Stable Diffusion | Gradio界面计划及webUI API调用
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：
https://i-blog.csdnimg.cn/blog_migrate/d7cb3ddc68d86fe07e27f4181607a13b.png
Adetailer 设置界面：
https://i-blog.csdnimg.cn/blog_migrate/9c77c32fbdf742d95949597ee78744f8.png
ControlNet 设置界面：
https://i-blog.csdnimg.cn/blog_migrate/f60c7db0f9a77a7487f677412d829169.png
X/Y/Z Plot 设置界面：
https://i-blog.csdnimg.cn/blog_migrate/939771b0cf62128c374b03fade0aced7.png
图生图界面：
https://i-blog.csdnimg.cn/blog_migrate/18293838584d20f9bae3453b9f2d6269.png
图片放大界面：
https://i-blog.csdnimg.cn/blog_migrate/953009bab4892cb2139c26d6148f9fd3.png
图片信息读取界面：
https://i-blog.csdnimg.cn/blog_migrate/9f11bd2d7d4359f0dbef87caf95fac09.png
3. Gradio界面计划及webUI API调用
import base64
import datetime
import io
import os
import re
import subprocess
import gradio as gr
import requests
from PIL import Image, PngImagePlugin
design_mode = 1
save_images = "Yes"
url = "http://127.0.0.1:7860"
if design_mode == 0:
cmd = "netstat -tulnp"
netstat_output = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True).stdout.splitlines()
for i in netstat_output:
if "stable-diffus" in i:
port = int(re.findall(r'\d+', i))
url = f"http://127.0.0.1:{port}"
output_dir = os.getcwd() + "/output/" + datetime.date.today().strftime("%Y-%m-%d")
os.makedirs(output_dir, exist_ok=True)
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
default = {
"prompt": "(best quality:1), (high quality:1), detailed/(extreme, highly, ultra/), realistic, 1girl/(beautiful, delicate, perfect/)",
"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",
"clip_skip": 1,
"width": 512,
"height": 768,
"size_step": 64,
"steps": 20,
"cfg": 7,
"ad_nums": 2,
"ad_model": ["face_yolov8n.pt", "hand_yolov8n.pt"],
"cn_nums": 3,
"cn_type": "Canny",
"gallery_height": 600,
"lora_weight": 0.8,
"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"]
}
samplers = []
response = requests.get(url=f"{url}/sdapi/v1/samplers").json()
for i in range(len(response)):
samplers.append(response["name"])
schedulers = []
response = requests.get(url=f"{url}/sdapi/v1/schedulers").json()
for i in range(len(response)):
schedulers.append(response["label"])
upscalers = []
response = requests.get(url=f"{url}/sdapi/v1/upscalers").json()
for i in range(len(response)):
upscalers.append(response["name"])
sd_models = []
sd_models_list = {}
response = requests.get(url=f"{url}/sdapi/v1/sd-models").json()
for i in range(len(response)):
path, sd_model = os.path.split(response["title"])
sd_model_name, sd_model_extension = os.path.splitext(sd_model)
if not sd_model_name in default["hidden_models"]:
sd_models.append(sd_model)
sd_models_list = response["title"]
sd_models = sorted(sd_models)
sd_vaes = ["Automatic", "None"]
response = requests.get(url=f"{url}/sdapi/v1/sd-vae").json()
for i in range(len(response)):
sd_vaes.append(response["model_name"])
embeddings = []
response = requests.get(url=f"{url}/sdapi/v1/embeddings").json()
for key in response["loaded"]:
embeddings.append(key)
extensions = []
response = requests.get(url=f"{url}/sdapi/v1/extensions").json()
for i in range(len(response)):
extensions.append(response["name"])
loras = []
loras_name = {}
loras_activation_text = {}
response = requests.get(url=f"{url}/sdapi/v1/loras").json()
for i in range(len(response)):
lora_name = response["name"]
lora_info = requests.get(url=f"{url}/tacapi/v1/lora-info/{lora_name}").json()
if lora_info and "sd version" in lora_info:
lora_type = lora_info["sd version"]
lora_name_type = f"{lora_name} ({lora_type})"
else:
lora_name_type = f"{lora_name}"
loras.append(lora_name_type)
loras_name = lora_name
if "activation text" in loras_activation_text:
loras_activation_text = lora_info["activation text"]
xyz_args = {}
xyz_plot_types = {}
last_choice = "Size"
response = requests.get(url=f"{url}/sdapi/v1/script-info").json()
for i in range(len(response)):
if response["name"] == "x/y/z plot":
if response["is_img2img"] == False:
xyz_plot_types["txt2img"] = response["args"]["choices"]
choice_index = xyz_plot_types["txt2img"].index(last_choice) + 1
xyz_plot_types["txt2img"] = xyz_plot_types["txt2img"][:choice_index]
else:
xyz_plot_types["img2img"] = response["args"]["choices"]
choice_index = xyz_plot_types["img2img"].index(last_choice) + 1
xyz_plot_types["img2img"] = xyz_plot_types["img2img"][:choice_index]
if "adetailer" in extensions:
ad_args = {"txt2img": {}, "img2img": {}}
ad_skip_img2img = False
ad_models = ["None"]
response = requests.get(url=f"{url}/adetailer/v1/ad_model").json()
for key in response["ad_model"]:
ad_models.append(key)
if "sd-webui-controlnet" in extensions:
cn_args = {"txt2img": {}, "img2img": {}}
cn_types = []
cn_types_list = {}
response = requests.get(url=f"{url}/controlnet/control_types").json()
for key in response["control_types"]:
cn_types.append(key)
cn_types_list = response["control_types"]
cn_default_type = default["cn_type"]
cn_module_list = cn_types_list["module_list"]
cn_model_list = cn_types_list["model_list"]
cn_default_option = cn_types_list["default_option"]
cn_default_model = cn_types_list["default_model"]
def save_image(image, part1, part2):
counter = 1
image_name = f"{part1}-{part2}-{counter}.png"
while os.path.exists(os.path.join(output_dir, image_name)):
counter += 1
image_name = f"{part1}-{part2}-{counter}.png"
image_path = os.path.join(output_dir, image_name)
image_metadata = PngImagePlugin.PngInfo()
for key, value in image.info.items():
if isinstance(key, str) and isinstance(value, str):
image_metadata.add_text(key, value)
image.save(image_path, format="PNG", pnginfo=image_metadata)
def pil_to_base64(image_pil):
buffer = io.BytesIO()
image_pil.save(buffer, format="png")
image_buffer = buffer.getbuffer()
image_base64 = base64.b64encode(image_buffer).decode("utf-8")
return image_base64
def base64_to_pil(image_base64):
image_binary = base64.b64decode(image_base64)
image_pil = Image.open(io.BytesIO(image_binary))
return image_pil
def format_prompt(prompt):
prompt = re.sub(r"\s+,", ",", prompt)
prompt = re.sub(r"\s+", " ", prompt)
prompt = re.sub(",,+", ",", prompt)
prompt = re.sub(",", ", ", prompt)
prompt = re.sub(r"\s+", " ", prompt)
prompt = re.sub(r"^,", "", prompt)
prompt = re.sub(r"^ ", "", prompt)
prompt = re.sub(r" $", "", prompt)
prompt = re.sub(r",$", "", prompt)
prompt = re.sub(": ", ":", prompt)
return prompt
def post_interrupt():
global interrupt
interrupt = True
requests.post(url=f"{url}/sdapi/v1/interrupt").json()
def gr_update_visible(visible):
return gr.update(visible=visible)
def ordinal(n: int) -> str:
d = {1: "st", 2: "nd", 3: "rd"}
return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))
def add_lora(prompt, lora):
lora_weight = default["lora_weight"]
prompt = re.sub(r"<[^<>]+>", "", prompt)
for elem in loras_activation_text:
prompt = re.sub(loras_activation_text, "", prompt)
prompt = format_prompt(prompt)
for elem in lora:
lora_name = loras_name
if elem in loras_activation_text:
lora_activation_text = loras_activation_text
else:
lora_activation_text = ""
if lora_activation_text == "":
prompt = f"{prompt}, <lora:{lora_name}:{lora_weight}>"
else:
prompt = f"{prompt}, <lora:{lora_name}:{lora_weight}> {lora_activation_text}"
return prompt
def add_embedding(negative_prompt, embedding):
for elem in embeddings:
negative_prompt = re.sub(f"{elem},", "", negative_prompt)
negative_prompt = format_prompt(negative_prompt)
for elem in embedding[::-1]:
negative_prompt = f"{elem}, {negative_prompt}"
return negative_prompt
def add_xyz_plot(payload, gen_type):
global xyz_args
if gen_type in xyz_args:
payload["script_name"] = "X/Y/Z plot"
payload["script_args"] = xyz_args
return payload
def xyz_update_args(*args):
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
global xyz_args
x_type = xyz_plot_types.index(x_type)
y_type = xyz_plot_types.index(y_type)
z_type = xyz_plot_types.index(z_type)
args =
if enable_xyz_plot == True:
xyz_args = args
else:
del xyz_args
def xyz_update_choices(xyz_type):
choices = []
if xyz_type == "Checkpoint name":
choices = sd_models
if xyz_type == "VAE":
choices = sd_vaes
if xyz_type == "Sampler":
choices = samplers
if xyz_type == "Schedule type":
choices = schedulers
if xyz_type == "Hires sampler":
choices = samplers
if xyz_type == "Hires upscaler":
choices = upscalers
if xyz_type == "Always discard next-to-last sigma":
choices = ["False", "True"]
if xyz_type == "SGM noise multiplier":
choices = ["False", "True"]
if xyz_type == "Refiner checkpoint":
choices = sd_models
if xyz_type == "RNG source":
choices = ["GPU", "CPU", "NV"]
if xyz_type == "FP8 mode":
choices = ["Disable", "Enable for SDXL", "Enable"]
if choices == []:
return gr.update(visible=True, value=None), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=True, choices=choices)
def xyz_blocks(gen_type):
with gr.Blocks() as demo:
with gr.Row():
xyz_gen_type = gr.Textbox(visible=False, value=gen_type)
enable_xyz_plot = gr.Checkbox(label="Enable")
with gr.Row():
x_type = gr.Dropdown(xyz_plot_types, label="X type", value=xyz_plot_types)
x_values = gr.Textbox(label="X values", lines=1)
x_values_dropdown = gr.Dropdown(label="X values", visible=False, multiselect=True, interactive=True)
with gr.Row():
y_type = gr.Dropdown(xyz_plot_types, label="Y type", value=xyz_plot_types)
y_values = gr.Textbox(label="Y values", lines=1)
y_values_dropdown = gr.Dropdown(label="Y values", visible=False, multiselect=True, interactive=True)
with gr.Row():
z_type = gr.Dropdown(xyz_plot_types, label="Z type", value=xyz_plot_types)
z_values = gr.Textbox(label="Z values", lines=1)
z_values_dropdown = gr.Dropdown(label="Z values", visible=False, multiselect=True, interactive=True)
with gr.Row():
with gr.Column():
draw_legend = gr.Checkbox(label='Draw legend', value=True)
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False)
vary_seeds_y = gr.Checkbox(label='Vary seeds for Y', value=False)
vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False)
with gr.Column():
include_lone_images = gr.Checkbox(label='Include Sub Images', value=True)
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False)
csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False)
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2)
x_type.change(fn=xyz_update_choices, inputs=x_type, outputs=)
y_type.change(fn=xyz_update_choices, inputs=y_type, outputs=)
z_type.change(fn=xyz_update_choices, inputs=z_type, outputs=)
xyz_inputs =
for gr_block in xyz_inputs:
if type(gr_block) is gr.components.slider.Slider:
gr_block.release(fn=xyz_update_args, inputs=xyz_inputs, outputs=None)
else:
gr_block.change(fn=xyz_update_args, inputs=xyz_inputs, outputs=None)
return demo
def add_adetailer(payload, gen_type):
global ad_args, ad_skip_img2img
args = ad_args
args = dict(sorted(args.items(), key=lambda x: x))
payload["alwayson_scripts"]["adetailer"] = {"args": []}
if args == {}:
return payload
if gen_type == "img2img":
payload["alwayson_scripts"]["adetailer"]["args"] =
else:
payload["alwayson_scripts"]["adetailer"]["args"] =
for i in args:
payload["alwayson_scripts"]["adetailer"]["args"].append(args)
return payload
def ad_update_args(*args):
if "sd-webui-controlnet" in extensions:
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
else:
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
global ad_args
args = {
"ad_model": ad_model,
"ad_model_classes": "",
"ad_prompt": ad_prompt,
"ad_negative_prompt": ad_negative_prompt,
"ad_confidence": ad_confidence,
"ad_mask_k_largest": ad_mask_k_largest,
"ad_mask_min_ratio": ad_mask_min_ratio,
"ad_mask_max_ratio": ad_mask_max_ratio,
"ad_dilate_erode": ad_dilate_erode,
"ad_x_offset": ad_x_offset,
"ad_y_offset": ad_y_offset,
"ad_mask_merge_invert": ad_mask_merge_invert,
"ad_mask_blur": ad_mask_blur,
"ad_denoising_strength": ad_denoising_strength,
"ad_inpaint_only_masked": ad_inpaint_only_masked,
"ad_inpaint_only_masked_padding": ad_inpaint_only_masked_padding,
"ad_use_inpaint_width_height": ad_use_inpaint_width_height,
"ad_inpaint_width": ad_inpaint_width,
"ad_inpaint_height": ad_inpaint_height,
"ad_use_steps": ad_use_steps,
"ad_steps": ad_steps,
"ad_use_cfg_scale": ad_use_cfg_scale,
"ad_cfg_scale": ad_cfg_scale,
"ad_use_checkpoint": ad_use_checkpoint,
"ad_checkpoint": ad_checkpoint,
"ad_use_vae": ad_use_vae,
"ad_vae": ad_vae,
"ad_use_sampler": ad_use_sampler,
"ad_sampler": ad_sampler,
"ad_scheduler": ad_scheduler,
"ad_use_noise_multiplier": ad_use_noise_multiplier,
"ad_noise_multiplier": ad_noise_multiplier,
"ad_use_clip_skip": ad_use_clip_skip,
"ad_clip_skip": ad_clip_skip,
"ad_restore_face": ad_restore_face,
}
if "sd-webui-controlnet" in extensions:
args["ad_controlnet_model"] = ad_controlnet_model
args["ad_controlnet_module"] = ad_controlnet_module
args["ad_controlnet_weight"] = ad_controlnet_weight
args["ad_controlnet_guidance_start"] = ad_controlnet_guidance_start
args["ad_controlnet_guidance_end"] = ad_controlnet_guidance_end
if enable_ad == True:
ad_args = args
else:
del ad_args
def ad_update_cn_module_choices(ad_controlnet_model):
if ad_controlnet_model == "control_v11f1p_sd15_depth ":
return gr.update(choices=["depth_midas", "depth_hand_refiner"], visible=True, value="depth_midas")
if ad_controlnet_model == "control_v11p_sd15_inpaint ":
return gr.update(choices=["inpaint_global_harmonious", "inpaint_only", "inpaint_only+lama"], visible=True, value="inpaint_global_harmonious")
if ad_controlnet_model == "control_v11p_sd15_lineart ":
return gr.update(choices=["lineart_coarse", "lineart_realistic", "lineart_anime", "lineart_anime_denoise"], visible=True, value="lineart_coarse")
if ad_controlnet_model == "control_v11p_sd15_openpose ":
return gr.update(choices=["openpose_full", "dw_openpose_full"], visible=True, value="openpose_full")
if ad_controlnet_model == "control_v11p_sd15_scribble ":
return gr.update(choices=["t2ia_sketch_pidi"], visible=True, value="t2ia_sketch_pidi")
if ad_controlnet_model == "control_v11p_sd15s2_lineart_anime ":
return gr.update(choices=["lineart_coarse", "lineart_realistic", "lineart_anime", "lineart_anime_denoise"], visible=True, value="lineart_coarse")
return gr.update(visible=False)
def ad_update_skip_img2img(arg):
global ad_skip_img2img
ad_skip_img2img = arg
def ad_blocks(i, gen_type):
with gr.Blocks() as demo:
ad_gen_type = gr.Textbox(visible=False, value=gen_type)
ad_num = gr.Textbox(visible=False, value=i)
enable_ad = gr.Checkbox(label="Enable")
ad_model = gr.Dropdown(ad_models, label="ADetailer model", value=default["ad_model"])
ad_prompt = gr.Textbox(show_label=False, placeholder="ADetailer prompt" + "\nIf blank, the main prompt is used.", lines=3)
ad_negative_prompt = gr.Textbox(show_label=False, placeholder="ADetailer negative prompt" + "\nIf blank, the main negative prompt is used.", lines=3)
with gr.Tab("Detection"):
with gr.Row():
ad_confidence = gr.Slider(label="Detection model confidence threshold", minimum=0, maximum=1, step=0.01, value=0.3)
ad_mask_min_ratio = gr.Slider(label="Mask min area ratio", minimum=0, maximum=1, step=0.001, value=0)
with gr.Row():
ad_mask_k_largest = gr.Slider(label="Mask only the top k largest (0 to disable)", minimum=0, maximum=10, step=1, value=0)
ad_mask_max_ratio = gr.Slider(label="Mask max area ratio", minimum=0, maximum=1, step=0.001, value=1)
with gr.Tab("Mask Preprocessing"):
with gr.Row():
ad_x_offset = gr.Slider(label="Mask x(→) offset", minimum=-200, maximum=200, step=1, value=0)
ad_y_offset = gr.Slider(label="Mask y(↑) offset", minimum=-200, maximum=200, step=1, value=0)
ad_dilate_erode = gr.Slider(label="Mask erosion (-) / dilation (+)", minimum=-128, maximum=128, step=4, value=4)
ad_mask_merge_invert = gr.Radio(["None", "Merge", "Merge and Invert"], label="Mask merge mode", value="None")
with gr.Tab("Inpainting"):
with gr.Row():
ad_mask_blur = gr.Slider(label="Inpaint mask blur", minimum=0, maximum=64, step=1, value=4)
ad_denoising_strength = gr.Slider(label="Inpaint denoising strength", minimum=0, maximum=1, step=0.01, value=0.4)
with gr.Row():
ad_inpaint_only_masked = gr.Checkbox(label="Inpaint only masked", value=True)
ad_use_inpaint_width_height = gr.Checkbox(label="Use separate width/height")
with gr.Row():
ad_inpaint_only_masked_padding = gr.Slider(label="Inpaint only masked padding, pixels", minimum=0, maximum=256, step=4, value=32)
with gr.Column():
ad_inpaint_width = gr.Slider(label="inpaint width", minimum=64, maximum=2048, step=default["size_step"], value=512)
ad_inpaint_height = gr.Slider(label="inpaint height", minimum=64, maximum=2048, step=default["size_step"], value=512)
with gr.Row():
ad_use_steps = gr.Checkbox(label="Use separate steps")
ad_use_cfg_scale = gr.Checkbox(label="Use separate CFG scale")
with gr.Row():
ad_steps = gr.Slider(label="ADetailer steps", minimum=1, maximum=150, step=1, value=28)
ad_cfg_scale = gr.Slider(label="ADetailer CFG scale", minimum=0,maximum=30, step=0.5, value=7)
with gr.Row():
ad_use_checkpoint = gr.Checkbox(label="Use separate checkpoint")
ad_use_vae = gr.Checkbox(label="Use separate VAE")
with gr.Row():
ckpts = ["Use same checkpoint"]
for model in sd_models:
ckpts.append(model)
ad_checkpoint = gr.Dropdown(ckpts, label="ADetailer checkpoint", value=ckpts)
vaes = ["Use same VAE"]
for vae in sd_vaes:
vaes.append(vae)
ad_vae = gr.Dropdown(vaes, label="ADetailer VAE", value=vaes)
ad_use_sampler = gr.Checkbox(label="Use separate sampler")
with gr.Row():
ad_sampler = gr.Dropdown(samplers, label="ADetailer sampler", value=samplers)
scheduler_names = ["Use same scheduler"]
for scheduler in schedulers:
scheduler_names.append(scheduler)
ad_scheduler = gr.Dropdown(scheduler_names, label="ADetailer scheduler", value=scheduler_names)
with gr.Row():
ad_use_noise_multiplier = gr.Checkbox(label="Use separate noise multiplier")
ad_use_clip_skip = gr.Checkbox(label="Use separate CLIP skip")
with gr.Row():
ad_noise_multiplier = gr.Slider(label="Noise multiplier for img2img", minimum=0.5, maximum=1.5, step=0.01, value=1)
ad_clip_skip = gr.Slider(label="ADetailer CLIP skip", minimum=1, maximum=12, step=1, value=1)
ad_restore_face = gr.Checkbox(label="Restore faces after ADetailer")
if "sd-webui-controlnet" in extensions:
with gr.Tab("ControlNet"):
with gr.Row():
ad_cn_models = ["None", "Passthrough", "control_v11f1p_sd15_depth ", "control_v11p_sd15_inpaint ", "control_v11p_sd15_lineart ", "control_v11p_sd15_openpose ", "control_v11p_sd15_scribble ", "control_v11p_sd15s2_lineart_anime "]
ad_controlnet_model = gr.Dropdown(ad_cn_models, label="ControlNet model", value="None")
ad_controlnet_module = gr.Dropdown(["None"], label="ControlNet module", value="None", visible=False)
ad_controlnet_model.change(fn= ad_update_cn_module_choices, inputs=ad_controlnet_model, outputs=ad_controlnet_module)
with gr.Row():
ad_controlnet_weight = gr.Slider(label="Control Weight", minimum=0, maximum=1, step=0.01, value=1)
ad_controlnet_guidance_start = gr.Slider(label="Starting Control Step", minimum=0, maximum=1, step=0.01, value=0)
ad_controlnet_guidance_end = gr.Slider(label="Ending Control Step", minimum=0, maximum=1, step=0.01, value=1)
if "sd-webui-controlnet" in extensions:
ad_inputs =
else:
ad_inputs =
for gr_block in ad_inputs:
if type(gr_block) is gr.components.slider.Slider:
gr_block.release(fn=ad_update_args, inputs=ad_inputs, outputs=None)
else:
gr_block.change(fn=ad_update_args, inputs=ad_inputs, outputs=None)
return demo
def add_controlnet(payload, gen_type):
global cn_args
args = cn_args
args = dict(sorted(args.items(), key=lambda x: x))
payload["alwayson_scripts"]["controlnet"] = {"args": []}
if args == {}:
return payload
for i in args:
payload["alwayson_scripts"]["controlnet"]["args"].append(args)
return payload
def cn_preprocess(cn_module, cn_input_image):
if cn_input_image is None:
return None
cn_input_image = pil_to_base64(cn_input_image)
payload = {
"controlnet_module": cn_module,
"controlnet_input_images":
}
response = requests.post(url=f"{url}/controlnet/detect", json=payload)
images_base64 = response.json()["images"]
image_pil = base64_to_pil(images_base64)
if save_images == "Yes":
save_image(image_pil, "ControlNet", "detect")
return image_pil
def cn_update_args(*args):
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
global cn_args
if not cn_input_image is None:
cn_input_image = pil_to_base64(cn_input_image)
if not cn_mask is None:
cn_mask = pil_to_base64(cn_mask)
args = {
"input_image": cn_input_image,
"module": cn_module,
"model": cn_model,
"low_vram": enable_low_vram,
"pixel_perfect": enable_pixel_perfect,
"mask": cn_mask,
"weight": cn_weight,
"guidance_start": cn_guidance_start,
"guidance_end": cn_guidance_end,
"processor_res": cn_resolution,
"control_mode": cn_control_mode,
"resize_mode": cn_resize_mode
}
if enable_cn == True:
cn_args = args
else:
del cn_args
def cn_update_choices(cn_type):
module_list = cn_types_list["module_list"]
model_list = cn_types_list["model_list"]
default_option = cn_types_list["default_option"]
default_model = cn_types_list["default_model"]
return gr.update(choices=module_list, value=default_option), gr.update(choices=model_list, value=default_model)
def cn_blocks(i, gen_type):
with gr.Blocks() as demo:
with gr.Row():
cn_gen_type = gr.Textbox(visible=False, value=gen_type)
cn_num = gr.Textbox(visible=False, value=i)
enable_cn = gr.Checkbox(label="Enable")
enable_low_vram = gr.Checkbox(label="Low VRAM")
enable_pixel_perfect = gr.Checkbox(label="Pixel Perfect")
enable_mask_upload = gr.Checkbox(label="Effective Region Mask")
with gr.Row():
cn_type = gr.Dropdown(cn_types, label="ControlNet type", value=cn_default_type)
cn_btn = gr.Button("Preprocess | 预处理", elem_id="button")
with gr.Row():
cn_module = gr.Dropdown(cn_module_list, label="ControlNet module", value=cn_default_option)
cn_model = gr.Dropdown(cn_model_list, label="ControlNet model", value=cn_default_model)
with gr.Row():
cn_input_image = gr.Image(type="pil")
cn_detect_image = gr.Image(label="Preprocessor Preview")
cn_mask = gr.Image(label="Effective Region Mask", interactive=True, visible=False)
with gr.Row():
cn_weight = gr.Slider(label="Control Weight", minimum=0, maximum=2, step=0.05, value=1)
cn_guidance_start = gr.Slider(label="Starting Control Step", minimum=0, maximum=1, step=0.01, value=0)
cn_guidance_end = gr.Slider(label="Ending Control Step", minimum=0, maximum=1, step=0.01, value=1)
cn_resolution = gr.Slider(label="Resolution", minimum=64, maximum=2048, step=default["size_step"], value=512)
cn_control_mode = gr.Radio(["Balanced", "My prompt is more important", "ControlNet is more important"], label="Control Mode", value="Balanced")
cn_resize_mode = gr.Radio(["Just Resize", "Crop and Resize", "Resize and Fill"], label="Resize Mode", value="Crop and Resize")
enable_mask_upload.change(fn=gr_update_visible, inputs=enable_mask_upload, outputs=cn_mask)
cn_type.change(fn=cn_update_choices, inputs=cn_type, outputs=)
cn_btn.click(fn=cn_preprocess, inputs=, outputs=cn_detect_image)
cn_inputs =
for gr_block in cn_inputs:
if type(gr_block) is gr.components.slider.Slider:
gr_block.release(fn=cn_update_args, inputs=cn_inputs, outputs=None)
else:
gr_block.change(fn=cn_update_args, inputs=cn_inputs, outputs=None)
return demo
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()):
global interrupt, xyz_args
interrupt = False
if denoising_strength >= 0:
gen_type = "img2img"
if input_image is None:
return None, None, None
else:
gen_type = "txt2img"
progress(0, desc=f"Loading {sd_model}")
payload = {
"sd_model_checkpoint": sd_models_list,
"sd_vae": sd_vae,
"CLIP_stop_at_last_layers": clip_skip,
"randn_source": randn_source
}
requests.post(url=f"{url}/sdapi/v1/options", json=payload)
if interrupt == True:
return None, None, None
progress(0, desc="Processing...")
images = []
images_info = []
if not input_image is None:
input_image = pil_to_base64(input_image)
for i in range(batch_count):
payload = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"batch_size": batch_size,
"seed": seed,
"sampler_name": sampler_name,
"scheduler": scheduler,
"steps": steps,
"cfg_scale": cfg_scale,
"width": width,
"height": height,
"init_images": ,
"denoising_strength": denoising_strength,
"alwayson_scripts": {}
}
if "adetailer" in extensions:
payload = add_adetailer(payload, gen_type)
if "sd-webui-controlnet" in extensions:
payload = add_controlnet(payload, gen_type)
payload = add_xyz_plot(payload, gen_type)
response = requests.post(url=f"{url}/sdapi/v1/{gen_type}", json=payload)
images_base64 = response.json()["images"]
for j in range(len(images_base64)):
image_pil = base64_to_pil(images_base64)
images.append(image_pil)
image_info = get_png_info(image_pil)
images_info.append(image_info)
if image_info == "None":
if save_images == "Yes":
if gen_type in xyz_args:
save_image(image_pil, "XYZ_Plot", "grid")
else:
save_image(image_pil, "ControlNet", "detect")
else:
seed = re.findall("Seed: +", image_info).split(": ")[-1]
if save_images == "Yes":
save_image(image_pil, sd_model, seed)
seed = int(seed) + 1
progress((i+1)/batch_count, desc=f"Batch count: {(i+1)}/{batch_count}")
if interrupt == True:
return images, images_info, datetime.datetime.now()
return images, images_info, datetime.datetime.now()
def gen_clear_geninfo():
return None
def gen_update_geninfo(images_info):
if images_info == [] or images_info is None:
return None
return images_info
def gen_update_selected_geninfo(images_info, evt: gr.SelectData):
return images_info
def gen_blocks(gen_type):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
prompt = gr.Textbox(placeholder="Prompt", show_label=False, value=default["prompt"], lines=3)
negative_prompt = gr.Textbox(placeholder="Negative prompt", show_label=False, value=default["negative_prompt"], lines=3)
if gen_type == "txt2img":
input_image = gr.Image(visible=False)
else:
input_image = gr.Image(type="pil")
with gr.Tab("Generation"):
with gr.Row():
sd_model = gr.Dropdown(sd_models, label="SD Model", value=sd_models)
sd_vae = gr.Dropdown(sd_vaes, label="SD VAE", value=sd_vaes)
clip_skip = gr.Slider(minimum=1, maximum=12, step=1, label="Clip skip", value=default["clip_skip"])
with gr.Row():
sampler_name = gr.Dropdown(samplers, label="Sampling method", value=samplers)
scheduler = gr.Dropdown(schedulers, label="Schedule type", value=schedulers)
steps = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps", value=default["steps"])
with gr.Row():
width = gr.Slider(minimum=64, maximum=2048, step=default["size_step"], label="Width", value=default["width"])
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label="Batch size", value=1)
with gr.Row():
height = gr.Slider(minimum=64, maximum=2048, step=default["size_step"], label="Height", value=default["height"])
batch_count = gr.Slider(minimum=1, maximum=100, step=1, label="Batch count", value=1)
with gr.Row():
cfg_scale = gr.Slider(minimum=1, maximum=30, step=0.5, label="CFG Scale", value=default["cfg"])
if gen_type == "txt2img":
denoising_strength = gr.Slider(minimum=-1, maximum=1, step=1, value=-1, visible=False)
else:
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Denoising strength", value=0.7)
with gr.Row():
randn_source = gr.Dropdown(["CPU", "GPU"], label="RNG", value="CPU")
seed = gr.Textbox(label="Seed", value=-1)
if "adetailer" in extensions:
with gr.Tab("ADetailer"):
if gen_type == "img2img":
with gr.Row():
ad_skip_img2img = gr.Checkbox(label="Skip img2img", visible=True)
ad_skip_img2img.change(fn=ad_update_skip_img2img, inputs=ad_skip_img2img, outputs=None)
for i in range(default["ad_nums"]):
with gr.Tab(f"ADetailer {ordinal(i + 1)}"): ad_blocks(i, gen_type)
if "sd-webui-controlnet" in extensions:
with gr.Tab("ControlNet"):
for i in range(default["cn_nums"]):
with gr.Tab(f"ControlNet Unit {i}"): cn_blocks(i, gen_type)
if not loras == [] or not embeddings == []:
with gr.Tab("Extra Networks"):
if not loras == []:
lora = gr.Dropdown(loras, label="Lora", multiselect=True, interactive=True)
lora.change(fn=add_lora, inputs=, outputs=prompt)
if not embeddings == []:
embedding = gr.Dropdown(embeddings, label="Embedding", multiselect=True, interactive=True)
embedding.change(fn=add_embedding, inputs=, outputs=negative_prompt)
with gr.Tab("X/Y/Z plot"): xyz_blocks(gen_type)
with gr.Column():
with gr.Row():
btn = gr.Button("Generate | 生成", elem_id="button")
btn2 = gr.Button("Interrupt | 终止")
gallery = gr.Gallery(preview=True, height=default["gallery_height"])
image_geninfo = gr.Markdown()
images_geninfo = gr.State()
update_geninfo = gr.Textbox(visible=False)
gen_inputs =
btn.click(fn=gen_clear_geninfo, inputs=None, outputs=image_geninfo)
btn.click(fn=generate, inputs=gen_inputs, outputs=)
btn2.click(fn=post_interrupt, inputs=None, outputs=None)
gallery.select(fn=gen_update_selected_geninfo, inputs=images_geninfo, outputs=image_geninfo)
update_geninfo.change(fn=gen_update_geninfo, inputs=images_geninfo, outputs=image_geninfo)
return demo
def extras(input_image, upscaler_1, upscaler_2, upscaling_resize, extras_upscaler_2_visibility, enable_gfpgan, gfpgan_visibility, enable_codeformer, codeformer_visibility, codeformer_weight):
if input_image is None:
return None
input_image = pil_to_base64(input_image)
if enable_gfpgan == False:
gfpgan_visibility = 0
if enable_codeformer == False:
codeformer_visibility = 0
payload = {
"gfpgan_visibility": gfpgan_visibility,
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
"upscaling_resize": upscaling_resize,
"upscaler_1": upscaler_1,
"upscaler_2": upscaler_2,
"extras_upscaler_2_visibility": extras_upscaler_2_visibility,
"image": input_image
}
response = requests.post(url=f"{url}/sdapi/v1/extra-single-image", json=payload)
images_base64 = response.json()["image"]
image_pil = base64_to_pil(images_base64)
if save_images == "Yes":
save_image(image_pil, "Extras", "image")
return image_pil
def extras_blocks():
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil")
with gr.Row():
upscaler_1 = gr.Dropdown(upscalers, label="Upscaler 1", value="R-ESRGAN 4x+")
upscaler_2 = gr.Dropdown(upscalers, label="Upscaler 2", value="None")
with gr.Row():
upscaling_resize = gr.Slider(minimum=1, maximum=8, step=0.05, label="Scale by", value=4)
extras_upscaler_2_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="Upscaler 2 visibility", value=0)
enable_gfpgan = gr.Checkbox(label="Enable GFPGAN")
gfpgan_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="GFPGAN Visibility", value=1)
enable_codeformer = gr.Checkbox(label="Enable CodeFormer")
codeformer_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="CodeFormer Visibility", value=1)
codeformer_weight = gr.Slider(minimum=0, maximum=1, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0)
with gr.Column():
with gr.Row():
btn = gr.Button("Generate | 生成", elem_id="button")
btn2 = gr.Button("Interrupt | 终止")
extra_image = gr.Image(label="Extras image")
btn.click(fn=extras, inputs=, outputs=extra_image)
btn2.click(fn=post_interrupt, inputs=None, outputs=None)
return demo
def get_png_info(image_pil):
image_info=[]
if image_pil is None:
return None
for key, value in image_pil.info.items():
image_info.append(value)
if not image_info == []:
image_info = image_info
image_info = re.sub(r"<", "\<", image_info)
image_info = re.sub(r">", "\>", image_info)
image_info = re.sub(r"\n", "<br>", image_info)
else:
image_info = "None"
return image_info
def png_info_blocks():
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_image = gr.Image(value=None, type="pil")
with gr.Column():
png_info = gr.Markdown()
input_image.change(fn=get_png_info, inputs=input_image, outputs=png_info)
return demo
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:
with gr.Tab("txt2img"): gen_blocks("txt2img")
with gr.Tab("img2img"): gen_blocks("img2img")
with gr.Tab("Extras"): extras_blocks()
with gr.Tab("PNG Info"): png_info_blocks()
demo.queue(concurrency_count=100).launch(inbrowser=True)
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