task_stablepy = {
'txt2img': 'txt2img',
'img2img': 'img2img',
'inpaint': 'inpaint',
'sdxl_canny T2I Adapter': 'sdxl_canny',
'sdxl_sketch T2I Adapter': 'sdxl_sketch',
'sdxl_lineart T2I Adapter': 'sdxl_lineart',
'sdxl_depth-midas T2I Adapter': 'sdxl_depth-midas',
'sdxl_openpose T2I Adapter': 'sdxl_openpose',
'sd_openpose ControlNet': 'openpose',
'sd_canny ControlNet': 'canny',
'sd_mlsd ControlNet': 'mlsd',
'sd_scribble ControlNet': 'scribble',
'sd_softedge ControlNet': 'softedge',
'sd_segmentation ControlNet': 'segmentation',
'sd_depth ControlNet': 'depth',
'sd_normalbae ControlNet': 'normalbae',
'sd_lineart ControlNet': 'lineart',
'sd_lineart_anime ControlNet': 'lineart_anime',
'sd_shuffle ControlNet': 'shuffle',
'sd_ip2p ControlNet': 'ip2p',
}
task_model_list = list(task_stablepy.keys())
#######################
# UTILS
#######################
import spaces
import os
from stablepy import Model_Diffusers
from stablepy.diffusers_vanilla.model import scheduler_names
from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
import torch
import re
preprocessor_controlnet = {
"openpose": [
"Openpose",
"None",
],
"scribble": [
"HED",
"Pidinet",
"None",
],
"softedge": [
"Pidinet",
"HED",
"HED safe",
"Pidinet safe",
"None",
],
"segmentation": [
"UPerNet",
"None",
],
"depth": [
"DPT",
"Midas",
"None",
],
"normalbae": [
"NormalBae",
"None",
],
"lineart": [
"Lineart",
"Lineart coarse",
"LineartAnime",
"None",
"None (anime)",
],
"shuffle": [
"ContentShuffle",
"None",
],
"canny": [
"Canny"
],
"mlsd": [
"MLSD"
],
"ip2p": [
"ip2p"
]
}
def download_things(directory, url, hf_token="", civitai_api_key=""):
url = url.strip()
if "drive.google.com" in url:
original_dir = os.getcwd()
os.chdir(directory)
os.system(f"gdown --fuzzy {url}")
os.chdir(original_dir)
elif "huggingface.co" in url:
url = url.replace("?download=true", "")
if "/blob/" in url:
url = url.replace("/blob/", "/resolve/")
user_header = f'"Authorization: Bearer {hf_token}"'
if hf_token:
os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
else:
os.system (f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
elif "civitai.com" in url:
if "?" in url:
url = url.split("?")[0]
if civitai_api_key:
url = url + f"?token={civitai_api_key}"
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
else:
print("\033[91mYou need an API key to download Civitai models.\033[0m")
else:
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
def get_model_list(directory_path):
model_list = []
valid_extensions = {'.ckpt' , '.pt', '.pth', '.safetensors', '.bin'}
for filename in os.listdir(directory_path):
if os.path.splitext(filename)[1] in valid_extensions:
name_without_extension = os.path.splitext(filename)[0]
file_path = os.path.join(directory_path, filename)
# model_list.append((name_without_extension, file_path))
model_list.append(file_path)
print('\033[34mFILE: ' + file_path + '\033[0m')
return model_list
def process_string(input_string):
parts = input_string.split('/')
if len(parts) == 2:
first_element = parts[1]
complete_string = input_string
result = (first_element, complete_string)
return result
else:
return None
directory_models = 'models'
os.makedirs(directory_models, exist_ok=True)
directory_loras = 'loras'
os.makedirs(directory_loras, exist_ok=True)
directory_vaes = 'vaes'
os.makedirs(directory_vaes, exist_ok=True)
# - **Download SD 1.5 Models**
download_model = "https://huggingface.co/frankjoshua/toonyou_beta6/resolve/main/toonyou_beta6.safetensors"
# - **Download VAEs**
download_vae = "https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/anything_fp16.safetensors"
# - **Download LoRAs**
download_lora = "https://civitai.com/api/download/models/97655, https://civitai.com/api/download/models/124358"
load_diffusers_format_model = ['stabilityai/stable-diffusion-xl-base-1.0', 'runwayml/stable-diffusion-v1-5']
CIVITAI_API_KEY = ""
hf_token = ""
# Download stuffs
for url in [url.strip() for url in download_model.split(',')]:
if not os.path.exists(f"./models/{url.split('/')[-1]}"):
download_things(directory_models, url, hf_token, CIVITAI_API_KEY)
for url in [url.strip() for url in download_vae.split(',')]:
if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
download_things(directory_vaes, url, hf_token, CIVITAI_API_KEY)
for url in [url.strip() for url in download_lora.split(',')]:
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
# Download Embeddings
directory_embeds = 'embedings'
os.makedirs(directory_embeds, exist_ok=True)
download_embeds = [
'https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt.pt',
'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
'https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors',
'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
'https://huggingface.co/embed/negative/resolve/main/bad-artist.pt',
'https://huggingface.co/embed/negative/resolve/main/ng_deepnegative_v1_75t.pt',
'https://huggingface.co/embed/negative/resolve/main/bad-artist-anime.pt',
'https://huggingface.co/embed/negative/resolve/main/bad-image-v2-39000.pt',
'https://huggingface.co/embed/negative/resolve/main/verybadimagenegative_v1.3.pt',
]
for url_embed in download_embeds:
if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
download_things(directory_embeds, url_embed, hf_token, CIVITAI_API_KEY)
# Build list models
embed_list = get_model_list(directory_embeds)
model_list = get_model_list(directory_models)
model_list = load_diffusers_format_model + model_list
lora_model_list = get_model_list(directory_loras)
lora_model_list.insert(0, "None")
vae_model_list = get_model_list(directory_vaes)
vae_model_list.insert(0, "None")
print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
upscaler_dict_gui = {
None : None,
"Lanczos" : "Lanczos",
"Nearest" : "Nearest",
"RealESRGAN_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
"RealESRNet_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
"RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
"RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
"realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
"realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
"realesr-general-wdn-x4v3" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
"4x-UltraSharp" : "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
"4x_foolhardy_Remacri" : "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
"Remacri4xExtraSmoother" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
"AnimeSharp4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
"lollypop" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth",
"RealisticRescaler4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth",
"NickelbackFS4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth"
}
def extract_parameters(input_string):
parameters = {}
input_string = input_string.replace("\n", "")
if not "Negative prompt:" in input_string:
print("Negative prompt not detected")
parameters["prompt"] = input_string
return parameters
parm = input_string.split("Negative prompt:")
parameters["prompt"] = parm[0]
if not "Steps:" in parm[1]:
print("Steps not detected")
parameters["neg_prompt"] = parm[1]
return parameters
parm = parm[1].split("Steps:")
parameters["neg_prompt"] = parm[0]
input_string = "Steps:" + parm[1]
# Extracting Steps
steps_match = re.search(r'Steps: (\d+)', input_string)
if steps_match:
parameters['Steps'] = int(steps_match.group(1))
# Extracting Size
size_match = re.search(r'Size: (\d+x\d+)', input_string)
if size_match:
parameters['Size'] = size_match.group(1)
width, height = map(int, parameters['Size'].split('x'))
parameters['width'] = width
parameters['height'] = height
# Extracting other parameters
other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string)
for param in other_parameters:
parameters[param[0]] = param[1].strip('"')
return parameters
#######################
# GUI
#######################
import spaces
import gradio as gr
from PIL import Image
import IPython.display
import time, json
from IPython.utils import capture
import logging
logging.getLogger("diffusers").setLevel(logging.ERROR)
import diffusers
diffusers.utils.logging.set_verbosity(40)
import warnings
warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
from stablepy import logger
logger.setLevel(logging.DEBUG)
class GuiSD:
def __init__(self):
self.model = None
@spaces.GPU
def infer(self, model, pipe_params):
images, image_list = model(**pipe_params)
return images
# @spaces.GPU
def generate_pipeline(
self,
prompt,
neg_prompt,
num_images,
steps,
cfg,
clip_skip,
seed,
lora1,
lora_scale1,
lora2,
lora_scale2,
lora3,
lora_scale3,
lora4,
lora_scale4,
lora5,
lora_scale5,
sampler,
img_height,
img_width,
model_name,
vae_model,
task,
image_control,
preprocessor_name,
preprocess_resolution,
image_resolution,
style_prompt, # list []
style_json_file,
image_mask,
strength,
low_threshold,
high_threshold,
value_threshold,
distance_threshold,
controlnet_output_scaling_in_unet,
controlnet_start_threshold,
controlnet_stop_threshold,
textual_inversion,
syntax_weights,
loop_generation,
leave_progress_bar,
disable_progress_bar,
image_previews,
display_images,
save_generated_images,
image_storage_location,
retain_compel_previous_load,
retain_detailfix_model_previous_load,
retain_hires_model_previous_load,
t2i_adapter_preprocessor,
t2i_adapter_conditioning_scale,
t2i_adapter_conditioning_factor,
upscaler_model_path,
upscaler_increases_size,
esrgan_tile,
esrgan_tile_overlap,
hires_steps,
hires_denoising_strength,
hires_sampler,
hires_prompt,
hires_negative_prompt,
hires_before_adetailer,
hires_after_adetailer,
xformers_memory_efficient_attention,
freeu,
generator_in_cpu,
adetailer_inpaint_only,
adetailer_verbose,
adetailer_sampler,
adetailer_active_a,
prompt_ad_a,
negative_prompt_ad_a,
strength_ad_a,
face_detector_ad_a,
person_detector_ad_a,
hand_detector_ad_a,
mask_dilation_a,
mask_blur_a,
mask_padding_a,
adetailer_active_b,
prompt_ad_b,
negative_prompt_ad_b,
strength_ad_b,
face_detector_ad_b,
person_detector_ad_b,
hand_detector_ad_b,
mask_dilation_b,
mask_blur_b,
mask_padding_b,
):
task = task_stablepy[task]
# First load
model_precision = torch.float16
if not self.model:
from stablepy import Model_Diffusers
print("Loading model...")
self.model = Model_Diffusers(
base_model_id=model_name,
task_name=task,
vae_model=vae_model if vae_model != "None" else None,
type_model_precision=model_precision
)
self.model.load_pipe(
model_name,
task_name=task,
vae_model=vae_model if vae_model != "None" else None,
type_model_precision=model_precision
)
if task != "txt2img" and not image_control:
raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")
if task == "inpaint" and not image_mask:
raise ValueError("No mask image found: Specify one in 'Image Mask'")
if upscaler_model_path in [None, "Lanczos", "Nearest"]:
upscaler_model = upscaler_model_path
else:
directory_upscalers = 'upscalers'
os.makedirs(directory_upscalers, exist_ok=True)
url_upscaler = upscaler_dict_gui[upscaler_model_path]
if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
download_things(directory_upscalers, url_upscaler, hf_token)
upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
print("No Textual inversion for SDXL")
logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
adetailer_params_A = {
"face_detector_ad" : face_detector_ad_a,
"person_detector_ad" : person_detector_ad_a,
"hand_detector_ad" : hand_detector_ad_a,
"prompt": prompt_ad_a,
"negative_prompt" : negative_prompt_ad_a,
"strength" : strength_ad_a,
# "image_list_task" : None,
"mask_dilation" : mask_dilation_a,
"mask_blur" : mask_blur_a,
"mask_padding" : mask_padding_a,
"inpaint_only" : adetailer_inpaint_only,
"sampler" : adetailer_sampler,
}
adetailer_params_B = {
"face_detector_ad" : face_detector_ad_b,
"person_detector_ad" : person_detector_ad_b,
"hand_detector_ad" : hand_detector_ad_b,
"prompt": prompt_ad_b,
"negative_prompt" : negative_prompt_ad_b,
"strength" : strength_ad_b,
# "image_list_task" : None,
"mask_dilation" : mask_dilation_b,
"mask_blur" : mask_blur_b,
"mask_padding" : mask_padding_b,
}
pipe_params = {
"prompt": prompt,
"negative_prompt": neg_prompt,
"img_height": img_height,
"img_width": img_width,
"num_images": num_images,
"num_steps": steps,
"guidance_scale": cfg,
"clip_skip": clip_skip,
"seed": seed,
"image": image_control,
"preprocessor_name": preprocessor_name,
"preprocess_resolution": preprocess_resolution,
"image_resolution": image_resolution,
"style_prompt": style_prompt if style_prompt else "",
"style_json_file": "",
"image_mask": image_mask, # only for Inpaint
"strength": strength, # only for Inpaint or ...
"low_threshold": low_threshold,
"high_threshold": high_threshold,
"value_threshold": value_threshold,
"distance_threshold": distance_threshold,
"lora_A": lora1 if lora1 != "None" else None,
"lora_scale_A": lora_scale1,
"lora_B": lora2 if lora2 != "None" else None,
"lora_scale_B": lora_scale2,
"lora_C": lora3 if lora3 != "None" else None,
"lora_scale_C": lora_scale3,
"lora_D": lora4 if lora4 != "None" else None,
"lora_scale_D": lora_scale4,
"lora_E": lora5 if lora5 != "None" else None,
"lora_scale_E": lora_scale5,
"textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [],
"syntax_weights": syntax_weights, # "Classic"
"sampler": sampler,
"xformers_memory_efficient_attention": xformers_memory_efficient_attention,
"gui_active": True,
"loop_generation": loop_generation,
"controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
"control_guidance_start": float(controlnet_start_threshold),
"control_guidance_end": float(controlnet_stop_threshold),
"generator_in_cpu": generator_in_cpu,
"FreeU": freeu,
"adetailer_A": adetailer_active_a,
"adetailer_A_params": adetailer_params_A,
"adetailer_B": adetailer_active_b,
"adetailer_B_params": adetailer_params_B,
"leave_progress_bar": leave_progress_bar,
"disable_progress_bar": disable_progress_bar,
"image_previews": image_previews,
"display_images": display_images,
"save_generated_images": save_generated_images,
"image_storage_location": image_storage_location,
"retain_compel_previous_load": retain_compel_previous_load,
"retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
"retain_hires_model_previous_load": retain_hires_model_previous_load,
"t2i_adapter_preprocessor": t2i_adapter_preprocessor,
"t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
"t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
"upscaler_model_path": upscaler_model,
"upscaler_increases_size": upscaler_increases_size,
"esrgan_tile": esrgan_tile,
"esrgan_tile_overlap": esrgan_tile_overlap,
"hires_steps": hires_steps,
"hires_denoising_strength": hires_denoising_strength,
"hires_prompt": hires_prompt,
"hires_negative_prompt": hires_negative_prompt,
"hires_sampler": hires_sampler,
"hires_before_adetailer": hires_before_adetailer,
"hires_after_adetailer": hires_after_adetailer
}
# print(pipe_params)
return self.infer(self.model, pipe_params)
sd_gen = GuiSD()
title_tab_one = "SD Interactive
"
title_tab_adetailer = "Adetailer
"
title_tab_hires = "High-resolution
"
title_tab_settings = "Settings
"
CSS ="""
.contain { display: flex; flex-direction: column; }
#component-0 { height: 100%; }
#gallery { flex-grow: 1; }
"""
with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
gr.Markdown("# 🧩 DiffuseCraft")
gr.Markdown(
f"""
### This demo uses [diffusers](https://github.com/huggingface/diffusers) to perform different tasks in image generation.
"""
)
with gr.Tab("Generation"):
with gr.Row():
with gr.Column(scale=2):
task_gui = gr.Dropdown(label="Task", choices=task_model_list, value=task_model_list[0])
model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0], allow_custom_value=True)
prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt")
neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt")
generate_button = gr.Button(value="GENERATE", variant="primary")
result_images = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
columns=[2],
rows=[3],
object_fit="contain",
# height="auto",
interactive=False,
preview=True,
selected_index=50,
)
with gr.Column(scale=1):
steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=30, label="Steps")
cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.5, label="CFG")
sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler a")
img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width")
clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
free_u_gui = gr.Checkbox(value=True, label="FreeU")
seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed")
num_images_gui = gr.Slider(minimum=1, maximum=8, step=1, value=1, label="Images")
prompt_s_options = [("Compel (default) format: (word)weight", "Compel"), ("Classic (sd1.5 long prompts) format: (word:weight)", "Classic")]
prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=prompt_s_options, value=prompt_s_options[0][1])
vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list)
with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True):
image_control = gr.Image(label="Image ControlNet/Inpaint/Img2img", type="filepath")
image_mask_gui = gr.Image(label="Image Mask", type="filepath")
strength_gui = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, value=0.35, label="Strength")
image_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution")
preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=preprocessor_controlnet["canny"])
def change_preprocessor_choices(task):
if task in preprocessor_controlnet.keys():
choices_task = preprocessor_controlnet[task]
else:
choices_task = preprocessor_controlnet["canny"]
return gr.update(choices=choices_task, value=choices_task[0])
task_gui.change(
change_preprocessor_choices,
[task_gui],
[preprocessor_name_gui],
)
preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocess Resolution")
low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="Canny low threshold")
high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="Canny high threshold")
value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="Hough value threshold (MLSD)")
distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="Hough distance threshold (MLSD)")
control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
with gr.Accordion("T2I adapter", open=False, visible=True):
t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1, label="Adapter Conditioning Scale")
adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55, label="Adapter Conditioning Factor (%)")
with gr.Accordion("LoRA", open=False, visible=False):
lora1_gui = gr.Dropdown(label="Lora1", choices=lora_model_list)
lora_scale_1_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 1")
lora2_gui = gr.Dropdown(label="Lora2", choices=lora_model_list)
lora_scale_2_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 2")
lora3_gui = gr.Dropdown(label="Lora3", choices=lora_model_list)
lora_scale_3_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 3")
lora4_gui = gr.Dropdown(label="Lora4", choices=lora_model_list)
lora_scale_4_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 4")
lora5_gui = gr.Dropdown(label="Lora5", choices=lora_model_list)
lora_scale_5_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1, label="Lora Scale 5")
with gr.Accordion("Styles", open=False, visible=True):
try:
style_names_found = sd_gen.model.STYLE_NAMES
except:
style_names_found = STYLE_NAMES
style_prompt_gui = gr.Dropdown(
style_names_found,
multiselect=True,
value=None,
label="Style Prompt",
interactive=True,
)
style_json_gui = gr.File(label="Style JSON File")
style_button = gr.Button("Load styles")
def load_json_style_file(json):
if not sd_gen.model:
gr.Info("First load the model")
return gr.update(value=None, choices=STYLE_NAMES)
sd_gen.model.load_style_file(json)
gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)
style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])
with gr.Accordion("Textual inversion", open=False, visible=False):
active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
with gr.Accordion("Hires fix", open=False, visible=True):
upscaler_keys = list(upscaler_dict_gui.keys())
upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=upscaler_keys, value=upscaler_keys[0])
upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.5, label="Upscale by")
esrgan_tile_gui = gr.Slider(minimum=0, value=100, maximum=500, step=1, label="ESRGAN Tile")
esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=10, label="ESRGAN Tile Overlap")
hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=["Use same sampler"] + scheduler_names[:-1], value="Use same sampler")
hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
with gr.Accordion("Detailfix", open=False, visible=False):
# Adetailer Inpaint Only
adetailer_inpaint_only_gui = gr.Checkbox(label="Inpaint only", value=True)
# Adetailer Verbose
adetailer_verbose_gui = gr.Checkbox(label="Verbose", value=False)
# Adetailer Sampler
adetailer_sampler_options = ["Use same sampler"] + scheduler_names[:-1]
adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=adetailer_sampler_options, value="Use same sampler")
with gr.Accordion("Detailfix A", open=False, visible=True):
# Adetailer A
adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=True)
person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
mask_dilation_a_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
mask_blur_a_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
mask_padding_a_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
with gr.Accordion("Detailfix B", open=False, visible=True):
# Adetailer B
adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=True)
person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
mask_dilation_b_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
mask_blur_b_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
mask_padding_b_gui = gr.Number(label="Mask padding:", value=32, minimum=1)
with gr.Accordion("Other settings", open=False, visible=False):
hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation")
leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
image_previews_gui = gr.Checkbox(value=False, label="Image Previews")
display_images_gui = gr.Checkbox(value=False, label="Display Images")
save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load")
retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load")
xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")
generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")
with gr.Tab("Inpaint mask maker", render=True):
def create_mask_now(img, invert):
import numpy as np
import time
time.sleep(0.5)
transparent_image = img["layers"][0]
# Extract the alpha channel
alpha_channel = np.array(transparent_image)[:, :, 3]
# Create a binary mask by thresholding the alpha channel
binary_mask = alpha_channel > 1
if invert:
print("Invert")
# Invert the binary mask so that the drawn shape is white and the rest is black
binary_mask = np.invert(binary_mask)
# Convert the binary mask to a 3-channel RGB mask
rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
# Convert the mask to uint8
rgb_mask = rgb_mask.astype(np.uint8) * 255
return img["background"], rgb_mask
with gr.Row():
with gr.Column(scale=2):
# image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
image_base = gr.ImageEditor(
sources=["upload", "clipboard"],
# crop_size="1:1",
# enable crop (or disable it)
# transforms=["crop"],
brush=gr.Brush(
default_size="16", # or leave it as 'auto'
color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
#default_color="black", # html names are supported
colors=[
"rgba(0, 0, 0, 1)", # rgb(a)
"rgba(0, 0, 0, 0.1)",
"rgba(255, 255, 255, 0.1)",
# "hsl(360, 120, 120)" # in fact any valid colorstring
]
),
eraser=gr.Eraser(default_size="16")
)
invert_mask = gr.Checkbox(value=False, label="Invert mask")
btn = gr.Button("Create mask")
with gr.Column(scale=1):
img_source = gr.Image(interactive=False)
img_result = gr.Image(label="Mask image", show_label=True, interactive=False)
btn_send = gr.Button("Send to the first tab")
btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])
def send_img(img_source, img_result):
return img_source, img_result
btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])
generate_button.click(
fn=sd_gen.generate_pipeline,
inputs=[
prompt_gui,
neg_prompt_gui,
num_images_gui,
steps_gui,
cfg_gui,
clip_skip_gui,
seed_gui,
lora1_gui,
lora_scale_1_gui,
lora2_gui,
lora_scale_2_gui,
lora3_gui,
lora_scale_3_gui,
lora4_gui,
lora_scale_4_gui,
lora5_gui,
lora_scale_5_gui,
sampler_gui,
img_height_gui,
img_width_gui,
model_name_gui,
vae_model_gui,
task_gui,
image_control,
preprocessor_name_gui,
preprocess_resolution_gui,
image_resolution_gui,
style_prompt_gui,
style_json_gui,
image_mask_gui,
strength_gui,
low_threshold_gui,
high_threshold_gui,
value_threshold_gui,
distance_threshold_gui,
control_net_output_scaling_gui,
control_net_start_threshold_gui,
control_net_stop_threshold_gui,
active_textual_inversion_gui,
prompt_syntax_gui,
loop_generation_gui,
leave_progress_bar_gui,
disable_progress_bar_gui,
image_previews_gui,
display_images_gui,
save_generated_images_gui,
image_storage_location_gui,
retain_compel_previous_load_gui,
retain_detailfix_model_previous_load_gui,
retain_hires_model_previous_load_gui,
t2i_adapter_preprocessor_gui,
adapter_conditioning_scale_gui,
adapter_conditioning_factor_gui,
upscaler_model_path_gui,
upscaler_increases_size_gui,
esrgan_tile_gui,
esrgan_tile_overlap_gui,
hires_steps_gui,
hires_denoising_strength_gui,
hires_sampler_gui,
hires_prompt_gui,
hires_negative_prompt_gui,
hires_before_adetailer_gui,
hires_after_adetailer_gui,
xformers_memory_efficient_attention_gui,
free_u_gui,
generator_in_cpu_gui,
adetailer_inpaint_only_gui,
adetailer_verbose_gui,
adetailer_sampler_gui,
adetailer_active_a_gui,
prompt_ad_a_gui,
negative_prompt_ad_a_gui,
strength_ad_a_gui,
face_detector_ad_a_gui,
person_detector_ad_a_gui,
hand_detector_ad_a_gui,
mask_dilation_a_gui,
mask_blur_a_gui,
mask_padding_a_gui,
adetailer_active_b_gui,
prompt_ad_b_gui,
negative_prompt_ad_b_gui,
strength_ad_b_gui,
face_detector_ad_b_gui,
person_detector_ad_b_gui,
hand_detector_ad_b_gui,
mask_dilation_b_gui,
mask_blur_b_gui,
mask_padding_b_gui,
],
outputs=[result_images],
queue=True,
)
app.queue() # default_concurrency_limit=40
app.launch(
# max_threads=40,
# share=False,
show_error=True,
# quiet=False,
debug=True,
# allowed_paths=["./assets/"],
)