  341.         "ad_inpaint_width": ad_inpaint_width,
  342.         "ad_inpaint_height": ad_inpaint_height,
  343.         "ad_use_steps": ad_use_steps,
  344.         "ad_steps": ad_steps,
  345.         "ad_use_cfg_scale": ad_use_cfg_scale,
  346.         "ad_cfg_scale": ad_cfg_scale,
  347.         "ad_use_checkpoint": ad_use_checkpoint,
  348.         "ad_checkpoint": ad_checkpoint,
  349.         "ad_use_vae": ad_use_vae,
  350.         "ad_vae": ad_vae,
  351.         "ad_use_sampler": ad_use_sampler,
  352.         "ad_sampler": ad_sampler,
  353.         "ad_scheduler": ad_scheduler,
  354.         "ad_use_noise_multiplier": ad_use_noise_multiplier,
  355.         "ad_noise_multiplier": ad_noise_multiplier,
  356.         "ad_use_clip_skip": ad_use_clip_skip,
  357.         "ad_clip_skip": ad_clip_skip,
  358.         "ad_restore_face": ad_restore_face,
  359.     }
  360.     if "sd-webui-controlnet" in extensions:
  361.         args["ad_controlnet_model"] = ad_controlnet_model
  362.         args["ad_controlnet_module"] = ad_controlnet_module
  363.         args["ad_controlnet_weight"] = ad_controlnet_weight
  364.         args["ad_controlnet_guidance_start"] = ad_controlnet_guidance_start
  365.         args["ad_controlnet_guidance_end"] = ad_controlnet_guidance_end
  366.     if enable_ad == True:
  367.         ad_args[ad_gen_type][ad_num] = args
  368.     else:
  369.         del ad_args[ad_gen_type][ad_num]
  370.  
  371. def ad_update_cn_module_choices(ad_controlnet_model):
  372.     if ad_controlnet_model == "control_v11f1p_sd15_depth [1a8eb83c]":
  373.         return gr.update(choices=["depth_midas", "depth_hand_refiner"], visible=True, value="depth_midas")
  374.     if ad_controlnet_model == "control_v11p_sd15_inpaint [dfe64acb]":
  375.         return gr.update(choices=["inpaint_global_harmonious", "inpaint_only", "inpaint_only+lama"], visible=True, value="inpaint_global_harmonious")
  376.     if ad_controlnet_model == "control_v11p_sd15_lineart [2c3004a6]":
  377.         return gr.update(choices=["lineart_coarse", "lineart_realistic", "lineart_anime", "lineart_anime_denoise"], visible=True, value="lineart_coarse")
  378.     if ad_controlnet_model == "control_v11p_sd15_openpose [52e0ea54]":
  379.         return gr.update(choices=["openpose_full", "dw_openpose_full"], visible=True, value="openpose_full")
  380.     if ad_controlnet_model == "control_v11p_sd15_scribble [46a6fcd7]":
  381.         return gr.update(choices=["t2ia_sketch_pidi"], visible=True, value="t2ia_sketch_pidi")
  382.     if ad_controlnet_model == "control_v11p_sd15s2_lineart_anime [19a26aa8]":
  383.         return gr.update(choices=["lineart_coarse", "lineart_realistic", "lineart_anime", "lineart_anime_denoise"], visible=True, value="lineart_coarse")
  384.     return gr.update(visible=False)
  385.  
  386. def ad_update_skip_img2img(arg):
  387.     global ad_skip_img2img
  388.     ad_skip_img2img = arg
  389.  
  390. def ad_blocks(i, gen_type):
  391.     with gr.Blocks() as demo:
  392.         ad_gen_type = gr.Textbox(visible=False, value=gen_type)
  393.         ad_num = gr.Textbox(visible=False, value=i)
  394.         enable_ad = gr.Checkbox(label="Enable")
  395.         ad_model = gr.Dropdown(ad_models, label="ADetailer model", value=default["ad_model"][i])
  396.         ad_prompt = gr.Textbox(show_label=False, placeholder="ADetailer prompt" + "\nIf blank, the main prompt is used.", lines=3)
  397.         ad_negative_prompt = gr.Textbox(show_label=False, placeholder="ADetailer negative prompt" + "\nIf blank, the main negative prompt is used.", lines=3)
  398.         with gr.Tab("Detection"):
  399.             with gr.Row():
  400.                 ad_confidence = gr.Slider(label="Detection model confidence threshold", minimum=0, maximum=1, step=0.01, value=0.3)
  401.                 ad_mask_min_ratio = gr.Slider(label="Mask min area ratio", minimum=0, maximum=1, step=0.001, value=0)
  402.             with gr.Row():
  403.                 ad_mask_k_largest = gr.Slider(label="Mask only the top k largest (0 to disable)", minimum=0, maximum=10, step=1, value=0)
  404.                 ad_mask_max_ratio = gr.Slider(label="Mask max area ratio", minimum=0, maximum=1, step=0.001, value=1)
  405.         with gr.Tab("Mask Preprocessing"):
  406.             with gr.Row():
  407.                 ad_x_offset = gr.Slider(label="Mask x(→) offset", minimum=-200, maximum=200, step=1, value=0)
  408.                 ad_y_offset = gr.Slider(label="Mask y(↑) offset", minimum=-200, maximum=200, step=1, value=0)
  409.             ad_dilate_erode = gr.Slider(label="Mask erosion (-) / dilation (+)", minimum=-128, maximum=128, step=4, value=4)
  410.             ad_mask_merge_invert = gr.Radio(["None", "Merge", "Merge and Invert"], label="Mask merge mode", value="None")
  411.         with gr.Tab("Inpainting"):
  412.             with gr.Row():
  413.                 ad_mask_blur = gr.Slider(label="Inpaint mask blur", minimum=0, maximum=64, step=1, value=4)
  414.                 ad_denoising_strength = gr.Slider(label="Inpaint denoising strength", minimum=0, maximum=1, step=0.01, value=0.4)
  415.             with gr.Row():
  416.                 ad_inpaint_only_masked = gr.Checkbox(label="Inpaint only masked", value=True)
  417.                 ad_use_inpaint_width_height = gr.Checkbox(label="Use separate width/height")
  418.             with gr.Row():
  419.                 ad_inpaint_only_masked_padding = gr.Slider(label="Inpaint only masked padding, pixels", minimum=0, maximum=256, step=4, value=32)
  420.                 with gr.Column():
  421.                     ad_inpaint_width = gr.Slider(label="inpaint width", minimum=64, maximum=2048, step=default["size_step"], value=512)
  422.                     ad_inpaint_height = gr.Slider(label="inpaint height", minimum=64, maximum=2048, step=default["size_step"], value=512)
  423.             with gr.Row():
  424.                 ad_use_steps = gr.Checkbox(label="Use separate steps")
  425.                 ad_use_cfg_scale = gr.Checkbox(label="Use separate CFG scale")
  426.             with gr.Row():
  427.                 ad_steps = gr.Slider(label="ADetailer steps", minimum=1, maximum=150, step=1, value=28)
  428.                 ad_cfg_scale = gr.Slider(label="ADetailer CFG scale", minimum=0,  maximum=30, step=0.5, value=7)
  429.             with gr.Row():
  430.                 ad_use_checkpoint = gr.Checkbox(label="Use separate checkpoint")
  431.                 ad_use_vae = gr.Checkbox(label="Use separate VAE")
  432.             with gr.Row():
  433.                 ckpts = ["Use same checkpoint"]
  434.                 for model in sd_models:
  435.                     ckpts.append(model)
  436.                 ad_checkpoint = gr.Dropdown(ckpts, label="ADetailer checkpoint", value=ckpts[0])
  437.                 vaes = ["Use same VAE"]
  438.                 for vae in sd_vaes:
  439.                     vaes.append(vae)
  440.                 ad_vae = gr.Dropdown(vaes, label="ADetailer VAE", value=vaes[0])
  441.             ad_use_sampler = gr.Checkbox(label="Use separate sampler")
  442.             with gr.Row():
  443.                 ad_sampler = gr.Dropdown(samplers, label="ADetailer sampler", value=samplers[0])
  444.                 scheduler_names = ["Use same scheduler"]
  445.                 for scheduler in schedulers:
  446.                     scheduler_names.append(scheduler)
  447.                 ad_scheduler = gr.Dropdown(scheduler_names, label="ADetailer scheduler", value=scheduler_names[0])
  448.             with gr.Row():
  449.                 ad_use_noise_multiplier = gr.Checkbox(label="Use separate noise multiplier")
  450.                 ad_use_clip_skip = gr.Checkbox(label="Use separate CLIP skip")
  451.             with gr.Row():
  452.                 ad_noise_multiplier = gr.Slider(label="Noise multiplier for img2img", minimum=0.5, maximum=1.5, step=0.01, value=1)
  453.                 ad_clip_skip = gr.Slider(label="ADetailer CLIP skip", minimum=1, maximum=12, step=1, value=1)
  454.             ad_restore_face = gr.Checkbox(label="Restore faces after ADetailer")
  455.         if "sd-webui-controlnet" in extensions:
  456.             with gr.Tab("ControlNet"):
  457.                 with gr.Row():
  458.                     ad_cn_models = ["None", "Passthrough", "control_v11f1p_sd15_depth [1a8eb83c]", "control_v11p_sd15_inpaint [dfe64acb]", "control_v11p_sd15_lineart [2c3004a6]", "control_v11p_sd15_openpose [52e0ea54]", "control_v11p_sd15_scribble [46a6fcd7]", "control_v11p_sd15s2_lineart_anime [19a26aa8]"]
  459.                     ad_controlnet_model = gr.Dropdown(ad_cn_models, label="ControlNet model", value="None")
  460.                     ad_controlnet_module = gr.Dropdown(["None"], label="ControlNet module", value="None", visible=False)
  461.                     ad_controlnet_model.change(fn= ad_update_cn_module_choices, inputs=ad_controlnet_model, outputs=ad_controlnet_module)
  462.                 with gr.Row():
  463.                     ad_controlnet_weight = gr.Slider(label="Control Weight", minimum=0, maximum=1, step=0.01, value=1)
  464.                     ad_controlnet_guidance_start = gr.Slider(label="Starting Control Step", minimum=0, maximum=1, step=0.01, value=0)
  465.                     ad_controlnet_guidance_end = gr.Slider(label="Ending Control Step", minimum=0, maximum=1, step=0.01, value=1)
  466.         if "sd-webui-controlnet" in extensions:
  467.             ad_inputs = [ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face, ad_controlnet_model, ad_controlnet_module, ad_controlnet_weight, ad_controlnet_guidance_start, ad_controlnet_guidance_end]
  468.         else:
  469.             ad_inputs = [ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face]
  470.         for gr_block in ad_inputs:
  471.             if type(gr_block) is gr.components.slider.Slider:
  472.                 gr_block.release(fn=ad_update_args, inputs=ad_inputs, outputs=None)
  473.             else:
  474.                 gr_block.change(fn=ad_update_args, inputs=ad_inputs, outputs=None)
  475.     return demo
  476.  
  477. def add_controlnet(payload, gen_type):
  478.     global cn_args
  479.     args = cn_args[gen_type]
  480.     args = dict(sorted(args.items(), key=lambda x: x[0]))
  481.     payload["alwayson_scripts"]["controlnet"] = {"args": []}
  482.     if args == {}:
  483.         return payload
  484.     for i in args:
  485.         payload["alwayson_scripts"]["controlnet"]["args"].append(args[i])
  486.     return payload
  487.  
  488. def cn_preprocess(cn_module, cn_input_image):
  489.     if cn_input_image is None:
  490.         return None
  491.     cn_input_image = pil_to_base64(cn_input_image)
  492.     payload = {
  493.         "controlnet_module": cn_module,
  494.         "controlnet_input_images": [cn_input_image]
  495.     }
  496.     response = requests.post(url=f"{url}/controlnet/detect", json=payload)
  497.     images_base64 = response.json()["images"][0]
  498.     image_pil = base64_to_pil(images_base64)
  499.     if save_images == "Yes":
  500.         save_image(image_pil, "ControlNet", "detect")
  501.     return image_pil
  502.  
  503. def cn_update_args(*args):
  504.     cn_gen_type, cn_num, enable_cn, enable_low_vram, enable_pixel_perfect, cn_module, cn_model, cn_input_image, cn_mask, cn_weight, cn_guidance_start, cn_guidance_end, cn_resolution, cn_control_mode, cn_resize_mode = args
  505.     global cn_args
  506.     if not cn_input_image is None:
  507.         cn_input_image = pil_to_base64(cn_input_image)
  508.     if not cn_mask is None:
  509.         cn_mask = pil_to_base64(cn_mask)
  510.     args = {
  511.         "input_image": cn_input_image,
  512.         "module": cn_module,
  513.         "model": cn_model,
  514.         "low_vram": enable_low_vram,
  515.         "pixel_perfect": enable_pixel_perfect,
  516.         "mask": cn_mask,
  517.         "weight": cn_weight,
  518.         "guidance_start": cn_guidance_start,
  519.         "guidance_end": cn_guidance_end,
  520.         "processor_res": cn_resolution,
  521.         "control_mode": cn_control_mode,
  522.         "resize_mode": cn_resize_mode
  523.     }
  524.     if enable_cn == True:
  525.         cn_args[cn_gen_type][cn_num] = args
  526.     else:
  527.         del cn_args[cn_gen_type][cn_num]
  528.  
  529. def cn_update_choices(cn_type):
  530.     module_list = cn_types_list[cn_type]["module_list"]
  531.     model_list = cn_types_list[cn_type]["model_list"]
  532.     default_option = cn_types_list[cn_type]["default_option"]
  533.     default_model = cn_types_list[cn_type]["default_model"]
  534.     return gr.update(choices=module_list, value=default_option), gr.update(choices=model_list, value=default_model)
  535.  
  536. def cn_blocks(i, gen_type):
  537.     with gr.Blocks() as demo:
  538.         with gr.Row():
  539.             cn_gen_type = gr.Textbox(visible=False, value=gen_type)
  540.             cn_num = gr.Textbox(visible=False, value=i)
  541.             enable_cn = gr.Checkbox(label="Enable")
  542.             enable_low_vram = gr.Checkbox(label="Low VRAM")
  543.             enable_pixel_perfect = gr.Checkbox(label="Pixel Perfect")
  544.             enable_mask_upload = gr.Checkbox(label="Effective Region Mask")
  545.         with gr.Row():
  546.             cn_type = gr.Dropdown(cn_types, label="ControlNet type", value=cn_default_type)
  547.             cn_btn = gr.Button("Preprocess | 预处理", elem_id="button")
  548.         with gr.Row():
  549.             cn_module = gr.Dropdown(cn_module_list, label="ControlNet module", value=cn_default_option)
  550.             cn_model = gr.Dropdown(cn_model_list, label="ControlNet model", value=cn_default_model)
  551.         with gr.Row():
  552.             cn_input_image = gr.Image(type="pil")
  553.             cn_detect_image = gr.Image(label="Preprocessor Preview")
  554.             cn_mask = gr.Image(label="Effective Region Mask", interactive=True, visible=False)
  555.         with gr.Row():
  556.             cn_weight = gr.Slider(label="Control Weight", minimum=0, maximum=2, step=0.05, value=1)
  557.             cn_guidance_start = gr.Slider(label="Starting Control Step", minimum=0, maximum=1, step=0.01, value=0)
  558.             cn_guidance_end = gr.Slider(label="Ending Control Step", minimum=0, maximum=1, step=0.01, value=1)
  559.         cn_resolution = gr.Slider(label="Resolution", minimum=64, maximum=2048, step=default["size_step"], value=512)
  560.         cn_control_mode = gr.Radio(["Balanced", "My prompt is more important", "ControlNet is more important"], label="Control Mode", value="Balanced")
  561.         cn_resize_mode = gr.Radio(["Just Resize", "Crop and Resize", "Resize and Fill"], label="Resize Mode", value="Crop and Resize")
  562.         enable_mask_upload.change(fn=gr_update_visible, inputs=enable_mask_upload, outputs=cn_mask)
  563.         cn_type.change(fn=cn_update_choices, inputs=cn_type, outputs=[cn_module, cn_model])
  564.         cn_btn.click(fn=cn_preprocess, inputs=[cn_module, cn_input_image], outputs=cn_detect_image)
  565.         cn_inputs = [cn_gen_type, cn_num, enable_cn, enable_low_vram, enable_pixel_perfect, cn_module, cn_model, cn_input_image, cn_mask, cn_weight, cn_guidance_start, cn_guidance_end, cn_resolution, cn_control_mode, cn_resize_mode]
  566.         for gr_block in cn_inputs:
  567.             if type(gr_block) is gr.components.slider.Slider:
  568.                 gr_block.release(fn=cn_update_args, inputs=cn_inputs, outputs=None)
  569.             else:
  570.                 gr_block.change(fn=cn_update_args, inputs=cn_inputs, outputs=None)
  571.     return demo
  572.  
  573. def generate(input_image, sd_model, sd_vae, sampler_name, scheduler, clip_skip, steps, width, batch_size, height, batch_count, cfg_scale, randn_source, seed, denoising_strength, prompt, negative_prompt, progress=gr.Progress()):
  574.     global interrupt, xyz_args
  575.     interrupt = False
  576.     if denoising_strength >= 0:
  577.         gen_type = "img2img"
  578.         if input_image is None:
  579.             return None, None, None
  580.     else:
  581.         gen_type = "txt2img"
  582.     progress(0, desc=f"Loading {sd_model}")
  583.     payload = {
  584.         "sd_model_checkpoint": sd_models_list[sd_model],
  585.         "sd_vae": sd_vae,
  586.         "CLIP_stop_at_last_layers": clip_skip,
  587.         "randn_source": randn_source
  588.     }
  589.     requests.post(url=f"{url}/sdapi/v1/options", json=payload)
  590.     if interrupt == True:
  591.             return None, None, None
  592.     progress(0, desc="Processing...")
  593.     images = []
  594.     images_info = []
  595.     if not input_image is None:
  596.         input_image = pil_to_base64(input_image)
  597.     for i in range(batch_count):
  598.         payload = {
  599.             "prompt": prompt,
  600.             "negative_prompt": negative_prompt,
  601.             "batch_size": batch_size,
  602.             "seed": seed,
  603.             "sampler_name": sampler_name,
  604.             "scheduler": scheduler,
  605.             "steps": steps,
  606.             "cfg_scale": cfg_scale,
  607.             "width": width,
  608.             "height": height,
  609.             "init_images": [input_image],
  610.             "denoising_strength": denoising_strength,
  611.             "alwayson_scripts": {}
  612.         }
  613.         if "adetailer" in extensions:
  614.             payload = add_adetailer(payload, gen_type)
  615.         if "sd-webui-controlnet" in extensions:
  616.             payload = add_controlnet(payload, gen_type)
  617.         payload = add_xyz_plot(payload, gen_type)
  618.         response = requests.post(url=f"{url}/sdapi/v1/{gen_type}", json=payload)
  619.         images_base64 = response.json()["images"]
  620.         for j in range(len(images_base64)):
  621.             image_pil = base64_to_pil(images_base64[j])
  622.             images.append(image_pil)
  623.             image_info = get_png_info(image_pil)
  624.             images_info.append(image_info)
  625.             if image_info == "None":
  626.                 if save_images == "Yes":
  627.                     if gen_type in xyz_args:
  628.                         save_image(image_pil, "XYZ_Plot", "grid")
  629.                     else:
  630.                         save_image(image_pil, "ControlNet", "detect")
  631.             else:
  632.                 seed = re.findall("Seed: [0-9]+", image_info)[0].split(": ")[-1]
  633.                 if save_images == "Yes":
  634.                     save_image(image_pil, sd_model, seed)
  635.         seed = int(seed) + 1
  636.         progress((i+1)/batch_count, desc=f"Batch count: {(i+1)}/{batch_count}")
  637.         if interrupt == True:
  638.             return images, images_info, datetime.datetime.now()
  639.     return images, images_info, datetime.datetime.now()
  640.  
  641. def gen_clear_geninfo():
  642.     return None
  643.  
  644. def gen_update_geninfo(images_info):
  645.     if images_info == [] or images_info is None:
  646.         return None
  647.     return images_info[0]
  648.  
  649. def gen_update_selected_geninfo(images_info, evt: gr.SelectData):
  650.     return images_info[evt.index]
  651.  
  652. def gen_blocks(gen_type):
  653.     with gr.Blocks() as demo:
  654.         with gr.Row():
  655.             with gr.Column():
  656.                 prompt = gr.Textbox(placeholder="Prompt", show_label=False, value=default["prompt"], lines=3)
  657.                 negative_prompt = gr.Textbox(placeholder="Negative prompt", show_label=False, value=default["negative_prompt"], lines=3)
  658.                 if gen_type == "txt2img":
  659.                     input_image = gr.Image(visible=False)
  660.                 else:
  661.                     input_image = gr.Image(type="pil")
  662.  
  663.                 with gr.Tab("Generation"):
  664.                     with gr.Row():
  665.                         sd_model = gr.Dropdown(sd_models, label="SD Model", value=sd_models[0])
  666.                         sd_vae = gr.Dropdown(sd_vaes, label="SD VAE", value=sd_vaes[0])
  667.                         clip_skip = gr.Slider(minimum=1, maximum=12, step=1, label="Clip skip", value=default["clip_skip"])
  668.                     with gr.Row():
  669.                         sampler_name = gr.Dropdown(samplers, label="Sampling method", value=samplers[0])
  670.                         scheduler = gr.Dropdown(schedulers, label="Schedule type", value=schedulers[0])
  671.                         steps = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps", value=default["steps"])
  672.                     with gr.Row():
  673.                         width = gr.Slider(minimum=64, maximum=2048, step=default["size_step"], label="Width", value=default["width"])
  674.                         batch_size = gr.Slider(minimum=1, maximum=8, step=1, label="Batch size", value=1)
  675.                     with gr.Row():
  676.                         height = gr.Slider(minimum=64, maximum=2048, step=default["size_step"], label="Height", value=default["height"])
  677.                         batch_count = gr.Slider(minimum=1, maximum=100, step=1, label="Batch count", value=1)
  678.                     with gr.Row():
  679.                         cfg_scale = gr.Slider(minimum=1, maximum=30, step=0.5, label="CFG Scale", value=default["cfg"])
  680.                         if gen_type == "txt2img":
  681.                             denoising_strength = gr.Slider(minimum=-1, maximum=1, step=1, value=-1, visible=False)
  682.                         else:
  683.                             denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Denoising strength", value=0.7)
  684.                     with gr.Row():
  685.                         randn_source = gr.Dropdown(["CPU", "GPU"], label="RNG", value="CPU")
  686.                         seed = gr.Textbox(label="Seed", value=-1)
  687.  
  688.                 if "adetailer" in extensions:
  689.                     with gr.Tab("ADetailer"):
  690.                         if gen_type == "img2img":
  691.                             with gr.Row():
  692.                                 ad_skip_img2img = gr.Checkbox(label="Skip img2img", visible=True)
  693.                                 ad_skip_img2img.change(fn=ad_update_skip_img2img, inputs=ad_skip_img2img, outputs=None)
  694.                         for i in range(default["ad_nums"]):
  695.                             with gr.Tab(f"ADetailer {ordinal(i + 1)}"): ad_blocks(i, gen_type)
  696.  
  697.                 if "sd-webui-controlnet" in extensions:
  698.                     with gr.Tab("ControlNet"):
  699.                         for i in range(default["cn_nums"]):
  700.                             with gr.Tab(f"ControlNet Unit {i}"): cn_blocks(i, gen_type)
  701.  
  702.                 if not loras == [] or not embeddings == []:
  703.                     with gr.Tab("Extra Networks"):
  704.                         if not loras == []:
  705.                             lora = gr.Dropdown(loras, label="Lora", multiselect=True, interactive=True)
  706.                             lora.change(fn=add_lora, inputs=[prompt, lora], outputs=prompt)
  707.                         if not embeddings == []:
  708.                             embedding = gr.Dropdown(embeddings, label="Embedding", multiselect=True, interactive=True)
  709.                             embedding.change(fn=add_embedding, inputs=[negative_prompt, embedding], outputs=negative_prompt)
  710.  
  711.                 with gr.Tab("X/Y/Z plot"): xyz_blocks(gen_type)
  712.  
  713.             with gr.Column():
  714.                 with gr.Row():
  715.                     btn = gr.Button("Generate | 生成", elem_id="button")
  716.                     btn2 = gr.Button("Interrupt | 终止")
  717.                 gallery = gr.Gallery(preview=True, height=default["gallery_height"])
  718.                 image_geninfo = gr.Markdown()
  719.                 images_geninfo = gr.State()
  720.                 update_geninfo = gr.Textbox(visible=False)
  721.             gen_inputs = [input_image, sd_model, sd_vae, sampler_name, scheduler, clip_skip, steps, width, batch_size, height, batch_count, cfg_scale, randn_source, seed, denoising_strength, prompt, negative_prompt]
  722.             btn.click(fn=gen_clear_geninfo, inputs=None, outputs=image_geninfo)
  723.             btn.click(fn=generate, inputs=gen_inputs, outputs=[gallery, images_geninfo, update_geninfo])
  724.             btn2.click(fn=post_interrupt, inputs=None, outputs=None)
  725.             gallery.select(fn=gen_update_selected_geninfo, inputs=images_geninfo, outputs=image_geninfo)
  726.             update_geninfo.change(fn=gen_update_geninfo, inputs=images_geninfo, outputs=image_geninfo)
  727.     return demo
  728.  
  729. def extras(input_image, upscaler_1, upscaler_2, upscaling_resize, extras_upscaler_2_visibility, enable_gfpgan, gfpgan_visibility, enable_codeformer, codeformer_visibility, codeformer_weight):
  730.     if input_image is None:
  731.         return None
  732.     input_image = pil_to_base64(input_image)
  733.     if enable_gfpgan == False:
  734.         gfpgan_visibility = 0
  735.     if enable_codeformer == False:
  736.         codeformer_visibility = 0
  737.     payload = {
  738.         "gfpgan_visibility": gfpgan_visibility,
  739.         "codeformer_visibility": codeformer_visibility,
  740.         "codeformer_weight": codeformer_weight,
  741.         "upscaling_resize": upscaling_resize,
  742.         "upscaler_1": upscaler_1,
  743.         "upscaler_2": upscaler_2,
  744.         "extras_upscaler_2_visibility": extras_upscaler_2_visibility,
  745.         "image": input_image
  746.     }
  747.     response = requests.post(url=f"{url}/sdapi/v1/extra-single-image", json=payload)
  748.     images_base64 = response.json()["image"]
  749.     image_pil = base64_to_pil(images_base64)
  750.     if save_images == "Yes":
  751.         save_image(image_pil, "Extras", "image")
  752.     return image_pil
  753.  
  754. def extras_blocks():
  755.     with gr.Blocks() as demo:
  756.         with gr.Row():
  757.             with gr.Column():
  758.                 input_image = gr.Image(type="pil")
  759.                 with gr.Row():
  760.                     upscaler_1 = gr.Dropdown(upscalers, label="Upscaler 1", value="R-ESRGAN 4x+")
  761.                     upscaler_2 = gr.Dropdown(upscalers, label="Upscaler 2", value="None")
  762.                 with gr.Row():
  763.                     upscaling_resize = gr.Slider(minimum=1, maximum=8, step=0.05, label="Scale by", value=4)
  764.                     extras_upscaler_2_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="Upscaler 2 visibility", value=0)
  765.                 enable_gfpgan = gr.Checkbox(label="Enable GFPGAN")
  766.                 gfpgan_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="GFPGAN Visibility", value=1)
  767.                 enable_codeformer = gr.Checkbox(label="Enable CodeFormer")
  768.                 codeformer_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="CodeFormer Visibility", value=1)
  769.                 codeformer_weight = gr.Slider(minimum=0, maximum=1, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0)
  770.             with gr.Column():
  771.                 with gr.Row():
  772.                     btn = gr.Button("Generate | 生成", elem_id="button")
  773.                     btn2 = gr.Button("Interrupt | 终止")
  774.                 extra_image = gr.Image(label="Extras image")
  775.             btn.click(fn=extras, inputs=[input_image, upscaler_1, upscaler_2, upscaling_resize, extras_upscaler_2_visibility, enable_gfpgan, gfpgan_visibility, enable_codeformer, codeformer_visibility, codeformer_weight], outputs=extra_image)
  776.             btn2.click(fn=post_interrupt, inputs=None, outputs=None)
  777.     return demo
  778.  
  779. def get_png_info(image_pil):
  780.     image_info=[]
  781.     if image_pil is None:
  782.         return None
  783.     for key, value in image_pil.info.items():
  784.         image_info.append(value)
  785.     if not image_info == []:
  786.         image_info = image_info[0]
  787.         image_info = re.sub(r"<", "\<", image_info)
  788.         image_info = re.sub(r">", "\>", image_info)
  789.         image_info = re.sub(r"\n", "<br>", image_info)
  790.     else:
  791.         image_info = "None"
  792.     return image_info
  793.  
  794. def png_info_blocks():
  795.     with gr.Blocks() as demo:
  796.         with gr.Row():
  797.             with gr.Column():
  798.                 input_image = gr.Image(value=None, type="pil")
  799.             with gr.Column():
  800.                 png_info = gr.Markdown()
  801.             input_image.change(fn=get_png_info, inputs=input_image, outputs=png_info)
  802.     return demo
  803.  
  804. with gr.Blocks(css="#button {background: #FFE1C0; color: #FF453A} .block.padded:not(.gradio-accordion) {padding: 0 !important;} div.form {border-width: 0; box-shadow: none; background: white; gap: 0.5em;}") as demo:
  805.     with gr.Tab("txt2img"): gen_blocks("txt2img")
  806.     with gr.Tab("img2img"): gen_blocks("img2img")
  807.     with gr.Tab("Extras"): extras_blocks()
  808.     with gr.Tab("PNG Info"): png_info_blocks()
  809.  
  810. demo.queue(concurrency_count=100).launch(inbrowser=True)

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