app.py

import spaces
import gradio as gr
import json
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image, AutoPipelineForInpainting, GGUFQuantizationConfig
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from diffusers import (FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel, FluxControlNetImg2ImgPipeline,
                       FluxTransformer2DModel, FluxControlNetInpaintPipeline, FluxImg2ImgPipeline, FluxInpaintPipeline, FluxFillPipeline, FluxControlPipeline)
from transformers import T5EncoderModel
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download, HfApi
import os
import copy
import random
import time
import requests
import pandas as pd
import numpy as np
from pathlib import Path

from env import models, models_dev, models_schnell, models_fill, models_canny, models_depth, num_loras, num_cns, HF_TOKEN, single_file_base_models
from mod import (clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists, get_model_trigger,
                 description_ui, compose_lora_json, is_valid_lora, fuse_loras, turbo_loras, save_image, preprocess_i2i_image,
                 get_trigger_word, enhance_prompt, set_control_union_image, get_canny_image, get_depth_image,
                 get_control_union_mode, set_control_union_mode, get_control_params, translate_to_en)
from modutils import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
                      download_my_lora_flux, get_all_lora_tupled_list, apply_lora_prompt_flux,
                      update_loras_flux, update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD,
                      get_t2i_model_info, download_hf_file, save_image_history)
from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
from tagger.fl2flux import predict_tags_fl2_flux

#Load prompts for randomization
df = pd.read_csv('prompts.csv', header=None)
prompt_values = df.values.flatten()

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Initialize the base model
base_model = models[0]
controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
#controlnet_model_union_repo = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro'
dtype = torch.bfloat16
#dtype = torch.float8_e4m3fn
device = "cuda" if torch.cuda.is_available() else "cpu"
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype, token=HF_TOKEN)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype, token=HF_TOKEN)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1, token=HF_TOKEN)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model, vae=good_vae, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
 tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype, token=HF_TOKEN)
controlnet_union = None
controlnet = None
last_model = models[0]
last_cn_on = False
last_task = "Text-to-Image"
last_dtype_str = "BF16"
#controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
#controlnet = FluxMultiControlNetModel([controlnet_union])
#controlnet.config = controlnet_union.config

MAX_SEED = 2**32-1
TASK_TYPE_T2I = ["Text-to-Image"]
TASK_TYPE_I2I = ["Image-to-Image", "Inpainting", "Flux Fill"] # , "Canny", "Depth"

def unload_lora():
    global pipe, pipe_i2i
    try:
        #pipe.unfuse_lora()
        pipe.unload_lora_weights()
        #pipe_i2i.unfuse_lora()
        pipe_i2i.unload_lora_weights()
    except Exception as e:
        print(e)

def download_file_mod(url, directory=os.getcwd()):
    path = download_hf_file(directory, url, hf_token=HF_TOKEN)
    if not path: raise Exception(f"Download error: {url}")
    return path

def print_progress(desc: str, proceed: float=0.0, progress=gr.Progress(track_tqdm=True)):
    progress(proceed, desc=desc)
    print(desc)

#@spaces.GPU(duration=30)
def load_quantized_control(control_repo: str, dtype, hf_token):
    transformer = FluxTransformer2DModel.from_pretrained(control_repo, subfolder="transformer", torch_dtype=dtype, token=hf_token).to("cpu")
    text_encoder_2 = T5EncoderModel.from_pretrained(control_repo, subfolder="text_encoder_2", torch_dtype=dtype, token=hf_token).to("cpu")
    return transformer, text_encoder_2

def load_pipeline(pipe, pipe_i2i, repo_id: str, cn_on: bool, model_type: str, task: str, dtype_str: str, hf_token: str, progress=gr.Progress(track_tqdm=True)):
    try:
        controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
        if task == "Flux Fill" or repo_id in models_fill:
            model_type = "fill"
            if repo_id in set(models_dev + models_schnell): repo_id = models_fill[0]
        if dtype_str == "BF16": dtype = torch.bfloat16
        else: dtype = torch.bfloat16
        single_file_base_model = single_file_base_models.get(model_type, models[0])
        kwargs = {}
        transformer_model = FluxTransformer2DModel
        t5_model = T5EncoderModel
        if task == "Flux Fill":
            pipeline = FluxFillPipeline
            pipeline_i2i = FluxFillPipeline
        elif task == "Canny" or task == "Depth":
            pipeline = DiffusionPipeline
            pipeline_i2i = FluxControlPipeline
        elif cn_on: # with ControlNet
            print_progress(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}", 0, progress)
            controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype, token=hf_token)
            controlnet = FluxMultiControlNetModel([controlnet_union])
            controlnet.config = controlnet_union.config
            pipeline = FluxControlNetPipeline
            pipeline_i2i = FluxControlNetInpaintPipeline if task == "Inpainting" else FluxControlNetImg2ImgPipeline
            kwargs["controlnet"] = controlnet
        else: # without ControlNet
            print_progress(f"Loading model: {repo_id}", 0, progress)
            pipeline = DiffusionPipeline
            pipeline_i2i = AutoPipelineForInpainting if task == "Inpainting" else AutoPipelineForImage2Image
        if task == "Canny" or task == "Depth": # FluxControlPipeline
            if task == "Canny": control_repo = models_canny[0]
            elif task == "Depth": control_repo = models_depth[0]
            transformer = transformer_model.from_pretrained(control_repo, subfolder="transformer", torch_dtype=dtype, token=hf_token)
            text_encoder_2 = t5_model.from_pretrained(control_repo, subfolder="text_encoder_2", torch_dtype=dtype, token=hf_token)
            #transformer, text_encoder_2 = load_quantized_control(control_repo, dtype, hf_token)
            pipe = pipeline.from_pretrained(models_dev[0], transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=dtype, token=hf_token)
            pipe_i2i = pipeline_i2i.from_pipe(pipe, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=dtype)
        elif ".safetensors" in repo_id or ".gguf" in repo_id: # from single file
            file_url = repo_id.replace("/resolve/main/", "/blob/main/").replace("?download=true", "")
            if ".gguf" in file_url: transformer = transformer_model.from_single_file(file_url, subfolder="transformer",
                quantization_config=GGUFQuantizationConfig(compute_dtype=dtype), torch_dtype=dtype, config=single_file_base_model)
            else: transformer = transformer_model.from_single_file(file_url, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model)
            if not transformer: transformer = transformer_model.from_pretrained(single_file_base_model, subfolder="transformer", torch_dtype=dtype, token=hf_token)
            pipe = pipeline.from_pretrained(single_file_base_model, transformer=transformer, torch_dtype=dtype, token=hf_token, **kwargs)
            pipe_i2i = pipeline_i2i.from_pretrained(single_file_base_model, vae=pipe.vae, transformer=pipe.transformer,
                text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2,
                torch_dtype=dtype, token=hf_token, **kwargs)
        else: # from diffusers repo
            pipe = pipeline.from_pretrained(repo_id, torch_dtype=dtype, token=hf_token, **kwargs)
            pipe_i2i = pipeline_i2i.from_pretrained(repo_id, vae=pipe.vae, transformer=pipe.transformer, text_encoder=pipe.text_encoder,
                tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype, token=hf_token, **kwargs)
        if cn_on: print_progress(f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}", 1, progress)
        else: print_progress(f"Model loaded: {repo_id}", 1, progress)
    except Exception as e:
        print(e)
        gr.Warning(f"Failed to load pipeline: {e}")
    finally:
        return pipe, pipe_i2i

#load_pipeline.zerogpu = True

# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
# https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux
#@spaces.GPU()
def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, model_type: str, task: str, dtype_str: str, progress=gr.Progress(track_tqdm=True)):
    global pipe, pipe_i2i, taef1, good_vae, controlnet_union, controlnet, last_model, last_cn_on, last_task, last_dtype_str, dtype
    try:
        if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on and task == last_task and dtype_str == last_dtype_str)\
            or ((not is_repo_name(repo_id) or not is_repo_exists(repo_id)) and not ".safetensors" in repo_id): return gr.update() #  and not ".gguf" in repo_id
        unload_lora()
        pipe.to("cpu")
        pipe_i2i.to("cpu")
        good_vae.to("cpu")
        taef1.to("cpu")
        if controlnet is not None: controlnet.to("cpu")
        if controlnet_union is not None: controlnet_union.to("cpu")
        pipe, pipe_i2i = load_pipeline(pipe, pipe_i2i, repo_id, cn_on, model_type, task, dtype_str, HF_TOKEN, progress)
        clear_cache()
        last_model = repo_id
        last_cn_on = cn_on
        last_task = task
        last_dtype_str = dtype_str
    except Exception as e:
        print(f"Model load Error: {repo_id} {e}")
        raise gr.Error(f"Model load Error: {repo_id} {e}") from e
    return gr.update()

change_base_model.zerogpu = True

def is_repo_public(repo_id: str):
    api = HfApi()
    try:
        if api.repo_exists(repo_id=repo_id, token=False): return True
        else: return False
    except Exception as e:
        print(f"Error: Failed to connect {repo_id}. {e}")
        return False

def calc_sigmas(num_inference_steps: int, sigmas_factor: float):
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
    sigmas = sigmas * sigmas_factor
    return sigmas

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def download_file(url, directory=None):
    if directory is None:
        directory = os.getcwd()  # Use current working directory if not specified

    # Get the filename from the URL
    filename = url.split('/')[-1]
    
    # Full path for the downloaded file
    filepath = os.path.join(directory, filename)
    
    # Download the file
    response = requests.get(url)
    response.raise_for_status()  # Raise an exception for bad status codes
    
    # Write the content to the file
    with open(filepath, 'wb') as file:
        file.write(response.content)
    
    return filepath

def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
    selected_index = evt.index
    selected_indices = selected_indices or []
    if selected_index in selected_indices:
        selected_indices.remove(selected_index)
    else:
        if len(selected_indices) < 2:
            selected_indices.append(selected_index)
        else:
            gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
            return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), width, height, gr.update(), gr.update()

    selected_info_1 = "Select a LoRA 1"
    selected_info_2 = "Select a LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
        lora_image_2 = lora2['image']

    if selected_indices:
        last_selected_lora = loras_state[selected_indices[-1]]
        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
    else:
        new_placeholder = "Type a prompt"

    return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2

def remove_lora_1(selected_indices, loras_state):
    if len(selected_indices) >= 1:
        selected_indices.pop(0)
    selected_info_1 = "Select LoRA 1"
    selected_info_2 = "Select LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def remove_lora_2(selected_indices, loras_state):
    if len(selected_indices) >= 2:
        selected_indices.pop(1)
    selected_info_1 = "Select LoRA 1"
    selected_info_2 = "Select LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras_state[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras_state[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def randomize_loras(selected_indices, loras_state):
    if len(loras_state) < 2:
        raise gr.Error("Not enough LoRAs to randomize.")
    selected_indices = random.sample(range(len(loras_state)), 2)
    lora1 = loras_state[selected_indices[0]]
    lora2 = loras_state[selected_indices[1]]
    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = lora1['image']
    lora_image_2 = lora2['image']
    random_prompt = random.choice(prompt_values)
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, random_prompt

def download_loras_images(loras_json_orig: list[dict]):
    api = HfApi(token=HF_TOKEN)
    loras_json = []
    for lora in loras_json_orig:
        repo = lora.get("repo", None)
        if repo is None or not api.repo_exists(repo_id=repo, token=HF_TOKEN):
            print(f"LoRA '{repo}' is not exsit.")
            continue
        if "title" not in lora.keys() or "trigger_word" not in lora.keys() or "image" not in lora.keys():
            title, _repo, _path, trigger_word, image_def = check_custom_model(repo)
            if "title" not in lora.keys(): lora["title"] = title
            if "trigger_word" not in lora.keys(): lora["trigger_word"] = trigger_word
            if "image" not in lora.keys(): lora["image"] = image_def
        image = lora.get("image", None)
        try:
            if not is_repo_public(repo) and image is not None and "http" in image and repo in image: image = download_file_mod(image)
            lora["image"] = image if image else "/home/user/app/custom.png"
        except Exception as e:
            print(f"Failed to download LoRA '{repo}''s image '{image if image else ''}'. {e}")
            lora["image"] = "/home/user/app/custom.png"
        loras_json.append(lora)
    return loras_json

def add_custom_lora(custom_lora, selected_indices, current_loras, gallery):
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            if image is not None and "http" in image and not is_repo_public(repo) and repo in image:
                try:
                    image = download_file_mod(image)
                except Exception as e:
                    print(e)
                    image = None
            print(f"Loaded custom LoRA: {repo}")
            existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None)
            if existing_item_index is None:
                if repo.endswith(".safetensors") and repo.startswith("http"):
                    #repo = download_file(repo)
                    repo = download_file_mod(repo)
                new_item = {
                    "image": image if image else "/home/user/app/custom.png",
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(f"New LoRA: {new_item}")
                existing_item_index = len(current_loras)
                current_loras.append(new_item)
            
            # Update gallery
            gallery_items = [(item["image"], item["title"]) for item in current_loras]
            # Update selected_indices if there's room
            if len(selected_indices) < 2:
                selected_indices.append(existing_item_index)
            else:
                gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")

            # Update selected_info and images
            selected_info_1 = "Select a LoRA 1"
            selected_info_2 = "Select a LoRA 2"
            lora_scale_1 = 1.15
            lora_scale_2 = 1.15
            lora_image_1 = None
            lora_image_2 = None
            if len(selected_indices) >= 1:
                lora1 = current_loras[selected_indices[0]]
                selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
                lora_image_1 = lora1['image'] if lora1['image'] else None
            if len(selected_indices) >= 2:
                lora2 = current_loras[selected_indices[1]]
                selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
                lora_image_2 = lora2['image'] if lora2['image'] else None
            print("Finished adding custom LoRA")
            return (
                current_loras,
                gr.update(value=gallery_items),
                selected_info_1, 
                selected_info_2,
                selected_indices,
                lora_scale_1,
                lora_scale_2,
                lora_image_1,
                lora_image_2
            )
        except Exception as e:
            print(e)
            gr.Warning(str(e))
            return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
    else:
        return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()

def remove_custom_lora(selected_indices, current_loras, gallery):
    if current_loras:
        custom_lora_repo = current_loras[-1]['repo']
        # Remove from loras list
        current_loras = current_loras[:-1]
        # Remove from selected_indices if selected
        custom_lora_index = len(current_loras)
        if custom_lora_index in selected_indices:
            selected_indices.remove(custom_lora_index)
    # Update gallery
    gallery_items = [(item["image"], item["title"]) for item in current_loras]
    # Update selected_info and images
    selected_info_1 = "Select a LoRA 1"
    selected_info_2 = "Select a LoRA 2"
    lora_scale_1 = 1.15
    lora_scale_2 = 1.15
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = current_loras[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = current_loras[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return (
        current_loras,
        gr.update(value=gallery_items),
        selected_info_1,
        selected_info_2,
        selected_indices,
        lora_scale_1,
        lora_scale_2,
        lora_image_1,
        lora_image_2
    )

@spaces.GPU(duration=70)
@torch.inference_mode()
def generate_image(prompt_mash: str, steps: int, seed: int, cfg_scale: float, width: int, height: int, sigmas_factor: float, cn_on: bool, progress=gr.Progress(track_tqdm=True)):
    global pipe, taef1, good_vae, controlnet, controlnet_union
    try:
        good_vae.to(device)
        taef1.to(device)
        generator = torch.Generator(device=device).manual_seed(int(float(seed)))
        sigmas = calc_sigmas(steps, sigmas_factor)
        
        with calculateDuration("Generating image"):
            # Generate image
            modes, images, scales = get_control_params()
            if not cn_on or len(modes) == 0: # without ControlNet
                pipe.to(device)
                pipe.vae = taef1
                pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
                print_progress("Start Inference.")
                for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
                    prompt=prompt_mash,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    width=width,
                    height=height,
                    generator=generator,
                    joint_attention_kwargs={"scale": 1.0},
                    output_type="pil",
                    good_vae=good_vae,
                    sigmas=sigmas,
                ):
                    yield img
            else: # with ControlNet
                pipe.to(device)
                pipe.vae = good_vae
                if controlnet_union is not None: controlnet_union.to(device)
                if controlnet is not None: controlnet.to(device)
                pipe.enable_model_cpu_offload()
                print_progress("Start Inference with ControlNet.")
                for img in pipe(
                    prompt=prompt_mash,
                    control_image=images,
                    control_mode=modes,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    width=width,
                    height=height,
                    controlnet_conditioning_scale=scales,
                    generator=generator,
                    joint_attention_kwargs={"scale": 1.0},
                    sigmas=sigmas,
                ).images:
                    yield img
    except Exception as e:
        print(e)
        raise gr.Error(f"Inference Error: {e}") from e

@spaces.GPU(duration=70)
@torch.inference_mode()
def generate_image_to_image(prompt_mash: str, image_input_path_dict: dict, image_strength: float, task_type: str, blur_mask: bool, blur_factor: float,

                            steps: int, cfg_scale: float, width: int, height: int, sigmas_factor: float, seed: int, cn_on: bool, progress=gr.Progress(track_tqdm=True)):
    global pipe_i2i, good_vae, controlnet, controlnet_union
    try:
        good_vae.to(device)
        generator = torch.Generator(device=device).manual_seed(int(float(seed)))
        image_input_path = image_input_path_dict['background']
        mask_path = image_input_path_dict['layers'][0]
        is_mask = True if task_type == "Inpainting" or task_type == "Flux Fill" else False
        is_fill = True if task_type == "Flux Fill" else False
        is_depth = True if task_type == "Depth" else False
        is_canny = True if task_type == "Canny" else False
        kwargs = {}
        if not is_fill: kwargs["strength"] = image_strength
        if sigmas_factor < 1.0 and task_type != "Image-to-Image": kwargs["sigmas"] = calc_sigmas(steps, sigmas_factor)

        with calculateDuration("Generating image"):
            # Generate image
            modes, images, scales = get_control_params()
            if not cn_on or len(modes) == 0: # without ControlNet
                pipe_i2i.to(device)
                pipe_i2i.vae = good_vae
                image_input = load_image(image_input_path)
                if is_mask:
                    mask_input = load_image(mask_path)
                    if blur_mask: mask_input = pipe_i2i.mask_processor.blur(mask_input, blur_factor=blur_factor)
                    kwargs["mask_image"] = mask_input
                    if is_fill: print_progress("Start Flux Fill Inference.")
                    else: print_progress("Start Inpainting Inference.")
                elif is_canny:
                    image_input = get_canny_image(image_input, height, width)
                    print_progress("Start Canny Inference.")
                elif is_depth:
                    image_input = get_depth_image(image_input, height, width)
                    print_progress("Start Depth Inference.")
                else: print_progress("Start I2I Inference.")
                final_image = pipe_i2i(
                    prompt=prompt_mash,
                    image=image_input,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    #width=width,
                    #height=height,
                    generator=generator,
                    joint_attention_kwargs={"scale": 1.0},
                    output_type="pil",
                    **kwargs,
                ).images[0]
                return final_image 
            else: # with ControlNet
                pipe_i2i.to(device)
                pipe_i2i.vae = good_vae
                image_input = load_image(image_input_path)
                if controlnet_union is not None: controlnet_union.to(device)
                if controlnet is not None: controlnet.to(device)
                if is_mask:
                    mask_input = load_image(mask_path)
                    if blur_mask: mask_input = pipe_i2i.mask_processor.blur(mask_input, blur_factor=blur_factor)
                    kwargs["mask_image"] = mask_input
                    if is_fill: print_progress("Start Flux Fill Inference with ControlNet.")
                    else: print_progress("Start Inpainting Inference with ControlNet.")
                else: print_progress("Start I2I Inference with ControlNet.")
                pipe_i2i.enable_model_cpu_offload()
                final_image = pipe_i2i(
                    prompt=prompt_mash,
                    control_image=images,
                    control_mode=modes,
                    image=image_input,
                    num_inference_steps=steps,
                    guidance_scale=cfg_scale,
                    #width=width,
                    #height=height,
                    controlnet_conditioning_scale=scales,
                    generator=generator,
                    joint_attention_kwargs={"scale": 1.0},
                    output_type="pil",
                    **kwargs,
                ).images[0]
                return final_image
    except Exception as e:
        print(e)
        raise gr.Error(f"I2I Inference Error: {e}") from e

def run_lora(prompt: str, image_input: dict, image_strength: float, task_type: str, turbo_mode: str, blur_mask: bool, blur_factor: float,

             cfg_scale: float, steps: int, selected_indices, lora_scale_1: float, lora_scale_2: float,

             randomize_seed: bool, seed: int, width: int, height: int, sigmas_factor: float, loras_state,

             lora_json, cn_on: bool, translate_on: bool, progress=gr.Progress(track_tqdm=True)):
    global pipe, pipe_i2i
    if not selected_indices and not is_valid_lora(lora_json):
        gr.Info("LoRA isn't selected.")
    #    raise gr.Error("You must select a LoRA before proceeding.")
    progress(0, desc="Preparing Inference.")

    selected_loras = [loras_state[idx] for idx in selected_indices]

    if task_type in set(TASK_TYPE_I2I): is_i2i = True
    else: is_i2i = False
    if translate_on: prompt = translate_to_en(prompt)

    # Build the prompt with trigger words
    prepends = []
    appends = []
    for lora in selected_loras:
        trigger_word = lora.get('trigger_word', '')
        if trigger_word:
            if lora.get("trigger_position") == "prepend":
                prepends.append(trigger_word)
            else:
                appends.append(trigger_word)
    prompt_mash = " ".join(prepends + [prompt] + appends)
    print("Prompt Mash: ", prompt_mash) #

    # Unload previous LoRA weights
    with calculateDuration("Unloading LoRA"):
        unload_lora()

    print(pipe.get_active_adapters()) #
    print(pipe_i2i.get_active_adapters()) #

    clear_cache() #

    # Build the prompt for External LoRAs
    prompt_mash = prompt_mash + get_model_trigger(last_model)
    lora_names = []
    lora_weights = []
    # Load Turbo LoRA weights
    if turbo_mode != "None":
        if is_i2i: pipe_i2i, lora_names, lora_weights, steps = turbo_loras(pipe_i2i, turbo_mode, lora_names, lora_weights)
        else: pipe, lora_names, lora_weights, steps = turbo_loras(pipe, turbo_mode, lora_names, lora_weights)

    # Load External LoRA weights
    if is_valid_lora(lora_json):
        with calculateDuration("Loading External LoRA weights"):
            if is_i2i: pipe_i2i, lora_names, lora_weights = fuse_loras(pipe_i2i, lora_json, lora_names, lora_weights)
            else: pipe, lora_names, lora_weights = fuse_loras(pipe, lora_json, lora_names, lora_weights)
            trigger_word = get_trigger_word(lora_json)
            prompt_mash = f"{prompt_mash} {trigger_word}"
    print("Prompt Mash: ", prompt_mash) #

    # Load LoRA weights with respective scales
    if selected_indices:
        with calculateDuration("Loading LoRA weights"):
            for idx, lora in enumerate(selected_loras):
                lora_name = f"lora_{idx}"
                lora_names.append(lora_name)
                print(f"Lora Name: {lora_name}")
                lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
                lora_path = lora['repo']
                weight_name = lora.get("weights")
                print(f"Lora Path: {lora_path}")
                if is_i2i:
                    pipe_i2i.load_lora_weights(
                        lora_path, 
                        weight_name=weight_name if weight_name else None,
                        low_cpu_mem_usage=False,
                        adapter_name=lora_name,
                        token=HF_TOKEN
                    )
                else:
                    pipe.load_lora_weights(
                        lora_path, 
                        weight_name=weight_name if weight_name else None,
                        low_cpu_mem_usage=False,
                        adapter_name=lora_name,
                        token=HF_TOKEN
                    )
            print("Loaded LoRAs:", lora_names)
    if selected_indices or is_valid_lora(lora_json):
        if is_i2i: pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
        else: pipe.set_adapters(lora_names, adapter_weights=lora_weights)

    print(pipe.get_active_adapters()) #
    print(pipe_i2i.get_active_adapters()) #

    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
    
    # Generate image
    progress(0, desc="Running Inference.")
    if is_i2i:
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, task_type, blur_mask, blur_factor,
                                              steps, cfg_scale, width, height, sigmas_factor, seed, cn_on)
        yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, sigmas_factor, cn_on)
        # Consume the generator to get the final image
        final_image = None
        step_counter = 0
        for image in image_generator:
            step_counter+=1
            final_image = image
            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
            yield image, seed, gr.update(value=progress_bar, visible=True)
        yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(value=progress_bar, visible=False)

run_lora.zerogpu = True

def get_huggingface_safetensors(link):
    split_link = link.split("/")
    if len(split_link) == 2:
        model_card = ModelCard.load(link, token=HF_TOKEN)
        base_model = model_card.data.get("base_model")
        print(f"Base model: {base_model}")
        if base_model not in ["black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"]:
            #raise Exception("Not a FLUX LoRA!")
            gr.Warning("Not a FLUX LoRA?")
        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(token=HF_TOKEN)
        safetensors_name = None
        try:
            list_of_files = fs.ls(link, detail=False)
            for file in list_of_files:
                if file.endswith(".safetensors"):
                    safetensors_name = file.split("/")[-1]
                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 Exception as e:
            print(e)
            raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
        if not safetensors_name:
            raise gr.Error("No *.safetensors file found in the repository")
        return split_link[1], link, safetensors_name, trigger_word, image_url
    else:
        raise gr.Error("Invalid Hugging Face repository link")

def check_custom_model(link):
    if link.endswith(".safetensors"):
        # Treat as direct link to the LoRA weights
        title = os.path.basename(link)
        repo = link
        path = None  # No specific weight name
        trigger_word = ""
        image_url = None
        return title, repo, path, trigger_word, image_url
    elif link.startswith("https://"):
        if "huggingface.co" in link:
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
        else:
            raise Exception("Unsupported URL")
    else:
        # Assume it's a Hugging Face model path
        return get_huggingface_safetensors(link)

def update_history(new_image, history):
    """Updates the history gallery with the new image."""
    if history is None:
        history = []
    history.insert(0, new_image)
    return history

loras = download_loras_images(loras)

css = '''

#gen_column{align-self: stretch}

#gen_btn{height: 100%}

#title{text-align: center}

#title h1{font-size: 3em; display:inline-flex; align-items:center}

#title img{width: 100px; margin-right: 0.25em}

#gallery .grid-wrap{height: 5vh}

#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}

.custom_lora_card{margin-bottom: 1em}

.card_internal{display: flex;height: 100px;margin-top: .5em}

.card_internal img{margin-right: 1em}

.styler{--form-gap-width: 0px !important}

#progress{height:30px}

#progress .generating{display:none}

.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}

.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}

#component-8, .button_total{height: 100%; align-self: stretch;}

#loaded_loras [data-testid="block-info"]{font-size:80%}

#custom_lora_structure{background: var(--block-background-fill)}

#custom_lora_btn{margin-top: auto;margin-bottom: 11px}

#random_btn{font-size: 300%}

#component-11{align-self: stretch;}

.info { align-items: center; text-align: center; }

.desc [src$='#float'] { float: right; margin: 20px; }

'''
with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=css, delete_cache=(60, 3600)) as app:
    with gr.Tab("FLUX LoRA the Explorer"):
        title = gr.HTML(
            """<h1><img src="https://huggingface.co/spaces/John6666/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA">FLUX LoRA the Explorer Mod</h1>""",
            elem_id="title",
        )
        loras_state = gr.State(loras)
        selected_indices = gr.State([])
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Group():
                    with gr.Accordion("Generate Prompt from Image", open=False):
                        tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
                        with gr.Accordion(label="Advanced options", open=False):
                            tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
                            tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
                            neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
                            v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2, visible=False)
                            v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2, visible=False)
                            v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False, visible=False)
                        tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-Flux"], label="Algorithms", value=["Use WD Tagger"])
                        tagger_generate_from_image = gr.Button(value="Generate Prompt from Image")
                    prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt", show_copy_button=True)
                    with gr.Row():
                        prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary")
                        auto_trans = gr.Checkbox(label="Auto translate to English", value=False, elem_classes="info")
            with gr.Column(scale=1, elem_id="gen_column"):
                generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn", elem_classes=["button_total"])
        with gr.Row(elem_id="loaded_loras"):
            with gr.Column(scale=1, min_width=25):
                randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
            with gr.Column(scale=8):
                with gr.Row():
                    with gr.Column(scale=0, min_width=50):
                        lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
                    with gr.Column(scale=3, min_width=100):
                        selected_info_1 = gr.Markdown("Select a LoRA 1")
                    with gr.Column(scale=5, min_width=50):
                        lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
                with gr.Row():
                    remove_button_1 = gr.Button("Remove", size="sm")
            with gr.Column(scale=8):
                with gr.Row():
                    with gr.Column(scale=0, min_width=50):
                        lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
                    with gr.Column(scale=3, min_width=100):
                        selected_info_2 = gr.Markdown("Select a LoRA 2")
                    with gr.Column(scale=5, min_width=50):
                        lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
                with gr.Row():
                    remove_button_2 = gr.Button("Remove", size="sm")
        with gr.Row():
            with gr.Column():
                selected_info = gr.Markdown("")
                gallery = gr.Gallery([(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False,
                                     columns=4, elem_id="gallery", show_share_button=False, interactive=False)
                with gr.Group():
                    with gr.Row(elem_id="custom_lora_structure"):
                        custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="multimodalart/vintage-ads-flux", scale=3, min_width=150)
                        add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
                    remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
                    gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
            with gr.Column():
                progress_bar = gr.Markdown(elem_id="progress",visible=False)
                result = gr.Image(label="Generated Image", format="png", type="filepath", show_share_button=False, interactive=False)
                with gr.Accordion("History", open=False):
                    history_files = gr.Files(interactive=False, visible=False)
                    history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False, format="png",
                                                 show_share_button=False, show_download_button=True)
                    history_clear_button = gr.Button(value="Clear History", variant="secondary")
                    history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)
        with gr.Row():
            with gr.Accordion("Advanced Settings", open=True):
                with gr.Tab("Generation Settings"):
                    with gr.Column():
                        with gr.Group():
                            with gr.Row():
                                model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id or path of single safetensors file to want to use.",
                                                         choices=models, value=models[0], allow_custom_value=True, min_width=320, scale=5)
                                model_type = gr.Radio(label="Model type", info="Model type of single safetensors file",
                                                      choices=list(single_file_base_models.keys()), value=list(single_file_base_models.keys())[0], scale=1)
                            model_info = gr.Markdown(elem_classes="info")
                        with gr.Row():
                            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                            steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                        with gr.Row():
                            randomize_seed = gr.Checkbox(True, label="Randomize seed")
                            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                        with gr.Row():
                            turbo_mode = gr.Radio(label="Turbo mode", choices=["None", "Hyper-FLUX.1-dev-8steps", "Hyper-FLUX.1-dev-16steps", "FLUX.1-Turbo-Alpha 8-steps"], value="None")
                            sigmas_factor = gr.Slider(label="Sigmas factor", minimum=0.01, maximum=1.00, step=0.01, value=0.95)
                            data_type = gr.Radio(label="Data type", choices=["BF16"], value="BF16", visible=False)
                            disable_model_cache = gr.Checkbox(False, label="Disable model caching")
                with gr.Tab("Image-to-Image"):
                    with gr.Row():
                        with gr.Column():
                            #input_image = gr.Image(label="Input image", type="filepath", height=256, sources=["upload", "clipboard"], show_share_button=False)
                            input_image = gr.ImageEditor(label='Input image', type='filepath', sources=["upload", "clipboard"], image_mode='RGB', show_share_button=False, show_fullscreen_button=False,
                                                         layers=False, brush=gr.Brush(colors=["white"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"), value=None,
                                                         canvas_size=(384, 384), width=384, height=512)
                        with gr.Column():

                            task_type = gr.Radio(label="Task", choices=TASK_TYPE_T2I+TASK_TYPE_I2I, value=TASK_TYPE_T2I[0])
                            image_strength = gr.Slider(label="Strength", info="Lower means more image influence in I2I, opposite in Inpaint", minimum=0.01, maximum=1.0, step=0.01, value=0.75)
                            blur_mask = gr.Checkbox(label="Blur mask", value=False)
                            blur_factor = gr.Slider(label="Blur factor", minimum=0, maximum=50, step=1, value=33)
                            input_image_preprocess = gr.Checkbox(True, label="Preprocess Input image")
                with gr.Tab("More LoRA"):
                    with gr.Accordion("External LoRA", open=True):
                        with gr.Column():
                            deselect_lora_button = gr.Button("Remove External LoRAs", variant="secondary")
                            lora_repo_json = gr.JSON(value=[{}] * num_loras, visible=False)
                            lora_repo = [None] * num_loras
                            lora_weights = [None] * num_loras
                            lora_trigger = [None] * num_loras
                            lora_wt = [None] * num_loras
                            lora_info = [None] * num_loras
                            lora_copy = [None] * num_loras
                            lora_md = [None] * num_loras
                            lora_num = [None] * num_loras
                            with gr.Row():
                                for i in range(num_loras):
                                    with gr.Column():
                                        lora_repo[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Repo", choices=get_all_lora_tupled_list(), info="Input LoRA Repo ID", value="", allow_custom_value=True, min_width=320)
                                        with gr.Row():
                                            lora_weights[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Filename", choices=[], info="Optional", value="", allow_custom_value=True)
                                            lora_trigger[i] = gr.Textbox(label=f"LoRA {int(i+1)} Trigger Prompt", lines=1, max_lines=4, value="")
                                            lora_wt[i] = gr.Slider(label=f"LoRA {int(i+1)} Scale", minimum=-3, maximum=3, step=0.01, value=1.00)
                                        with gr.Row():
                                            lora_info[i] = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
                                            lora_copy[i] = gr.Button(value="Copy example to prompt", visible=False)
                                            lora_md[i] = gr.Markdown(value="", visible=False)
                                            lora_num[i] = gr.Number(i, visible=False)
                            with gr.Accordion("From URL", open=True, visible=True):
                                with gr.Row():
                                    lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Flux.1 D", "Flux.1 S"], value=["Flux.1 D"])
                                    lora_search_civitai_sort = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Most Downloaded")
                                    lora_search_civitai_period = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="Month")
                                with gr.Row():
                                    lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1)
                                    lora_search_civitai_tag = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
                                    lora_search_civitai_user = gr.Textbox(label="Username", lines=1)
                                lora_search_civitai_submit = gr.Button("Search on Civitai")
                                with gr.Row():
                                    lora_search_civitai_json = gr.JSON(value={}, visible=False)
                                    lora_search_civitai_desc = gr.Markdown(value="", visible=False, elem_classes="desc")
                                with gr.Accordion("Select from Gallery", open=False):
                                    lora_search_civitai_gallery = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False)
                                lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
                                lora_download_url = gr.Textbox(label="LoRA URL", placeholder="https://civitai.com/api/download/models/28907", lines=1)
                                with gr.Row():
                                    lora_download = [None] * num_loras
                                    for i in range(num_loras):
                                        lora_download[i] = gr.Button(f"Get and set LoRA to {int(i+1)}")
                with gr.Tab("ControlNet", visible=False):
                    with gr.Column():
                        cn_on = gr.Checkbox(False, label="Use ControlNet")
                        cn_mode = [None] * num_cns
                        cn_scale = [None] * num_cns
                        cn_image = [None] * num_cns
                        cn_image_ref = [None] * num_cns
                        cn_res = [None] * num_cns
                        cn_num = [None] * num_cns
                        with gr.Row():
                            for i in range(num_cns):
                                with gr.Column():
                                    cn_mode[i] = gr.Radio(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0])
                                    with gr.Row():
                                        cn_scale[i] = gr.Slider(label=f"ControlNet {int(i+1)} Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.75)
                                        cn_res[i] = gr.Slider(label=f"ControlNet {int(i+1)} Preprocess resolution", minimum=128, maximum=512, value=384, step=1)
                                        cn_num[i] = gr.Number(i, visible=False)
                                    with gr.Row():
                                        cn_image_ref[i] = gr.Image(label="Image Reference", type="pil", format="png", height=256, sources=["upload", "clipboard"], show_share_button=False)
                                        cn_image[i] = gr.Image(label="Control Image", type="pil", format="png", height=256, show_share_button=False, interactive=False)

    gallery.select(
        update_selection,
        inputs=[selected_indices, loras_state, width, height],
        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2])
    remove_button_1.click(
        remove_lora_1,
        inputs=[selected_indices, loras_state],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    remove_button_2.click(
        remove_lora_2,
        inputs=[selected_indices, loras_state],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    randomize_button.click(
        randomize_loras,
        inputs=[selected_indices, loras_state],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, prompt]
    )
    add_custom_lora_button.click(
        add_custom_lora,
        inputs=[custom_lora, selected_indices, loras_state, gallery],
        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    remove_custom_lora_button.click(
        remove_custom_lora,
        inputs=[selected_indices, loras_state, gallery],
        outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=change_base_model,
        inputs=[model_name, cn_on, disable_model_cache, model_type, task_type, data_type],
        outputs=[result],
        queue=True,
        show_api=False,
        trigger_mode="once",
    ).success(
        fn=run_lora,
        inputs=[prompt, input_image, image_strength, task_type, turbo_mode, blur_mask, blur_factor, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2,
                randomize_seed, seed, width, height, sigmas_factor, loras_state, lora_repo_json, cn_on, auto_trans], 
        outputs=[result, seed, progress_bar],
        queue=True,
        show_api=True,
    ).success(save_image_history, [result, history_gallery, history_files, model_name], [history_gallery, history_files], queue=False, show_api=False)

    input_image.clear(lambda: gr.update(value="Text-to-Image"), None, [task_type], queue=False, show_api=False)
    input_image.upload(preprocess_i2i_image, [input_image, input_image_preprocess, height, width], [input_image], queue=False, show_api=False)#\
    #.success(lambda: gr.update(value="Image-to-Image"), None, [task_type], queue=False, show_api=False)
    gr.on(
        triggers=[model_name.change, cn_on.change],
        fn=get_t2i_model_info,
        inputs=[model_name], 
        outputs=[model_info],
        queue=False,
        show_api=False,
        trigger_mode="once",
    )
    prompt_enhance.click(enhance_prompt, [prompt], [prompt], queue=False, show_api=False)

    gr.on(
        triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
        fn=search_civitai_lora,
        inputs=[lora_search_civitai_query, lora_search_civitai_basemodel, lora_search_civitai_sort, lora_search_civitai_period,
                lora_search_civitai_tag, lora_search_civitai_user, lora_search_civitai_gallery],
        outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query, lora_search_civitai_gallery],
        scroll_to_output=True,
        queue=True,
        show_api=False,
    )
    lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True)  # fn for api
    lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False)
    lora_search_civitai_gallery.select(update_civitai_selection, None, [lora_search_civitai_result], queue=False, show_api=False)

    for i, l in enumerate(lora_repo):
        deselect_lora_button.click(lambda: ("", 1.0), None, [lora_repo[i], lora_wt[i]], queue=False, show_api=False)
        gr.on(
            triggers=[lora_download[i].click],
            fn=download_my_lora_flux,
            inputs=[lora_download_url, lora_repo[i]],
            outputs=[lora_repo[i]],
            scroll_to_output=True,
            queue=True,
            show_api=False,
        )
        gr.on(
            triggers=[lora_repo[i].change, lora_wt[i].change],
            fn=update_loras_flux,
            inputs=[prompt, lora_repo[i], lora_wt[i]],
            outputs=[prompt, lora_repo[i], lora_wt[i], lora_info[i], lora_md[i]],
            queue=False,
            trigger_mode="once",
            show_api=False,
        ).success(get_repo_safetensors, [lora_repo[i]], [lora_weights[i]], queue=False, show_api=False
        ).success(apply_lora_prompt_flux, [lora_info[i]], [lora_trigger[i]], queue=False, show_api=False
        ).success(compose_lora_json, [lora_repo_json, lora_num[i], lora_repo[i], lora_wt[i], lora_weights[i], lora_trigger[i]], [lora_repo_json], queue=False, show_api=False)
        
    for i, m in enumerate(cn_mode):
        gr.on(
            triggers=[cn_mode[i].change, cn_scale[i].change],
            fn=set_control_union_mode,
            inputs=[cn_num[i], cn_mode[i], cn_scale[i]],
            outputs=[cn_on],
            queue=True,
            show_api=False,
        ).success(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
        cn_image_ref[i].upload(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)

    tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
    ).success(
        predict_tags_wd,
        [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
        [v2_series, v2_character, prompt, v2_copy],
        show_api=False,
    ).success(predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
    ).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)

    with gr.Tab("FLUX Prompt Generator"):
        from prompt import (PromptGenerator, HuggingFaceInferenceNode, florence_caption,
            ARTFORM, PHOTO_TYPE, ROLES, HAIRSTYLES, LIGHTING, COMPOSITION, POSE, BACKGROUND,
            PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM, PLACE,
            FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, FEMALE_BODY_TYPES, MALE_BODY_TYPES,
            FEMALE_CLOTHING, MALE_CLOTHING, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, pg_title)

        prompt_generator = PromptGenerator()
        huggingface_node = HuggingFaceInferenceNode()

        gr.HTML(pg_title)

        with gr.Row():
            with gr.Column(scale=2):
                with gr.Accordion("Basic Settings"):
                    pg_custom = gr.Textbox(label="Custom Input Prompt (optional)")
                    pg_subject = gr.Textbox(label="Subject (optional)")
                    pg_gender = gr.Radio(["female", "male"], label="Gender", value="female")
                    
                    # Add the radio button for global option selection
                    pg_global_option = gr.Radio(
                        ["Disabled", "Random", "No Figure Rand"],
                        label="Set all options to:",
                        value="Disabled"
                    )
                
                with gr.Accordion("Artform and Photo Type", open=False):
                    pg_artform = gr.Dropdown(["disabled", "random"] + ARTFORM, label="Artform", value="disabled")
                    pg_photo_type = gr.Dropdown(["disabled", "random"] + PHOTO_TYPE, label="Photo Type", value="disabled")
            
                with gr.Accordion("Character Details", open=False):
                    pg_body_types = gr.Dropdown(["disabled", "random"] + FEMALE_BODY_TYPES + MALE_BODY_TYPES, label="Body Types", value="disabled")
                    pg_default_tags = gr.Dropdown(["disabled", "random"] + FEMALE_DEFAULT_TAGS + MALE_DEFAULT_TAGS, label="Default Tags", value="disabled")
                    pg_roles = gr.Dropdown(["disabled", "random"] + ROLES, label="Roles", value="disabled")
                    pg_hairstyles = gr.Dropdown(["disabled", "random"] + HAIRSTYLES, label="Hairstyles", value="disabled")
                    pg_clothing = gr.Dropdown(["disabled", "random"] + FEMALE_CLOTHING + MALE_CLOTHING, label="Clothing", value="disabled")
            
                with gr.Accordion("Scene Details", open=False):
                    pg_place = gr.Dropdown(["disabled", "random"] + PLACE, label="Place", value="disabled")
                    pg_lighting = gr.Dropdown(["disabled", "random"] + LIGHTING, label="Lighting", value="disabled")
                    pg_composition = gr.Dropdown(["disabled", "random"] + COMPOSITION, label="Composition", value="disabled")
                    pg_pose = gr.Dropdown(["disabled", "random"] + POSE, label="Pose", value="disabled")
                    pg_background = gr.Dropdown(["disabled", "random"] + BACKGROUND, label="Background", value="disabled")
            
                with gr.Accordion("Style and Artist", open=False):
                    pg_additional_details = gr.Dropdown(["disabled", "random"] + FEMALE_ADDITIONAL_DETAILS + MALE_ADDITIONAL_DETAILS, label="Additional Details", value="disabled")
                    pg_photography_styles = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled")
                    pg_device = gr.Dropdown(["disabled", "random"] + DEVICE, label="Device", value="disabled")
                    pg_photographer = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHER, label="Photographer", value="disabled")
                    pg_artist = gr.Dropdown(["disabled", "random"] + ARTIST, label="Artist", value="disabled")
                    pg_digital_artform = gr.Dropdown(["disabled", "random"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled")
                
                pg_generate_button = gr.Button("Generate Prompt")

            with gr.Column(scale=2):
                with gr.Accordion("Image and Caption", open=False):
                    pg_input_image = gr.Image(label="Input Image (optional)")
                    pg_caption_output = gr.Textbox(label="Generated Caption", lines=3)
                    pg_create_caption_button = gr.Button("Create Caption")
                    pg_add_caption_button = gr.Button("Add Caption to Prompt")

                with gr.Accordion("Prompt Generation", open=True):
                    pg_output = gr.Textbox(label="Generated Prompt / Input Text", lines=4)
                    pg_t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True)
                    pg_clip_l_output = gr.Textbox(label="CLIP L Output", visible=True)
                    pg_clip_g_output = gr.Textbox(label="CLIP G Output", visible=True)
            
            with gr.Column(scale=2):
                with gr.Accordion("Prompt Generation with LLM", open=False):
                    pg_happy_talk = gr.Checkbox(label="Happy Talk", value=True)
                    pg_compress = gr.Checkbox(label="Compress", value=True)
                    pg_compression_level = gr.Radio(["soft", "medium", "hard"], label="Compression Level", value="hard")
                    pg_poster = gr.Checkbox(label="Poster", value=False)
                    pg_custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5)
                pg_generate_text_button = gr.Button("Generate Prompt with LLM (Llama 3.1 70B)")
                pg_text_output = gr.Textbox(label="Generated Text", lines=10)

        def create_caption(image):
            if image is not None:
                return florence_caption(image)
            return ""

        pg_create_caption_button.click(
            create_caption,
            inputs=[pg_input_image],
            outputs=[pg_caption_output]
        )

        def generate_prompt_with_dynamic_seed(*args):
            # Generate a new random seed
            dynamic_seed = random.randint(0, 1000000)
            
            # Call the generate_prompt function with the dynamic seed
            result = prompt_generator.generate_prompt(dynamic_seed, *args)
            
            # Return the result along with the used seed
            return [dynamic_seed] + list(result)

        pg_generate_button.click(
            generate_prompt_with_dynamic_seed,
            inputs=[pg_custom, pg_subject, pg_gender, pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles,
                    pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform,
                    pg_place, pg_lighting, pg_clothing, pg_composition, pg_pose, pg_background, pg_input_image],
            outputs=[gr.Number(label="Used Seed", visible=False), pg_output, gr.Number(visible=False), pg_t5xxl_output, pg_clip_l_output, pg_clip_g_output]
        ) #

        pg_add_caption_button.click(
            prompt_generator.add_caption_to_prompt,
            inputs=[pg_output, pg_caption_output],
            outputs=[pg_output]
        )

        pg_generate_text_button.click(
            huggingface_node.generate,
            inputs=[pg_output, pg_happy_talk, pg_compress, pg_compression_level, pg_poster, pg_custom_base_prompt],
            outputs=pg_text_output
        )

        def update_all_options(choice):
            updates = {}
            if choice == "Disabled":
                for dropdown in [
                    pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
                    pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
                    pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
                ]:
                    updates[dropdown] = gr.update(value="disabled")
            elif choice == "Random":
                for dropdown in [
                    pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
                    pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
                    pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
                ]:
                    updates[dropdown] = gr.update(value="random")
            else:  # No Figure Random
                for dropdown in [pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_pose, pg_additional_details]:
                    updates[dropdown] = gr.update(value="disabled")
                for dropdown in [pg_artform, pg_place, pg_lighting, pg_composition, pg_background, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform]:
                    updates[dropdown] = gr.update(value="random")
            return updates
        
        pg_global_option.change(
            update_all_options,
            inputs=[pg_global_option],
            outputs=[
                pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing,
                pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details,
                pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform
            ]
        )

    with gr.Tab("PNG Info"):
        def extract_exif_data(image):
            if image is None: return ""

            try:
                metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']

                for key in metadata_keys:
                    if key in image.info:
                        return image.info[key]

                return str(image.info)

            except Exception as e:
                return f"Error extracting metadata: {str(e)}"

        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],
                )

    description_ui()
    gr.LoginButton()
    gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")

app.queue()
app.launch(ssr_mode=False)