import spaces
import os
from stablepy import (
Model_Diffusers,
SCHEDULE_TYPE_OPTIONS,
SCHEDULE_PREDICTION_TYPE_OPTIONS,
check_scheduler_compatibility,
TASK_AND_PREPROCESSORS,
FACE_RESTORATION_MODELS,
)
from constants import (
DIRECTORY_MODELS,
DIRECTORY_LORAS,
DIRECTORY_VAES,
DIRECTORY_EMBEDS,
DIRECTORY_UPSCALERS,
DOWNLOAD_MODEL,
DOWNLOAD_VAE,
DOWNLOAD_LORA,
LOAD_DIFFUSERS_FORMAT_MODEL,
DIFFUSERS_FORMAT_LORAS,
DOWNLOAD_EMBEDS,
CIVITAI_API_KEY,
HF_TOKEN,
TASK_STABLEPY,
TASK_MODEL_LIST,
UPSCALER_DICT_GUI,
UPSCALER_KEYS,
PROMPT_W_OPTIONS,
WARNING_MSG_VAE,
SDXL_TASK,
MODEL_TYPE_TASK,
POST_PROCESSING_SAMPLER,
SUBTITLE_GUI,
HELP_GUI,
EXAMPLES_GUI_HELP,
EXAMPLES_GUI,
RESOURCES,
DIFFUSERS_CONTROLNET_MODEL,
)
from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
import torch
import re
from stablepy import (
scheduler_names,
IP_ADAPTERS_SD,
IP_ADAPTERS_SDXL,
)
import time
from PIL import ImageFile
from utils import (
download_things,
get_model_list,
extract_parameters,
get_my_lora,
get_model_type,
extract_exif_data,
create_mask_now,
download_diffuser_repo,
get_used_storage_gb,
delete_model,
progress_step_bar,
html_template_message,
escape_html,
)
from image_processor import preprocessor_tab
from datetime import datetime
import gradio as gr
import logging
import diffusers
import warnings
from stablepy import logger
from diffusers import FluxPipeline
# import urllib.parse
ImageFile.LOAD_TRUNCATED_IMAGES = True
torch.backends.cuda.matmul.allow_tf32 = True
# os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"
print(os.getenv("SPACES_ZERO_GPU"))
directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_UPSCALERS]
for directory in directories:
os.makedirs(directory, exist_ok=True)
# 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)
embed_list = [
(os.path.splitext(os.path.basename(emb))[0], emb) for emb in embed_list
]
single_file_model_list = get_model_list(DIRECTORY_MODELS)
model_list = LOAD_DIFFUSERS_FORMAT_MODEL + single_file_model_list
lora_model_list = get_model_list(DIRECTORY_LORAS)
lora_model_list.insert(0, "None")
lora_model_list = lora_model_list + DIFFUSERS_FORMAT_LORAS
vae_model_list = get_model_list(DIRECTORY_VAES)
vae_model_list.insert(0, "BakedVAE")
vae_model_list.insert(0, "None")
print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
flux_repo = "camenduru/FLUX.1-dev-diffusers"
flux_pipe = FluxPipeline.from_pretrained(
flux_repo,
transformer=None,
torch_dtype=torch.bfloat16,
).to("cuda")
components = flux_pipe.components
components.pop("transformer", None)
components.pop("scheduler", None)
delete_model(flux_repo)
# components = None
#######################
# GUI
#######################
logging.getLogger("diffusers").setLevel(logging.ERROR)
diffusers.utils.logging.set_verbosity(40)
warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
logger.setLevel(logging.DEBUG)
CSS = """
.contain { display: flex; flex-direction: column; }
#component-0 { height: 100%; }
#gallery { flex-grow: 1; }
#load_model { height: 50px; }
"""
class GuiSD:
def __init__(self, stream=True):
self.model = None
self.status_loading = False
self.sleep_loading = 4
self.last_load = datetime.now()
self.inventory = []
def update_storage_models(self, storage_floor_gb=24, required_inventory_for_purge=3):
while get_used_storage_gb() > storage_floor_gb:
if len(self.inventory) < required_inventory_for_purge:
break
removal_candidate = self.inventory.pop(0)
delete_model(removal_candidate)
def update_inventory(self, model_name):
if model_name not in single_file_model_list:
self.inventory = [
m for m in self.inventory if m != model_name
] + [model_name]
print(self.inventory)
def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):
# download link model > model_name
self.update_storage_models()
vae_model = vae_model if vae_model != "None" else None
model_type = get_model_type(model_name)
dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16
if not os.path.exists(model_name):
_ = download_diffuser_repo(
repo_name=model_name,
model_type=model_type,
revision="main",
token=True,
)
self.update_inventory(model_name)
for i in range(68):
if not self.status_loading:
self.status_loading = True
if i > 0:
time.sleep(self.sleep_loading)
print("Previous model ops...")
break
time.sleep(0.5)
print(f"Waiting queue {i}")
yield "Waiting queue"
self.status_loading = True
yield f"Loading model: {model_name}"
if vae_model == "BakedVAE":
vae_model = model_name
elif vae_model:
vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5"
if model_type != vae_type:
gr.Warning(WARNING_MSG_VAE)
print("Loading model...")
try:
start_time = time.time()
if self.model is None:
self.model = Model_Diffusers(
base_model_id=model_name,
task_name=TASK_STABLEPY[task],
vae_model=vae_model,
type_model_precision=dtype_model,
retain_task_model_in_cache=False,
controlnet_model=controlnet_model,
device="cpu",
env_components=components,
)
self.model.advanced_params(image_preprocessor_cuda_active=True)
else:
if self.model.base_model_id != model_name:
load_now_time = datetime.now()
elapsed_time = max((load_now_time - self.last_load).total_seconds(), 0)
if elapsed_time <= 9:
print("Waiting for the previous model's time ops...")
time.sleep(9 - elapsed_time)
self.model.device = torch.device("cpu")
self.model.load_pipe(
model_name,
task_name=TASK_STABLEPY[task],
vae_model=vae_model,
type_model_precision=dtype_model,
retain_task_model_in_cache=False,
controlnet_model=controlnet_model,
)
end_time = time.time()
self.sleep_loading = max(min(int(end_time - start_time), 10), 4)
except Exception as e:
self.last_load = datetime.now()
self.status_loading = False
self.sleep_loading = 4
raise e
self.last_load = datetime.now()
self.status_loading = False
yield f"Model loaded: {model_name}"
# @spaces.GPU(duration=59)
@torch.inference_mode()
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,
lora6,
lora_scale6,
lora7,
lora_scale7,
sampler,
schedule_type,
schedule_prediction_type,
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,
recolor_gamma_correction,
tile_blur_sigma,
controlnet_output_scaling_in_unet,
controlnet_start_threshold,
controlnet_stop_threshold,
textual_inversion,
syntax_weights,
upscaler_model_path,
upscaler_increases_size,
upscaler_tile_size,
upscaler_tile_overlap,
hires_steps,
hires_denoising_strength,
hires_sampler,
hires_prompt,
hires_negative_prompt,
hires_before_adetailer,
hires_after_adetailer,
hires_schedule_type,
hires_guidance_scale,
controlnet_model,
loop_generation,
leave_progress_bar,
disable_progress_bar,
image_previews,
display_images,
save_generated_images,
filename_pattern,
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,
guidance_rescale,
image_ip1,
mask_ip1,
model_ip1,
mode_ip1,
scale_ip1,
image_ip2,
mask_ip2,
model_ip2,
mode_ip2,
scale_ip2,
pag_scale,
face_restoration_model,
face_restoration_visibility,
face_restoration_weight,
):
info_state = html_template_message("Navigating latent space...")
yield info_state, gr.update(), gr.update()
vae_model = vae_model if vae_model != "None" else None
loras_list = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
vae_msg = f"VAE: {vae_model}" if vae_model else ""
msg_lora = ""
print("Config model:", model_name, vae_model, loras_list)
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),
]
if not hasattr(self.model.pipe, "transformer"):
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)
concurrency = 5
self.model.stream_config(concurrency=concurrency, latent_resize_by=1, vae_decoding=False)
if task != "txt2img" and not image_control:
raise ValueError("Reference image is required. Please upload one in 'Image ControlNet/Inpaint/Img2img'.")
if task in ["inpaint", "repaint"] and not image_mask:
raise ValueError("Mask image not found. Upload one in 'Image Mask' to proceed.")
if "https://" not in str(UPSCALER_DICT_GUI[upscaler_model_path]):
upscaler_model = upscaler_model_path
else:
url_upscaler = UPSCALER_DICT_GUI[upscaler_model_path]
if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"):
download_things(DIRECTORY_UPSCALERS, url_upscaler, HF_TOKEN)
upscaler_model = f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"
logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
adetailer_params_A = {
"face_detector_ad": face_detector_ad_a,
"person_detector_ad": person_detector_ad_a,
"hand_detector_ad": hand_detector_ad_a,
"prompt": prompt_ad_a,
"negative_prompt": negative_prompt_ad_a,
"strength": strength_ad_a,
# "image_list_task" : None,
"mask_dilation": mask_dilation_a,
"mask_blur": mask_blur_a,
"mask_padding": mask_padding_a,
"inpaint_only": adetailer_inpaint_only,
"sampler": adetailer_sampler,
}
adetailer_params_B = {
"face_detector_ad": face_detector_ad_b,
"person_detector_ad": person_detector_ad_b,
"hand_detector_ad": hand_detector_ad_b,
"prompt": prompt_ad_b,
"negative_prompt": negative_prompt_ad_b,
"strength": strength_ad_b,
# "image_list_task" : None,
"mask_dilation": mask_dilation_b,
"mask_blur": mask_blur_b,
"mask_padding": mask_padding_b,
}
pipe_params = {
"prompt": prompt,
"negative_prompt": neg_prompt,
"img_height": img_height,
"img_width": img_width,
"num_images": num_images,
"num_steps": steps,
"guidance_scale": cfg,
"clip_skip": clip_skip,
"pag_scale": float(pag_scale),
"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,
"recolor_gamma_correction": float(recolor_gamma_correction),
"tile_blur_sigma": int(tile_blur_sigma),
"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,
"lora_F": lora6 if lora6 != "None" else None,
"lora_scale_F": lora_scale6,
"lora_G": lora7 if lora7 != "None" else None,
"lora_scale_G": lora_scale7,
"textual_inversion": embed_list if textual_inversion else [],
"syntax_weights": syntax_weights, # "Classic"
"sampler": sampler,
"schedule_type": schedule_type,
"schedule_prediction_type": schedule_prediction_type,
"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,
"filename_pattern": filename_pattern,
"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,
"upscaler_tile_size": upscaler_tile_size,
"upscaler_tile_overlap": upscaler_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,
"hires_schedule_type": hires_schedule_type,
"hires_guidance_scale": hires_guidance_scale,
"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,
"face_restoration_model": face_restoration_model,
"face_restoration_visibility": face_restoration_visibility,
"face_restoration_weight": face_restoration_weight,
}
# kwargs for diffusers pipeline
if guidance_rescale:
pipe_params["guidance_rescale"] = guidance_rescale
self.model.device = torch.device("cuda:0")
if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * self.model.num_loras:
self.model.pipe.transformer.to(self.model.device)
print("transformer to cuda")
actual_progress = 0
info_images = gr.update()
for img, [seed, image_path, metadata] in self.model(**pipe_params):
info_state = progress_step_bar(actual_progress, steps)
actual_progress += concurrency
if image_path:
info_images = f"Seeds: {str(seed)}"
if vae_msg:
info_images = info_images + "
" + vae_msg
if "Cannot copy out of meta tensor; no data!" in self.model.last_lora_error:
msg_ram = "Unable to process the LoRAs due to high RAM usage; please try again later."
print(msg_ram)
msg_lora += f"
{msg_ram}"
for status, lora in zip(self.model.lora_status, self.model.lora_memory):
if status:
msg_lora += f"
Loaded: {lora}"
elif status is not None:
msg_lora += f"
Error with: {lora}"
if msg_lora:
info_images += msg_lora
info_images = info_images + "
" + "GENERATION DATA:
" + escape_html(metadata[-1]) + "
-------
"
download_links = "
".join(
[
f'Download Image {i + 1}'
for i, path in enumerate(image_path)
]
)
if save_generated_images:
info_images += f"
{download_links}"
info_state = "COMPLETE"
yield info_state, img, info_images
def dynamic_gpu_duration(func, duration, *args):
# @torch.inference_mode()
@spaces.GPU(duration=duration)
def wrapped_func():
yield from func(*args)
return wrapped_func()
@spaces.GPU
def dummy_gpu():
return None
def sd_gen_generate_pipeline(*args):
gpu_duration_arg = int(args[-1]) if args[-1] else 59
verbose_arg = int(args[-2])
load_lora_cpu = args[-3]
generation_args = args[:-3]
lora_list = [
None if item == "None" else item
for item in [args[7], args[9], args[11], args[13], args[15], args[17], args[19]]
]
lora_status = [None] * sd_gen.model.num_loras
msg_load_lora = "Updating LoRAs in GPU..."
if load_lora_cpu:
msg_load_lora = "Updating LoRAs in CPU..."
if lora_list != sd_gen.model.lora_memory and lora_list != [None] * sd_gen.model.num_loras:
yield msg_load_lora, gr.update(), gr.update()
# Load lora in CPU
if load_lora_cpu:
lora_status = sd_gen.model.load_lora_on_the_fly(
lora_A=lora_list[0], lora_scale_A=args[8],
lora_B=lora_list[1], lora_scale_B=args[10],
lora_C=lora_list[2], lora_scale_C=args[12],
lora_D=lora_list[3], lora_scale_D=args[14],
lora_E=lora_list[4], lora_scale_E=args[16],
lora_F=lora_list[5], lora_scale_F=args[18],
lora_G=lora_list[6], lora_scale_G=args[20],
)
print(lora_status)
sampler_name = args[21]
schedule_type_name = args[22]
_, _, msg_sampler = check_scheduler_compatibility(
sd_gen.model.class_name, sampler_name, schedule_type_name
)
if msg_sampler:
gr.Warning(msg_sampler)
if verbose_arg:
for status, lora in zip(lora_status, lora_list):
if status:
gr.Info(f"LoRA loaded in CPU: {lora}")
elif status is not None:
gr.Warning(f"Failed to load LoRA: {lora}")
if lora_status == [None] * sd_gen.model.num_loras and sd_gen.model.lora_memory != [None] * sd_gen.model.num_loras and load_lora_cpu:
lora_cache_msg = ", ".join(
str(x) for x in sd_gen.model.lora_memory if x is not None
)
gr.Info(f"LoRAs in cache: {lora_cache_msg}")
msg_request = f"Requesting {gpu_duration_arg}s. of GPU time.\nModel: {sd_gen.model.base_model_id}"
if verbose_arg:
gr.Info(msg_request)
print(msg_request)
yield msg_request.replace("\n", "
"), gr.update(), gr.update()
start_time = time.time()
# yield from sd_gen.generate_pipeline(*generation_args)
yield from dynamic_gpu_duration(
sd_gen.generate_pipeline,
gpu_duration_arg,
*generation_args,
)
end_time = time.time()
execution_time = end_time - start_time
msg_task_complete = (
f"GPU task complete in: {int(round(execution_time, 0) + 1)} seconds"
)
if verbose_arg:
gr.Info(msg_task_complete)
print(msg_task_complete)
yield msg_task_complete, gr.update(), gr.update()
@spaces.GPU(duration=15)
def process_upscale(image, upscaler_name, upscaler_size):
if image is None: return None
from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
from stablepy import load_upscaler_model
image = image.convert("RGB")
exif_image = extract_exif_data(image)
name_upscaler = UPSCALER_DICT_GUI[upscaler_name]
if "https://" in str(name_upscaler):
if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"):
download_things(DIRECTORY_UPSCALERS, name_upscaler, HF_TOKEN)
name_upscaler = f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"
scaler_beta = load_upscaler_model(model=name_upscaler, tile=0, tile_overlap=8, device="cuda", half=True)
image_up = scaler_beta.upscale(image, upscaler_size, True)
image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)
return image_path
# https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space/discussions/1#674969a022b99c122af5d407
dynamic_gpu_duration.zerogpu = True
sd_gen_generate_pipeline.zerogpu = True
sd_gen = GuiSD()
with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
gr.Markdown("# 🧩 DiffuseCraft")
gr.Markdown(SUBTITLE_GUI)
with gr.Tab("Generation"):
with gr.Row():
with gr.Column(scale=2):
def update_task_options(model_name, task_name):
new_choices = MODEL_TYPE_TASK[get_model_type(model_name)]
if task_name not in new_choices:
task_name = "txt2img"
return gr.update(value=task_name, choices=new_choices)
task_gui = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0], allow_custom_value=True)
prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt", label="Prompt")
neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt", value="lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality, very displeasing, (bad)")
with gr.Row(equal_height=False):
set_params_gui = gr.Button(value="↙️", variant="secondary", size="sm")
clear_prompt_gui = gr.Button(value="🗑️", variant="secondary", size="sm")
set_random_seed = gr.Button(value="🎲", variant="secondary", size="sm")
generate_button = gr.Button(value="GENERATE IMAGE", variant="primary")
model_name_gui.change(
update_task_options,
[model_name_gui, task_gui],
[task_gui],
)
load_model_gui = gr.HTML(elem_id="load_model", elem_classes="contain")
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.Row(equal_height=False, variant="default"):
gpu_duration_gui = gr.Number(minimum=5, maximum=240, value=59, show_label=False, container=False, info="GPU time duration (seconds)")
with gr.Column():
verbose_info_gui = gr.Checkbox(value=False, container=False, label="Status info")
load_lora_cpu_gui = gr.Checkbox(value=False, container=False, label="Load LoRAs on CPU")
with gr.Column(scale=1):
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., label="CFG")
sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler")
schedule_type_gui = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
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")
pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
with gr.Row():
clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
free_u_gui = gr.Checkbox(value=False, label="FreeU")
with gr.Row(equal_height=False):
def run_set_params_gui(base_prompt, name_model):
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"),
"CFG scale": gr.update(value=7.), # cfg
"Clip skip": gr.update(value=True),
"Model": gr.update(value=name_model),
"Schedule type": gr.update(value="Automatic"),
"PAG": gr.update(value=.0),
"FreeU": gr.update(value=False),
}
valid_keys = list(valid_receptors.keys())
parameters = extract_parameters(base_prompt)
# print(parameters)
if "Sampler" in parameters:
value_sampler = parameters["Sampler"]
for s_type in SCHEDULE_TYPE_OPTIONS:
if s_type in value_sampler:
value_sampler = value_sampler.replace(s_type, "").strip()
parameters["Sampler"] = value_sampler
parameters["Schedule type"] = s_type
for key, val in parameters.items():
# print(val)
if key in valid_keys:
try:
if key == "Sampler":
if val not in scheduler_names:
continue
if key == "Schedule type":
if val not in SCHEDULE_TYPE_OPTIONS:
val = "Automatic"
elif key == "Clip skip":
if "," in str(val):
val = val.replace(",", "")
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 = re.sub(r'\s+', ' ', re.sub(r',+', ',', val)).strip()
if key in ["Steps", "width", "height", "Seed"]:
val = int(val)
if key == "FreeU":
val = True
if key in ["CFG scale", "PAG"]:
val = float(val)
if key == "Model":
filtered_models = [m for m in model_list if val in m]
if filtered_models:
val = filtered_models[0]
else:
val = name_model
if key == "Seed":
continue
valid_receptors[key] = gr.update(value=val)
# print(val, type(val))
# print(valid_receptors)
except Exception as e:
print(str(e))
return [value for value in valid_receptors.values()]
set_params_gui.click(
run_set_params_gui, [prompt_gui, model_name_gui], [
prompt_gui,
neg_prompt_gui,
steps_gui,
img_width_gui,
img_height_gui,
seed_gui,
sampler_gui,
cfg_gui,
clip_skip_gui,
model_name_gui,
schedule_type_gui,
pag_scale_gui,
free_u_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=5, step=1, value=1, label="Images")
prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_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):
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=4., step=0.1, value=1.2, label="Upscale by")
upscaler_tile_size_gui = gr.Slider(minimum=0, maximum=512, step=16, value=0, label="Upscaler Tile Size", info="0 = no tiling")
upscaler_tile_overlap_gui = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler 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=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
hires_schedule_type_gui = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
hires_guidance_scale_gui = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used")
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):
def lora_dropdown(label, visible=True):
return gr.Dropdown(label=label, choices=lora_model_list, value="None", allow_custom_value=True, visible=visible)
def lora_scale_slider(label, visible=True):
return gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label=label, visible=visible)
lora1_gui = lora_dropdown("Lora1")
lora_scale_1_gui = lora_scale_slider("Lora Scale 1")
lora2_gui = lora_dropdown("Lora2")
lora_scale_2_gui = lora_scale_slider("Lora Scale 2")
lora3_gui = lora_dropdown("Lora3")
lora_scale_3_gui = lora_scale_slider("Lora Scale 3")
lora4_gui = lora_dropdown("Lora4")
lora_scale_4_gui = lora_scale_slider("Lora Scale 4")
lora5_gui = lora_dropdown("Lora5")
lora_scale_5_gui = lora_scale_slider("Lora Scale 5")
lora6_gui = lora_dropdown("Lora6", visible=False)
lora_scale_6_gui = lora_scale_slider("Lora Scale 6", visible=False)
lora7_gui = lora_dropdown("Lora7", visible=False)
lora_scale_7_gui = lora_scale_slider("Lora Scale 7", visible=False)
with gr.Accordion("From URL", open=False, visible=True):
text_lora = gr.Textbox(
label="LoRA's download URL",
placeholder="https://civitai.com/api/download/models/28907",
lines=1,
info="It has to be .safetensors files, and you can also download them from Hugging Face.",
)
romanize_text = gr.Checkbox(value=False, label="Transliterate name", visible=False)
button_lora = gr.Button("Get and Refresh the LoRA Lists")
new_lora_status = gr.HTML()
button_lora.click(
get_my_lora,
[text_lora, romanize_text],
[lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui, new_lora_status]
)
with gr.Accordion("Face restoration", open=False, visible=True):
face_rest_options = [None] + FACE_RESTORATION_MODELS
face_restoration_model_gui = gr.Dropdown(label="Face restoration model", choices=face_rest_options, value=face_rest_options[0])
face_restoration_visibility_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=1., label="Visibility")
face_restoration_weight_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=.5, label="Weight", info="(0 = maximum effect, 1 = minimum effect)")
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, repaint and inpaint."
)
image_resolution_gui = gr.Slider(
minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
info="The maximum proportional size of the generated image based on the uploaded image."
)
controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0])
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 (%)")
preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
def change_preprocessor_choices(task):
task = TASK_STABLEPY[task]
if task in TASK_AND_PREPROCESSORS.keys():
choices_task = TASK_AND_PREPROCESSORS[task]
else:
choices_task = TASK_AND_PREPROCESSORS["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="Preprocessor 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="'MLSD' Hough value threshold")
distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
recolor_gamma_correction_gui = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
tile_blur_sigma_gui = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")
with gr.Accordion("T2I adapter", open=False, visible=False):
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 Exception:
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_gui = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
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=False)
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=False)
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):
schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
guidance_rescale_gui = gr.Number(label="CFG rescale:", value=0., step=0.01, minimum=0., maximum=1.5)
save_generated_images_gui = gr.Checkbox(value=True, label="Create a download link for the images")
filename_pattern_gui = gr.Textbox(label="Filename pattern", value="model,seed", placeholder="model,seed,sampler,schedule_type,img_width,img_height,guidance_scale,num_steps,vae,prompt_section,neg_prompt_section", lines=1)
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=False, label="Display Images")
image_previews_gui = gr.Checkbox(value=True, label="Image Previews")
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):
gr.Markdown(HELP_GUI)
gr.Markdown(EXAMPLES_GUI_HELP)
gr.Examples(
examples=EXAMPLES_GUI,
fn=sd_gen.generate_pipeline,
inputs=[
prompt_gui,
neg_prompt_gui,
steps_gui,
cfg_gui,
seed_gui,
lora1_gui,
lora_scale_1_gui,
sampler_gui,
img_height_gui,
img_width_gui,
model_name_gui,
task_gui,
image_control,
image_resolution_gui,
strength_gui,
control_net_output_scaling_gui,
control_net_start_threshold_gui,
control_net_stop_threshold_gui,
prompt_syntax_gui,
upscaler_model_path_gui,
gpu_duration_gui,
load_lora_cpu_gui,
],
outputs=[load_model_gui, result_images, actual_task_info],
cache_examples=False,
)
gr.Markdown(RESOURCES)
with gr.Tab("Inpaint mask maker", render=True):
with gr.Row():
with gr.Column(scale=2):
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])
with gr.Tab("PNG Info"):
with gr.Row():
with gr.Column():
image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
with gr.Column():
result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
image_metadata.change(
fn=extract_exif_data,
inputs=[image_metadata],
outputs=[result_metadata],
)
with gr.Tab("Upscaler"):
with gr.Row():
with gr.Column():
USCALER_TAB_KEYS = [name for name in UPSCALER_KEYS[9:]]
image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
upscaler_tab = gr.Dropdown(label="Upscaler", choices=USCALER_TAB_KEYS, value=USCALER_TAB_KEYS[5])
upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")
with gr.Column():
result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")
generate_button_up_tab.click(
fn=process_upscale,
inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
outputs=[result_up_tab],
)
with gr.Tab("Preprocessor", render=True):
preprocessor_tab()
generate_button.click(
fn=sd_gen.load_new_model,
inputs=[
model_name_gui,
vae_model_gui,
task_gui,
controlnet_model_gui,
],
outputs=[load_model_gui],
queue=True,
show_progress="minimal",
).success(
fn=sd_gen_generate_pipeline, # 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,
lora6_gui,
lora_scale_6_gui,
lora7_gui,
lora_scale_7_gui,
sampler_gui,
schedule_type_gui,
schedule_prediction_type_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,
recolor_gamma_correction_gui,
tile_blur_sigma_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,
upscaler_tile_size_gui,
upscaler_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,
hires_schedule_type_gui,
hires_guidance_scale_gui,
controlnet_model_gui,
loop_generation_gui,
leave_progress_bar_gui,
disable_progress_bar_gui,
image_previews_gui,
display_images_gui,
save_generated_images_gui,
filename_pattern_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,
guidance_rescale_gui,
image_ip1,
mask_ip1,
model_ip1,
mode_ip1,
scale_ip1,
image_ip2,
mask_ip2,
model_ip2,
mode_ip2,
scale_ip2,
pag_scale_gui,
face_restoration_model_gui,
face_restoration_visibility_gui,
face_restoration_weight_gui,
load_lora_cpu_gui,
verbose_info_gui,
gpu_duration_gui,
],
outputs=[load_model_gui, result_images, actual_task_info],
queue=True,
show_progress="minimal",
)
app.queue()
app.launch(
show_error=True,
debug=True,
allowed_paths=["./images/"],
)