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
import gradio as gr
from PIL import Image
import IPython.display
import time, json
from IPython.utils import capture
import logging
logging.getLogger("diffusers").setLevel(logging.ERROR)
import diffusers
diffusers.utils.logging.set_verbosity(40)
import warnings
warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
from stablepy import logger
logger.setLevel(logging.CRITICAL)
from env import (
hf_token,
hf_read_token, # to use only for private repos
CIVITAI_API_KEY,
HF_LORA_PRIVATE_REPOS1,
HF_LORA_PRIVATE_REPOS2,
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,
)
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 (
to_list,
list_uniq,
list_sub,
get_model_id_list,
get_tupled_embed_list,
get_tupled_model_list,
get_lora_model_list,
download_private_repo,
)
# - **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(load_diffusers_format_model + get_model_id_list())
## END MOD
CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
hf_token = os.environ.get("HF_TOKEN")
# Download stuffs
for url in [url.strip() for url in download_model.split(',')]:
if not os.path.exists(f"./models/{url.split('/')[-1]}"):
download_things(directory_models, url, hf_token, CIVITAI_API_KEY)
for url in [url.strip() for url in download_vae.split(',')]:
if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
download_things(directory_vaes, url, hf_token, CIVITAI_API_KEY)
for url in [url.strip() for url in download_lora.split(',')]:
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
# Download Embeddings
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
## BEGIN MOD
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
prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
global lora_model_list
lora_model_list = get_lora_model_list()
## 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(1, desc="Inference preparation completed. Starting inference...")
info_state = f"PROCESSING "
info_state += ">"
info_state = f"COMPLETED. Seeds: {str(seed)}"
if vae_msg:
info_state = info_state + "<br>" + vae_msg
if msg_lora:
info_state = info_state + "<br>" + "<br>".join(msg_lora)
return self.infer_short(self.model, pipe_params), info_state
## END MOD
from pathlib import Path
from modutils import (
safe_float,
escape_lora_basename,
to_lora_key,
to_lora_path,
get_local_model_list,
get_private_lora_model_lists,
get_valid_lora_name,
get_valid_lora_path,
get_valid_lora_wt,
get_lora_info,
normalize_prompt_list,
get_civitai_info,
search_lora_on_civitai,
)
sd_gen = GuiSD()
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
sampler = "Euler a", vae = None, progress=gr.Progress(track_tqdm=True)):
import PIL
import numpy as np
MAX_SEED = np.iinfo(np.int32).max
images: list[tuple[PIL.Image.Image, str | None]] = []
info: str = ""
progress(0, desc="Preparing...")
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed).seed()
prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
progress(0.5, desc="Preparing...")
lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
lora1 = get_valid_lora_path(lora1)
lora2 = get_valid_lora_path(lora2)
lora3 = get_valid_lora_path(lora3)
lora4 = get_valid_lora_path(lora4)
lora5 = get_valid_lora_path(lora5)
progress(1, desc="Preparation completed. Starting inference preparation...")
sd_gen.load_new_model(model_name, vae, task_model_list[0])
images, info = sd_gen.generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
lora4, lora4_wt, lora5, lora5_wt, sampler,
height, width, model_name, vae, task_model_list[0], None, "Canny", 512, 1024,
None, None, None, 0.35, 100, 200, 0.1, 0.1, 1.0, 0., 1., False, "Classic", None,
1.0, 100, 10, 30, 0.55, "Use same sampler", "", "",
False, True, 1, True, False, False, False, False, "./images", False, False, False, True, 1, 0.55,
False, False, False, True, False, "Use same sampler", False, "", "", 0.35, True, True, False, 4, 4, 32,
False, "", "", 0.35, True, True, False, 4, 4, 32,
True, None, None, "plus_face", "original", 0.7, None, None, "base", "style", 0.7
)
progress(1, desc="Inference completed.")
output_image = images[0][0] if images else None
return output_image
@spaces.GPU
def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
sampler = "Euler a", vae = None, progress=gr.Progress(track_tqdm=True)):
return gr.update(visible=True)
infer.zerogpu = True
_infer.zerogpu = True
def pass_result(result):
return result
def get_samplers():
return scheduler_names
def get_vaes():
return vae_model_list
show_diffusers_model_list_detail = False
cached_diffusers_model_tupled_list = get_tupled_model_list(load_diffusers_format_model)
def get_diffusers_model_list():
if show_diffusers_model_list_detail:
return cached_diffusers_model_tupled_list
else:
return load_diffusers_format_model
def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = ""):
global show_diffusers_model_list_detail
show_diffusers_model_list_detail = is_enable
new_value = model_name
index = 0
if model_name in set(load_diffusers_format_model):
index = load_diffusers_format_model.index(model_name)
if is_enable:
new_value = cached_diffusers_model_tupled_list[index][1]
else:
new_value = load_diffusers_format_model[index]
return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list())
def get_t2i_model_info(repo_id: str):
from huggingface_hub import HfApi
api = HfApi()
try:
if " " in repo_id or not api.repo_exists(repo_id): return ""
model = api.model_info(repo_id=repo_id)
except Exception as e:
print(f"Error: Failed to get {repo_id}'s info. ")
return ""
if model.private or model.gated: return ""
tags = model.tags
info = []
url = f"https://huggingface.co/{repo_id}/"
if not 'diffusers' in tags: return ""
if 'diffusers:StableDiffusionXLPipeline' in tags:
info.append("SDXL")
elif 'diffusers:StableDiffusionPipeline' in tags:
info.append("SD1.5")
if model.card_data and model.card_data.tags:
info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
info.append(f"DLs: {model.downloads}")
info.append(f"likes: {model.likes}")
info.append(model.last_modified.strftime("lastmod: %Y-%m-%d"))
md = f"Model Info: {', '.join(info)}, [Model Repo]({url})"
return gr.update(value=md)
def load_model_prompt_dict():
import json
dict = {}
try:
with open('model_dict.json', encoding='utf-8') as f:
dict = json.load(f)
except Exception:
pass
return dict
model_prompt_dict = load_model_prompt_dict()
model_recom_prompt_enabled = True
animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres")
animagine_nps = to_list("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]")
pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
pony_nps = to_list("source_pony, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed")
other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly")
default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres")
default_nps = to_list("score_6, score_5, score_4, 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]")
def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"):
if not model_recom_prompt_enabled or not model_name: return prompt, neg_prompt
prompts = to_list(prompt)
neg_prompts = to_list(neg_prompt)
prompts = list_sub(prompts, animagine_ps + pony_ps + other_ps)
neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + other_nps)
last_empty_p = [""] if not prompts and type != "None" else []
last_empty_np = [""] if not neg_prompts and type != "None" else []
ps = []
nps = []
if model_name in model_prompt_dict.keys():
ps = to_list(model_prompt_dict[model_name]["prompt"])
nps = to_list(model_prompt_dict[model_name]["negative_prompt"])
else:
ps = default_ps
nps = default_nps
prompts = prompts + ps
neg_prompts = neg_prompts + nps
prompt = ", ".join(list_uniq(prompts) + last_empty_p)
neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
return prompt, neg_prompt
def enable_model_recom_prompt(is_enable: bool = True):
global model_recom_prompt_enabled
model_recom_prompt_enabled = is_enable
return is_enable
private_lora_dict = {}
try:
with open('lora_dict.json', encoding='utf-8') as f:
d = json.load(f)
for k, v in d.items():
private_lora_dict[escape_lora_basename(k)] = v
except Exception:
pass
private_lora_model_list = get_private_lora_model_lists()
loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
civitai_lora_last_results = {} # {"URL to download": {search results}, ...}
all_lora_list = []
def get_all_lora_list():
global all_lora_list
loras = get_lora_model_list()
all_lora_list = loras.copy()
return loras
def get_all_lora_tupled_list():
global loras_dict
models = get_all_lora_list()
if not models: return []
tupled_list = []
for model in models:
#if not model: continue # to avoid GUI-related bug
basename = Path(model).stem
key = to_lora_key(model)
items = None
if key in loras_dict.keys():
items = loras_dict.get(key, None)
else:
items = get_civitai_info(model)
if items != None:
loras_dict[key] = items
name = basename
value = model
if items and items[2] != "":
if items[1] == "Pony":
name = f"{basename} (for {items[1]}🐴, {items[2]})"
else:
name = f"{basename} (for {items[1]}, {items[2]})"
tupled_list.append((name, value))
return tupled_list
def update_lora_dict(path: str):
global loras_dict
key = to_lora_key(path)
if key in loras_dict.keys(): return
items = get_civitai_info(path)
if items == None: return
loras_dict[key] = items
def download_lora(dl_urls: str):
global loras_url_to_path_dict
dl_path = ""
before = get_local_model_list(directory_loras)
urls = []
for url in [url.strip() for url in dl_urls.split(',')]:
local_path = f"{directory_loras}/{url.split('/')[-1]}"
if not Path(local_path).exists():
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
urls.append(url)
after = get_local_model_list(directory_loras)
new_files = list_sub(after, before)
i = 0
for file in new_files:
path = Path(file)
if path.exists():
new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
path.resolve().rename(new_path.resolve())
loras_url_to_path_dict[urls[i]] = str(new_path)
update_lora_dict(str(new_path))
dl_path = str(new_path)
i += 1
return dl_path
def copy_lora(path: str, new_path: str):
import shutil
if path == new_path: return new_path
cpath = Path(path)
npath = Path(new_path)
if cpath.exists():
try:
shutil.copy(str(cpath.resolve()), str(npath.resolve()))
except Exception:
return None
update_lora_dict(str(npath))
return new_path
else:
return None
def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str):
path = download_lora(dl_urls)
if path:
if not lora1 or lora1 == "None":
lora1 = path
elif not lora2 or lora2 == "None":
lora2 = path
elif not lora3 or lora3 == "None":
lora3 = path
elif not lora4 or lora4 == "None":
lora4 = path
elif not lora5 or lora5 == "None":
lora5 = path
choices = get_all_lora_tupled_list()
return gr.update(value=lora1, choices=choices), gr.update(value=lora2, choices=choices), gr.update(value=lora3, choices=choices),\
gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices)
def set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
import re
lora1 = get_valid_lora_name(lora1)
lora2 = get_valid_lora_name(lora2)
lora3 = get_valid_lora_name(lora3)
lora4 = get_valid_lora_name(lora4)
lora5 = get_valid_lora_name(lora5)
if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
lora1_wt = get_valid_lora_wt(prompt, lora1, lora1_wt)
lora2_wt = get_valid_lora_wt(prompt, lora2, lora2_wt)
lora3_wt = get_valid_lora_wt(prompt, lora3, lora3_wt)
lora4_wt = get_valid_lora_wt(prompt, lora4, lora4_wt)
lora5_wt = get_valid_lora_wt(prompt, lora5, lora5_wt)
on1, label1, tag1, md1 = get_lora_info(lora1)
on2, label2, tag2, md2 = get_lora_info(lora2)
on3, label3, tag3, md3 = get_lora_info(lora3)
on4, label4, tag4, md4 = get_lora_info(lora4)
on5, label5, tag5, md5 = get_lora_info(lora5)
lora_paths = [lora1, lora2, lora3, lora4, lora5]
prompts = prompt.split(",") if prompt else []
for p in prompts:
p = str(p).strip()
if "<lora" in p:
result = re.findall(r'<lora:(.+?):(.+?)>', p)
if not result: continue
key = result[0][0]
wt = result[0][1]
path = to_lora_path(key)
if not key in loras_dict.keys() or not path:
path = get_valid_lora_name(path)
if not path or path == "None": continue
if path in lora_paths:
continue
elif not on1:
lora1 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora1_wt = safe_float(wt)
on1 = True
elif not on2:
lora2 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora2_wt = safe_float(wt)
on2 = True
elif not on3:
lora3 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora3_wt = safe_float(wt)
on3 = True
elif not on4:
lora4 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora4_wt = safe_float(wt)
on4, label4, tag4, md4 = get_lora_info(lora4)
elif not on5:
lora5 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora5_wt = safe_float(wt)
on5 = True
return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
def apply_lora_prompt(prompt: str, lora_info: str):
if lora_info == "None": return gr.update(value=prompt)
tags = prompt.split(",") if prompt else []
prompts = normalize_prompt_list(tags)
lora_tag = lora_info.replace("/",",")
lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
lora_prompts = normalize_prompt_list(lora_tags)
empty = [""]
prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
return gr.update(value=prompt)
def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
import re
on1, label1, tag1, md1 = get_lora_info(lora1)
on2, label2, tag2, md2 = get_lora_info(lora2)
on3, label3, tag3, md3 = get_lora_info(lora3)
on4, label4, tag4, md4 = get_lora_info(lora4)
on5, label5, tag5, md5 = get_lora_info(lora5)
lora_paths = [lora1, lora2, lora3, lora4, lora5]
prompts = prompt.split(",") if prompt else []
output_prompts = []
for p in prompts:
p = str(p).strip()
if "<lora" in p:
result = re.findall(r'<lora:(.+?):(.+?)>', p)
if not result: continue
key = result[0][0]
wt = result[0][1]
path = to_lora_path(key)
if not key in loras_dict.keys() or not path: continue
if path in lora_paths:
output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
elif p:
output_prompts.append(p)
lora_prompts = []
if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
choices = get_all_lora_tupled_list()
return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\
gr.update(value=tag1, label=label1, visible=on1), gr.update(visible=on1), gr.update(value=md1, visible=on1),\
gr.update(value=lora2, choices=choices), gr.update(value=lora2_wt),\
gr.update(value=tag2, label=label2, visible=on2), gr.update(visible=on2), gr.update(value=md2, visible=on2),\
gr.update(value=lora3, choices=choices), gr.update(value=lora3_wt),\
gr.update(value=tag3, label=label3, visible=on3), gr.update(visible=on3), gr.update(value=md3, visible=on3),\
gr.update(value=lora4, choices=choices), gr.update(value=lora4_wt),\
gr.update(value=tag4, label=label4, visible=on4), gr.update(visible=on4), gr.update(value=md4, visible=on4),\
gr.update(value=lora5, choices=choices), gr.update(value=lora5_wt),\
gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
def search_civitai_lora(query, base_model):
global civitai_lora_last_results
items = search_lora_on_civitai(query, base_model)
if not items: return gr.update(choices=[("", "")], value="", visible=False),\
gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
civitai_lora_last_results = {}
choices = []
for item in items:
base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
value = item['dl_url']
choices.append((name, value))
civitai_lora_last_results[value] = item
if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
result = civitai_lora_last_results.get(choices[0][1], "None")
md = result['md'] if result else ""
return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
gr.update(visible=True), gr.update(visible=True)
def select_civitai_lora(search_result):
if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
result = civitai_lora_last_results.get(search_result, "None")
md = result['md'] if result else ""
return gr.update(value=search_result), gr.update(value=md, visible=True)
def search_civitai_lora_json(query, base_model):
results = {}
items = search_lora_on_civitai(query, base_model)
if not items: return gr.update(value=results)
for item in items:
results[item['dl_url']] = item
return gr.update(value=results)
quality_prompt_list = [
{
"name": "None",
"prompt": "",
"negative_prompt": "lowres",
},
{
"name": "Animagine Common",
"prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
"negative_prompt": "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]",
},
{
"name": "Pony Anime Common",
"prompt": "source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres",
"negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
},
{
"name": "Pony Common",
"prompt": "source_anime, score_9, score_8_up, score_7_up",
"negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
},
{
"name": "Animagine Standard v3.0",
"prompt": "masterpiece, best quality",
"negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name",
},
{
"name": "Animagine Standard v3.1",
"prompt": "masterpiece, best quality, very aesthetic, absurdres",
"negative_prompt": "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]",
},
{
"name": "Animagine Light v3.1",
"prompt": "(masterpiece), best quality, very aesthetic, perfect face",
"negative_prompt": "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
},
{
"name": "Animagine Heavy v3.1",
"prompt": "(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details",
"negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, very displeasing",
},
]
style_list = [
{
"name": "None",
"prompt": "",
"negative_prompt": "",
},
{
"name": "Cinematic",
"prompt": "cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
"negative_prompt": "cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
},
{
"name": "Photographic",
"prompt": "cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed",
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
},
{
"name": "Anime",
"prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed",
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
},
{
"name": "Manga",
"prompt": "manga style, vibrant, high-energy, detailed, iconic, Japanese comic style",
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
},
{
"name": "Digital Art",
"prompt": "concept art, digital artwork, illustrative, painterly, matte painting, highly detailed",
"negative_prompt": "photo, photorealistic, realism, ugly",
},
{
"name": "Pixel art",
"prompt": "pixel-art, low-res, blocky, pixel art style, 8-bit graphics",
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
},
{
"name": "Fantasy art",
"prompt": "ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
},
{
"name": "Neonpunk",
"prompt": "neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
},
{
"name": "3D Model",
"prompt": "professional 3d model, octane render, highly detailed, volumetric, dramatic lighting",
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
},
]
preset_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list}
def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None"):
def to_list(s):
return [x.strip() for x in s.split(",") if not s == ""]
def list_sub(a, b):
return [e for e in a if e not in b]
def list_uniq(l):
return sorted(set(l), key=l.index)
animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres")
animagine_nps = to_list("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]")
pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
prompts = to_list(prompt)
neg_prompts = to_list(neg_prompt)
all_styles_ps = []
all_styles_nps = []
for d in style_list:
all_styles_ps.extend(to_list(str(d.get("prompt", ""))))
all_styles_nps.extend(to_list(str(d.get("negative_prompt", ""))))
all_quality_ps = []
all_quality_nps = []
for d in quality_prompt_list:
all_quality_ps.extend(to_list(str(d.get("prompt", ""))))
all_quality_nps.extend(to_list(str(d.get("negative_prompt", ""))))
quality_ps = to_list(preset_quality[quality_key][0])
quality_nps = to_list(preset_quality[quality_key][1])
styles_ps = to_list(preset_styles[styles_key][0])
styles_nps = to_list(preset_styles[styles_key][1])
prompts = list_sub(prompts, animagine_ps + pony_ps + all_styles_ps + all_quality_ps)
neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + all_styles_nps + all_quality_nps)
last_empty_p = [""] if not prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
last_empty_np = [""] if not neg_prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
if type == "Animagine":
prompts = prompts + animagine_ps
neg_prompts = neg_prompts + animagine_nps
elif type == "Pony":
prompts = prompts + pony_ps
neg_prompts = neg_prompts + pony_nps
prompts = prompts + styles_ps + quality_ps
neg_prompts = neg_prompts + styles_nps + quality_nps
prompt = ", ".join(list_uniq(prompts) + last_empty_p)
neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
return gr.update(value=prompt), gr.update(value=neg_prompt)