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
import shutil
import random
from stablepy import (
CONTROLNET_MODEL_IDS,
VALID_TASKS,
T2I_PREPROCESSOR_NAME,
FLASH_LORA,
SCHEDULER_CONFIG_MAP,
scheduler_names,
IP_ADAPTER_MODELS,
IP_ADAPTERS_SD,
IP_ADAPTERS_SDXL,
REPO_IMAGE_ENCODER,
ALL_PROMPT_WEIGHT_OPTIONS,
SD15_TASKS,
SDXL_TASKS,
)
import urllib.parse
from config import (
MINIMUM_IMAGE_NUMBER,
MAXIMUM_IMAGE_NUMBER,
DEFAULT_NEGATIVE_PROMPT,
DEFAULT_POSITIVE_PROMPT
)
from models.vae import VAE_LIST as download_vae
from models.checkpoints import CHECKPOINT_LIST as download_model
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",
"Lineart (anime)",
"None",
"None (anime)",
],
"shuffle": [
"ContentShuffle",
"None",
],
"canny": [
"Canny"
],
"mlsd": [
"MLSD"
],
"ip2p": [
"ip2p"
]
}
task_stablepy = {
'txt2img': 'txt2img',
'img2img': 'img2img',
'inpaint': 'inpaint',
# 'canny T2I Adapter': 'sdxl_canny_t2i', # NO HAVE STEP CALLBACK PARAMETERS SO NOT WORKS WITH DIFFUSERS 0.29.0
# 'sketch T2I Adapter': 'sdxl_sketch_t2i',
# 'lineart T2I Adapter': 'sdxl_lineart_t2i',
# 'depth-midas T2I Adapter': 'sdxl_depth-midas_t2i',
# 'openpose T2I Adapter': 'sdxl_openpose_t2i',
'openpose ControlNet': 'openpose',
'canny ControlNet': 'canny',
'mlsd ControlNet': 'mlsd',
'scribble ControlNet': 'scribble',
'softedge ControlNet': 'softedge',
'segmentation ControlNet': 'segmentation',
'depth ControlNet': 'depth',
'normalbae ControlNet': 'normalbae',
'lineart ControlNet': 'lineart',
# 'lineart_anime ControlNet': 'lineart_anime',
'shuffle ControlNet': 'shuffle',
'ip2p ControlNet': 'ip2p',
'optical pattern ControlNet': 'pattern',
'tile realistic': 'sdxl_tile_realistic',
}
task_model_list = list(task_stablepy.keys())
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", "")
# url = urllib.parse.quote(url, safe=':/') # fix encoding
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 "
f"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: 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 LoRAs**
download_lora = (
"https://civitai.com/api/download/models/423719, "
"https://civitai.com/api/download/models/50503, "
"https://civitai.com/api/download/models/133160, "
"https://civitai.com/api/download/models/29332, "
"https://huggingface.co/Leopain/color/resolve/main/Coloring_book_-_LineArt.safetensors, "
"https://civitai.com/api/download/models/135867, "
"https://civitai.com/api/download/models/145907, "
"https://huggingface.co/Linaqruf/anime-detailer-xl-lora/resolve/main/anime-detailer-xl.safetensors?download=true, "
"https://huggingface.co/Linaqruf/style-enhancer-xl-lora/resolve/main/style-enhancer-xl.safetensors?download=true, "
"https://civitai.com/api/download/models/28609, "
"https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SD15-8steps-CFG-lora.safetensors?download=true, "
"https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SDXL-8steps-CFG-lora.safetensors?download=true, "
"https://civitai.com/api/download/models/30666 "
)
load_diffusers_format_model = [
'stabilityai/stable-diffusion-xl-base-1.0',
'cagliostrolab/animagine-xl-3.1',
'misri/epicrealismXL_v7FinalDestination',
'misri/juggernautXL_juggernautX',
'misri/zavychromaxl_v80',
'SG161222/RealVisXL_V4.0',
'misri/newrealityxlAllInOne_Newreality40',
'eienmojiki/Anything-XL',
'eienmojiki/Starry-XL-v5.2',
'gsdf/CounterfeitXL',
'kitty7779/ponyDiffusionV6XL',
'John6666/ebara-mfcg-pony-mix-v12-sdxl',
'John6666/t-ponynai3-v51-sdxl',
'yodayo-ai/kivotos-xl-2.0',
'yodayo-ai/holodayo-xl-2.1',
'digiplay/majicMIX_sombre_v2',
'digiplay/majicMIX_realistic_v6',
'digiplay/majicMIX_realistic_v7',
'digiplay/DreamShaper_8',
'digiplay/BeautifulArt_v1',
'digiplay/DarkSushi2.5D_v1',
'digiplay/darkphoenix3D_v1.1',
'digiplay/BeenYouLiteL11_diffusers',
'rubbrband/revAnimated_v2Rebirth',
'youknownothing/cyberrealistic_v50',
'votepurchase/counterfeitV30_v30',
'Meina/MeinaMix_V11',
'Meina/MeinaUnreal_V5',
'Meina/MeinaPastel_V7',
'rubbrband/realcartoon3d_v16',
'rubbrband/realcartoonRealistic_v14',
]
CIVITAI_API_KEY: str = os.environ.get("CIVITAI_API_KEY")
hf_token: str = os.environ.get("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/blob/main/bad_prompt_version2.pt',
'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.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")
def get_my_lora(link_url):
for url in [url.strip() for url in link_url.split(',')]:
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
new_lora_model_list = get_model_list(directory_loras)
new_lora_model_list.insert(0, "None")
return gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
),
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)
def info_html(json_data, title, subtitle):
return f"""
{title}
Details
{subtitle}
"""
class GuiSD:
def __init__(self, stream=True):
self.model = None
print("Loading model...")
self.model = Model_Diffusers(
base_model_id="cagliostrolab/animagine-xl-3.1",
task_name="txt2img",
vae_model=None,
type_model_precision=torch.float16,
retain_task_model_in_cache=False,
)
def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
yield f"Loading model: {model_name}"
vae_model = vae_model if vae_model != "None" else None
if model_name in model_list:
model_is_xl = "xl" in model_name.lower()
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
model_type = "SDXL" if model_is_xl else "SD 1.5"
incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
if incompatible_vae:
vae_model = None
self.model.load_pipe(
model_name,
task_name=task_stablepy[task],
vae_model=vae_model if vae_model != "None" else None,
type_model_precision=torch.float16,
retain_task_model_in_cache=False,
)
yield f"Model loaded: {model_name}"
@spaces.GPU(duration=60)
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,
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,
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,
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,
retain_task_cache_gui,
image_ip1,
mask_ip1,
model_ip1,
mode_ip1,
scale_ip1,
image_ip2,
mask_ip2,
model_ip2,
mode_ip2,
scale_ip2,
):
vae_model = vae_model if vae_model != "None" else None
loras_list = [lora1, lora2, lora3, lora4, lora5]
vae_msg = f"VAE: {vae_model}" if vae_model else ""
msg_lora = []
if model_name in model_list:
model_is_xl = "xl" in model_name.lower()
sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
model_type = "SDXL" if model_is_xl else "SD 1.5"
incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
if incompatible_vae:
msg_inc_vae = (
f"The selected VAE is for a {'SD 1.5' if model_is_xl else 'SDXL'} model, but you"
f" are using a {model_type} model. The default VAE "
"will be used."
)
gr.Info(msg_inc_vae)
vae_msg = msg_inc_vae
vae_model = None
for la in loras_list:
if la is not None and la != "None" and la in lora_model_list:
print(la)
lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}."
gr.Info(msg_inc_lora)
msg_lora.append(msg_inc_lora)
task = task_stablepy[task]
params_ip_img: list = []
params_ip_msk: list = []
params_ip_model: list = []
params_ip_mode: list = []
params_ip_scale: list = []
all_adapters = [
(image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
(image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
]
for imgip, mskip, modelip, modeip, scaleip in all_adapters:
if imgip:
params_ip_img.append(imgip)
if mskip:
params_ip_msk.append(mskip)
params_ip_model.append(modelip)
params_ip_mode.append(modeip)
params_ip_scale.append(scaleip)
# First load
model_precision = torch.float16
if not self.model:
from modelstream import Model_Diffusers2
print("Loading model...")
self.model = Model_Diffusers2(
base_model_id=model_name,
task_name=task,
vae_model=vae_model if vae_model != "None" else None,
type_model_precision=model_precision,
retain_task_model_in_cache=retain_task_cache_gui,
)
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]}"
logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
print("Config model:", model_name, vae_model, loras_list)
self.model.load_pipe(
model_name,
task_name=task,
vae_model=vae_model if vae_model != "None" else None,
type_model_precision=model_precision,
retain_task_model_in_cache=retain_task_cache_gui,
)
if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
print("No Textual inversion for SDXL")
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,
"ip_adapter_image": params_ip_img,
"ip_adapter_mask": params_ip_msk,
"ip_adapter_model": params_ip_model,
"ip_adapter_mode": params_ip_mode,
"ip_adapter_scale": params_ip_scale,
}
# print(pipe_params)
random_number = random.randint(1, 100)
if random_number < 25 and num_images < 3:
if (not upscaler_model and
steps < 45 and
task in ["txt2img", "img2img"] and
not adetailer_active_a and
not adetailer_active_b):
num_images *= 2
pipe_params["num_images"] = num_images
gr.Info("Num images x 2 🎉")
# Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
info_state = f"PROCESSING "
for img, seed, data in self.model(**pipe_params):
info_state += ">"
if data:
info_state = f"COMPLETED. Seeds: {str(seed)}"
if vae_msg:
info_state = info_state + "
" + vae_msg
if msg_lora:
info_state = info_state + "
" + "
".join(msg_lora)
yield img, info_state
sd_gen = GuiSD()
CSS = """
.contain { display: flex; flex-direction: column; }
#component-0 { height: 100%; }
#gallery { flex-grow: 1; }
"""
sdxl_task = [k for k, v in task_stablepy.items() if v in SDXL_TASKS]
sd_task = [k for k, v in task_stablepy.items() if v in SD15_TASKS]
def update_task_options(model_name, task_name):
if model_name in model_list:
if "xl" in model_name.lower():
new_choices = sdxl_task
else:
new_choices = sd_task
if task_name not in new_choices:
task_name = "txt2img"
return gr.update(value=task_name, choices=new_choices)
else:
return gr.update(value=task_name, choices=task_model_list)
with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
gr.Markdown("# 🧩 (Ivan) 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=sdxl_task,
value=task_model_list[-6] or 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 Positive prompt",
label="Prompt",
value=DEFAULT_POSITIVE_PROMPT
)
neg_prompt_gui = gr.Textbox(
lines=3,
placeholder="Enter Neg prompt",
label="Negative prompt",
value=DEFAULT_NEGATIVE_PROMPT
)
with gr.Row(equal_height=False):
set_params_gui = gr.Button(value="↙️")
clear_prompt_gui = gr.Button(value="🗑️")
set_random_seed = gr.Button(value="🎲")
generate_button = gr.Button(
value="GENERATE",
variant="primary"
)
model_name_gui.change(
update_task_options,
[model_name_gui, task_gui],
[task_gui],
)
load_model_gui = gr.HTML()
result_images = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
columns=[2],
rows=[2],
object_fit="contain",
# height="auto",
interactive=False,
preview=False,
selected_index=50,
)
actual_task_info = gr.HTML()
with gr.Column(scale=1):
steps_gui = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=43,
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="DPM++ 2M Karras"
)
img_width_gui = gr.Slider(
minimum=64,
maximum=4096,
step=8,
value=1024,
label="Img Width"
)
img_height_gui = gr.Slider(
minimum=64,
maximum=4096,
step=8,
value=1024,
label="Img Height"
)
seed_gui = gr.Number(
minimum=-1,
maximum=9999999999,
value=-1,
label="Seed"
)
with gr.Row():
clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
free_u_gui = gr.Checkbox(value=True, label="FreeU")
with gr.Row(equal_height=False):
def run_set_params_gui(base_prompt):
valid_receptors = { # default values
"prompt": gr.update(value=base_prompt),
"neg_prompt": gr.update(value=""),
"Steps": gr.update(value=30),
"width": gr.update(value=1024),
"height": gr.update(value=1024),
"Seed": gr.update(value=-1),
"Sampler": gr.update(value="Euler a"),
"scale": gr.update(value=7.5), # cfg
"skip": gr.update(value=True),
}
valid_keys = list(valid_receptors.keys())
parameters = extract_parameters(base_prompt)
for key, val in parameters.items():
# print(val)
if key in valid_keys:
if key == "Sampler":
if val not in scheduler_names:
continue
elif key == "skip":
if int(val) >= 2:
val = True
if key == "prompt":
if ">" in val and "<" in val:
val = re.sub(r'<[^>]+>', '', val)
print("Removed LoRA written in the prompt")
if key in ["prompt", "neg_prompt"]:
val = val.strip()
if key in ["Steps", "width", "height", "Seed"]:
val = int(val)
if key == "scale":
val = float(val)
if key == "Seed":
continue
valid_receptors[key] = gr.update(value=val)
# print(val, type(val))
# print(valid_receptors)
return [value for value in valid_receptors.values()]
set_params_gui.click(
run_set_params_gui, [prompt_gui], [
prompt_gui,
neg_prompt_gui,
steps_gui,
img_width_gui,
img_height_gui,
seed_gui,
sampler_gui,
cfg_gui,
clip_skip_gui,
],
)
def run_clear_prompt_gui():
return gr.update(value=""), gr.update(value="")
clear_prompt_gui.click(
run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
)
def run_set_random_seed():
return -1
set_random_seed.click(
run_set_random_seed, [], seed_gui
)
num_images_gui = gr.Slider(
minimum=MINIMUM_IMAGE_NUMBER,
maximum=MAXIMUM_IMAGE_NUMBER,
step=1,
value=1,
label="Images"
)
prompt_s_options = [
("Classic format: (word:weight)", "Classic"),
("Compel format: (word)weight", "Compel"),
("Classic-original format: (word:weight)", "Classic-original"),
("Classic-no_norm format: (word:weight)", "Classic-no_norm"),
("Classic-ignore", "Classic-ignore"),
("None", "None"),
]
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("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=1.4,
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("LoRA", open=False, visible=True):
lora1_gui = gr.Dropdown(
label="Lora1",
choices=lora_model_list
)
lora_scale_1_gui = gr.Slider(
minimum=-2,
maximum=2,
step=0.01,
value=0.33,
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=0.33,
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=0.33,
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=0.33,
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=0.33,
label="Lora Scale 5"
)
with gr.Accordion("From URL", open=False, visible=True):
text_lora = gr.Textbox(
label="URL",
placeholder="http://...my_lora_url.safetensors",
lines=1
)
button_lora = gr.Button("Get and update lists of LoRAs")
button_lora.click(
get_my_lora,
[text_lora],
[lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui]
)
with gr.Accordion("IP-Adapter", open=False, visible=True): ##############
IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
MODE_IP_OPTIONS = [
"original",
"style",
"layout",
"style+layout"
]
with gr.Accordion("IP-Adapter 1", open=False, visible=True):
image_ip1 = gr.Image(label="IP Image", type="filepath")
mask_ip1 = gr.Image(label="IP Mask", type="filepath")
model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS)
mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS)
scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
with gr.Accordion("IP-Adapter 2", open=False, visible=True):
image_ip2 = gr.Image(label="IP Image", type="filepath")
mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath")
model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS)
mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS)
scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
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=0.55,
label="Strength",
info="This option adjusts the level of changes for img2img and inpainting."
)
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):
task = task_stablepy[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("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("Detailfix", open=False, visible=True):
# 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=True):
image_previews_gui = gr.Checkbox(value=True, label="Image Previews")
hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")
with gr.Accordion("More settings", open=False, visible=False):
loop_generation_gui = gr.Slider(
minimum=1,
value=1,
label="Loop Generation"
)
retain_task_cache_gui = gr.Checkbox(
value=False,
label="Retain task model in cache"
)
leave_progress_bar_gui = gr.Checkbox(
value=True,
label="Leave Progress Bar"
)
disable_progress_bar_gui = gr.Checkbox(
value=False,
label="Disable Progress Bar"
)
display_images_gui = gr.Checkbox(
value=True,
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"
)
# example and Help Section
with gr.Accordion("Examples and help", open=False, visible=True):
gr.Markdown(
"""### Help:
- The current space runs on a ZERO GPU which is assigned for approximately 60 seconds; Therefore, \
if you submit expensive tasks, the operation may be canceled upon reaching the \
maximum allowed time with 'GPU TASK ABORTED'.
- Distorted or strange images often result from high prompt weights, \
so it's best to use low weights and scales, and consider using Classic variants like 'Classic-original'.
- For better results with Pony Diffusion, \
try using sampler DPM++ 1s or DPM2 with Compel or Classic prompt weights.
"""
)
gr.Markdown(
"""### The following examples perform specific tasks:
1. Generation with SDXL and upscale
2. Generation with SDXL
3. ControlNet Canny SDXL
4. Optical pattern (Optical illusion) SDXL
5. Convert an image to a coloring drawing
6. ControlNet OpenPose SD 1.5
- Different tasks can be performed, such as img2img or using the IP adapter, \
to preserve a person's appearance or a specific style based on an image.
"""
)
gr.Examples(
examples=[
[
"""
1girl, loli, serious, cocky, sporty, basketball jersey, lakers, basketball, half body,
(basketball court), sweaty, dribble, high detailed, sunny, day light,
score_9, score_8_up, score_7_up, very aesthetic,
layered, white hair, featuring soft waves and a slight outward curl at the ends,
parted in the middle, (short hair),
red glowing eyes, beautiful hazel red eyes, highly detailed eyes, thin eyebrows,
detailed black eyebrows,
long eyelashes,
detailed kornea,
fisheye,
blush,
parted lips, gorgeous lips, pink thin lips,
detailed ear, human ears, human ear, highly detailed ears, highly detailed ear, detailed ears,
perfect anatomy,
five fingers,
two hands,
short girl, narrow body, detailed face, petite,
medium boobs,
armpits, (naval),
""",
"""
(EasyNegative:1.05), easynegative, bad_prompt_version2, (poorly rendered), ugly, disfigured,
cross eyed, cloned face, bad symmetry, bad anatomy, low quality, blurry, text, watermark, logo,
signature, jpeg, artifacts, monochrome, paintings, oil, (hands:1.15), European Woman, woman,
noise, dark skin, (3d), By bad artist -neg,bhands-neg, canvas frame,
""",
1,
43,
7.5,
True,
-1,
"None",
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"DPM++ 2M Karras",
1152,
896,
"cagliostrolab/animagine-xl-3.1",
None, # vae
"txt2img",
None, # img conttol
"Canny", # preprocessor
512, # preproc resolution
1024, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.35, # strength
100, # low th canny
200, # high th canny
0.1, # value mstd
0.1, # distance mstd
1.0, # cn scale
0., # cn start
1., # cn end
False, # ti
"Classic",
"Nearest",
],
[
"""
score_9, score_8_up, score_8, medium breasts, cute, eyelashes , princess Zelda OOT, cute
small face, long hair, crown braid, hairclip, pointy ears, soft curvy body, solo,
looking at viewer, smile, blush, white dress, medium body, (((holding the Master Sword))),
standing, deep forest in the background
""",
"""
score_6, score_5, score_4, busty, ugly face, mutated hands, low res,
blurry face, black and white,
""",
1,
30,
5.,
True,
-1,
"None",
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"DPM++ 2M Karras",
1024,
1024,
"kitty7779/ponyDiffusionV6XL",
None, # vae
"txt2img",
None, # img conttol
"Canny", # preprocessor
512, # preproc resolution
1024, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.35, # strength
100, # low th canny
200, # high th canny
0.1, # value mstd
0.1, # distance mstd
1.0, # cn scale
0., # cn start
1., # cn end
False, # ti
"Classic",
"Nearest",
],
[
"""
((masterpiece)), best quality, blonde disco girl, detailed face, realistic face,
realistic hair, dynamic pose, pink pvc, intergalactic disco background,
pastel lights, dynamic contrast, airbrush, fine detail, 70s vibe, midriff
""",
"""
(worst quality:1.2), (bad quality:1.2), (poor quality:1.2), (missing fingers:1.2),
bad-artist-anime, bad-artist, bad-picture-chill-75v
""",
1,
48,
3.5,
True,
-1,
"None",
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"DPM++ 2M SDE Lu",
1024,
1024,
"misri/epicrealismXL_v7FinalDestination",
None, # vae
"canny ControlNet",
"image.webp", # img conttol
"Canny", # preprocessor
1024, # preproc resolution
1024, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.35, # strength
100, # low th canny
200, # high th canny
0.1, # value mstd
0.1, # distance mstd
1.0, # cn scale
0., # cn start
1., # cn end
False, # ti
"Classic",
None,
],
[
"cinematic scenery old city ruins",
"""
(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch),
(illustration, 3d, 2d, painting, cartoons, sketch, blurry, film grain, noise),
(low quality, worst quality:1.2)
""",
1,
50,
4.,
True,
-1,
"None",
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"DPM++ 2M Karras",
1024,
1024,
"misri/juggernautXL_juggernautX",
None, # vae
"optical pattern ControlNet",
"spiral_no_transparent.png", # img conttol
"Canny", # preprocessor
512, # preproc resolution
1024, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.35, # strength
100, # low th canny
200, # high th canny
0.1, # value mstd
0.1, # distance mstd
1.0, # cn scale
0.05, # cn start
0.75, # cn end
False, # ti
"Classic",
None,
],
[
"""
black and white, line art, coloring drawing, clean line art, black strokes, no background,
white, black, free lines, black scribbles, on paper,
A blend of comic book art and lineart full of black and white color, masterpiece,
high-resolution, trending on Pixiv fan box, palette knife, brush strokes, two-dimensional,
planar vector, T-shirt design, stickers, and T-shirt design, vector art, fantasy art,
Adobe Illustrator, hand-painted, digital painting, low polygon, soft lighting, aerial view,
isometric style, retro aesthetics, 8K resolution, black sketch lines, monochrome, invert color
""",
"""
color, red, green, yellow, colored, duplicate, blurry, abstract, disfigured, deformed,
animated, toy, figure, framed, 3d, bad art, poorly drawn, extra limbs, close up, b&w,
weird colors, blurry, watermark, blur haze, 2 heads, long neck, watermark, elongated body,
cropped image, out of frame, draft, deformed hands, twisted fingers, double image,
malformed hands, multiple heads, extra limb, ugly, poorly drawn hands, missing limb,
cut-off, over satured, grain, lowères, bad anatomy, poorly drawn face, mutation, mutated,
floating limbs, disconnected limbs, out of focus, long body, disgusting, extra fingers,
groos proportions, missing arms, mutated hands, cloned face, missing legs, ugly, tiling,
poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs,
disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, watermark, grainy,
signature, cut off, draft, deformed, blurry, bad anatomy, disfigured, poorly drawn face,
mutation, bluelish, blue
""",
1,
20,
4.,
True,
-1,
"loras/Coloring_book_-_LineArt.safetensors",
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"DPM++ 2M SDE Karras",
1024,
1024,
"cagliostrolab/animagine-xl-3.1",
None, # vae
"lineart ControlNet",
"color_image.png", # img conttol
"Lineart", # preprocessor
512, # preproc resolution
896, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.35, # strength
100, # low th canny
200, # high th canny
0.1, # value mstd
0.1, # distance mstd
1.0, # cn scale
0., # cn start
1., # cn end
False, # ti
"Compel",
None,
],
[
"1girl,face,curly hair,red hair,white background,",
"(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark,",
1,
38,
5.,
True,
-1,
"None",
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"DPM++ 2M SDE Karras",
512,
512,
"digiplay/majicMIX_realistic_v7",
None, # vae
"openpose ControlNet",
"image.webp", # img conttol
"Canny", # preprocessor
512, # preproc resolution
1024, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.35, # strength
100, # low th canny
200, # high th canny
0.1, # value mstd
0.1, # distance mstd
1.0, # cn scale
0., # cn start
0.9, # cn end
False, # ti
"Compel",
"Nearest",
],
],
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,
upscaler_model_path_gui,
],
outputs=[result_images],
cache_examples=False,
)
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.load_new_model,
inputs=[
model_name_gui,
vae_model_gui,
task_gui
],
outputs=[load_model_gui],
queue=True,
show_progress="minimal",
).success(
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,
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,
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,
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,
retain_task_cache_gui,
image_ip1,
mask_ip1,
model_ip1,
mode_ip1,
scale_ip1,
image_ip2,
mask_ip2,
model_ip2,
mode_ip2,
scale_ip2,
],
outputs=[result_images, actual_task_info],
queue=True,
show_progress="minimal",
)
app.queue()
app.launch(
show_error=True,
debug=True,
)