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
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 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
## BEGIN MOD
from modutils import (
list_uniq,
download_private_repo,
get_model_id_list,
get_tupled_embed_list,
get_lora_model_list,
get_all_lora_tupled_list,
update_loras,
apply_lora_prompt,
set_prompt_loras,
get_my_lora,
upload_file_lora,
move_file_lora,
search_civitai_lora,
select_civitai_lora,
set_textual_inversion_prompt,
get_model_pipeline,
change_interface_mode,
get_t2i_model_info,
get_tupled_model_list,
save_gallery_images,
set_optimization,
set_sampler_settings,
set_quick_presets,
process_style_prompt,
optimization_list,
preset_styles,
preset_quality,
preset_sampler_setting,
)
from env import (
hf_token,
CIVITAI_API_KEY,
HF_LORA_ESSENTIAL_PRIVATE_REPO,
HF_VAE_PRIVATE_REPO,
HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO,
HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO,
directory_models,
directory_loras,
directory_vaes,
directory_embeds,
directory_embeds_sdxl,
directory_embeds_positive_sdxl,
load_diffusers_format_model,
download_model_list,
download_lora_list,
download_vae_list,
download_embeds,
)
# - **Download Models**
download_model = ", ".join(download_model_list)
# - **Download VAEs**
download_vae = ", ".join(download_vae_list)
# - **Download LoRAs**
download_lora = ", ".join(download_lora_list)
download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, directory_loras, True)
download_private_repo(HF_VAE_PRIVATE_REPO, directory_vaes, False)
load_diffusers_format_model = list_uniq(get_model_id_list() + load_diffusers_format_model)
## END MOD
# 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
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
## BEGIN MOD
lora_model_list = get_lora_model_list()
vae_model_list = get_model_list(directory_vaes)
vae_model_list.insert(0, "None")
download_private_repo(HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, directory_embeds_sdxl, False)
download_private_repo(HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, directory_embeds_positive_sdxl, False)
embed_sdxl_list = get_model_list(directory_embeds_sdxl) + get_model_list(directory_embeds_positive_sdxl)
def get_embed_list(pipeline_name):
return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list)
## END MOD
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")
## BEGIN MOD
from stablepy import logger
logger.setLevel(logging.CRITICAL)
from v2 import (
V2_ALL_MODELS,
v2_random_prompt,
v2_upsampling_prompt,
)
from utils import (
gradio_copy_text,
COPY_ACTION_JS,
gradio_copy_prompt,
V2_ASPECT_RATIO_OPTIONS,
V2_RATING_OPTIONS,
V2_LENGTH_OPTIONS,
V2_IDENTITY_OPTIONS
)
from tagger import (
predict_tags_wd,
convert_danbooru_to_e621_prompt,
remove_specific_prompt,
insert_recom_prompt,
insert_model_recom_prompt,
compose_prompt_to_copy,
translate_prompt,
select_random_character,
)
def description_ui():
gr.Markdown(
"""
## Danbooru Tags Transformer V2 Demo with WD Tagger
(Image =>) Prompt => Upsampled longer prompt
- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).
- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft)
"""
)
## END MOD
def info_html(json_data, title, subtitle):
return f"""
{title}
Details
{subtitle}
"""
class GuiSD:
def __init__(self):
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 infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
progress(0, desc="Start inference...")
images, image_list = model(**pipe_params)
progress(1, desc="Inference completed.")
if not isinstance(images, list): images = [images]
img = []
for image in images:
img.append((image, None))
return img
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
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,
progress=gr.Progress(track_tqdm=True),
):
progress(0, desc="Preparing inference...")
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 = []
## BEGIN MOD
global lora_model_list
lora_model_list = get_lora_model_list()
lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5 = \
set_prompt_loras(prompt, syntax_weights, lora1, lora_scale1, lora2, lora_scale2, lora3,
lora_scale3, lora4, lora_scale4, lora5, lora_scale5)
prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
## END MOD
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 = []
params_ip_msk = []
params_ip_model = []
params_ip_mode = []
params_ip_scale = []
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 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,
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,
)
## BEGIN MOD
# if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
# print("No Textual inversion for SDXL")
## END MOD
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,
## BEGIN MOD
"textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
## END MOD
"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,
}
# 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")
progress(0, desc="Preparation completed. Starting inference...")
info_state = f"PROCESSING "
info_state += ">"
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 self.infer_short(self.model, pipe_params), info_state
sd_gen = GuiSD()
## BEGIN MOD
CSS ="""
.gradio-container, #main {width:100%; height:100%; max-width:100%; padding-left:0; padding-right:0; margin-left:0; margin-right:0; !important;}
.contain {display: flex; flex-direction: column; !important;}
#component-0 {height: 100%; !important;}
#gallery {flex-grow: 1; !important;}
#model-info { text-align: center; }
"""
## END MOD
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)
## BEGIN MOD
with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", elem_id="main", css=CSS) as app:
gr.Markdown("# 🧩 DiffuseCraft Mod")
gr.Markdown(
f"""
This space is a modification of [r3gm's DiffuseCraft](https://huggingface.co/spaces/r3gm/DiffuseCraft).
"""
)
with gr.Row():
with gr.Tab("Generation"):
with gr.Column():
interface_mode_gui = gr.Radio(label="Quick settings", choices=["Simple", "Standard", "Fast", "LoRA"], value="Standard")
with gr.Accordion("Model and Task", open=False) as menu_model:
task_gui = gr.Dropdown(label="Task", choices=sdxl_task, value=task_model_list[0])
with gr.Group():
model_name_gui = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_tupled_model_list(model_list), value="votepurchase/animagine-xl-3.1", allow_custom_value=True)
model_info_gui = gr.Markdown(elem_id="model-info")
with gr.Row():
quick_model_type_gui = gr.Radio(label="Model Type", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
quick_genre_gui = gr.Radio(label="Genre", choices=["Anime", "Photo"], value="Anime", interactive=True)
quick_speed_gui = gr.Radio(label="Speed", choices=["Fast", "Standard", "Heavy"], value="Standard", interactive=True)
quick_aspect_gui = gr.Radio(label="Aspect Ratio", choices=["1:1", "3:4"], value="1:1", interactive=True)
with gr.Accordion(label="Advanced options", open=False):
quality_selector_gui = gr.Dropdown(label="Quality Tags Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
style_selector_gui = gr.Dropdown(label="Style Preset", interactive=True, choices=list(preset_styles.keys()), value="None")
sampler_selector_gui = gr.Dropdown(label="Sampler Quick Settings", interactive=True, choices=list(preset_sampler_setting.keys()), value="None")
optimization_gui = gr.Dropdown(label="Optimization for SDXL", choices=list(optimization_list.keys()), value="None", interactive=True)
with gr.Group():
with gr.Accordion("Prompt from Image", open=False) as menu_from_image:
input_image_gui = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
with gr.Accordion(label="Advanced options", open=False):
general_threshold_gui = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
character_threshold_gui = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
tag_type_gui = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
recom_prompt_gui = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
keep_tags_gui = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
generate_from_image_btn_gui = gr.Button(value="GENERATE TAGS FROM IMAGE")
prompt_gui = gr.Textbox(lines=6, placeholder="1girl, solo, ...", label="Prompt", show_copy_button=True)
with gr.Accordion("Negative prompt, etc.", open=False) as menu_negative:
neg_prompt_gui = gr.Textbox(lines=3, placeholder="lowres, (bad), ...", label="Negative prompt", show_copy_button=True)
translate_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
insert_prompt_gui = gr.Radio(label="Insert reccomended positive / negative prompt", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)
with gr.Accordion(label="Advanced options", open=False):
prompt_type_gui = gr.Radio(label="Convert tags to", choices=["danbooru", "e621"], value="e621", visible=False)
prompt_type_button = gr.Button(value="Convert prompt to Pony e621 style", size="sm", variant="secondary")
with gr.Row():
character_dbt = gr.Textbox(lines=1, placeholder="kafuu chino, ...", label="Character names")
series_dbt = gr.Textbox(lines=1, placeholder="Is the order a rabbit?, ...", label="Series names")
random_character_gui = gr.Button(value="Random character 🎲", size="sm", variant="secondary")
model_name_dbt = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0], visible=False)
aspect_ratio_dbt = gr.Radio(label="Aspect ratio", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
length_dbt = gr.Radio(label="Length", choices=list(V2_LENGTH_OPTIONS), value="very_long", visible=False)
identity_dbt = gr.Radio(label="Keep identity", choices=list(V2_IDENTITY_OPTIONS), value="lax", visible=False)
ban_tags_dbt = gr.Textbox(label="Ban tags", placeholder="alternate costumen, ...", value="futanari, censored, furry, furrification", visible=False)
copy_button_dbt = gr.Button(value="Copy to clipboard", visible=False)
rating_dbt = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
generate_db_random_button = gr.Button(value="EXTEND PROMPT 🎲")
with gr.Row():
set_random_seed = gr.Button(value="Seed 🎲", size="sm")
set_params_gui = gr.Button(value="Params ↙️", size="sm")
clear_prompt_gui = gr.Button(value="Clear 🗑️", size="sm")
generate_button = gr.Button(value="GENERATE IMAGE", size="lg", 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,
show_share_button=False,
show_download_button=True,
selected_index=50,
format="png",
)
result_images_files = gr.Files(interactive=False, visible=False)
actual_task_info = gr.HTML()
with gr.Accordion("Generation settings", open=False, visible=True) as menu_gen:
steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=28, label="Steps")
cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.0, label="CFG")
sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler a")
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")
with gr.Row():
clip_skip_gui = gr.Checkbox(value=False, label="Layer 2 Clip Skip")
free_u_gui = gr.Checkbox(value=False, label="FreeU")
seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed")
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=1, maximum=4, step=1, value=1, label="Images")
prompt_s_options = [
("Compel format: (word)weight", "Compel"),
("Classic format: (word:weight)", "Classic"),
("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[1][1])
vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
with gr.Accordion("Hires fix", open=False, visible=True) as menu_hires:
upscaler_keys = list(upscaler_dict_gui.keys())
upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=upscaler_keys, value=upscaler_dict_gui[upscaler_keys[0]])
upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.0, 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) as menu_lora:
lora1_gui = gr.Dropdown(label="LoRA1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
lora_scale_1_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 1")
with gr.Row():
with gr.Group():
lora1_info_gui = gr.Textbox(label="LoRA1 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
lora1_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
lora1_desc_gui = gr.Markdown(value="", visible=False)
lora2_gui = gr.Dropdown(label="LoRA2", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
lora_scale_2_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 2")
with gr.Row():
with gr.Group():
lora2_info_gui = gr.Textbox(label="LoRA2 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
lora2_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
lora2_desc_gui = gr.Markdown(value="", visible=False)
lora3_gui = gr.Dropdown(label="LoRA3", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
lora_scale_3_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 3")
with gr.Row():
with gr.Group():
lora3_info_gui = gr.Textbox(label="LoRA3 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
lora3_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
lora3_desc_gui = gr.Markdown(value="", visible=False)
lora4_gui = gr.Dropdown(label="LoRA4", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
lora_scale_4_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 4")
with gr.Row():
with gr.Group():
lora4_info_gui = gr.Textbox(label="LoRA4 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
lora4_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
lora4_desc_gui = gr.Markdown(value="", visible=False)
lora5_gui = gr.Dropdown(label="LoRA5", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
lora_scale_5_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 5")
with gr.Row():
with gr.Group():
lora5_info_gui = gr.Textbox(label="LoRA5 prompts", info="Example of prompt", value="None", show_copy_button=True, interactive=False, visible=False)
lora5_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
lora5_desc_gui = gr.Markdown(value="", visible=False)
with gr.Accordion("From URL", open=True, visible=True):
with gr.Row():
search_civitai_query_lora = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
search_civitai_basemodel_lora = gr.CheckboxGroup(label="Search LoRA for", choices=["Pony", "SD 1.5", "SDXL 1.0"], value=["Pony", "SDXL 1.0"])
search_civitai_button_lora = gr.Button("Search on Civitai")
search_civitai_result_lora = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
search_civitai_desc_lora = gr.Markdown(value="", visible=False)
text_lora = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
button_lora = gr.Button("Get and update lists of LoRAs")
with gr.Accordion("From Local", open=True, visible=True):
file_output_lora = gr.File(label="Uploaded LoRA", file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple", interactive=False, visible=False)
upload_button_lora = gr.UploadButton(label="Upload LoRA from your disk (very slow)", file_types=['.ckpt' , '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple")
with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True) as menu_i2i:
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"], value=preprocessor_controlnet["canny"][0])
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("Advanced functions", open=False, visible=True) as menu_advanced:
with gr.Accordion("IP-Adapter", open=False, visible=True) as menu_ipa:##############
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("T2I adapter", open=False, visible=True) as menu_t2i:
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) as menu_styles:
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=True) as menu_ti:
active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
use_textual_inversion_gui = gr.CheckboxGroup(choices=get_embed_list(get_model_pipeline(model_name_gui.value)) if active_textual_inversion_gui.value else [], value=None, label="Use Textual Invertion in prompt")
def update_textual_inversion_gui(active_textual_inversion_gui, model_name_gui):
return gr.update(choices=get_embed_list(get_model_pipeline(model_name_gui)) if active_textual_inversion_gui else [])
active_textual_inversion_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
model_name_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui])
with gr.Accordion("Detailfix", open=False, visible=True) as menu_detail:
# 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) as menu_other:
image_previews_gui = gr.Checkbox(value=False, 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=True, 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=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")
with gr.Accordion("Examples and help", open=False, visible=True) as menu_example:
gr.Examples(
examples=[
[
"1girl, souryuu asuka langley, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors, masterpiece, best quality, very aesthetic, absurdres",
"nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
1,
30,
7.5,
True,
-1,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"Euler a",
1152,
896,
"votepurchase/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",
None,
],
[
"solo, princess Zelda OOT, score_9, score_8_up, score_8, medium breasts, cute, eyelashes, cute small face, long hair, crown braid, hairclip, pointy ears, soft curvy body, 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,
"Euler a",
1024,
1024,
"votepurchase/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",
None,
],
[
"1girl, oomuro sakurako, yuru yuri, official art, school uniform, anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
"photo, deformed, black and white, realism, disfigured, low contrast, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
1,
40,
7.0,
True,
-1,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"Euler a",
1024,
1024,
"Raelina/Rae-Diffusion-XL-V2",
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",
None,
],
[
"1girl, akaza akari, yuru yuri, official art, anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
"photo, deformed, black and white, realism, disfigured, low contrast, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
1,
35,
7.0,
True,
-1,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"Euler a",
1024,
1024,
"Raelina/Raemu-XL-V4",
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",
None,
],
[
"yoshida yuuko, machikado mazoku, 1girl, solo, demon horns,horns, school uniform, long hair, open mouth, skirt, demon girl, ahoge, shiny, shiny hair, anime artwork",
"nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
1,
50,
7.,
True,
-1,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
None,
1.0,
"Euler a",
1024,
1024,
"votepurchase/animagine-xl-3.1",
None, # vae
"img2img",
"color_image.png", # img conttol
"Canny", # preprocessor
512, # preproc resolution
1024, # img resolution
None, # Style prompt
None, # Style json
None, # img Mask
0.6, # 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,
],
],
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,
elem_id="examples",
)
## END MOD
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])
## BEGIN MOD
interface_mode_gui.change(
change_interface_mode,
[interface_mode_gui],
[menu_model, menu_from_image, menu_negative, menu_gen, menu_hires, menu_lora, menu_advanced,
menu_example, task_gui, quick_speed_gui],
queue=False,
)
model_name_gui.change(get_t2i_model_info, [model_name_gui], [model_info_gui], queue=False)
gr.on(
triggers=[quick_model_type_gui.change, quick_genre_gui.change, quick_speed_gui.change, quick_aspect_gui.change],
fn=set_quick_presets,
inputs=[quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui],
outputs=[quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui, insert_prompt_gui],
queue=False,
trigger_mode="once",
)
gr.on(
triggers=[quality_selector_gui.change, style_selector_gui.change, insert_prompt_gui.change],
fn=process_style_prompt,
inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui],
outputs=[prompt_gui, neg_prompt_gui, quick_model_type_gui],
queue=False,
trigger_mode="once",
)
sampler_selector_gui.change(set_sampler_settings, [sampler_selector_gui], [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui], queue=False)
optimization_gui.change(set_optimization, [optimization_gui, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], [steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], queue=False)
gr.on(
triggers=[lora1_gui.change, lora_scale_1_gui.change, lora2_gui.change, lora_scale_2_gui.change,
lora3_gui.change, lora_scale_3_gui.change, lora4_gui.change, lora_scale_4_gui.change,
lora5_gui.change, lora_scale_5_gui.change, prompt_syntax_gui.change],
fn=update_loras,
inputs=[prompt_gui, prompt_syntax_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],
outputs=[prompt_gui, lora1_gui, lora_scale_1_gui, lora1_info_gui, lora1_copy_gui, lora1_desc_gui,
lora2_gui, lora_scale_2_gui, lora2_info_gui, lora2_copy_gui, lora2_desc_gui,
lora3_gui, lora_scale_3_gui, lora3_info_gui, lora3_copy_gui, lora3_desc_gui,
lora4_gui, lora_scale_4_gui, lora4_info_gui, lora4_copy_gui, lora4_desc_gui,
lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui],
queue=False,
trigger_mode="once",
)
lora1_copy_gui.click(apply_lora_prompt, [prompt_gui, lora1_info_gui], [prompt_gui], queue=False)
lora2_copy_gui.click(apply_lora_prompt, [prompt_gui, lora2_info_gui], [prompt_gui], queue=False)
lora3_copy_gui.click(apply_lora_prompt, [prompt_gui, lora3_info_gui], [prompt_gui], queue=False)
lora4_copy_gui.click(apply_lora_prompt, [prompt_gui, lora4_info_gui], [prompt_gui], queue=False)
lora5_copy_gui.click(apply_lora_prompt, [prompt_gui, lora5_info_gui], [prompt_gui], queue=False)
gr.on(
triggers=[search_civitai_button_lora.click, search_civitai_query_lora.submit],
fn=search_civitai_lora,
inputs=[search_civitai_query_lora, search_civitai_basemodel_lora],
outputs=[search_civitai_result_lora, search_civitai_desc_lora, search_civitai_button_lora, search_civitai_query_lora],
queue=True,
scroll_to_output=True,
)
search_civitai_result_lora.change(select_civitai_lora, [search_civitai_result_lora], [text_lora, search_civitai_desc_lora], queue=False, scroll_to_output=True)
button_lora.click(get_my_lora, [text_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui], scroll_to_output=True)
upload_button_lora.upload(upload_file_lora, [upload_button_lora], [file_output_lora, upload_button_lora]).success(
move_file_lora, [file_output_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui], scroll_to_output=True)
use_textual_inversion_gui.change(set_textual_inversion_prompt, [use_textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui], [prompt_gui, neg_prompt_gui])
generate_from_image_btn_gui.click(
lambda: ("", "", ""), None, [series_dbt, character_dbt, prompt_gui], queue=False,
).success(
predict_tags_wd,
[input_image_gui, prompt_gui, image_algorithms, general_threshold_gui, character_threshold_gui],
[series_dbt, character_dbt, prompt_gui, copy_button_dbt],
).success(
compose_prompt_to_copy, [character_dbt, series_dbt, prompt_gui], [prompt_gui], queue=False,
).success(
remove_specific_prompt, [prompt_gui, keep_tags_gui], [prompt_gui], queue=False,
).success(
convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
).success(
insert_recom_prompt, [prompt_gui, neg_prompt_gui, recom_prompt_gui], [prompt_gui, neg_prompt_gui], queue=False,
)
prompt_type_button.click(convert_danbooru_to_e621_prompt, [prompt_gui, prompt_type_gui], [prompt_gui], queue=False)
random_character_gui.click(select_random_character, [series_dbt, character_dbt], [series_dbt, character_dbt], queue=False)
generate_db_random_button.click(
v2_random_prompt,
[prompt_gui, series_dbt, character_dbt,
rating_dbt, aspect_ratio_dbt, length_dbt, identity_dbt, ban_tags_dbt, model_name_dbt],
[prompt_gui, series_dbt, character_dbt],
).success(
convert_danbooru_to_e621_prompt, [prompt_gui, tag_type_gui], [prompt_gui], queue=False,
)
translate_prompt_button.click(translate_prompt, [prompt_gui], [prompt_gui], queue=False)
translate_prompt_button.click(translate_prompt, [character_dbt], [character_dbt], queue=False)
translate_prompt_button.click(translate_prompt, [series_dbt], [series_dbt], queue=False)
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="full",
).success(save_gallery_images, [result_images], [result_images, result_images_files, result_images_files], queue=False)
with gr.Tab("Danbooru Tags Transformer with WD Tagger", render=True):
with gr.Column(scale=2):
with gr.Group():
input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
with gr.Accordion(label="Advanced options", open=False):
general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
image_algorithms = gr.CheckboxGroup(["Use WD Tagger"], label="Algorithms", value=["Use WD Tagger"], visible=False)
keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
with gr.Group():
with gr.Row():
input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
pick_random_character = gr.Button(value="Random character 🎲", size="sm")
input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
input_tags_to_copy = gr.Textbox(value="", visible=False)
with gr.Row():
copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
with gr.Accordion(label="Advanced options", open=False):
input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")
input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
with gr.Row():
with gr.Group():
output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
with gr.Row():
copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
copy_prompt_btn = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
with gr.Group():
output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
with gr.Row():
copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
copy_prompt_btn_pony = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
description_ui()
translate_input_prompt_button.click(translate_prompt, inputs=[input_general], outputs=[input_general], queue=False)
translate_input_prompt_button.click(translate_prompt, inputs=[input_character], outputs=[input_character], queue=False)
translate_input_prompt_button.click(translate_prompt, inputs=[input_copyright], outputs=[input_copyright], queue=False)
generate_from_image_btn.click(
lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False,
).success(
predict_tags_wd,
[input_image, input_general, image_algorithms, general_threshold, character_threshold],
[input_copyright, input_character, input_general, copy_input_btn],
).success(
remove_specific_prompt, inputs=[input_general, keep_tags], outputs=[input_general], queue=False,
).success(
convert_danbooru_to_e621_prompt, inputs=[input_general, input_tag_type], outputs=[input_general], queue=False,
).success(
insert_recom_prompt, inputs=[input_general, dummy_np, recom_prompt], outputs=[input_general, dummy_np], queue=False,
).success(lambda: gr.update(interactive=True), None, [copy_prompt_btn_input], queue=False)
copy_input_btn.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
.success(gradio_copy_text, inputs=[input_tags_to_copy], js=COPY_ACTION_JS)
copy_prompt_btn_input.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy])\
.success(gradio_copy_prompt, inputs=[input_tags_to_copy], outputs=[prompt_gui])
pick_random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character])
generate_btn.click(
v2_upsampling_prompt,
[model_name, input_copyright, input_character, input_general,
input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
[output_text],
).success(
convert_danbooru_to_e621_prompt, inputs=[output_text, tag_type], outputs=[output_text_pony], queue=False,
).success(
insert_recom_prompt, inputs=[output_text, dummy_np, recom_animagine], outputs=[output_text, dummy_np], queue=False,
).success(
insert_recom_prompt, inputs=[output_text_pony, dummy_np, recom_pony], outputs=[output_text_pony, dummy_np], queue=False,
).success(lambda: (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)),
None, [copy_btn, copy_btn_pony, copy_prompt_btn, copy_prompt_btn_pony], queue=False)
copy_btn.click(gradio_copy_text, inputs=[output_text], js=COPY_ACTION_JS)
copy_btn_pony.click(gradio_copy_text, inputs=[output_text_pony], js=COPY_ACTION_JS)
copy_prompt_btn.click(gradio_copy_prompt, inputs=[output_text], outputs=[prompt_gui])
copy_prompt_btn_pony.click(gradio_copy_prompt, inputs=[output_text_pony], outputs=[prompt_gui])
gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires Space with GPU available.)", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1")
app.queue()
app.launch(
show_error=False,
debug=False,
)
## END MOD