app.py

import gradio as gr
import torch
torch.jit.script = lambda f: f
import timm
import time
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard
from safetensors.torch import load_file
from share_btn import community_icon_html, loading_icon_html, share_js
from cog_sdxl_dataset_and_utils import TokenEmbeddingsHandler

import lora
import copy
import json
import gc
import random
from urllib.parse import quote
import gdown
import os
import re
import requests

import diffusers
from diffusers.utils import load_image
from diffusers.models import ControlNetModel
from diffusers import AutoencoderKL, DPMSolverMultistepScheduler
import cv2
import torch
import numpy as np
from PIL import Image

from insightface.app import FaceAnalysis
from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInstantIDImg2ImgPipeline, draw_kps
from controlnet_aux import ZoeDetector

from compel import Compel, ReturnedEmbeddingsType
#import spaces

#from gradio_imageslider import ImageSlider

with open("sdxl_loras.json", "r") as file:
    data = json.load(file)
    sdxl_loras_raw = [
        {
            "image": item["image"],
            "title": item["title"],
            "repo": item["repo"],
            "trigger_word": item["trigger_word"],
            "weights": item["weights"],
            "is_compatible": item["is_compatible"],
            "is_pivotal": item.get("is_pivotal", False),
            "text_embedding_weights": item.get("text_embedding_weights", None),
            "likes": item.get("likes", 0),
            "downloads": item.get("downloads", 0),
            "is_nc": item.get("is_nc", False),
            "new": item.get("new", False),
        }
        for item in data
    ]

with open("defaults_data.json", "r") as file:
    lora_defaults = json.load(file)
    

#device = "cuda" 
if torch.cuda.is_available():
    device = "cuda"
    dtype = torch.float16
elif torch.backends.mps.is_available():
    device = "mps"
    dtype = torch.float32
else:
    device = "cpu"
    dtype = torch.float32


state_dicts = {}

for item in sdxl_loras_raw:
    saved_name = hf_hub_download(item["repo"], item["weights"])
    
    if not saved_name.endswith('.safetensors'):
        state_dict = torch.load(saved_name)
    else:
        state_dict = load_file(saved_name)
    
    state_dicts[item["repo"]] = {
        "saved_name": saved_name,
        "state_dict": state_dict
    }

sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True]
    
# download models
hf_hub_download(
    repo_id="InstantX/InstantID",
    filename="ControlNetModel/config.json",
    local_dir="data/checkpoints",
)
hf_hub_download(
    repo_id="InstantX/InstantID",
    filename="ControlNetModel/diffusion_pytorch_model.safetensors",
    local_dir="data/checkpoints",
)
hf_hub_download(
    repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="data/checkpoints"
)
hf_hub_download(
    repo_id="latent-consistency/lcm-lora-sdxl",
    filename="pytorch_lora_weights.safetensors",
    local_dir="data/checkpoints",
)
## download antelopev2
#if not os.path.exists("data/antelopev2.zip"):
#    gdown.download(url="https://drive.google.com/file/d/18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8/view?usp=sharing", output="data/", quiet=False, fuzzy=True)
#    os.system("unzip /data/antelopev2.zip -d /data/models/")

app = FaceAnalysis(name='antelopev2', root='data', providers=['CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))

# prepare models under ./checkpoints
face_adapter = f'data/checkpoints/ip-adapter.bin'
controlnet_path = f'data/checkpoints/ControlNetModel'

# load IdentityNet
st = time.time()
identitynet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=dtype)
zoedepthnet = ControlNetModel.from_pretrained("diffusers/controlnet-zoe-depth-sdxl-1.0",torch_dtype=dtype)
et = time.time()
elapsed_time = et - st
print('Loading ControlNet took: ', elapsed_time, 'seconds')
st = time.time()
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
et = time.time()
elapsed_time = et - st
print('Loading VAE took: ', elapsed_time, 'seconds')
st = time.time()
pipe = StableDiffusionXLInstantIDImg2ImgPipeline.from_pretrained("rubbrband/albedobaseXL_v21",
                                                                 vae=vae,
                                                                 controlnet=[identitynet, zoedepthnet],
                                                                 torch_dtype=dtype)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
pipe.load_ip_adapter_instantid(face_adapter)
pipe.set_ip_adapter_scale(0.8)
et = time.time()
elapsed_time = et - st
print('Loading pipeline took: ', elapsed_time, 'seconds')
st = time.time()
compel = Compel(tokenizer=[pipe.tokenizer, pipe.tokenizer_2] , text_encoder=[pipe.text_encoder, pipe.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True])
et = time.time()
elapsed_time = et - st
print('Loading Compel took: ', elapsed_time, 'seconds')

st = time.time()
zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
et = time.time()
elapsed_time = et - st
print('Loading Zoe took: ', elapsed_time, 'seconds')
zoe.to(device)
pipe.to(device)

last_lora = ""
last_fused = False
js = '''
var button = document.getElementById('button');
// Add a click event listener to the button
button.addEventListener('click', function() {
    element.classList.add('selected');
});
'''
#lora_archive = "/data"
lora_archive = "data"

def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative, is_new=False):
    lora_repo = sdxl_loras[selected_state.index]["repo"]
    new_placeholder = "Type a prompt to use your selected LoRA"
    weight_name = sdxl_loras[selected_state.index]["weights"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }"

    for lora_list in lora_defaults:
        if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]:
            face_strength = lora_list.get("face_strength", 0.85)
            image_strength = lora_list.get("image_strength", 0.15)
            weight = lora_list.get("weight", 0.9)
            depth_control_scale = lora_list.get("depth_control_scale", 0.8)
            negative = lora_list.get("negative", "")
    
    if(is_new):
        if(selected_state.index == 0):
            selected_state.index = -9999
        else:
            selected_state.index *= -1
    
    return (
        updated_text,
        gr.update(placeholder=new_placeholder),
        face_strength,
        image_strength,
        weight,
        depth_control_scale,
        negative,
        selected_state
    )

def center_crop_image_as_square(img):
    square_size = min(img.size)
    
    left = (img.width - square_size) / 2
    top = (img.height - square_size) / 2
    right = (img.width + square_size) / 2
    bottom = (img.height + square_size) / 2
    
    img_cropped = img.crop((left, top, right, bottom))
    return img_cropped
    
def check_selected(selected_state, custom_lora):
    if not selected_state and not custom_lora:
        raise gr.Error("You must select a style")

def merge_incompatible_lora(full_path_lora, lora_scale):
    for weights_file in [full_path_lora]:
                if ";" in weights_file:
                    weights_file, multiplier = weights_file.split(";")
                    multiplier = float(multiplier)
                else:
                    multiplier = lora_scale

                lora_model, weights_sd = lora.create_network_from_weights(
                    multiplier,
                    full_path_lora,
                    pipe.vae,
                    pipe.text_encoder,
                    pipe.unet,
                    for_inference=True,
                )
                lora_model.merge_to(
                    pipe.text_encoder, pipe.unet, weights_sd, dtype, device
                )
                del weights_sd
                del lora_model
#@spaces.GPU
def generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index, st):
    print(loaded_state_dict)
    et = time.time()
    elapsed_time = et - st
    print('Getting into the decorated function took: ', elapsed_time, 'seconds')
    global last_fused, last_lora
    print("Last LoRA: ", last_lora)
    print("Current LoRA: ", repo_name)
    print("Last fused: ", last_fused)
    #prepare face zoe
    st = time.time()
    with torch.no_grad():
        image_zoe = zoe(face_image)
    width, height = face_kps.size
    images = [face_kps, image_zoe.resize((height, width))]
    et = time.time()
    elapsed_time = et - st
    print('Zoe Depth calculations took: ', elapsed_time, 'seconds')
    if last_lora != repo_name:
        if(last_fused):
            st = time.time()
            pipe.unfuse_lora()
            pipe.unload_lora_weights()
            et = time.time()
            elapsed_time = et - st
            print('Unfuse and unload LoRA took: ', elapsed_time, 'seconds')
        st = time.time()
        pipe.load_lora_weights(loaded_state_dict)
        pipe.fuse_lora(lora_scale)
        et = time.time()
        elapsed_time = et - st
        print('Fuse and load LoRA took: ', elapsed_time, 'seconds')
        last_fused = True
        is_pivotal = sdxl_loras[selected_state_index]["is_pivotal"]
        if(is_pivotal):
            #Add the textual inversion embeddings from pivotal tuning models
            text_embedding_name = sdxl_loras[selected_state_index]["text_embedding_weights"]
            embedding_path = hf_hub_download(repo_id=repo_name, filename=text_embedding_name, repo_type="model")
            state_dict_embedding = load_file(embedding_path)
            try:
                pipe.unload_textual_inversion()
                pipe.load_textual_inversion(state_dict_embedding["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
                pipe.load_textual_inversion(state_dict_embedding["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
            except:
                pipe.unload_textual_inversion()
                pipe.load_textual_inversion(state_dict_embedding["text_encoders_0"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
                pipe.load_textual_inversion(state_dict_embedding["text_encoders_1"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)

    print("Processing prompt...")
    st = time.time()
    conditioning, pooled = compel(prompt)
    if(negative):
        negative_conditioning, negative_pooled = compel(negative)
    else:
        negative_conditioning, negative_pooled = None, None
    et = time.time()
    elapsed_time = et - st
    print('Prompt processing took: ', elapsed_time, 'seconds')
    print("Processing image...")
    st = time.time()
    image = pipe(
        prompt_embeds=conditioning,
        pooled_prompt_embeds=pooled,
        negative_prompt_embeds=negative_conditioning,
        negative_pooled_prompt_embeds=negative_pooled,
        width=1024,
        height=1024,
        image_embeds=face_emb,
        image=face_image,
        strength=1-image_strength,
        control_image=images,
        num_inference_steps=20,
        guidance_scale = guidance_scale,
        controlnet_conditioning_scale=[face_strength, depth_control_scale],
    ).images[0]
    et = time.time()
    elapsed_time = et - st
    print('Image processing took: ', elapsed_time, 'seconds')
    last_lora = repo_name
    return image
    
def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, custom_lora, progress=gr.Progress(track_tqdm=True)):
    print("Custom LoRA: ", custom_lora)
    custom_lora_path = custom_lora[0] if custom_lora else None
    selected_state_index = selected_state.index if selected_state else -1
    st = time.time()
    face_image = center_crop_image_as_square(face_image)
    try:
        face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
        face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
        face_emb = face_info['embedding']
        face_kps = draw_kps(face_image, face_info['kps'])
    except: 
        raise gr.Error("No face found in your image. Only face images work here. Try again")
    et = time.time()
    elapsed_time = et - st
    print('Cropping and calculating face embeds took: ', elapsed_time, 'seconds')

    st = time.time()

    if(custom_lora_path):
        prompt = f"{prompt} {custom_lora[1]}"
    else:
        for lora_list in lora_defaults:
            if lora_list["model"] == sdxl_loras[selected_state_index]["repo"]:
                prompt_full = lora_list.get("prompt", None)
                if(prompt_full):
                    prompt = prompt_full.replace("<subject>", prompt)

    print("Prompt:", prompt) 
    if(prompt == ""):
        prompt = "a person"
    
    #print("Selected State: ", selected_state_index)
    #print(sdxl_loras[selected_state_index]["repo"])
    if negative == "":
        negative = None
    print("Custom Loaded LoRA: ", custom_lora_path)
    if not selected_state and not custom_lora_path:
        raise gr.Error("You must select a style")
    elif custom_lora_path:
        repo_name = custom_lora_path
        full_path_lora = custom_lora_path
    else:
        repo_name = sdxl_loras[selected_state_index]["repo"]
        weight_name = sdxl_loras[selected_state_index]["weights"]
        full_path_lora = state_dicts[repo_name]["saved_name"]
    print("Full path LoRA ", full_path_lora)
    #loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
    cross_attention_kwargs = None
    et = time.time()
    elapsed_time = et - st
    print('Small content processing took: ', elapsed_time, 'seconds')

    st = time.time()
    image = generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale, repo_name, full_path_lora, lora_scale, sdxl_loras, selected_state_index, st)
    return image, gr.update(visible=True)

def shuffle_gallery(sdxl_loras):
    random.shuffle(sdxl_loras)
    return [(item["image"], item["title"]) for item in sdxl_loras], sdxl_loras

def classify_gallery(sdxl_loras):
    sorted_gallery = sorted(sdxl_loras, key=lambda x: x.get("likes", 0), reverse=True)
    return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery

def swap_gallery(order, sdxl_loras):
    if(order == "random"):
        return shuffle_gallery(sdxl_loras)
    else:
        return classify_gallery(sdxl_loras)
        
def deselect():
  return gr.Gallery(selected_index=None)

def get_huggingface_safetensors(link):
  split_link = link.split("/")
  if(len(split_link) == 2):
            model_card = ModelCard.load(link)
            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
            trigger_word = model_card.data.get("instance_prompt", "")
            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
            fs = HfFileSystem()
            try:
                list_of_files = fs.ls(link, detail=False)
                for file in list_of_files:
                    if(file.endswith(".safetensors")):
                        safetensors_name = file.replace("/", "_")
                        if(not os.path.exists(f"{lora_archive}/{safetensors_name}")):
                          fs.get_file(file, lpath=f"{lora_archive}/{safetensors_name}")
                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
                      image_elements = file.split("/")
                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
            except:
              gr.Warning("You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
              raise Exception("You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
            return split_link[1], f"{lora_archive}/{safetensors_name}", trigger_word, image_url

def get_civitai_safetensors(link):
  link_split = link.split("civitai.com/")
  pattern = re.compile(r'models\/(\d+)')
  regex_match = pattern.search(link_split[1])
  if(regex_match):
    civitai_model_id = regex_match.group(1)
  else:
    gr.Warning("No CivitAI model id found in your URL")
    raise Exception("No CivitAI model id found in your URL")
  model_request_url = f"https://civitai.com/api/v1/models/{civitai_model_id}?token={os.getenv('CIVITAI_TOKEN')}"
  x = requests.get(model_request_url)
  if(x.status_code != 200):
      raise Exception("Invalid CivitAI URL")
  model_data = x.json()
#  if(model_data["nsfw"] == True or model_data["nsfwLevel"] > 2):
#    gr.Warning("The model is tagged by CivitAI as adult content and cannot be used in this shared environment.")
#    raise Exception("The model is tagged by CivitAI as adult content and cannot be used in this shared environment.")
#  elif(model_data["type"] != "LORA"):
  if(model_data["type"] != "LORA"):
    gr.Warning("The model isn't tagged at CivitAI as a LoRA")
    raise Exception("The model isn't tagged at CivitAI as a LoRA")
  model_link_download = None
  image_url = None
  trigger_word = ""
  for model in model_data["modelVersions"]:
    if(model["baseModel"] == "SDXL 1.0"):
      model_link_download = f"{model['downloadUrl']}/?token={os.getenv('CIVITAI_TOKEN')}"
      safetensors_name = model["files"][0]["name"]
      if(not os.path.exists(f"{lora_archive}/{safetensors_name}")):
        safetensors_file_request = requests.get(model_link_download)
        if(safetensors_file_request.status_code != 200):
            raise Exception("Invalid CivitAI download link")
        with open(f"{lora_archive}/{safetensors_name}", 'wb') as file:
            file.write(safetensors_file_request.content)
      trigger_word = model.get("trainedWords", [""])[0]
      for image in model["images"]:
        image_url = image["url"]
        break
#        if(image["nsfwLevel"] == 1):
#          image_url = image["url"]
#          break
      break
  if(not model_link_download):
    gr.Warning("We couldn't find a SDXL LoRA on the model you've sent")
    raise Exception("We couldn't find a SDXL LoRA on the model you've sent")
  return model_data["name"], f"{lora_archive}/{safetensors_name}", trigger_word, image_url
  
def check_custom_model(link):
    if(link.startswith("https://")):
        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
        elif(link.startswith("https://civitai.com") or link.startswith("https://www.civitai.com")):
            return get_civitai_safetensors(link)
    else: 
        return get_huggingface_safetensors(link)

def show_loading_widget():
    return gr.update(visible=True)

def load_custom_lora(link):
    try:
        title, path, trigger_word, image = check_custom_model(link)
        card = f'''
        <div class="custom_lora_card">
          <span>Loaded custom LoRA:</span>
          <div class="card_internal">
            <img src="{image}" />
            <div>
                <h3>{title}</h3>
                <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
            </div>
          </div>
        </div>
        '''
        return gr.update(visible=True), card, gr.update(visible=True), [path, trigger_word], gr.Gallery(selected_index=None), f"Custom: {path}"
    except Exception as e:
        gr.Warning("Invalid LoRA: either you entered an invalid link, a non-SDXL LoRA or a LoRA with mature content")
        return gr.update(visible=True), "Invalid LoRA: either you entered an invalid link, a non-SDXL LoRA or a LoRA with mature content", gr.update(visible=False), None, gr.update(visible=True), gr.update(visible=True)
    
def remove_custom_lora():
    return "", gr.update(visible=False), gr.update(visible=False), None
with gr.Blocks(css="custom.css") as demo:
    gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
    title = gr.HTML(
        """<h1>
<span>Face to All<br><small style="
    font-size: 13px;
    display: block;
    font-weight: normal;
    opacity: 0.75;
">🧨 diffusers InstantID + ControlNet<br> inspired by fofr's <a href="https://github.com/fofr/cog-face-to-many" target="_blank">face-to-many</a></small></span></h1>""",
        elem_id="title",
    )
    selected_state = gr.State()
    custom_loaded_lora = gr.State()
    with gr.Row(elem_id="main_app"):
        with gr.Column(scale=4, elem_id="box_column"):
            with gr.Group(elem_id="gallery_box"):
                photo = gr.Image(label="Upload a picture of yourself", interactive=True, type="pil", height=300)
                selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", )
                #order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio")
                #new_gallery = gr.Gallery(
                #    label="New LoRAs",
                #    elem_id="gallery_new",
                #    columns=3,
                #    value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False)
                gallery = gr.Gallery(
                    #value=[(item["image"], item["title"]) for item in sdxl_loras],
                    label="Pick a style from the gallery",
                    allow_preview=False,
                    columns=4,
                    elem_id="gallery",
                    show_share_button=False,
                    height=550
                )
                custom_model = gr.Textbox(label="or enter a custom Hugging Face or CivitAI SDXL LoRA (Example: https://civitai.com/models/262880/eldritch-comics-comic-book-style-illustration?modelVersionId=305491)", placeholder="Paste Hugging Face or CivitAI model path...")
                custom_model_card = gr.HTML(visible=False)
                custom_model_button = gr.Button("Remove custom LoRA", visible=False)
        with gr.Column(scale=5):
            with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, info="Describe your subject (optional)", value="a person", elem_id="prompt")
                button = gr.Button("Run", elem_id="run_button")
            result = gr.Image(
                interactive=False, label="Generated Image", elem_id="result-image"
            )
            with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
                community_icon = gr.HTML(community_icon_html)
                loading_icon = gr.HTML(loading_icon_html)
                share_button = gr.Button("Share to community", elem_id="share-btn")
            with gr.Accordion("Advanced options", open=False):
                negative = gr.Textbox(label="Negative Prompt")
                weight = gr.Slider(0, 10, value=0.9, step=0.1, label="LoRA weight")
                face_strength = gr.Slider(0, 1, value=0.85, step=0.01, label="Face strength", info="Higher values increase the face likeness but reduce the creative liberty of the models")
                image_strength = gr.Slider(0, 1, value=0.15, step=0.01, label="Image strength", info="Higher values increase the similarity with the structure/colors of the original photo")
                guidance_scale = gr.Slider(0, 50, value=7, step=0.1, label="Guidance Scale")
                depth_control_scale = gr.Slider(0, 1, value=0.8, step=0.01, label="Zoe Depth ControlNet strenght")
            prompt_title = gr.Markdown(
                value="### Click on a LoRA in the gallery to select it",
                visible=True,
                elem_id="selected_lora",
            )
    #order_gallery.change(
    #    fn=swap_gallery,
    #    inputs=[order_gallery, gr_sdxl_loras],
    #    outputs=[gallery, gr_sdxl_loras],
    #    queue=False
    #)
    custom_model.input(
        fn=load_custom_lora,
        inputs=[custom_model],
        outputs=[custom_model_card, custom_model_card, custom_model_button, custom_loaded_lora, gallery, prompt_title],
        queue=False
    )
    custom_model_button.click(
        fn=remove_custom_lora,
        outputs=[custom_model, custom_model_button, custom_model_card, custom_loaded_lora]
    )
    gallery.select(
        fn=update_selection,
        inputs=[gr_sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative],
        outputs=[prompt_title, prompt, face_strength, image_strength, weight, depth_control_scale, negative, selected_state],
        queue=False,
        show_progress=False
    )
    #new_gallery.select(
    #    fn=update_selection,
    #    inputs=[gr_sdxl_loras_new, gr.State(True)],
    #    outputs=[prompt_title, prompt, prompt, selected_state, gallery],
    #    queue=False,
    #    show_progress=False
    #)
    prompt.submit(
        fn=check_selected,
        inputs=[selected_state, custom_loaded_lora],
        queue=False,
        show_progress=False
    ).success(
        fn=run_lora,
        inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras, custom_loaded_lora],
        outputs=[result, share_group],
    )
    button.click(
        fn=check_selected,
        inputs=[selected_state, custom_loaded_lora],
        queue=False,
        show_progress=False
    ).success(
        fn=run_lora,
        inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras, custom_loaded_lora],
        outputs=[result, share_group],
    )
    share_button.click(None, [], [], js=share_js)
    demo.load(fn=classify_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False, js=js)
demo.queue(max_size=20)
demo.launch(share=True)