import os
import time
import sys
import threading
import argparse
from mmgp import offload, safetensors2, profile_type 
try:
    import triton
except ImportError:
    pass
from pathlib import Path
from datetime import datetime
import gradio as gr
import random
import json
import wan
from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS, SUPPORTED_SIZES, VACE_SIZE_CONFIGS
from wan.utils.utils import cache_video
from wan.modules.attention import get_attention_modes, get_supported_attention_modes
import torch
import gc
import traceback
import math 
import typing
import asyncio
import inspect
from wan.utils import prompt_parser
import base64
import io
from PIL import Image
import zipfile
import tempfile
import atexit
import shutil
import glob
from transformers.utils import logging
logging.set_verbosity_error

from tqdm import tqdm
import requests
global_queue_ref = []
AUTOSAVE_FILENAME = "queue.zip"
PROMPT_VARS_MAX = 10

target_mmgp_version = "3.4.7"
WanGP_version = "5.3"
prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None

from importlib.metadata import version
mmgp_version = version("mmgp")
if mmgp_version != target_mmgp_version:
    print(f"Incorrect version of mmgp ({mmgp_version}), version {target_mmgp_version} is needed. Please upgrade with the command 'pip install -r requirements.txt'")
    exit()
lock = threading.Lock()
current_task_id = None
task_id = 0


def download_ffmpeg():
    if os.name != 'nt': return
    exes = ['ffmpeg.exe', 'ffprobe.exe', 'ffplay.exe']
    if all(os.path.exists(e) for e in exes): return
    api_url = 'https://api.github.com/repos/GyanD/codexffmpeg/releases/latest'
    r = requests.get(api_url, headers={'Accept': 'application/vnd.github+json'})
    assets = r.json().get('assets', [])
    zip_asset = next((a for a in assets if 'essentials_build.zip' in a['name']), None)
    if not zip_asset: return
    zip_url = zip_asset['browser_download_url']
    zip_name = zip_asset['name']
    with requests.get(zip_url, stream=True) as resp:
        total = int(resp.headers.get('Content-Length', 0))
        with open(zip_name, 'wb') as f, tqdm(total=total, unit='B', unit_scale=True) as pbar:
            for chunk in resp.iter_content(chunk_size=8192):
                f.write(chunk)
                pbar.update(len(chunk))
    with zipfile.ZipFile(zip_name) as z:
        for f in z.namelist():
            if f.endswith(tuple(exes)) and '/bin/' in f:
                z.extract(f)
                os.rename(f, os.path.basename(f))
    os.remove(zip_name)

def format_time(seconds):
    if seconds < 60:
        return f"{seconds:.1f}s"
    elif seconds < 3600:
        minutes = seconds / 60
        return f"{minutes:.1f}m"
    else:
        hours = int(seconds // 3600)
        minutes = int((seconds % 3600) // 60)
        return f"{hours}h {minutes}m"
def pil_to_base64_uri(pil_image, format="png", quality=75):
    if pil_image is None:
        return None

    if isinstance(pil_image, str):
        from wan.utils.utils import get_video_frame
        pil_image = get_video_frame(pil_image, 0)

    buffer = io.BytesIO()
    try:
        img_to_save = pil_image
        if format.lower() == 'jpeg' and pil_image.mode == 'RGBA':
            img_to_save = pil_image.convert('RGB')
        elif format.lower() == 'png' and pil_image.mode not in ['RGB', 'RGBA', 'L', 'P']:
             img_to_save = pil_image.convert('RGBA')
        elif pil_image.mode == 'P':
             img_to_save = pil_image.convert('RGBA' if 'transparency' in pil_image.info else 'RGB')
        if format.lower() == 'jpeg':
            img_to_save.save(buffer, format=format, quality=quality)
        else:
            img_to_save.save(buffer, format=format)
        img_bytes = buffer.getvalue()
        encoded_string = base64.b64encode(img_bytes).decode("utf-8")
        return f"data:image/{format.lower()};base64,{encoded_string}"
    except Exception as e:
        print(f"Error converting PIL to base64: {e}")
        return None

def is_integer(n):
    try:
        float(n)
    except ValueError:
        return False
    else:
        return float(n).is_integer()


def process_prompt_and_add_tasks(state, model_choice):
 
    if state.get("validate_success",0) != 1:
        return
    
    state["validate_success"] = 0

    model_filename = state["model_filename"]

    model_type = get_model_type(model_filename)
    inputs = state.get(model_type, None)
    if model_choice != model_type or inputs ==None:
        raise gr.Error("Webform can not be used as the App has been restarted since the form was displayed. Please refresh the page")
    
    inputs["state"] =  state
    inputs.pop("lset_name")
    if inputs == None:
        gr.Warning("Internal state error: Could not retrieve inputs for the model.")
        gen = get_gen_info(state)
        queue = gen.get("queue", [])
        return get_queue_table(queue)
    prompt = inputs["prompt"]
    if len(prompt) ==0:
        gr.Info("Prompt cannot be empty.")
        gen = get_gen_info(state)
        queue = gen.get("queue", [])
        return get_queue_table(queue)
    prompt, errors = prompt_parser.process_template(prompt)
    if len(errors) > 0:
        gr.Info("Error processing prompt template: " + errors)
        return
    
    inputs["model_filename"] = model_filename
    prompts = prompt.replace("\r", "").split("\n")
    prompts = [prompt.strip() for prompt in prompts if len(prompt.strip())>0 and not prompt.startswith("#")]
    if len(prompts) ==0:
        gr.Info("Prompt cannot be empty.")
        gen = get_gen_info(state)
        queue = gen.get("queue", [])
        return get_queue_table(queue)

    resolution = inputs["resolution"]
    width, height = resolution.split("x")
    width, height = int(width), int(height)
    # if test_class_i2v(model_filename):
        # if "480p" in  model_filename and not "Fun" in model_filename and width * height > 848*480:
        #     gr.Info("You must use the 720P image to video model to generate videos with a resolution equivalent to 720P")
            # return
        # resolution = str(width) + "*" + str(height)  
        # if  resolution not in ['720*1280', '1280*720', '480*832', '832*480']:
        #     gr.Info(f"Resolution {resolution} not supported by image 2 video")
        #     return

    if "1.3B" in  model_filename and width * height > 848*480 and any( model in model_filename for model in ["image2video", "text2video"] ):
        gr.Info("You must use the 14B model to generate videos with a resolution equivalent to 720P")
        return

    if "diffusion_forcing" in model_filename or "ltxv" in model_filename or "Vace" in model_filename:
        video_length = inputs["video_length"]
        sliding_window_size = inputs["sliding_window_size"]
        if  video_length > sliding_window_size:
            gr.Info(f"The Number of Frames to generate ({video_length}) is greater than the Sliding Window Size ({sliding_window_size}) , multiple Windows will be generated")

    if "phantom" in model_filename or "hunyuan_video_custom" in model_filename:
        image_refs = inputs["image_refs"]

        if image_refs  == None :
            gr.Info("You must provide an Image Reference") 
            return
        if len(image_refs) > 1 and "hunyuan_video_custom" in model_filename:
            gr.Info("Only one Image Reference (a person) is supported for the moment by Hunyuan Custom") 
            return
        if any(isinstance(image[0], str) for image in image_refs) :
            gr.Info("Reference Image should be an Image") 
            return

        if isinstance(image_refs, list):
            image_refs = [ convert_image(tup[0]) for tup in image_refs ]
        

        if len(prompts) > 0:
            prompts = ["\n".join(prompts)]

        for single_prompt  in prompts:
            extra_inputs = {
                "prompt" : single_prompt,
                "image_refs": image_refs,
            }
            inputs.update(extra_inputs) 
            add_video_task(**inputs)

    elif "diffusion_forcing" in model_filename or "ltxv" in model_filename:
        image_start = inputs["image_start"]
        image_end = inputs["image_end"]
        video_source = inputs["video_source"]
        keep_frames_video_source = inputs["keep_frames_video_source"]
        image_prompt_type = inputs["image_prompt_type"]

        if len(keep_frames_video_source) > 0:
            if not is_integer(keep_frames_video_source):
                gr.Info("The number of frames to keep must be an integer") 
                return

        if "V" in image_prompt_type:
            if video_source == None or len(video_source) == 0:
                gr.Info("You must provide a Video to continue") 
                return
            image_start = None

        if "S" in image_prompt_type:
            if image_start  == None :
                gr.Info("You must provide a Start Image") 
                return
            if len(image_start) > 1:
                gr.Info("Only one Start Image is supported for the moment") 
                return
            if isinstance(image_start[0][0], str) :
                gr.Info("Start Image should be an Image") 
                return

            image_start = [ convert_image(tup[0]) for tup in image_start ]
            video_source = None

        if "E" in image_prompt_type:
            if image_end  == None :
                gr.Info("You must provide an End Image") 
                return
            if len(image_end) > 1:
                gr.Info("Only one End Image is supported for the moment") 
                return
            if isinstance(image_end[0][0], str) :
                gr.Info("End Image should be an Image") 
                return

            image_end = [ convert_image(tup[0]) for tup in image_end ]
            video_source = None

        if "T" in image_prompt_type:
            image_start = None
            image_end = None
            video_source = None

        if len(prompts) > 0:
            prompts = ["\n".join(prompts)]

        for single_prompt  in prompts:
            extra_inputs = {
                "prompt" : single_prompt,
                "image_start" : image_start,
                "image_end" : image_end,
                "video_source" : video_source,
            }
            inputs.update(extra_inputs) 
            add_video_task(**inputs)
    elif "recam" in model_filename:
        video_source = inputs["video_source"]
        if video_source == None:
            gr.Info("You must provide a Source Video")
            return
        frames = get_resampled_video(video_source, 0, 81, 16)
        if len(frames)<81:
            gr.Info("Recammaster source video should be at least 81 frames once the resampling at 16 fps has been done")
            return
        for single_prompt  in prompts:
            extra_inputs = {
                "prompt" : single_prompt,
                "video_source" : video_source,
            }
            inputs.update(extra_inputs) 
            add_video_task(**inputs)

    elif "Vace" in model_filename:
        video_prompt_type = inputs["video_prompt_type"]
        image_refs = inputs["image_refs"]
        video_guide = inputs["video_guide"]
        video_mask = inputs["video_mask"]
        
        # if "1.3B" in model_filename :                
        #     resolution_reformated = str(height) + "*" + str(width) 
        #     if not resolution_reformated in VACE_SIZE_CONFIGS:
        #         res = (" and ").join(VACE_SIZE_CONFIGS.keys())
        #         gr.Info(f"Video Resolution for Vace model is not supported. Only {res} resolutions are allowed.")
        #         return
        if "I" in video_prompt_type:
            if image_refs == None:
                gr.Info("You must provide at least one Refererence Image")
                return
        else:
            image_refs = None
        if "V" in video_prompt_type:
            if video_guide == None:
                gr.Info("You must provide a Control Video")
                return
        else:
            video_guide = None
        if "M" in video_prompt_type:
            if video_mask == None:
                gr.Info("You must provide a Video Mask ")
                return
        else:
            video_mask = None
        if "O" in video_prompt_type :
            keep_frames_video_guide= inputs["keep_frames_video_guide"] 
            video_length = inputs["video_length"]
            if len(keep_frames_video_guide) > 0: 
                gr.Info("Keeping Frames with Extending Video is not yet supported")
                return
                # gr.Info(f"Warning : you have asked to reuse all the frames of the control Video in the Alternate Video Ending it. Please make sure the number of frames of the control Video is lower than the total number of frames to generate otherwise it won't make a difference.")
            # elif keep_frames >= video_length:
            #     gr.Info(f"The number of frames in the control Video to reuse ({keep_frames_video_guide}) in Alternate Video Ending can not be bigger than the total number of frames ({video_length}) to generate.")
            #     return
        elif "V" in video_prompt_type:
            keep_frames_video_guide= inputs["keep_frames_video_guide"] 
            video_length = inputs["video_length"]
            _, error = parse_keep_frames_video_guide(keep_frames_video_guide, video_length)
            if len(error) > 0:
                gr.Info(f"Invalid Keep Frames property: {error}")
                return

        if isinstance(image_refs, list):
            image_refs = [ convert_image(tup[0]) for tup in image_refs ]        

        if len(prompts) > 0:
            prompts = ["\n".join(prompts)]

        for single_prompt  in prompts:
            extra_inputs = {
                "prompt" : single_prompt,
                "image_refs": image_refs,
                "video_guide" : video_guide,
                "video_mask" : video_mask ,
            }
            inputs.update(extra_inputs) 
            add_video_task(**inputs)
    elif test_class_i2v(model_filename) :
        image_prompt_type = inputs["image_prompt_type"]

        image_start = inputs["image_start"]
        image_end = inputs["image_end"]
        if image_start == None or isinstance(image_start, list) and len(image_start) == 0:
            return
        if not "E" in image_prompt_type:
            image_end = None
        if not isinstance(image_start, list):
            image_start = [image_start]
        if not all( not isinstance(img[0], str) for img in image_start) :
            gr.Info("Start Image should be an Image") 
            return
        image_start = [ convert_image(tup[0]) for tup in image_start ]

        if image_end != None:
            if not isinstance(image_end , list):
                image_end  = [image_end]
            if not all( not isinstance(img[0], str) for img in image_end) :
                gr.Info("End Image should be an Image") 
                return
            image_end  = [ convert_image(tup[0]) for tup in image_end  ]
            if len(image_start) != len(image_end):
                gr.Info("The number of start and end images should be the same ")
                return 
                            
        if inputs["multi_images_gen_type"] == 0:
            new_prompts = []
            new_image_start = []
            new_image_end = []
            for i in range(len(prompts) * len(image_start) ):
                new_prompts.append(  prompts[ i % len(prompts)] )
                new_image_start.append(image_start[i // len(prompts)] )
                if image_end != None:
                    new_image_end.append(image_end[i // len(prompts)] )
            prompts = new_prompts
            image_start = new_image_start 
            if image_end != None:
                image_end = new_image_end 
        else:
            if len(prompts) >= len(image_start):
                if len(prompts) % len(image_start) != 0:
                    raise gr.Error("If there are more text prompts than input images the number of text prompts should be dividable by the number of images")
                rep = len(prompts) // len(image_start)
                new_image_start = []
                new_image_end = []
                for i, _ in enumerate(prompts):
                    new_image_start.append(image_start[i//rep] )
                    if image_end != None:
                        new_image_end.append(image_end[i//rep] )
                image_start = new_image_start 
                if image_end != None:
                    image_end = new_image_end 
            else: 
                if len(image_start) % len(prompts)  !=0:
                    raise gr.Error("If there are more input images than text prompts the number of images should be dividable by the number of text prompts")
                rep = len(image_start) // len(prompts)  
                new_prompts = []
                for i, _ in enumerate(image_start):
                    new_prompts.append(  prompts[ i//rep] )
                prompts = new_prompts

  
        if image_start == None:
            image_start = [None] * len(prompts)
        if image_end == None:
            image_end = [None] * len(prompts)

        for single_prompt, start, end in zip(prompts, image_start, image_end) :
            extra_inputs = {
                "prompt" : single_prompt,
                "image_start": start,
                "image_end" : end,
                "video_source": None,
            }
            inputs.update(extra_inputs) 
            add_video_task(**inputs)
    else:
        for single_prompt in prompts :
            extra_inputs = {
                "prompt" : single_prompt,
            }
            inputs.update(extra_inputs) 
            add_video_task(**inputs)

    gen = get_gen_info(state)
    gen["prompts_max"] = len(prompts) + gen.get("prompts_max",0)
    state["validate_success"] = 1
    queue= gen.get("queue", [])
    return update_queue_data(queue)

def get_preview_images(inputs):
    inputs_to_query = ["image_start", "image_end", "video_guide", "image_refs","video_mask", "video_source"]
    start_image_data = None
    end_image_data = None
    for name in inputs_to_query:
        image= inputs.get(name, None)
        if image != None:
            image= [image] if not isinstance(image, list) else image
            if start_image_data == None:
                start_image_data = image
            else:
                end_image_data = image
                break
    if start_image_data != None and len(start_image_data) > 1 and  end_image_data  == None:
        end_image_data = start_image_data [1:]
        start_image_data = start_image_data [:1] 
    return start_image_data, end_image_data 

def add_video_task(**inputs):
    global task_id
    state = inputs["state"]
    gen = get_gen_info(state)
    queue = gen["queue"]
    task_id += 1
    current_task_id = task_id

    start_image_data, end_image_data = get_preview_images(inputs)

    queue.append({
        "id": current_task_id,
        "params": inputs.copy(),
        "repeats": inputs["repeat_generation"],
        "length": inputs["video_length"],
        "steps": inputs["num_inference_steps"],
        "prompt": inputs["prompt"],
        "start_image_data": start_image_data,
        "end_image_data": end_image_data,
        "start_image_data_base64": [pil_to_base64_uri(img, format="jpeg", quality=70) for img in start_image_data] if start_image_data != None else None,
        "end_image_data_base64": [pil_to_base64_uri(img, format="jpeg", quality=70) for img in end_image_data] if end_image_data != None else None
    })
    return update_queue_data(queue)

def update_task_thumbnails(task,  inputs):
    start_image_data, end_image_data = get_preview_images(inputs)

    task.update({
        "start_image_data_base64": [pil_to_base64_uri(img, format="jpeg", quality=70) for img in start_image_data] if start_image_data != None else None,
        "end_image_data_base64": [pil_to_base64_uri(img, format="jpeg", quality=70) for img in end_image_data] if end_image_data != None else None
    })

def move_up(queue, selected_indices):
    if not selected_indices or len(selected_indices) == 0:
        return update_queue_data(queue)
    idx = selected_indices[0]
    if isinstance(idx, list):
        idx = idx[0]
    idx = int(idx)
    with lock:
        if idx > 0:
            idx += 1
            queue[idx], queue[idx-1] = queue[idx-1], queue[idx]
    return update_queue_data(queue)

def move_down(queue, selected_indices):
    if not selected_indices or len(selected_indices) == 0:
        return update_queue_data(queue)
    idx = selected_indices[0]
    if isinstance(idx, list):
        idx = idx[0]
    idx = int(idx)
    with lock:
        idx += 1
        if idx < len(queue)-1:
            queue[idx], queue[idx+1] = queue[idx+1], queue[idx]
    return update_queue_data(queue)

def remove_task(queue, selected_indices):
    if not selected_indices or len(selected_indices) == 0:
        return update_queue_data(queue)
    idx = selected_indices[0]
    if isinstance(idx, list):
        idx = idx[0]
    idx = int(idx) + 1
    with lock:
        if idx < len(queue):
            if idx == 0:
                wan_model._interrupt = True
            del queue[idx]
    return update_queue_data(queue)

def update_global_queue_ref(queue):
    global global_queue_ref
    with lock:
        global_queue_ref = queue[:]

def save_queue_action(state):
    gen = get_gen_info(state)
    queue = gen.get("queue", [])

    if not queue or len(queue) <=1 :
        gr.Info("Queue is empty. Nothing to save.")
        return ""

    zip_buffer = io.BytesIO()

    with tempfile.TemporaryDirectory() as tmpdir:
        queue_manifest = []
        file_paths_in_zip = {}

        for task_index, task in enumerate(queue):
            if task is None or not isinstance(task, dict) or task.get('id') is None: continue

            params_copy = task.get('params', {}).copy()
            task_id_s = task.get('id', f"task_{task_index}")

            image_keys = ["image_start", "image_end", "image_refs"]
            video_keys = ["video_guide", "video_mask", "video_source", "audio_guide"]

            for key in image_keys:
                images_pil = params_copy.get(key)
                if images_pil is None:
                    continue

                is_originally_list = isinstance(images_pil, list)
                if not is_originally_list:
                    images_pil = [images_pil]

                image_filenames_for_json = []
                for img_index, pil_image in enumerate(images_pil):
                    if not isinstance(pil_image, Image.Image):
                         print(f"Warning: Expected PIL Image for key '{key}' in task {task_id_s}, got {type(pil_image)}. Skipping image.")
                         continue

                    img_id = id(pil_image)
                    if img_id in file_paths_in_zip:
                         image_filenames_for_json.append(file_paths_in_zip[img_id])
                         continue

                    img_filename_in_zip = f"task{task_id_s}_{key}_{img_index}.png"
                    img_save_path = os.path.join(tmpdir, img_filename_in_zip)

                    try:
                        pil_image.save(img_save_path, "PNG")
                        image_filenames_for_json.append(img_filename_in_zip)
                        file_paths_in_zip[img_id] = img_filename_in_zip
                        print(f"Saved image: {img_filename_in_zip}")
                    except Exception as e:
                        print(f"Error saving image {img_filename_in_zip} for task {task_id_s}: {e}")

                if image_filenames_for_json:
                     params_copy[key] = image_filenames_for_json if is_originally_list else image_filenames_for_json[0]
                else:
                     pass
                    #  params_copy.pop(key, None) #cant pop otherwise crash during reload

            for key in video_keys:
                video_path_orig = params_copy.get(key)
                if video_path_orig is None or not isinstance(video_path_orig, str):
                    continue

                if video_path_orig in file_paths_in_zip:
                    params_copy[key] = file_paths_in_zip[video_path_orig]
                    continue

                if not os.path.isfile(video_path_orig):
                    print(f"Warning: Video file not found for key '{key}' in task {task_id_s}: {video_path_orig}. Skipping video.")
                    params_copy.pop(key, None)
                    continue

                _, extension = os.path.splitext(video_path_orig)
                vid_filename_in_zip = f"task{task_id_s}_{key}{extension if extension else '.mp4'}"
                vid_save_path = os.path.join(tmpdir, vid_filename_in_zip)

                try:
                    shutil.copy2(video_path_orig, vid_save_path)
                    params_copy[key] = vid_filename_in_zip
                    file_paths_in_zip[video_path_orig] = vid_filename_in_zip
                    print(f"Copied video: {video_path_orig} -> {vid_filename_in_zip}")
                except Exception as e:
                    print(f"Error copying video {video_path_orig} to {vid_filename_in_zip} for task {task_id_s}: {e}")
                    params_copy.pop(key, None)


            params_copy.pop('state', None)
            params_copy.pop('start_image_data_base64', None)
            params_copy.pop('end_image_data_base64', None)
            params_copy.pop('start_image_data', None)
            params_copy.pop('end_image_data', None)
            task.pop('start_image_data', None)
            task.pop('end_image_data', None)

            manifest_entry = {
                "id": task.get('id'),
                "params": params_copy,
            }
            manifest_entry = {k: v for k, v in manifest_entry.items() if v is not None}
            queue_manifest.append(manifest_entry)

        manifest_path = os.path.join(tmpdir, "queue.json")
        try:
            with open(manifest_path, 'w', encoding='utf-8') as f:
                json.dump(queue_manifest, f, indent=4)
        except Exception as e:
            print(f"Error writing queue.json: {e}")
            gr.Warning("Failed to create queue manifest.")
            return None

        try:
            with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zf:
                zf.write(manifest_path, arcname="queue.json")

                for file_id, saved_file_rel_path in file_paths_in_zip.items():
                    saved_file_abs_path = os.path.join(tmpdir, saved_file_rel_path)
                    if os.path.exists(saved_file_abs_path):
                        zf.write(saved_file_abs_path, arcname=saved_file_rel_path)
                        print(f"Adding to zip: {saved_file_rel_path}")
                    else:
                        print(f"Warning: File {saved_file_rel_path} (ID: {file_id}) not found during zipping.")

            zip_buffer.seek(0)
            zip_binary_content = zip_buffer.getvalue()
            zip_base64 = base64.b64encode(zip_binary_content).decode('utf-8')
            print(f"Queue successfully prepared as base64 string ({len(zip_base64)} chars).")
            return zip_base64

        except Exception as e:
            print(f"Error creating zip file in memory: {e}")
            gr.Warning("Failed to create zip data for download.")
            return None
        finally:
            zip_buffer.close()

def load_queue_action(filepath, state, evt:gr.EventData):
    global task_id

    gen = get_gen_info(state)
    original_queue = gen.get("queue", [])
    delete_autoqueue_file  = False 
    if evt.target == None:

        if original_queue or not Path(AUTOSAVE_FILENAME).is_file():
            return
        print(f"Autoloading queue from {AUTOSAVE_FILENAME}...")
        filename = AUTOSAVE_FILENAME
        delete_autoqueue_file = True
    else:
        if not filepath or not hasattr(filepath, 'name') or not Path(filepath.name).is_file():
            print("[load_queue_action] Warning: No valid file selected or file not found.")
            return update_queue_data(original_queue)
        filename = filepath.name


    save_path_base = server_config.get("save_path", "outputs")
    loaded_cache_dir = os.path.join(save_path_base, "_loaded_queue_cache")


    newly_loaded_queue = []
    max_id_in_file = 0
    error_message = ""
    local_queue_copy_for_global_ref = None

    try:
        print(f"[load_queue_action] Attempting to load queue from: {filename}")
        os.makedirs(loaded_cache_dir, exist_ok=True)
        print(f"[load_queue_action] Using cache directory: {loaded_cache_dir}")

        with tempfile.TemporaryDirectory() as tmpdir:
            with zipfile.ZipFile(filename, 'r') as zf:
                if "queue.json" not in zf.namelist(): raise ValueError("queue.json not found in zip file")
                print(f"[load_queue_action] Extracting {filename} to {tmpdir}")
                zf.extractall(tmpdir)
                print(f"[load_queue_action] Extraction complete.")

            manifest_path = os.path.join(tmpdir, "queue.json")
            print(f"[load_queue_action] Reading manifest: {manifest_path}")
            with open(manifest_path, 'r', encoding='utf-8') as f:
                loaded_manifest = json.load(f)
            print(f"[load_queue_action] Manifest loaded. Processing {len(loaded_manifest)} tasks.")

            for task_index, task_data in enumerate(loaded_manifest):
                if task_data is None or not isinstance(task_data, dict):
                    print(f"[load_queue_action] Skipping invalid task data at index {task_index}")
                    continue

                params = task_data.get('params', {})
                task_id_loaded = task_data.get('id', 0)
                max_id_in_file = max(max_id_in_file, task_id_loaded)
                params['state'] = state

                image_keys = ["image_start", "image_end", "image_refs"]
                video_keys = ["video_guide", "video_mask", "video_source", "audio_guide"]

                loaded_pil_images = {}
                loaded_video_paths = {}

                for key in image_keys:
                    image_filenames = params.get(key)
                    if image_filenames is None: continue

                    is_list = isinstance(image_filenames, list)
                    if not is_list: image_filenames = [image_filenames]

                    loaded_pils = []
                    for img_filename_in_zip in image_filenames:
                         if not isinstance(img_filename_in_zip, str):
                             print(f"[load_queue_action] Warning: Non-string filename found for image key '{key}'. Skipping.")
                             continue
                         img_load_path = os.path.join(tmpdir, img_filename_in_zip)
                         if not os.path.exists(img_load_path):
                             print(f"[load_queue_action] Image file not found in extracted data: {img_load_path}. Skipping.")
                             continue
                         try:
                             pil_image = Image.open(img_load_path)
                             pil_image.load()
                             converted_image = convert_image(pil_image)
                             loaded_pils.append(converted_image)
                             pil_image.close()
                             print(f"Loaded image: {img_filename_in_zip} for key {key}")
                         except Exception as img_e:
                             print(f"[load_queue_action] Error loading image {img_filename_in_zip}: {img_e}")
                    if loaded_pils:
                        params[key] = loaded_pils if is_list else loaded_pils[0]
                        loaded_pil_images[key] = params[key]
                    else:
                        params.pop(key, None)

                for key in video_keys:
                    video_filename_in_zip = params.get(key)
                    if video_filename_in_zip is None or not isinstance(video_filename_in_zip, str):
                        continue

                    video_load_path = os.path.join(tmpdir, video_filename_in_zip)
                    if not os.path.exists(video_load_path):
                        print(f"[load_queue_action] Video file not found in extracted data: {video_load_path}. Skipping.")
                        params.pop(key, None)
                        continue

                    persistent_video_path = os.path.join(loaded_cache_dir, video_filename_in_zip)
                    try:
                        shutil.copy2(video_load_path, persistent_video_path)
                        params[key] = persistent_video_path
                        loaded_video_paths[key] = persistent_video_path
                        print(f"Loaded video: {video_filename_in_zip} -> {persistent_video_path}")
                    except Exception as vid_e:
                        print(f"[load_queue_action] Error copying video {video_filename_in_zip} to cache: {vid_e}")
                        params.pop(key, None)

                primary_preview_pil_list, secondary_preview_pil_list = get_preview_images(params)

                start_b64 = [pil_to_base64_uri(primary_preview_pil_list[0], format="jpeg", quality=70)] if isinstance(primary_preview_pil_list, list) and primary_preview_pil_list else None
                end_b64 = [pil_to_base64_uri(secondary_preview_pil_list[0], format="jpeg", quality=70)] if isinstance(secondary_preview_pil_list, list) and secondary_preview_pil_list else None

                top_level_start_image = params.get("image_start") or params.get("image_refs")
                top_level_end_image = params.get("image_end")

                runtime_task = {
                    "id": task_id_loaded,
                    "params": params.copy(),
                    "repeats": params.get('repeat_generation', 1),
                    "length": params.get('video_length'),
                    "steps": params.get('num_inference_steps'),
                    "prompt": params.get('prompt'),
                    "start_image_data": top_level_start_image,
                    "end_image_data": top_level_end_image,
                    "start_image_data_base64": start_b64,
                    "end_image_data_base64": end_b64,
                }
                newly_loaded_queue.append(runtime_task)
                print(f"[load_queue_action] Reconstructed task {task_index+1}/{len(loaded_manifest)}, ID: {task_id_loaded}")

        with lock:
            print("[load_queue_action] Acquiring lock to update state...")
            gen["queue"] = newly_loaded_queue[:]
            local_queue_copy_for_global_ref = gen["queue"][:]

            current_max_id_in_new_queue = max([t['id'] for t in newly_loaded_queue if 'id' in t] + [0])
            if current_max_id_in_new_queue >= task_id:
                 new_task_id = current_max_id_in_new_queue + 1
                 print(f"[load_queue_action] Updating global task_id from {task_id} to {new_task_id}")
                 task_id = new_task_id
            else:
                 print(f"[load_queue_action] Global task_id ({task_id}) is > max in file ({current_max_id_in_new_queue}). Not changing task_id.")

            gen["prompts_max"] = len(newly_loaded_queue)
            print("[load_queue_action] State update complete. Releasing lock.")

        if local_queue_copy_for_global_ref is not None:
             print("[load_queue_action] Updating global queue reference...")
             update_global_queue_ref(local_queue_copy_for_global_ref)
        else:
             print("[load_queue_action] Warning: Skipping global ref update as local copy is None.")

        print(f"[load_queue_action] Queue load successful. Returning DataFrame update for {len(newly_loaded_queue)} tasks.")
        return update_queue_data(newly_loaded_queue)

    except (ValueError, zipfile.BadZipFile, FileNotFoundError, Exception) as e:
        error_message = f"Error during queue load: {e}"
        print(f"[load_queue_action] Caught error: {error_message}")
        traceback.print_exc()
        gr.Warning(f"Failed to load queue: {error_message[:200]}")

        print("[load_queue_action] Load failed. Returning DataFrame update for original queue.")
        return update_queue_data(original_queue)
    finally:
        if delete_autoqueue_file:
            if os.path.isfile(filename):
                os.remove(filename)
                print(f"Clear Queue: Deleted autosave file '{filename}'.")

        if filepath and hasattr(filepath, 'name') and filepath.name and os.path.exists(filepath.name):
             if tempfile.gettempdir() in os.path.abspath(filepath.name):
                 try:
                     os.remove(filepath.name)
                     print(f"[load_queue_action] Removed temporary upload file: {filepath.name}")
                 except OSError as e:
                     print(f"[load_queue_action] Info: Could not remove temp file {filepath.name}: {e}")
             else:
                  print(f"[load_queue_action] Info: Did not remove non-temporary file: {filepath.name}")

def clear_queue_action(state):
    gen = get_gen_info(state)
    queue = gen.get("queue", [])
    aborted_current = False
    cleared_pending = False

    with lock:
        if "in_progress" in gen and gen["in_progress"]:
            print("Clear Queue: Signalling abort for in-progress task.")
            gen["abort"] = True
            gen["extra_orders"] = 0
            if wan_model is not None:
                wan_model._interrupt = True
            aborted_current = True

        if queue:
             if len(queue) > 1 or (len(queue) == 1 and queue[0] is not None and queue[0].get('id') is not None):
                 print(f"Clear Queue: Clearing {len(queue)} tasks from queue.")
                 queue.clear()
                 cleared_pending = True
             else:
                 pass

        if aborted_current or cleared_pending:
            gen["prompts_max"] = 0

    if cleared_pending:
        try:
            if os.path.isfile(AUTOSAVE_FILENAME):
                os.remove(AUTOSAVE_FILENAME)
                print(f"Clear Queue: Deleted autosave file '{AUTOSAVE_FILENAME}'.")
        except OSError as e:
            print(f"Clear Queue: Error deleting autosave file '{AUTOSAVE_FILENAME}': {e}")
            gr.Warning(f"Could not delete the autosave file '{AUTOSAVE_FILENAME}'. You may need to remove it manually.")

    if aborted_current and cleared_pending:
        gr.Info("Queue cleared and current generation aborted.")
    elif aborted_current:
        gr.Info("Current generation aborted.")
    elif cleared_pending:
        gr.Info("Queue cleared.")
    else:
        gr.Info("Queue is already empty or only contains the active task (which wasn't aborted now).")

    return update_queue_data([])

def quit_application():
    print("Save and Quit requested...")
    autosave_queue()
    import signal
    os.kill(os.getpid(), signal.SIGINT)

def start_quit_process():
    return 5, gr.update(visible=False), gr.update(visible=True)

def cancel_quit_process():
    return -1, gr.update(visible=True), gr.update(visible=False)

def show_countdown_info_from_state(current_value: int):
    if current_value > 0:
        gr.Info(f"Quitting in {current_value}...")
        return current_value - 1
    return current_value

def autosave_queue():
    global global_queue_ref
    if not global_queue_ref:
        print("Autosave: Queue is empty, nothing to save.")
        return

    print(f"Autosaving queue ({len(global_queue_ref)} items) to {AUTOSAVE_FILENAME}...")
    temp_state_for_save = {"gen": {"queue": global_queue_ref}}
    zip_file_path = None
    try:

        def _save_queue_to_file(queue_to_save, output_filename):
             if not queue_to_save: return None

             with tempfile.TemporaryDirectory() as tmpdir:
                queue_manifest = []
                file_paths_in_zip = {}

                for task_index, task in enumerate(queue_to_save):
                    if task is None or not isinstance(task, dict) or task.get('id') is None: continue

                    params_copy = task.get('params', {}).copy()
                    task_id_s = task.get('id', f"task_{task_index}")

                    image_keys = ["image_start", "image_end", "image_refs"]
                    video_keys = ["video_guide", "video_mask", "video_source", "audio_guide"]

                    for key in image_keys:
                        images_pil = params_copy.get(key)
                        if images_pil is None: continue
                        is_list = isinstance(images_pil, list)
                        if not is_list: images_pil = [images_pil]
                        image_filenames_for_json = []
                        for img_index, pil_image in enumerate(images_pil):
                            if not isinstance(pil_image, Image.Image): continue
                            img_id = id(pil_image)
                            if img_id in file_paths_in_zip:
                                image_filenames_for_json.append(file_paths_in_zip[img_id])
                                continue
                            img_filename_in_zip = f"task{task_id_s}_{key}_{img_index}.png"
                            img_save_path = os.path.join(tmpdir, img_filename_in_zip)
                            try:
                                pil_image.save(img_save_path, "PNG")
                                image_filenames_for_json.append(img_filename_in_zip)
                                file_paths_in_zip[img_id] = img_filename_in_zip
                            except Exception as e:
                                print(f"Autosave error saving image {img_filename_in_zip}: {e}")
                        if image_filenames_for_json:
                            params_copy[key] = image_filenames_for_json if is_list else image_filenames_for_json[0]
                        else:
                            params_copy.pop(key, None)

                    for key in video_keys:
                        video_path_orig = params_copy.get(key)
                        if video_path_orig is None or not isinstance(video_path_orig, str):
                            continue

                        if video_path_orig in file_paths_in_zip:
                            params_copy[key] = file_paths_in_zip[video_path_orig]
                            continue

                        if not os.path.isfile(video_path_orig):
                            print(f"Warning (Autosave): Video file not found for key '{key}' in task {task_id_s}: {video_path_orig}. Skipping.")
                            params_copy.pop(key, None)
                            continue

                        _, extension = os.path.splitext(video_path_orig)
                        vid_filename_in_zip = f"task{task_id_s}_{key}{extension if extension else '.mp4'}"
                        vid_save_path = os.path.join(tmpdir, vid_filename_in_zip)

                        try:
                            shutil.copy2(video_path_orig, vid_save_path)
                            params_copy[key] = vid_filename_in_zip
                            file_paths_in_zip[video_path_orig] = vid_filename_in_zip
                        except Exception as e:
                            print(f"Error (Autosave) copying video {video_path_orig} to {vid_filename_in_zip} for task {task_id_s}: {e}")
                            params_copy.pop(key, None)
                    params_copy.pop('state', None)
                    params_copy.pop('start_image_data_base64', None)
                    params_copy.pop('end_image_data_base64', None)
                    params_copy.pop('start_image_data', None)
                    params_copy.pop('end_image_data', None)

                    manifest_entry = {
                        "id": task.get('id'),
                        "params": params_copy,
                    }
                    manifest_entry = {k: v for k, v in manifest_entry.items() if v is not None}
                    queue_manifest.append(manifest_entry)

                manifest_path = os.path.join(tmpdir, "queue.json")
                with open(manifest_path, 'w', encoding='utf-8') as f: json.dump(queue_manifest, f, indent=4)
                with zipfile.ZipFile(output_filename, 'w', zipfile.ZIP_DEFLATED) as zf:
                    zf.write(manifest_path, arcname="queue.json")
                    for saved_file_rel_path in file_paths_in_zip.values():
                        saved_file_abs_path = os.path.join(tmpdir, saved_file_rel_path)
                        if os.path.exists(saved_file_abs_path):
                             zf.write(saved_file_abs_path, arcname=saved_file_rel_path)
                        else:
                             print(f"Warning (Autosave): File {saved_file_rel_path} not found during zipping.")
                return output_filename
             return None

        saved_path = _save_queue_to_file(global_queue_ref, AUTOSAVE_FILENAME)

        if saved_path:
            print(f"Queue autosaved successfully to {saved_path}")
        else:
            print("Autosave failed.")
    except Exception as e:
        print(f"Error during autosave: {e}")
        traceback.print_exc()

def finalize_generation_with_state(current_state):
     if not isinstance(current_state, dict) or 'gen' not in current_state:
         return gr.update(), gr.update(interactive=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False, value=""), gr.update(), current_state

     gallery_update, abort_btn_update, gen_btn_update, add_queue_btn_update, current_gen_col_update, gen_info_update = finalize_generation(current_state)
     accordion_update = gr.Accordion(open=False) if len(get_gen_info(current_state).get("queue", [])) <= 1 else gr.update()
     return gallery_update, abort_btn_update, gen_btn_update, add_queue_btn_update, current_gen_col_update, gen_info_update, accordion_update, current_state

def get_queue_table(queue):
    data = []
    if len(queue) == 1:
        return data 

    for i, item in enumerate(queue):
        if i==0:
            continue
        truncated_prompt = (item['prompt'][:97] + '...') if len(item['prompt']) > 100 else item['prompt']
        full_prompt = item['prompt'].replace('"', '&quot;')
        prompt_cell = f'<span title="{full_prompt}">{truncated_prompt}</span>'
        start_img_uri =item.get('start_image_data_base64')
        start_img_uri = start_img_uri[0] if start_img_uri !=None else None
        end_img_uri = item.get('end_image_data_base64')
        end_img_uri = end_img_uri[0] if end_img_uri !=None else None
        thumbnail_size = "50px"
        num_steps = item.get('steps')
        length = item.get('length')
        start_img_md = ""
        end_img_md = ""
        if start_img_uri:
            start_img_md = f'<img src="{start_img_uri}" alt="Start" style="max-width:{thumbnail_size}; max-height:{thumbnail_size}; display: block; margin: auto; object-fit: contain;" />'
        if end_img_uri:
            end_img_md = f'<img src="{end_img_uri}" alt="End" style="max-width:{thumbnail_size}; max-height:{thumbnail_size}; display: block; margin: auto; object-fit: contain;" />'


        data.append([item.get('repeats', "1"),
                    prompt_cell,
                    length,
                    num_steps,
                    start_img_md,
                    end_img_md,
                    "↑",
                    "↓",
                    "✖"
                    ])    
    return data
def update_queue_data(queue):
    update_global_queue_ref(queue)
    data = get_queue_table(queue)

    if len(data) == 0:
        return gr.DataFrame(visible=False)
    else:
        return gr.DataFrame(value=data, visible= True)

def create_html_progress_bar(percentage=0.0, text="Idle", is_idle=True):
    bar_class = "progress-bar-custom idle" if is_idle else "progress-bar-custom"
    bar_text_html = f'<div class="progress-bar-text">{text}</div>'

    html = f"""
    <div class="progress-container-custom">
        <div class="{bar_class}" style="width: {percentage:.1f}%;" role="progressbar" aria-valuenow="{percentage:.1f}" aria-valuemin="0" aria-valuemax="100">
           {bar_text_html}
        </div>
    </div>
    """
    return html

def update_generation_status(html_content):
    if(html_content):
        return gr.update(value=html_content)

def _parse_args():
    parser = argparse.ArgumentParser(
        description="Generate a video from a text prompt or image using Gradio")

    parser.add_argument(
        "--quantize-transformer",
        action="store_true",
        help="On the fly 'transformer' quantization"
    )

    parser.add_argument(
        "--share",
        action="store_true",
        help="Create a shared URL to access webserver remotely"
    )

    parser.add_argument(
        "--lock-config",
        action="store_true",
        help="Prevent modifying the configuration from the web interface"
    )

    parser.add_argument(
        "--lock-model",
        action="store_true",
        help="Prevent switch models"
    )

    parser.add_argument(
        "--preload",
        type=str,
        default="0",
        help="Megabytes of the diffusion model to preload in VRAM"
    )

    parser.add_argument(
        "--multiple-images",
        action="store_true",
        help="Allow inputting multiple images with image to video"
    )


    parser.add_argument(
        "--lora-dir-i2v",
        type=str,
        default="",
        help="Path to a directory that contains Wan i2v Loras "
    )


    parser.add_argument(
        "--lora-dir",
        type=str,
        default="", 
        help="Path to a directory that contains Wan t2v Loras"
    )

    parser.add_argument(
        "--lora-dir-hunyuan",
        type=str,
        default="loras_hunyuan", 
        help="Path to a directory that contains Hunyuan Video t2v Loras"
    )

    parser.add_argument(
        "--lora-dir-hunyuan-i2v",
        type=str,
        default="loras_hunyuan_i2v", 
        help="Path to a directory that contains Hunyuan Video i2v Loras"
    )


    parser.add_argument(
        "--lora-dir-ltxv",
        type=str,
        default="loras_ltxv", 
        help="Path to a directory that contains LTX Videos Loras"
    )



    parser.add_argument(
        "--check-loras",
        action="store_true",
        help="Filter Loras that are not valid"
    )


    parser.add_argument(
        "--lora-preset",
        type=str,
        default="",
        help="Lora preset to preload"
    )

    parser.add_argument(
        "--settings",
        type=str,
        default="settings",
        help="Path to settings folder"
    )


    # parser.add_argument(
    #     "--lora-preset-i2v",
    #     type=str,
    #     default="",
    #     help="Lora preset to preload for i2v"
    # )

    parser.add_argument(
        "--profile",
        type=str,
        default=-1,
        help="Profile No"
    )

    parser.add_argument(
        "--verbose",
        type=str,
        default=1,
        help="Verbose level"
    )

    parser.add_argument(
        "--steps",
        type=int,
        default=0,
        help="default denoising steps"
    )

    parser.add_argument(
        "--frames",
        type=int,
        default=0,
        help="default number of frames"
    )

    parser.add_argument(
        "--seed",
        type=int,
        default=-1,
        help="default generation seed"
    )

    parser.add_argument(
        "--advanced",
        action="store_true",
        help="Access advanced options by default"
    )

    parser.add_argument(
        "--fp16",
        action="store_true",
        help="For using fp16 transformer model"
    )

    parser.add_argument(
        "--bf16",
        action="store_true",
        help="For using bf16 transformer model"
    )

    parser.add_argument(
        "--server-port",
        type=str,
        default=0,
        help="Server port"
    )

    parser.add_argument(
        "--theme",
        type=str,
        default="",
        help="set UI Theme"
    )

    parser.add_argument(
        "--perc-reserved-mem-max",
        type=float,
        default=0,
        help="% of RAM allocated to Reserved RAM"
    )



    parser.add_argument(
        "--server-name",
        type=str,
        default="",
        help="Server name"
    )
    parser.add_argument(
        "--gpu",
        type=str,
        default="",
        help="Default GPU Device"
    )

    parser.add_argument(
        "--open-browser",
        action="store_true",
        help="open browser"
    )

    parser.add_argument(
        "--t2v",
        action="store_true",
        help="text to video mode"
    )

    parser.add_argument(
        "--i2v",
        action="store_true",
        help="image to video mode"
    )

    parser.add_argument(
        "--t2v-14B",
        action="store_true",
        help="text to video mode 14B model"
    )

    parser.add_argument(
        "--t2v-1-3B",
        action="store_true",
        help="text to video mode 1.3B model"
    )

    parser.add_argument(
        "--vace-1-3B",
        action="store_true",
        help="Vace ControlNet 1.3B model"
    )    
    parser.add_argument(
        "--i2v-1-3B",
        action="store_true",
        help="Fun InP image to video mode 1.3B model"
    )

    parser.add_argument(
        "--i2v-14B",
        action="store_true",
        help="image to video mode 14B model"
    )


    parser.add_argument(
        "--compile",
        action="store_true",
        help="Enable pytorch compilation"
    )

    parser.add_argument(
        "--listen",
        action="store_true",
        help="Server accessible on local network"
    )

    # parser.add_argument(
    #     "--fast",
    #     action="store_true",
    #     help="use Fast model"
    # )

    # parser.add_argument(
    #     "--fastest",
    #     action="store_true",
    #     help="activate the best config"
    # )

    parser.add_argument(
    "--attention",
    type=str,
    default="",
    help="attention mode"
    )

    parser.add_argument(
    "--vae-config",
    type=str,
    default="",
    help="vae config mode"
    )    

    args = parser.parse_args()

    return args

def get_lora_dir(model_filename):
    
    model_family = get_model_family(model_filename)
    i2v = test_class_i2v(model_filename)
    if model_family == "wan":
        lora_dir =args.lora_dir
        if i2v and len(lora_dir)==0:
            lora_dir =args.lora_dir_i2v
        if len(lora_dir) > 0:
            return lora_dir
        root_lora_dir = "loras_i2v" if i2v else "loras"

        if  "1.3B" in model_filename :
            lora_dir_1_3B = os.path.join(root_lora_dir, "1.3B")
            if os.path.isdir(lora_dir_1_3B ):
                return lora_dir_1_3B
        else:
            lora_dir_14B = os.path.join(root_lora_dir, "14B")
            if os.path.isdir(lora_dir_14B ):
                return lora_dir_14B
        return root_lora_dir    
    elif model_family == "ltxv":
            return args.lora_dir_ltxv
    elif model_family =="hunyuan":
        if i2v:
            return args.lora_dir_hunyuan_i2v
        else:
            return args.lora_dir_hunyuan
    else:
        raise Exception("loras unknown")

attention_modes_installed = get_attention_modes()
attention_modes_supported = get_supported_attention_modes()
args = _parse_args()

major, minor = torch.cuda.get_device_capability(args.gpu if len(args.gpu) > 0 else None)
if  major < 8:
    print("Switching to FP16 models when possible as GPU architecture doesn't support optimed BF16 Kernels")
    bfloat16_supported = False
else:
    bfloat16_supported = True

args.flow_reverse = True
processing_device = args.gpu
if len(processing_device) == 0:
    processing_device ="cuda"
# torch.backends.cuda.matmul.allow_fp16_accumulation = True
lock_ui_attention = False
lock_ui_transformer = False
lock_ui_compile = False

force_profile_no = int(args.profile)
verbose_level = int(args.verbose)
quantizeTransformer = args.quantize_transformer
check_loras = args.check_loras ==1
advanced = args.advanced

server_config_filename = "wgp_config.json"
if not os.path.isdir("settings"):
    os.mkdir("settings") 
if os.path.isfile("t2v_settings.json"):
    for f in glob.glob(os.path.join(".", "*_settings.json*")):
        target_file = os.path.join("settings",  Path(f).parts[-1] )
        shutil.move(f, target_file) 

if not os.path.isfile(server_config_filename) and os.path.isfile("gradio_config.json"):
    shutil.move("gradio_config.json", server_config_filename) 

if not os.path.isdir("ckpts/umt5-xxl/"):
    os.makedirs("ckpts/umt5-xxl/")
src_move = [ "ckpts/models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors", "ckpts/models_t5_umt5-xxl-enc-bf16.safetensors", "ckpts/models_t5_umt5-xxl-enc-quanto_int8.safetensors" ]
tgt_move = [ "ckpts/xlm-roberta-large/", "ckpts/umt5-xxl/", "ckpts/umt5-xxl/"]
for src,tgt in zip(src_move,tgt_move):
    if os.path.isfile(src):
        try:
            if os.path.isfile(tgt):
                shutil.remove(src)
            else:
                shutil.move(src, tgt)
        except:
            pass
    

if not Path(server_config_filename).is_file():
    server_config = {"attention_mode" : "auto",  
                     "transformer_types": [], 
                     "transformer_quantization": "int8",
                     "text_encoder_quantization" : "int8",
                     "save_path": "outputs", #os.path.join(os.getcwd(), 
                     "compile" : "",
                     "metadata_type": "metadata",
                     "default_ui": "t2v",
                     "boost" : 1,
                     "clear_file_list" : 5,
                     "vae_config": 0,
                     "profile" : profile_type.LowRAM_LowVRAM,
                     "preload_model_policy": [],
                     "UI_theme": "default" }

    with open(server_config_filename, "w", encoding="utf-8") as writer:
        writer.write(json.dumps(server_config))
else:
    with open(server_config_filename, "r", encoding="utf-8") as reader:
        text = reader.read()
    server_config = json.loads(text)

#   Deprecated models
for path in  ["wan2.1_Vace_1.3B_preview_bf16.safetensors", "sky_reels2_diffusion_forcing_1.3B_bf16.safetensors","sky_reels2_diffusion_forcing_720p_14B_bf16.safetensors",
"sky_reels2_diffusion_forcing_720p_14B_quanto_int8.safetensors", "sky_reels2_diffusion_forcing_720p_14B_quanto_fp16_int8.safetensors", "wan2.1_image2video_480p_14B_bf16.safetensors", "wan2.1_image2video_480p_14B_quanto_int8.safetensors",
"wan2.1_image2video_720p_14B_quanto_int8.safetensors", "wan2.1_image2video_720p_14B_quanto_fp16_int8.safetensors", "wan2.1_image2video_720p_14B_bf16.safetensors"
]:
    if Path(os.path.join("ckpts" , path)).is_file():
        print(f"Removing old version of model '{path}'. A new version of this model will be downloaded next time you use it.")
        os.remove( os.path.join("ckpts" , path))


wan_choices_t2v=["ckpts/wan2.1_text2video_1.3B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_quanto_int8.safetensors", "ckpts/wan2.1_Vace_1.3B_mbf16.safetensors", 
                         "ckpts/wan2.1_recammaster_1.3B_bf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_1.3B_mbf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_14B_bf16.safetensors",
                        "ckpts/sky_reels2_diffusion_forcing_14B_quanto_int8.safetensors",  "ckpts/sky_reels2_diffusion_forcing_720p_14B_mbf16.safetensors","ckpts/sky_reels2_diffusion_forcing_720p_14B_quanto_mbf16_int8.safetensors", 
                        "ckpts/wan2_1_phantom_1.3B_mbf16.safetensors", "ckpts/wan2.1_Vace_14B_mbf16.safetensors", "ckpts/wan2.1_Vace_14B_quanto_mbf16_int8.safetensors",
                        "ckpts/wan2.1_moviigen1.1_14B_mbf16.safetensors", "ckpts/wan2.1_moviigen1.1_14B_quanto_mbf16_int8.safetensors",
                        ]    
wan_choices_i2v=["ckpts/wan2.1_image2video_480p_14B_mbf16.safetensors", "ckpts/wan2.1_image2video_480p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_image2video_720p_14B_mbf16.safetensors",
                        "ckpts/wan2.1_image2video_720p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_Fun_InP_1.3B_bf16.safetensors", "ckpts/wan2.1_Fun_InP_14B_bf16.safetensors",
                        "ckpts/wan2.1_Fun_InP_14B_quanto_int8.safetensors", "ckpts/wan2.1_FLF2V_720p_14B_bf16.safetensors", "ckpts/wan2.1_FLF2V_720p_14B_quanto_int8.safetensors",
                        "ckpts/wan2.1_fantasy_speaking_14B_bf16.safetensors"]
ltxv_choices= ["ckpts/ltxv_0.9.7_13B_dev_bf16.safetensors", "ckpts/ltxv_0.9.7_13B_dev_quanto_bf16_int8.safetensors", "ckpts/ltxv_0.9.7_13B_distilled_lora128_bf16.safetensors"]

hunyuan_choices= ["ckpts/hunyuan_video_720_bf16.safetensors", "ckpts/hunyuan_video_720_quanto_int8.safetensors", "ckpts/hunyuan_video_i2v_720_bf16v2.safetensors", "ckpts/hunyuan_video_i2v_720_quanto_int8v2.safetensors",
                 "ckpts/hunyuan_video_custom_720_bf16.safetensors", "ckpts/hunyuan_video_custom_720_quanto_bf16_int8.safetensors" ]

transformer_choices = wan_choices_t2v + wan_choices_i2v + ltxv_choices + hunyuan_choices
def get_dependent_models(model_filename, quantization, dtype_policy ):
    if "fantasy" in model_filename:
        return [get_model_filename("i2v_720p", quantization, dtype_policy)]
    elif "ltxv_0.9.7_13B_distilled_lora128" in model_filename: 
        return [get_model_filename("ltxv_13B", quantization, dtype_policy)]
    else:
        return []
model_types = [ "t2v_1.3B", "t2v", "i2v", "i2v_720p", "flf2v_720p", "vace_1.3B","vace_14B","moviigen", "phantom_1.3B", "fantasy",  "fun_inp_1.3B", "fun_inp", "recam_1.3B",  "sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B", "ltxv_13B", "ltxv_13B_distilled", "hunyuan", "hunyuan_i2v", "hunyuan_custom"]
model_signatures = {"t2v": "text2video_14B", "t2v_1.3B" : "text2video_1.3B",   "fun_inp_1.3B" : "Fun_InP_1.3B",  "fun_inp" :  "Fun_InP_14B", 
                    "i2v" : "image2video_480p", "i2v_720p" : "image2video_720p" , "vace_1.3B" : "Vace_1.3B", "vace_14B" : "Vace_14B","recam_1.3B": "recammaster_1.3B", 
                    "flf2v_720p" : "FLF2V_720p", "sky_df_1.3B" : "sky_reels2_diffusion_forcing_1.3B", "sky_df_14B" : "sky_reels2_diffusion_forcing_14B", 
                    "sky_df_720p_14B" : "sky_reels2_diffusion_forcing_720p_14B",  "moviigen" :"moviigen",
                     "phantom_1.3B" : "phantom_1.3B", "fantasy" : "fantasy", "ltxv_13B" : "ltxv_0.9.7_13B_dev", "ltxv_13B_distilled" : "ltxv_0.9.7_13B_distilled",  "hunyuan" : "hunyuan_video_720", "hunyuan_i2v" : "hunyuan_video_i2v_720", "hunyuan_custom" : "hunyuan_video_custom" }


def get_model_type(model_filename):
    for model_type, signature in model_signatures.items():
        if signature in model_filename:
            return model_type        
    raise Exception("Unknown model:" + model_filename)

def get_model_family(model_filename):
    if "wan" in model_filename or "sky" in model_filename:
        return "wan"
    elif "ltxv" in model_filename:
        return "ltxv"
    elif "hunyuan" in model_filename:
        return "hunyuan"
    else:
        raise Exception(f"Unknown model family for model'{model_filename}'")
    
def test_class_i2v(model_filename):
    return "image2video" in model_filename or "Fun_InP" in model_filename  or "FLF2V" in model_filename or "fantasy" in model_filename or "hunyuan_video_i2v" in model_filename

def get_model_name(model_filename, description_container = [""]):
    if "Fun" in model_filename:
        model_name = "Fun InP image2video"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The Fun model is an alternative image 2 video that supports out the box End Image fixing (contrary to the original Wan image 2 video model). The 1.3B adds also image 2 to video capability  to the 1.3B model."
    elif "Vace" in model_filename:
        model_name = "Vace ControlNet"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The Vace ControlNet model is a powerful model that allows you to control the content of the generated video based of additional custom data : pose or depth video, images or objects you want to see in the video."
    elif "image" in model_filename:
        model_name = "Wan2.1 image2video"
        model_name += " 720p" if "720p" in model_filename else " 480p"
        model_name += " 14B"
        if "720p" in model_filename:
            description = "The standard Wan Image 2 Video specialized to generate 720p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)"
        else:
            description = "The standard Wan Image 2 Video specialized to generate 480p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)"
    elif "recam" in model_filename:
        model_name = "ReCamMaster"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The Recam Master in theory should allow you to replay a video by applying a different camera movement. The model supports only video that are at least 81 frames long (any frame beyond will be ignored)"
    elif "FLF2V" in model_filename:
        model_name = "Wan2.1 FLF2V"
        model_name += " 720p" if "720p" in model_filename else " 480p"
        model_name += " 14B"
        description = "The First Last Frame 2 Video model is the official model Image 2 Video model that support Start and End frames."
    elif "sky_reels2_diffusion_forcing" in model_filename:
        model_name = "SkyReels2 Diffusion Forcing"
        if "720p" in model_filename :
            model_name += " 720p"
        elif not "1.3B" in model_filename :
            model_name += " 540p"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The SkyReels 2 Diffusion Forcing model has been designed to generate very long videos that exceeds the usual 5s limit. You can also use this model to extend any existing video."
    elif "phantom" in model_filename:
        model_name = "Wan2.1 Phantom"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The Phantom model is specialized to transfer people or objects of your choice into a generated Video. It produces very nices results when used at 720p."
    elif "fantasy" in model_filename:
        model_name = "Wan2.1 Fantasy Speaking 720p"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The Fantasy Speaking model corresponds to the original Wan image 2 video model combined with the Fantasy Speaking extension to process an audio Input."
    elif "movii" in model_filename:
        model_name = "Wan2.1 MoviiGen 1080p 14B"
        description = "MoviiGen 1.1, a cutting-edge video generation model that excels in cinematic aesthetics and visual quality. Use it to generate videos in 720p or 1080p in the 21:9 ratio."
    elif "ltxv_0.9.7_13B_dev" in model_filename:
        model_name = "LTX Video 0.9.7 13B"
        description = "LTX Video is a fast model that can be used to generate long videos (up to 260 frames).It is recommended to keep the number of steps to 30 or you will need to update the file 'ltxv_video/configs/ltxv-13b-0.9.7-dev.yaml'.The LTX Video model expects very long prompts, so don't hesitate to use the Prompt Enhancer."
    elif "ltxv_0.9.7_13B_distilled" in model_filename:
        model_name = "LTX Video 0.9.7 Distilled 13B"
        description = "LTX Video is a fast model that can be used to generate long videos (up to 260 frames).This distilled version  is a very fast version and retains a high level of quality. The LTX Video model expects very long prompts, so don't hesitate to use the Prompt Enhancer."
    elif "hunyuan_video_720" in model_filename:
        model_name = "Hunyuan Video text2video 720p 13B"
        description = "Probably the best text 2 video model available."
    elif "hunyuan_video_i2v" in model_filename:
        model_name = "Hunyuan Video image2video 720p 13B"
        description = "A good looking image 2 video model, but not so good in prompt adherence."
    elif "hunyuan_video_custom" in model_filename:
        model_name = "Hunyuan Video Custom 720p 13B"
        description = "The Hunyuan Video Custom model is proably the best model  to transfer people (only people for the momment) as it is quite good to keep their identity. However it is slow as to get good results, you need to generate 720p videos with 30 steps."
    else:
        model_name = "Wan2.1 text2video"
        model_name += " 14B" if "14B" in model_filename else " 1.3B"
        description = "The original Wan Text 2 Video model. Most other models have been built on top of it" 
    description_container[0] = description
    return model_name


def get_model_filename(model_type, quantization ="int8", dtype_policy = ""):
    signature = model_signatures[model_type]
    choices = [ name for name in transformer_choices if signature in name]
    if len(quantization) == 0:
        quantization = "bf16"

    model_family =  get_model_family(choices[0]) 
    dtype = get_transformer_dtype(model_family, dtype_policy)
    if len(choices) <= 1:
        raw_filename = choices[0]
    else:
        sub_choices = [ name for name in choices if quantization in name]
        if len(sub_choices) > 0:
            dtype_str = "fp16" if dtype == torch.float16 else "bf16"
            new_sub_choices = [ name for name in sub_choices if dtype_str in name]
            sub_choices = new_sub_choices if len(new_sub_choices) > 0 else sub_choices
            raw_filename = sub_choices[0]
        else:
            raw_filename = choices[0]

    if dtype == torch.float16 and not "fp16" in raw_filename and model_family == "wan" :
        if "quanto_int8" in raw_filename:
            raw_filename = raw_filename.replace("quanto_int8", "quanto_fp16_int8")
        elif "quanto_bf16_int8" in raw_filename:
            raw_filename = raw_filename.replace("quanto_bf16_int8", "quanto_fp16_int8")
        elif "quanto_mbf16_int8" in raw_filename:
            raw_filename= raw_filename.replace("quanto_mbf16_int8", "quanto_mfp16_int8")
    return raw_filename

def get_transformer_dtype(model_family, transformer_dtype_policy):
    if len(transformer_dtype_policy) == 0:
        if not bfloat16_supported:
            return torch.float16
        else:
            if model_family == "wan"and False:
                return torch.float16
            else: 
                return torch.bfloat16
        return transformer_dtype
    elif transformer_dtype_policy =="fp16":
        return torch.float16
    else:
        return torch.bfloat16

def get_settings_file_name(model_filename):
    return  os.path.join(args.settings, get_model_type(model_filename) + "_settings.json")

def get_default_settings(filename):
    def get_default_prompt(i2v):
        if i2v:
            return "Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field."
        else:
            return "A large orange octopus is seen resting on the bottom of the ocean floor, blending in with the sandy and rocky terrain. Its tentacles are spread out around its body, and its eyes are closed. The octopus is unaware of a king crab that is crawling towards it from behind a rock, its claws raised and ready to attack. The crab is brown and spiny, with long legs and antennae. The scene is captured from a wide angle, showing the vastness and depth of the ocean. The water is clear and blue, with rays of sunlight filtering through. The shot is sharp and crisp, with a high dynamic range. The octopus and the crab are in focus, while the background is slightly blurred, creating a depth of field effect."
    i2v = test_class_i2v(filename)
    defaults_filename = get_settings_file_name(filename)
    if not Path(defaults_filename).is_file():
        ui_defaults = {
            "prompt": get_default_prompt(i2v),
            "resolution": "1280x720" if "720p" in filename else "832x480",
            "video_length": 81,
            "num_inference_steps": 30,
            "seed": -1,
            "repeat_generation": 1,
            "multi_images_gen_type": 0,        
            "guidance_scale": 5.0,
            "embedded_guidance_scale" : 6.0,
            "audio_guidance_scale": 5.0,
            "flow_shift": get_default_flow(filename, i2v),
            "negative_prompt": "",
            "activated_loras": [],
            "loras_multipliers": "",
            "tea_cache": 0.0,
            "tea_cache_start_step_perc": 0,
            "RIFLEx_setting": 0,
            "slg_switch": 0,
            "slg_layers": [9],
            "slg_start_perc": 10,
            "slg_end_perc": 90
        }

        if get_model_type(filename) in ("hunyuan","hunyuan_i2v"):
            ui_defaults.update({
                "guidance_scale": 7.0,
            })

        if get_model_type(filename) in ("sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B"):
            ui_defaults.update({
                "guidance_scale": 6.0,
                "flow_shift": 8,
                "sliding_window_discard_last_frames" : 0,
                "resolution": "1280x720" if "720p" in filename else "960x544",
                "sliding_window_size" : 121 if "720p" in filename else 97,
                "RIFLEx_setting": 2,
                "guidance_scale": 6,
                "flow_shift": 8,
            })


        if get_model_type(filename) in ("phantom_1.3B"):
            ui_defaults.update({
                "guidance_scale": 7.5,
                "flow_shift": 5,
                "resolution": "1280x720" 
            })

        elif get_model_type(filename) in ("hunyuan_custom"):
            ui_defaults.update({
                "guidance_scale": 7.5,
                "flow_shift": 13,
                "resolution": "1280x720" 
            })
        elif get_model_type(filename) in ("vace_14B"):
            ui_defaults.update({
                "sliding_window_discard_last_frames": 0,
            })
            

        with open(defaults_filename, "w", encoding="utf-8") as f:
            json.dump(ui_defaults, f, indent=4)
    else:
        with open(defaults_filename, "r", encoding="utf-8") as f:
            ui_defaults = json.load(f)
        prompts = ui_defaults.get("prompts", "")
        if len(prompts) > 0:
            ui_defaults["prompt"] = prompts
        image_prompt_type = ui_defaults.get("image_prompt_type", None)
        if image_prompt_type !=None and not isinstance(image_prompt_type, str):
            ui_defaults["image_prompt_type"] = "S" if image_prompt_type  == 0 else "SE"

    default_seed = args.seed
    if default_seed > -1:
        ui_defaults["seed"] = default_seed
    default_number_frames = args.frames
    if default_number_frames > 0:
        ui_defaults["video_length"] = default_number_frames
    default_number_steps = args.steps
    if default_number_steps > 0:
        ui_defaults["num_inference_steps"] = default_number_steps
    return ui_defaults

transformer_types = server_config.get("transformer_types", [])
transformer_type = transformer_types[0] if len(transformer_types) > 0 else  model_types[0]

transformer_quantization =server_config.get("transformer_quantization", "int8")

transformer_dtype_policy = server_config.get("transformer_dtype_policy", "")
if args.fp16:
    transformer_dtype_policy = "fp16" 
if args.bf16:
    transformer_dtype_policy = "bf16" 
transformer_filename = get_model_filename(transformer_type, transformer_quantization, transformer_dtype_policy)
text_encoder_quantization =server_config.get("text_encoder_quantization", "int8")
attention_mode = server_config["attention_mode"]
if len(args.attention)> 0:
    if args.attention in ["auto", "sdpa", "sage", "sage2", "flash", "xformers"]:
        attention_mode = args.attention
        lock_ui_attention = True
    else:
        raise Exception(f"Unknown attention mode '{args.attention}'")

profile =  force_profile_no if force_profile_no >=0 else server_config["profile"]
compile = server_config.get("compile", "")
boost = server_config.get("boost", 1)
vae_config = server_config.get("vae_config", 0)
if len(args.vae_config) > 0:
    vae_config = int(args.vae_config)

reload_needed = False
default_ui = server_config.get("default_ui", "t2v") 
save_path = server_config.get("save_path", os.path.join(os.getcwd(), "gradio_outputs"))
preload_model_policy = server_config.get("preload_model_policy", []) 


if args.t2v_14B or args.t2v: 
    transformer_filename = get_model_filename("t2v", transformer_quantization, transformer_dtype_policy)

if args.i2v_14B or args.i2v: 
    transformer_filename = get_model_filename("i2v", transformer_quantization, transformer_dtype_policy)

if args.t2v_1_3B:
    transformer_filename = get_model_filename("t2v_1.3B", transformer_quantization, transformer_dtype_policy)

if args.i2v_1_3B:
    transformer_filename = get_model_filename("fun_inp_1.3B", transformer_quantization, transformer_dtype_policy)

if args.vace_1_3B: 
    transformer_filename = get_model_filename("vace_1.3B", transformer_quantization, transformer_dtype_policy)

only_allow_edit_in_advanced = False
lora_preselected_preset = args.lora_preset
lora_preset_model = transformer_filename

if  args.compile: #args.fastest or
    compile="transformer"
    lock_ui_compile = True

model_filename = ""
#attention_mode="sage"
#attention_mode="sage2"
#attention_mode="flash"
#attention_mode="sdpa"
#attention_mode="xformers"
# compile = "transformer"

def get_loras_preprocessor(transformer, model_filename):
    preprocessor =  getattr(transformer, "preprocess_loras", None)
    if preprocessor == None:
        return None
    
    def preprocessor_wrapper(sd):
        return preprocessor(model_filename, sd)

    return preprocessor_wrapper

        
def get_model_manager(model_family):
    if model_family == "wan":
        return None
    elif model_family == "ltxv":
        from ltxv import model_def
        return model_def
    else:
        raise Exception("model family not supported")

def get_wan_text_encoder_filename(text_encoder_quantization):
    text_encoder_filename = "ckpts/umt5-xxl/models_t5_umt5-xxl-enc-bf16.safetensors"
    if text_encoder_quantization =="int8":
        text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_int8") 
    return text_encoder_filename

def get_ltxv_text_encoder_filename(text_encoder_quantization):
    text_encoder_filename = "ckpts/T5_xxl_1.1/T5_xxl_1.1_enc_bf16.safetensors"
    if text_encoder_quantization =="int8":
        text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8") 
    return text_encoder_filename

def get_hunyuan_text_encoder_filename(text_encoder_quantization):
    if text_encoder_quantization =="int8":
        text_encoder_filename = "ckpts/llava-llama-3-8b/llava-llama-3-8b-v1_1_vlm_quanto_int8.safetensors"
    else:
        text_encoder_filename = "ckpts/llava-llama-3-8b/llava-llama-3-8b-v1_1_vlm_fp16.safetensors"

    return text_encoder_filename


def download_models(transformer_filename):
    def computeList(filename):
        pos = filename.rfind("/")
        filename = filename[pos+1:]
        return [filename]        

    def process_files_def(repoId, sourceFolderList, fileList):
        targetRoot = "ckpts/" 
        for sourceFolder, files in zip(sourceFolderList,fileList ):
            if len(files)==0:
                if not Path(targetRoot + sourceFolder).exists():
                    snapshot_download(repo_id=repoId,  allow_patterns=sourceFolder +"/*", local_dir= targetRoot)
            else:
                for onefile in files:     
                    if len(sourceFolder) > 0: 
                        if not os.path.isfile(targetRoot + sourceFolder + "/" + onefile ):          
                            hf_hub_download(repo_id=repoId,  filename=onefile, local_dir = targetRoot, subfolder=sourceFolder)
                    else:
                        if not os.path.isfile(targetRoot + onefile ):          
                            hf_hub_download(repo_id=repoId,  filename=onefile, local_dir = targetRoot)

    from huggingface_hub import hf_hub_download, snapshot_download    

    shared_def = {
        "repoId" : "DeepBeepMeep/Wan2.1",
        "sourceFolderList" : [ "pose", "depth", "mask", "wav2vec", ""  ],
        "fileList" : [ [],[], ["sam_vit_h_4b8939_fp16.safetensors"], ["config.json", "feature_extractor_config.json", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json", "vocab.json"],
                [ "flownet.pkl"  ] ]
    }
    process_files_def(**shared_def)


    if server_config.get("enhancer_enabled", 0) == 1:
        enhancer_def = {
            "repoId" : "DeepBeepMeep/LTX_Video",
            "sourceFolderList" : [ "Florence2", "Llama3_2"  ],
            "fileList" : [ ["config.json", "configuration_florence2.py", "model.safetensors", "modeling_florence2.py", "preprocessor_config.json", "processing_florence2.py", "tokenizer.json", "tokenizer_config.json"],["config.json", "generation_config.json", "Llama3_2_quanto_bf16_int8.safetensors", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json"]  ]
        }
        process_files_def(**enhancer_def)


    model_family = get_model_family(transformer_filename)
    if model_family == "wan":        
        text_encoder_filename = get_wan_text_encoder_filename(text_encoder_quantization)    
        model_def = {
            "repoId" : "DeepBeepMeep/Wan2.1", 
            "sourceFolderList" :  ["xlm-roberta-large", "umt5-xxl", ""  ],
            "fileList" : [ [ "models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors", "sentencepiece.bpe.model", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json"], ["special_tokens_map.json", "spiece.model", "tokenizer.json", "tokenizer_config.json"] + computeList(text_encoder_filename) , ["Wan2.1_VAE.safetensors",  "fantasy_proj_model.safetensors" ] +  computeList(transformer_filename) ]   
        }
    elif model_family == "ltxv":
        text_encoder_filename = get_ltxv_text_encoder_filename(text_encoder_quantization)    
        model_def = {
            "repoId" : "DeepBeepMeep/LTX_Video", 
            "sourceFolderList" :  ["T5_xxl_1.1",  ""  ],
            "fileList" : [ ["added_tokens.json", "special_tokens_map.json", "spiece.model", "tokenizer_config.json"] + computeList(text_encoder_filename), ["ltxv_0.9.7_VAE.safetensors", "ltxv_0.9.7_spatial_upscaler.safetensors", "ltxv_scheduler.json"] + computeList(transformer_filename) ]   
        }
    elif model_family == "hunyuan":
        text_encoder_filename = get_hunyuan_text_encoder_filename(text_encoder_quantization)    
        model_def = {  
            "repoId" : "DeepBeepMeep/HunyuanVideo", 
            "sourceFolderList" :  [ "llava-llama-3-8b", "clip_vit_large_patch14", ""  ],
            "fileList" :[ ["config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "preprocessor_config.json"] + computeList(text_encoder_filename) , ["config.json", "merges.txt", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "vocab.json"],  [ "hunyuan_video_720_quanto_int8_map.json", "hunyuan_video_custom_VAE_fp32.safetensors", "hunyuan_video_custom_VAE_config.json", "hunyuan_video_VAE_fp32.safetensors", "hunyuan_video_VAE_config.json" , "hunyuan_video_720_quanto_int8_map.json"   ] + computeList(transformer_filename)  ]
        } 

    else:
        model_manager = get_model_manager(model_family)
        model_def = model_manager.get_files_def(transformer_filename, text_encoder_quantization)

    process_files_def(**model_def)


offload.default_verboseLevel = verbose_level


# download_models(transformer_filename) 

def sanitize_file_name(file_name, rep =""):
    return file_name.replace("/",rep).replace("\\",rep).replace(":",rep).replace("|",rep).replace("?",rep).replace("<",rep).replace(">",rep).replace("\"",rep).replace("\n",rep).replace("\r",rep) 

def extract_preset(model_filename, lset_name, loras):
    loras_choices = []
    loras_choices_files = []
    loras_mult_choices = ""
    prompt =""
    full_prompt =""
    lset_name = sanitize_file_name(lset_name)
    lora_dir = get_lora_dir(model_filename)
    if not lset_name.endswith(".lset"):
        lset_name_filename = os.path.join(lora_dir, lset_name + ".lset" ) 
    else:
        lset_name_filename = os.path.join(lora_dir, lset_name ) 
    error = ""
    if not os.path.isfile(lset_name_filename):
        error = f"Preset '{lset_name}' not found "
    else:
        missing_loras = []

        with open(lset_name_filename, "r", encoding="utf-8") as reader:
            text = reader.read()
        lset = json.loads(text)

        loras_choices_files = lset["loras"]
        for lora_file in loras_choices_files:
            choice = os.path.join(lora_dir, lora_file)
            if choice not in loras:
                missing_loras.append(lora_file)
            else:
                loras_choice_no = loras.index(choice)
                loras_choices.append(str(loras_choice_no))

        if len(missing_loras) > 0:
            error = f"Unable to apply Lora preset '{lset_name} because the following Loras files are missing or invalid: {missing_loras}"
        
        loras_mult_choices = lset["loras_mult"]
        prompt = lset.get("prompt", "")
        full_prompt = lset.get("full_prompt", False)
    return loras_choices, loras_mult_choices, prompt, full_prompt, error


    
def setup_loras(model_filename, transformer,  lora_dir, lora_preselected_preset, split_linear_modules_map = None):
    loras =[]
    loras_names = []
    default_loras_choices = []
    default_loras_multis_str = ""
    loras_presets = []
    default_lora_preset = ""
    default_lora_preset_prompt = ""

    from pathlib import Path

    lora_dir = get_lora_dir(model_filename)
    if lora_dir != None :
        if not os.path.isdir(lora_dir):
            raise Exception("--lora-dir should be a path to a directory that contains Loras")


    if lora_dir != None:
        dir_loras =  glob.glob( os.path.join(lora_dir , "*.sft") ) + glob.glob( os.path.join(lora_dir , "*.safetensors") ) 
        dir_loras.sort()
        loras += [element for element in dir_loras if element not in loras ]

        dir_presets =  glob.glob( os.path.join(lora_dir , "*.lset") ) 
        dir_presets.sort()
        loras_presets = [ Path(Path(file_path).parts[-1]).stem for file_path in dir_presets]

    if transformer !=None:
        loras = offload.load_loras_into_model(transformer, loras,  activate_all_loras=False, check_only= True, preprocess_sd=get_loras_preprocessor(transformer, model_filename), split_linear_modules_map = split_linear_modules_map) #lora_multiplier,

    if len(loras) > 0:
        loras_names = [ Path(lora).stem for lora in loras  ]

    if len(lora_preselected_preset) > 0:
        if not os.path.isfile(os.path.join(lora_dir, lora_preselected_preset + ".lset")):
            raise Exception(f"Unknown preset '{lora_preselected_preset}'")
        default_lora_preset = lora_preselected_preset
        default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, _ , error = extract_preset(model_filename, default_lora_preset, loras)
        if len(error) > 0:
            print(error[:200])
    return loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset


def load_wan_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
    filename = model_filename[-1]
    print(f"Loading '{filename}' model...")

    if test_class_i2v(model_filename[0]):
        cfg = WAN_CONFIGS['i2v-14B']
        model_factory = wan.WanI2V
    else:
        cfg = WAN_CONFIGS['t2v-14B']
        # cfg = WAN_CONFIGS['t2v-1.3B']    
        if get_model_type(filename) in ("sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B"):
            model_factory = wan.DTT2V
        else:
            model_factory = wan.WanT2V

    wan_model = model_factory(
        config=cfg,
        checkpoint_dir="ckpts",
        model_filename=model_filename,
        text_encoder_filename= get_wan_text_encoder_filename(text_encoder_quantization),
        quantizeTransformer = quantizeTransformer,
        dtype = dtype,
        VAE_dtype = VAE_dtype, 
        mixed_precision_transformer = mixed_precision_transformer
    )

    pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model }
    if hasattr(wan_model, "clip"):
        pipe["text_encoder_2"] = wan_model.clip.model
    return wan_model, pipe

def load_ltxv_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
    filename = model_filename[-1]
    print(f"Loading '{filename}' model...")
    from ltx_video.ltxv import LTXV

    ltxv_model = LTXV(
        model_filepath = model_filename,
        text_encoder_filepath = get_ltxv_text_encoder_filename(text_encoder_quantization),
        dtype = dtype,
        # quantizeTransformer = quantizeTransformer,
        VAE_dtype = VAE_dtype, 
        mixed_precision_transformer = mixed_precision_transformer
    )

    pipeline = ltxv_model.pipeline 
    pipe = {"transformer" : pipeline.video_pipeline.transformer, "vae" : pipeline.vae, "text_encoder" : pipeline.video_pipeline.text_encoder, "latent_upsampler" : pipeline.latent_upsampler}

    return ltxv_model, pipe

def load_hunyuan_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
    filename = model_filename[-1]
    print(f"Loading '{filename}' model...")
    from hyvideo.hunyuan import HunyuanVideoSampler

    hunyuan_model = HunyuanVideoSampler.from_pretrained(
        model_filepath = model_filename,
        text_encoder_filepath = get_hunyuan_text_encoder_filename(text_encoder_quantization),
        dtype = dtype,
        # quantizeTransformer = quantizeTransformer,
        VAE_dtype = VAE_dtype, 
        mixed_precision_transformer = mixed_precision_transformer
    )

    pipe = { "transformer" : hunyuan_model.model, "text_encoder" : hunyuan_model.text_encoder, "text_encoder_2" : hunyuan_model.text_encoder_2, "vae" : hunyuan_model.vae  }

    from hyvideo.modules.models import get_linear_split_map

    split_linear_modules_map = get_linear_split_map()
    hunyuan_model.model.split_linear_modules_map = split_linear_modules_map
    offload.split_linear_modules(hunyuan_model.model, split_linear_modules_map )


    return hunyuan_model, pipe

def get_transformer_model(model):
    if hasattr(model, "model"):
        return model.model
    elif hasattr(model, "transformer"):
        return model.transformer
    else:
        raise Exception("no transformer found")


def load_models(model_filename):
    global transformer_filename, transformer_loras_filenames
    model_family = get_model_family(model_filename)
    perc_reserved_mem_max = args.perc_reserved_mem_max
    preload =int(args.preload)
    if preload == 0:
        preload = server_config.get("preload_in_VRAM", 0)
    new_transformer_loras_filenames = None
    dependent_models = get_dependent_models(model_filename, quantization= transformer_quantization, dtype_policy =  transformer_dtype_policy) 
    new_transformer_loras_filenames = [model_filename]  if "_lora" in model_filename else None
    model_filelist = dependent_models + [model_filename]
    for filename in model_filelist: 
        download_models(filename)
    transformer_dtype = get_transformer_dtype(model_family, transformer_dtype_policy)
    VAE_dtype = torch.float16 if server_config.get("vae_precision","16") == "16" else torch.float
    mixed_precision_transformer =  server_config.get("mixed_precision","0") == "1"
    transformer_filename = None
    transformer_loras_filenames = None
    new_transformer_filename = model_filelist[-1] 
    if model_family == "wan" :
        wan_model, pipe = load_wan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
    elif model_family == "ltxv":
        wan_model, pipe = load_ltxv_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
    elif model_family == "hunyuan":
        wan_model, pipe = load_hunyuan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
    else:
        raise Exception(f"Model '{new_transformer_filename}' not supported.")
    wan_model._model_file_name = new_transformer_filename
    kwargs = { "extraModelsToQuantize": None }    
    if profile in (2, 4, 5):
        kwargs["budgets"] = { "transformer" : 100 if preload  == 0 else preload, "text_encoder" : 100 if preload  == 0 else preload, "*" : max(1000 if profile==5 else 3000 , preload) }
    elif profile == 3:
        kwargs["budgets"] = { "*" : "70%" }
 
    global prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer
    if server_config.get("enhancer_enabled", 0) == 1:
        from transformers import ( AutoModelForCausalLM, AutoProcessor, AutoTokenizer, LlamaForCausalLM )
        prompt_enhancer_image_caption_model = AutoModelForCausalLM.from_pretrained( "ckpts/Florence2", trust_remote_code=True)
        prompt_enhancer_image_caption_processor = AutoProcessor.from_pretrained( "ckpts/Florence2", trust_remote_code=True)
        prompt_enhancer_llm_model = offload.fast_load_transformers_model("ckpts/Llama3_2/Llama3_2_quanto_bf16_int8.safetensors") #, configKwargs= {"_attn_implementation" :"XXXsdpa"}
        prompt_enhancer_llm_tokenizer = AutoTokenizer.from_pretrained("ckpts/Llama3_2")
        pipe["prompt_enhancer_image_caption_model"] = prompt_enhancer_image_caption_model
        pipe["prompt_enhancer_llm_model"] = prompt_enhancer_llm_model
        prompt_enhancer_image_caption_model._model_dtype = torch.float
        if "budgets" in kwargs:
            kwargs["budgets"]["prompt_enhancer_llm_model"] = 5000
    else:
        prompt_enhancer_image_caption_model = None
        prompt_enhancer_image_caption_processor = None
        prompt_enhancer_llm_model = None
        prompt_enhancer_llm_tokenizer = None

        
    offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", coTenantsMap= {}, perc_reserved_mem_max = perc_reserved_mem_max , convertWeightsFloatTo = transformer_dtype, **kwargs)  
    if len(args.gpu) > 0:
        torch.set_default_device(args.gpu)
    transformer_filename = new_transformer_filename
    transformer_loras_filenames = new_transformer_loras_filenames
    return wan_model, offloadobj, pipe["transformer"] 

if not "P" in preload_model_policy:
    wan_model, offloadobj, transformer = None, None, None
    reload_needed = True
else:
    wan_model, offloadobj, transformer = load_models(transformer_filename)
    if check_loras:
        setup_loras(model_filename, transformer,  get_lora_dir(transformer_filename), "", None)
        exit()
    del transformer

gen_in_progress = False

def get_auto_attention():
    for attn in ["sage2","sage","sdpa"]:
        if attn in attention_modes_supported:
            return attn
    return "sdpa"

def get_default_flow(filename, i2v):
    return 7.0 if "480p" in filename and i2v else 5.0 





def generate_header(model_filename, compile, attention_mode):

    description_container = [""]
    get_model_name(model_filename, description_container)
    description  = description_container[0]
    header = "<DIV style='height:40px'>" + description + "</DIV>"

    header += "<DIV style='align:right;width:100%'><FONT SIZE=3>Attention mode <B>" + (attention_mode if attention_mode!="auto" else "auto/" + get_auto_attention() )
    if attention_mode not in attention_modes_installed:
        header += " -NOT INSTALLED-"
    elif attention_mode not in attention_modes_supported:
        header += " -NOT SUPPORTED-"
    header += "</B>"

    if compile:
        header += ", Pytorch compilation <B>ON</B>"
    if "fp16" in model_filename:
        header += ", Data Type <B>FP16</B>"
    else:
        header += ", Data Type <B>BF16</B>"

    if "int8" in model_filename:
        header += ", Quantization <B>Scaled Int8</B>"
    header += "<FONT></DIV>"

    return header

def apply_changes(  state,
                    transformer_types_choices,
                    transformer_dtype_policy_choice,
                    text_encoder_quantization_choice,
                    VAE_precision_choice,
                    mixed_precision_choice,
                    save_path_choice,
                    attention_choice,
                    compile_choice,
                    profile_choice,
                    vae_config_choice,
                    metadata_choice,
                    quantization_choice,
                    boost_choice = 1,
                    clear_file_list = 0,
                    preload_model_policy_choice = 1,
                    UI_theme_choice = "default",
                    enhancer_enabled_choice = 0,
                    fit_canvas_choice = 0,
                    preload_in_VRAM_choice = 0
):
    if args.lock_config:
        return
    if gen_in_progress:
        return "<DIV ALIGN=CENTER>Unable to change config when a generation is in progress</DIV>", gr.update(), gr.update()
    global offloadobj, wan_model, server_config, loras, loras_names, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset, loras_presets
    server_config = {"attention_mode" : attention_choice,  
                     "transformer_types": transformer_types_choices, 
                     "text_encoder_quantization" : text_encoder_quantization_choice,
                     "save_path" : save_path_choice,
                     "compile" : compile_choice,
                     "profile" : profile_choice,
                     "vae_config" : vae_config_choice,
                     "vae_precision" : VAE_precision_choice,
                     "mixed_precision" : mixed_precision_choice,
                     "metadata_type": metadata_choice,
                     "transformer_quantization" : quantization_choice,
                     "transformer_dtype_policy" : transformer_dtype_policy_choice,
                     "boost" : boost_choice,
                     "clear_file_list" : clear_file_list,
                     "preload_model_policy" : preload_model_policy_choice,
                     "UI_theme" : UI_theme_choice,
                     "fit_canvas": fit_canvas_choice,
                     "enhancer_enabled" : enhancer_enabled_choice,
                     "preload_in_VRAM" : preload_in_VRAM_choice
                       }

    if Path(server_config_filename).is_file():
        with open(server_config_filename, "r", encoding="utf-8") as reader:
            text = reader.read()
        old_server_config = json.loads(text)
        if lock_ui_transformer:
            server_config["transformer_filename"] = old_server_config["transformer_filename"]
        if lock_ui_attention:
            server_config["attention_mode"] = old_server_config["attention_mode"]
        if lock_ui_compile:
            server_config["compile"] = old_server_config["compile"]

    with open(server_config_filename, "w", encoding="utf-8") as writer:
        writer.write(json.dumps(server_config))

    changes = []
    for k, v in server_config.items():
        v_old = old_server_config.get(k, None)
        if v != v_old:
            changes.append(k)

    global attention_mode, profile, compile, vae_config, boost, lora_dir, reload_needed, preload_model_policy, transformer_quantization, transformer_dtype_policy, transformer_types, text_encoder_quantization
    attention_mode = server_config["attention_mode"]
    profile = server_config["profile"]
    compile = server_config["compile"]
    text_encoder_quantization = server_config["text_encoder_quantization"]
    vae_config = server_config["vae_config"]
    boost = server_config["boost"]
    preload_model_policy = server_config["preload_model_policy"]
    transformer_quantization = server_config["transformer_quantization"]
    transformer_dtype_policy = server_config["transformer_dtype_policy"]
    text_encoder_quantization = server_config["text_encoder_quantization"]
    transformer_types = server_config["transformer_types"]
    model_filename = get_model_filename(get_model_type(state["model_filename"]), transformer_quantization, transformer_dtype_policy)
    state["model_filename"] = model_filename
    if all(change in ["attention_mode", "vae_config", "boost", "save_path", "metadata_type", "clear_file_list", "fit_canvas"] for change in changes ):
        model_choice = gr.Dropdown()
    else:
        reload_needed = True
        model_choice = generate_dropdown_model_list(model_filename)

    header = generate_header(state["model_filename"], compile=compile, attention_mode= attention_mode)
    return "<DIV ALIGN=CENTER>The new configuration has been succesfully applied</DIV>", header, model_choice, gr.Row(visible= server_config["enhancer_enabled"] == 1)



from moviepy.editor import ImageSequenceClip
import numpy as np

def save_video(final_frames, output_path, fps=24):
    assert final_frames.ndim == 4 and final_frames.shape[3] == 3, f"invalid shape: {final_frames} (need t h w c)"
    if final_frames.dtype != np.uint8:
        final_frames = (final_frames * 255).astype(np.uint8)
    ImageSequenceClip(list(final_frames), fps=fps).write_videofile(output_path, verbose= False)


def get_gen_info(state):
    cache = state.get("gen", None)
    if cache == None:
        cache = dict()
        state["gen"] = cache
    return cache

def build_callback(state, pipe, send_cmd, status, num_inference_steps):
    gen = get_gen_info(state)
    gen["num_inference_steps"] = num_inference_steps
    def callback(step_idx, latent, force_refresh, read_state = False, override_num_inference_steps = -1, pass_no = -1):
        refresh_id =  gen.get("refresh", -1)
        if force_refresh or step_idx >= 0:
            pass
        else:
            refresh_id =  gen.get("refresh", -1)
            if refresh_id < 0:
                return
            UI_refresh = state.get("refresh", 0)
            if UI_refresh >= refresh_id:
                return  
        if override_num_inference_steps > 0:
            gen["num_inference_steps"] = override_num_inference_steps
            
        num_inference_steps = gen.get("num_inference_steps", 0)
        status = gen["progress_status"]
        state["refresh"] = refresh_id
        if read_state:
            phase, step_idx  = gen["progress_phase"] 
        else:
            step_idx += 1         
            if gen.get("abort", False):
                # pipe._interrupt = True
                phase = "Aborting"    
            elif step_idx  == num_inference_steps:
                phase = "VAE Decoding"    
            else:
                if pass_no <=0:
                    phase = "Denoising"
                elif pass_no == 1:
                    phase = "Denoising First Pass"
                elif pass_no == 2:
                    phase = "Denoising Second Pass"
                elif pass_no == 3:
                    phase = "Denoising Third Pass"
                else:
                    phase = f"Denoising {pass_no}th Pass"
                    
            gen["progress_phase"] = (phase, step_idx)
        status_msg = merge_status_context(status, phase)      
        if step_idx >= 0:
            progress_args = [(step_idx , num_inference_steps) , status_msg  ,  num_inference_steps]
        else:
            progress_args = [0, status_msg]
        
        # progress(*progress_args)
        send_cmd("progress", progress_args)
        if latent != None:
            latent = latent.to("cpu", non_blocking=True)
            send_cmd("preview", latent)
            
        # gen["progress_args"] = progress_args
            
    return callback
def abort_generation(state):
    gen = get_gen_info(state)
    if "in_progress" in gen and wan_model != None:

        wan_model._interrupt= True
        msg = "Processing Request to abort Current Generation"
        gen["status"] = msg
        gr.Info(msg)
        return gr.Button(interactive=  False)
    else:
        return gr.Button(interactive=  True)



def refresh_gallery(state): #, msg
    gen = get_gen_info(state)

    # gen["last_msg"] = msg
    file_list = gen.get("file_list", None)      
    choice = gen.get("selected",0)
    in_progress = "in_progress" in gen
    if in_progress:
        if gen.get("last_selected", True):
            choice = max(len(file_list) - 1,0)  

    queue = gen.get("queue", [])
    abort_interactive = not gen.get("abort", False)
    if not in_progress or len(queue) == 0:
        return gr.Gallery(selected_index=choice, value = file_list), gr.HTML("", visible= False),  gr.Button(visible=True), gr.Button(visible=False), gr.Row(visible=False), update_queue_data(queue), gr.Button(interactive=  abort_interactive), gr.Button(visible= False)
    else:
        task = queue[0]
        start_img_md = ""
        end_img_md = ""
        prompt =  task["prompt"]
        params = task["params"]
        model_filename = params["model_filename"] 
        onemorewindow_visible = "Vace"  in model_filename or "diffusion_forcing" in model_filename or "ltxv" in model_filename
        enhanced = False
        if  prompt.startswith("!enhanced!\n"):
            enhanced = True
            prompt = prompt[len("!enhanced!\n"):]
        if "\n" in prompt :
            prompts = prompt.split("\n")
            window_no= gen.get("window_no",1)
            if window_no > len(prompts):
                window_no = len(prompts)
            window_no -= 1
            prompts[window_no]="<B>" + prompts[window_no] + "</B>"
            prompt = "<BR><DIV style='height:8px'></DIV>".join(prompts)
        if enhanced:
            prompt = "<U><B>Enhanced:</B></U><BR>" + prompt
        list_uri = []
        start_img_uri = task.get('start_image_data_base64')
        if start_img_uri != None:
            list_uri += start_img_uri
        end_img_uri = task.get('end_image_data_base64')
        if end_img_uri != None:
            list_uri += end_img_uri

        thumbnail_size = "100px"
        thumbnails = ""
        for img_uri in list_uri:
            thumbnails += f'<TD><img src="{img_uri}" alt="Start" style="max-width:{thumbnail_size}; max-height:{thumbnail_size}; display: block; margin: auto; object-fit: contain;" /></TD>'
        
        html = "<STYLE> #PINFO, #PINFO  th, #PINFO td {border: 1px solid #CCCCCC;background-color:#FFFFFF;}</STYLE><TABLE WIDTH=100% ID=PINFO ><TR><TD width=100%>" + prompt + "</TD>" + thumbnails + "</TR></TABLE>" 
        html_output = gr.HTML(html, visible= True)
        return gr.Gallery(selected_index=choice, value = file_list), html_output, gr.Button(visible=False), gr.Button(visible=True), gr.Row(visible=True), update_queue_data(queue), gr.Button(interactive=  abort_interactive), gr.Button(visible= onemorewindow_visible)



def finalize_generation(state):
    gen = get_gen_info(state)
    choice = gen.get("selected",0)
    if "in_progress" in gen:
        del gen["in_progress"]
    if gen.get("last_selected", True):
        file_list = gen.get("file_list", [])
        choice = len(file_list) - 1


    gen["extra_orders"] = 0
    time.sleep(0.2)
    global gen_in_progress
    gen_in_progress = False
    return gr.Gallery(selected_index=choice), gr.Button(interactive=  True), gr.Button(visible= True), gr.Button(visible= False), gr.Column(visible= False), gr.HTML(visible= False, value="")


def select_video(state , event_data: gr.EventData):
    data=  event_data._data
    gen = get_gen_info(state)

    if data!=None:
        choice = data.get("index",0)
        file_list = gen.get("file_list", [])
        gen["last_selected"] = (choice + 1) >= len(file_list)
        gen["selected"] = choice
    return 

def expand_slist(slist, num_inference_steps ):
    new_slist= []
    inc =  len(slist) / num_inference_steps 
    pos = 0
    for i in range(num_inference_steps):
        new_slist.append(slist[ int(pos)])
        pos += inc
    return new_slist
def convert_image(image):

    from PIL import ImageOps
    from typing import cast
    image = image.convert('RGB')
    return cast(Image, ImageOps.exif_transpose(image))

def get_resampled_video(video_in, start_frame, max_frames, target_fps):
    from wan.utils.utils import resample

    import decord
    decord.bridge.set_bridge('torch')
    reader = decord.VideoReader(video_in)
    
    fps = reader.get_avg_fps()

    frame_nos = resample(fps, len(reader), max_target_frames_count= max_frames, target_fps=target_fps, start_target_frame= start_frame)
    frames_list = reader.get_batch(frame_nos)
    return frames_list

def preprocess_video(process_type, height, width, video_in, max_frames, start_frame=0, fit_canvas = False, target_fps = 16, block_size = 16):

    frames_list = get_resampled_video(video_in, start_frame, max_frames, target_fps)

    if len(frames_list) == 0:
        return None
    frame_height, frame_width, _ = frames_list[0].shape

    if fit_canvas :
        scale1  = min(height / frame_height, width /  frame_width)
        scale2  = min(height / frame_width, width /  frame_height)
        scale = max(scale1, scale2)
    else:
        scale =   ((height * width ) /  (frame_height * frame_width))**(1/2)

    new_height = (int(frame_height * scale) // block_size) * block_size
    new_width = (int(frame_width * scale) // block_size) * block_size
    # if fit_canvas :
    #     new_height = height
    #     new_width = width

    processed_frames_list = []
    for frame in frames_list:
        frame = Image.fromarray(np.clip(frame.cpu().numpy(), 0, 255).astype(np.uint8))
        frame = frame.resize((new_width,new_height), resample=Image.Resampling.LANCZOS) 
        processed_frames_list.append(frame)

    if process_type=="pose":
        from preprocessing.dwpose.pose import PoseBodyFaceVideoAnnotator
        cfg_dict = {
            "DETECTION_MODEL": "ckpts/pose/yolox_l.onnx",
            "POSE_MODEL": "ckpts/pose/dw-ll_ucoco_384.onnx",
            "RESIZE_SIZE": 1024
        }
        anno_ins = PoseBodyFaceVideoAnnotator(cfg_dict)
    elif process_type=="depth":
        from preprocessing.midas.depth import DepthVideoAnnotator
        cfg_dict = {
            "PRETRAINED_MODEL": "ckpts/depth/dpt_hybrid-midas-501f0c75.pt"
        }
        anno_ins = DepthVideoAnnotator(cfg_dict)
    elif process_type=="gray":
        from preprocessing.gray import GrayVideoAnnotator
        cfg_dict = {}
        anno_ins = GrayVideoAnnotator(cfg_dict)
    else:
        anno_ins = None
    
    if anno_ins == None:
        np_frames = [np.array(frame) for frame in processed_frames_list]
    else:
        np_frames = anno_ins.forward(processed_frames_list)

    # from preprocessing.dwpose.pose import save_one_video
    # save_one_video("test.mp4", np_frames, fps=8, quality=8, macro_block_size=None)

    torch_frames = []
    for np_frame in np_frames:
        torch_frame = torch.from_numpy(np_frame)
        torch_frames.append(torch_frame)

    return torch.stack(torch_frames) 


def parse_keep_frames_video_guide(keep_frames, video_length):
        
    def absolute(n):
        if n==0:
            return 0
        elif n < 0:
            return max(0, video_length + n)
        else:
            return min(n-1, video_length-1)

    if len(keep_frames) == 0:
        return [True] *video_length, "" 
    frames =[False] *video_length
    error = ""
    sections = keep_frames.split(" ")
    for section in sections:
        section = section.strip()
        if ":" in section:
            parts = section.split(":")
            if not is_integer(parts[0]):
                error =f"Invalid integer {parts[0]}"
                break
            start_range = absolute(int(parts[0]))
            if not is_integer(parts[1]):
                error =f"Invalid integer {parts[1]}"
                break
            end_range = absolute(int(parts[1]))
            for i in range(start_range, end_range + 1):
                frames[i] = True
        else:
            if not is_integer(section):
                error =f"Invalid integer {section}"
                break
            index = absolute(int(section))
            frames[index] = True

    if len(error ) > 0:
        return [], error
    for i in range(len(frames)-1, 0, -1):
        if frames[i]:
            break
    frames= frames[0: i+1]
    return  frames, error

def generate_video(
    task,
    send_cmd,
    prompt,
    negative_prompt,    
    resolution,
    video_length,
    seed,
    num_inference_steps,
    guidance_scale,
    audio_guidance_scale,
    flow_shift,
    embedded_guidance_scale,
    repeat_generation,
    multi_images_gen_type,
    tea_cache_setting,
    tea_cache_start_step_perc,    
    activated_loras,
    loras_multipliers,
    image_prompt_type,
    image_start,
    image_end,
    model_mode,
    video_source,
    keep_frames_video_source,
    video_prompt_type,
    image_refs,
    video_guide,
    keep_frames_video_guide,
    video_mask,
    audio_guide,
    sliding_window_size,
    sliding_window_overlap,
    sliding_window_overlap_noise,
    sliding_window_discard_last_frames,
    remove_background_images_ref,
    temporal_upsampling,
    spatial_upsampling,
    RIFLEx_setting,
    slg_switch,
    slg_layers,    
    slg_start_perc,
    slg_end_perc,
    cfg_star_switch,
    cfg_zero_step,
    prompt_enhancer,
    state,
    model_filename

):
    global wan_model, offloadobj, reload_needed
    gen = get_gen_info(state)
    torch.set_grad_enabled(False) 

    file_list = gen["file_list"]
    file_settings_list = gen["file_settings_list"]

    prompt_no = gen["prompt_no"]

    fit_canvas = server_config.get("fit_canvas", 0)
    # if wan_model == None:
    #     gr.Info("Unable to generate a Video while a new configuration is being applied.")
    #     return

    if "P" in preload_model_policy and not "U" in preload_model_policy:
        while wan_model == None:
            time.sleep(1)
        
    if model_filename !=  transformer_filename or reload_needed:
        wan_model = None
        if offloadobj is not None:
            offloadobj.release()
            offloadobj = None
        gc.collect()
        send_cmd("status", f"Loading model {get_model_name(model_filename)}...")
        wan_model, offloadobj, trans = load_models(model_filename)
        send_cmd("status", "Model loaded")
        reload_needed=  False

    if attention_mode == "auto":
        attn = get_auto_attention()
    elif attention_mode in attention_modes_supported:
        attn = attention_mode
    else:
        send_cmd("info", f"You have selected attention mode '{attention_mode}'. However it is not installed or supported on your system. You should either install it or switch to the default 'sdpa' attention.")
        send_cmd("exit")
        return
    
    width, height = resolution.split("x")
    width, height = int(width), int(height)
    resolution_reformated = str(height) + "*" + str(width) 

    if slg_switch == 0:
        slg_layers = None

    offload.shared_state["_attention"] =  attn
    device_mem_capacity = torch.cuda.get_device_properties(0).total_memory / 1048576
    VAE_tile_size = wan_model.vae.get_VAE_tile_size(vae_config, device_mem_capacity, server_config.get("vae_precision", "16") == "32")

    trans = get_transformer_model(wan_model)

    temp_filename = None

    prompts = prompt.split("\n")
    prompts = [part for part in prompts if len(prompt)>0]


    loras = state["loras"]
    if len(loras) > 0  or transformer_loras_filenames != None:
        def is_float(element: any) -> bool:
            if element is None: 
                return False
            try:
                float(element)
                return True
            except ValueError:
                return False
        list_mult_choices_nums = []
        if len(loras_multipliers) > 0:
            loras_mult_choices_list = loras_multipliers.replace("\r", "").split("\n")
            loras_mult_choices_list = [multi for multi in loras_mult_choices_list if len(multi)>0 and not multi.startswith("#")]
            loras_multipliers = " ".join(loras_mult_choices_list)
            list_mult_choices_str = loras_multipliers.split(" ")
            for i, mult in enumerate(list_mult_choices_str):
                mult = mult.strip()
                if "," in mult:
                    multlist = mult.split(",")
                    slist = []
                    for smult in multlist:
                        if not is_float(smult):                
                            raise gr.Error(f"Lora sub value no {i+1} ({smult}) in Multiplier definition '{multlist}' is invalid")
                        slist.append(float(smult))
                    slist = expand_slist(slist, num_inference_steps )
                    list_mult_choices_nums.append(slist)
                else:
                    if not is_float(mult):                
                        raise gr.Error(f"Lora Multiplier no {i+1} ({mult}) is invalid")
                    list_mult_choices_nums.append(float(mult))
        if len(list_mult_choices_nums ) < len(activated_loras):
            list_mult_choices_nums  += [1.0] * ( len(activated_loras) - len(list_mult_choices_nums ) )        
        loras_selected = [ lora for lora in loras if os.path.basename(lora) in activated_loras]
        pinnedLora = profile !=5 and transformer_loras_filenames == None #False # # # 
        split_linear_modules_map = getattr(trans,"split_linear_modules_map", None)
        if transformer_loras_filenames != None:
            loras_selected += transformer_loras_filenames
            list_mult_choices_nums.append(1.)
        offload.load_loras_into_model(trans, loras_selected, list_mult_choices_nums, activate_all_loras=True, preprocess_sd=get_loras_preprocessor(trans, model_filename), pinnedLora=pinnedLora, split_linear_modules_map = split_linear_modules_map) 
        errors = trans._loras_errors
        if len(errors) > 0:
            error_files = [msg for _ ,  msg  in errors]
            raise gr.Error("Error while loading Loras: " + ", ".join(error_files))
    seed = None if seed == -1 else seed
    # negative_prompt = "" # not applicable in the inference
    image2video = test_class_i2v(model_filename)
    current_video_length = video_length
    enable_RIFLEx = RIFLEx_setting == 0 and current_video_length > (6* 16) or RIFLEx_setting == 1
    # VAE Tiling
    device_mem_capacity = torch.cuda.get_device_properties(None).total_memory / 1048576

    diffusion_forcing = "diffusion_forcing" in model_filename
    ltxv = "ltxv" in model_filename
    vace = "Vace" in model_filename
    phantom = "phantom" in model_filename
    hunyuan_t2v = "hunyuan_video_720" in model_filename
    hunyuan_i2v = "hunyuan_video_i2v" in model_filename
    hunyuan_custom = "hunyuan_video_custom" in model_filename
    if diffusion_forcing or hunyuan_t2v or hunyuan_i2v or hunyuan_custom:
        fps = 24
    elif audio_guide != None:
        fps = 23
    elif ltxv:
        fps = 30
    else:
        fps = 16
    latent_size = 8 if ltxv else 4

    original_image_refs = image_refs 
    if image_refs != None and len(image_refs) > 0 and (hunyuan_custom or phantom or vace):
        send_cmd("progress", [0, get_latest_status(state, "Removing Images References Background")])
        os.environ["U2NET_HOME"] = os.path.join(os.getcwd(), "ckpts", "rembg")
        from wan.utils.utils import resize_and_remove_background
        image_refs = resize_and_remove_background(image_refs, width, height, remove_background_images_ref, fit_into_canvas= not vace)
        update_task_thumbnails(task, locals())
        send_cmd("output")

    joint_pass = boost ==1 #and profile != 1 and profile != 3  
   # TeaCache   
    trans.enable_teacache = tea_cache_setting > 0
    if trans.enable_teacache:
        trans.teacache_multiplier = tea_cache_setting
        trans.rel_l1_thresh = 0
        trans.teacache_start_step =  int(tea_cache_start_step_perc*num_inference_steps/100)
        if get_model_family(model_filename) == "wan":
            if image2video:
                if '720p' in model_filename:
                    trans.coefficients = [-114.36346466,   65.26524496,  -18.82220707,    4.91518089,   -0.23412683]
                else:
                    trans.coefficients = [-3.02331670e+02,  2.23948934e+02, -5.25463970e+01,  5.87348440e+00, -2.01973289e-01]
            else:
                if '1.3B' in model_filename:
                    trans.coefficients = [2.39676752e+03, -1.31110545e+03,  2.01331979e+02, -8.29855975e+00, 1.37887774e-01]
                elif '14B' in model_filename:
                    trans.coefficients = [-5784.54975374,  5449.50911966, -1811.16591783,   256.27178429, -13.02252404]
                else:
                        raise gr.Error("Teacache not supported for this model")
    source_video = None
    target_camera = None
    if "recam" in model_filename:
        source_video = preprocess_video("", width=width, height=height,video_in=video_source, max_frames= current_video_length, start_frame = 0, fit_canvas= fit_canvas == 1)
        target_camera = model_mode

    audio_proj_split = None
    audio_scale = None
    audio_context_lens = None
    if audio_guide != None:
        from fantasytalking.infer import parse_audio
        import librosa
        duration = librosa.get_duration(path=audio_guide)
        current_video_length = min(int(fps * duration // 4) * 4 + 5, current_video_length)    
        audio_proj_split, audio_context_lens = parse_audio(audio_guide, num_frames= current_video_length, fps= fps, device= processing_device  )
        audio_scale = 1.0

    import random
    if seed == None or seed <0:
        seed = random.randint(0, 999999999)

    torch.set_grad_enabled(False) 
    global save_path
    os.makedirs(save_path, exist_ok=True)
    abort = False
    gc.collect()
    torch.cuda.empty_cache()
    wan_model._interrupt = False
    gen["abort"] = False
    gen["prompt"] = prompt    
    repeat_no = 0
    extra_generation = 0
    initial_total_windows = 0
    if diffusion_forcing or vace or ltxv:
        reuse_frames = min(sliding_window_size - 4, sliding_window_overlap)
    else:
        reuse_frames = 0
    if (diffusion_forcing or ltxv) and source_video != None:
        current_video_length +=  sliding_window_overlap
    sliding_window = (vace or diffusion_forcing or ltxv) and current_video_length > sliding_window_size

    discard_last_frames = sliding_window_discard_last_frames
    default_max_frames_to_generate = current_video_length
    if sliding_window:
        left_after_first_window = current_video_length - sliding_window_size + discard_last_frames
        initial_total_windows= 1 + math.ceil(left_after_first_window / (sliding_window_size - discard_last_frames - reuse_frames))
        current_video_length = sliding_window_size
    else:
        initial_total_windows = 1

    first_window_video_length = current_video_length
    original_prompts = prompts.copy()
    gen["sliding_window"] = sliding_window    
    while not abort: 
        extra_generation += gen.get("extra_orders",0)
        gen["extra_orders"] = 0
        total_generation = repeat_generation + extra_generation
        gen["total_generation"] = total_generation         
        if repeat_no >= total_generation:
            break
        repeat_no +=1
        gen["repeat_no"] = repeat_no
        src_video, src_mask, src_ref_images = None, None, None
        prefix_video = None
        prefix_video_frames_count = 0 
        frames_already_processed = None
        pre_video_guide = None
        overlapped_latents = None
        window_no = 0
        extra_windows = 0
        guide_start_frame = 0
        current_video_length = first_window_video_length
        gen["extra_windows"] = 0
        gen["total_windows"] = 1
        gen["window_no"] = 1
        num_frames_generated = 0
        max_frames_to_generate = default_max_frames_to_generate
        start_time = time.time()
        if prompt_enhancer_image_caption_model != None and prompt_enhancer !=None and len(prompt_enhancer)>0:
            text_encoder_max_tokens = 256
            send_cmd("progress", [0, get_latest_status(state, "Enhancing Prompt")])
            from ltx_video.utils.prompt_enhance_utils import generate_cinematic_prompt
            prompt_images = []
            if "I" in prompt_enhancer:
                if image_start != None:
                    prompt_images.append(image_start)
                if original_image_refs != None:
                    prompt_images +=  original_image_refs[:1]
            if len(original_prompts) == 0 and not "T" in prompt_enhancer:
                pass
            else:
                from wan.utils.utils import seed_everything
                seed_everything(seed)
                # for i, original_prompt in enumerate(original_prompts):
                prompts = generate_cinematic_prompt(
                    prompt_enhancer_image_caption_model,
                    prompt_enhancer_image_caption_processor,
                    prompt_enhancer_llm_model,
                    prompt_enhancer_llm_tokenizer,
                    original_prompts if "T" in prompt_enhancer else ["an image"],
                    prompt_images if len(prompt_images) > 0 else None,
                    max_new_tokens=text_encoder_max_tokens,
                )
                print(f"Enhanced prompts: {prompts}" )
                task["prompt"] = "\n".join(["!enhanced!"] + prompts)
                send_cmd("output")
                prompt = prompts[0]
                abort = gen.get("abort", False)

        while not abort:
            if sliding_window:
                prompt =  prompts[window_no] if window_no < len(prompts) else prompts[-1]
            new_extra_windows = gen.get("extra_windows",0)
            gen["extra_windows"] = 0
            extra_windows += new_extra_windows
            max_frames_to_generate +=  new_extra_windows * (sliding_window_size - discard_last_frames - reuse_frames)
            sliding_window = sliding_window  or extra_windows > 0
            if sliding_window and window_no > 0:
                num_frames_generated -= reuse_frames
                if (max_frames_to_generate - prefix_video_frames_count - num_frames_generated) <  latent_size:
                    break
                current_video_length = min(sliding_window_size, ((max_frames_to_generate - num_frames_generated - prefix_video_frames_count + reuse_frames + discard_last_frames) // latent_size) * latent_size + 1 )

            total_windows = initial_total_windows + extra_windows
            gen["total_windows"] = total_windows
            if window_no >= total_windows:
                break
            window_no += 1
            gen["window_no"] = window_no
            return_latent_slice = None 
            if reuse_frames > 0:                
                return_latent_slice = slice(-(reuse_frames - 1 + discard_last_frames ) // latent_size, None if discard_last_frames == 0 else -(discard_last_frames // latent_size) )

            if hunyuan_custom:
                src_ref_images  = image_refs
            elif phantom:
                src_ref_images = image_refs.copy() if image_refs != None else None
            elif diffusion_forcing or ltxv or vace and "O" in video_prompt_type:
                if vace:
                   video_source =  video_guide
                   video_guide = None
                if video_source != None and len(video_source) > 0 and window_no == 1:
                    keep_frames_video_source= 1000 if len(keep_frames_video_source) ==0 else int(keep_frames_video_source) 
                    keep_frames_video_source =  (keep_frames_video_source // latent_size  ) * latent_size + 1  
                    prefix_video  = preprocess_video(None, width=width, height=height,video_in=video_source, max_frames= keep_frames_video_source , start_frame = 0, fit_canvas= fit_canvas, target_fps = fps, block_size = 32 if ltxv else 16)
                    prefix_video  = prefix_video .permute(3, 0, 1, 2)
                    prefix_video  = prefix_video .float().div_(127.5).sub_(1.) # c, f, h, w
                    pre_video_guide =  prefix_video[:, -reuse_frames:]
                    prefix_video_frames_count = pre_video_guide.shape[1]
                    if vace:
                        height, width  = pre_video_guide.shape[-2:]     
            if vace:
                image_refs_copy = image_refs.copy() if image_refs != None else None # required since prepare_source do inplace modifications
                video_guide_copy = video_guide
                video_mask_copy = video_mask
                if any(process in video_prompt_type for process in ("P", "D", "G")) :
                    preprocess_type = None
                    if "P" in video_prompt_type :
                        progress_args = [0, get_latest_status(state,"Extracting Open Pose Information")]
                        preprocess_type = "pose"
                    elif "D" in video_prompt_type :
                        progress_args = [0, get_latest_status(state,"Extracting Depth Information")]
                        preprocess_type = "depth"
                    elif "G" in video_prompt_type :
                        progress_args = [0, get_latest_status(state,"Extracting Gray Level Information")]
                        preprocess_type = "gray"

                    if preprocess_type != None :
                        send_cmd("progress", progress_args)
                        video_guide_copy = preprocess_video(preprocess_type, width=width, height=height,video_in=video_guide, max_frames= current_video_length if window_no == 1 else current_video_length - reuse_frames, start_frame = guide_start_frame, fit_canvas = fit_canvas, target_fps = fps)
                keep_frames_parsed, error = parse_keep_frames_video_guide(keep_frames_video_guide, max_frames_to_generate)
                if len(error) > 0:
                    raise gr.Error(f"invalid keep frames {keep_frames_video_guide}")
                keep_frames_parsed = keep_frames_parsed[guide_start_frame: guide_start_frame + current_video_length]

                if window_no == 1:
                    image_size = (height, width) #  default frame dimensions until it is set by video_src (if there is any)
                

                src_video, src_mask, src_ref_images = wan_model.prepare_source([video_guide_copy],
                                                                        [video_mask_copy ],
                                                                        [image_refs_copy], 
                                                                        current_video_length, image_size = image_size, device ="cpu",
                                                                        original_video= "O" in video_prompt_type,
                                                                        keep_frames=keep_frames_parsed,
                                                                        start_frame = guide_start_frame,
                                                                        pre_src_video = [pre_video_guide],
                                                                        fit_into_canvas = fit_canvas 
                                                                        )
            if window_no ==  1:                
                conditioning_latents_size = ( (prefix_video_frames_count-1) // latent_size) + 1 if prefix_video_frames_count > 0 else 0
            else:
                conditioning_latents_size = ( (reuse_frames-1) // latent_size) + 1

            status = get_latest_status(state)
            gen["progress_status"] = status 
            gen["progress_phase"] = ("Encoding Prompt", -1 )
            callback = build_callback(state, trans, send_cmd, status, num_inference_steps)
            progress_args = [0, merge_status_context(status, "Encoding Prompt")]
            send_cmd("progress", progress_args)

            if trans.enable_teacache:
                trans.teacache_counter = 0
                trans.num_steps = num_inference_steps                
                trans.teacache_skipped_steps = 0    
                trans.previous_residual = None
                trans.previous_modulated_input = None

            # samples = torch.empty( (1,2)) #for testing
            # if False:
            
            try:
                samples = wan_model.generate(
                    input_prompt = prompt,
                    image_start = image_start,  
                    image_end = image_end if image_end != None else None,
                    input_frames = src_video,
                    input_ref_images=  src_ref_images,
                    input_masks = src_mask,
                    input_video= pre_video_guide  if diffusion_forcing or ltxv else source_video,
                    target_camera= target_camera,
                    frame_num=(current_video_length // latent_size)* latent_size + 1,
                    height =  height,
                    width = width,
                    fit_into_canvas = fit_canvas == 1,
                    shift=flow_shift,
                    sampling_steps=num_inference_steps,
                    guide_scale=guidance_scale,
                    embedded_guidance_scale=embedded_guidance_scale,
                    n_prompt=negative_prompt,
                    seed=seed,
                    callback=callback,
                    enable_RIFLEx = enable_RIFLEx,
                    VAE_tile_size = VAE_tile_size,
                    joint_pass = joint_pass,
                    slg_layers = slg_layers,
                    slg_start = slg_start_perc/100,
                    slg_end = slg_end_perc/100,
                    cfg_star_switch = cfg_star_switch,
                    cfg_zero_step = cfg_zero_step,
                    audio_cfg_scale= audio_guidance_scale,
                    audio_proj= audio_proj_split,
                    audio_scale= audio_scale,
                    audio_context_lens= audio_context_lens,
                    ar_step = model_mode, #5
                    causal_block_size = 5,
                    causal_attention = True,
                    fps = fps,
                    overlapped_latents = overlapped_latents,
                    return_latent_slice= return_latent_slice,
                    overlap_noise = sliding_window_overlap_noise,
                    conditioning_latents_size = conditioning_latents_size,
                    model_filename = model_filename,
                )
            except Exception as e:
                if temp_filename!= None and  os.path.isfile(temp_filename):
                    os.remove(temp_filename)
                offload.last_offload_obj.unload_all()
                offload.unload_loras_from_model(trans)
                # if compile:
                #     cache_size = torch._dynamo.config.cache_size_limit                                      
                #     torch.compiler.reset()
                #     torch._dynamo.config.cache_size_limit = cache_size

                gc.collect()
                torch.cuda.empty_cache()
                s = str(e)
                keyword_list = {"CUDA out of memory" : "VRAM", "Tried to allocate":"VRAM", "CUDA error: out of memory": "RAM", "CUDA error: too many resources requested": "RAM"}
                crash_type = ""
                for keyword, tp  in keyword_list.items():
                    if keyword in s:
                        crash_type = tp 
                        break
                state["prompt"] = ""
                if crash_type == "VRAM":
                    new_error = "The generation of the video has encountered an error: it is likely that you have unsufficient VRAM and you should therefore reduce the video resolution or its number of frames."
                elif crash_type == "RAM":
                    new_error = "The generation of the video has encountered an error: it is likely that you have unsufficient RAM and / or Reserved RAM allocation should be reduced using 'perc_reserved_mem_max' or using a different Profile."
                else:
                    new_error =  gr.Error(f"The generation of the video has encountered an error, please check your terminal for more information. '{s}'")
                tb = traceback.format_exc().split('\n')[:-1] 
                print('\n'.join(tb))
                send_cmd("error", new_error)
                clear_status(state)
                return
            finally:
                trans.previous_residual = None
                trans.previous_modulated_input = None

            if trans.enable_teacache:
                print(f"Teacache Skipped Steps:{trans.teacache_skipped_steps}/{trans.num_steps}" )

            if samples != None:
                if isinstance(samples, dict):
                    overlapped_latents = samples.get("latent_slice", None)
                    samples= samples["x"]
                samples = samples.to("cpu")
            offload.last_offload_obj.unload_all()
            gc.collect()
            torch.cuda.empty_cache()

            # time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss")
            # save_prompt = "_in_" + original_prompts[0]
            # file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(save_prompt[:50]).strip()}.mp4"
            # sample = samples.cpu()
            # cache_video( tensor=sample[None].clone(), save_file=os.path.join(save_path, file_name), fps=16, nrow=1, normalize=True, value_range=(-1, 1))

            if samples == None:
                abort = True
                state["prompt"] = ""
                send_cmd("output")  
            else:
                sample = samples.cpu()
                # if True: # for testing
                #     torch.save(sample, "output.pt")
                # else:
                #     sample =torch.load("output.pt")
                if gen.get("extra_windows",0) > 0:
                    sliding_window = True 
                if sliding_window :
                    guide_start_frame += current_video_length
                    if discard_last_frames > 0:
                        sample = sample[: , :-discard_last_frames]
                        guide_start_frame -= discard_last_frames
                    if reuse_frames == 0:
                        pre_video_guide =  sample[:,9999 :].clone()
                    else:
                        pre_video_guide =  sample[:, -reuse_frames:].clone()
                num_frames_generated += sample.shape[1] 


                if prefix_video != None:
                    if reuse_frames == 0:
                        sample = torch.cat([ prefix_video[:, :], sample], dim = 1)
                    else:
                        sample = torch.cat([ prefix_video[:, :-reuse_frames], sample], dim = 1)
                    prefix_video = None
                if sliding_window and window_no > 1:
                    if reuse_frames == 0:
                        sample = sample[: , :]
                    else:
                        sample = sample[: , reuse_frames:]
                    guide_start_frame -= reuse_frames 

                exp = 0
                if len(temporal_upsampling) > 0 or len(spatial_upsampling) > 0:                
                    progress_args = [(num_inference_steps , num_inference_steps) , status + " - Upsampling"  ,  num_inference_steps]
                    send_cmd("progress", progress_args)

                if temporal_upsampling == "rife2":
                    exp = 1
                elif temporal_upsampling == "rife4":
                    exp = 2
                output_fps = fps
                if exp > 0: 
                    from rife.inference import temporal_interpolation
                    if sliding_window and window_no > 1:
                        sample = torch.cat([frames_already_processed[:, -2:-1], sample], dim=1)
                        sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device)
                        sample = sample[:, 1:]
                    else:
                        sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device)

                    output_fps = output_fps * 2**exp

                if len(spatial_upsampling) > 0:
                    from wan.utils.utils import resize_lanczos # need multithreading or to do lanczos with cuda
                    if spatial_upsampling == "lanczos1.5":
                        scale = 1.5
                    else:
                        scale = 2
                    sample = (sample + 1) / 2
                    h, w = sample.shape[-2:]
                    h *= scale
                    w *= scale
                    h = int(h)
                    w = int(w)
                    new_frames =[]
                    for i in range( sample.shape[1] ):
                        frame = sample[:, i]
                        frame = resize_lanczos(frame, h, w)
                        frame = frame.unsqueeze(1)
                        new_frames.append(frame)
                    sample = torch.cat(new_frames, dim=1)
                    new_frames = None
                    sample = sample * 2 - 1

                if sliding_window :
                    if frames_already_processed == None:
                        frames_already_processed = sample
                    else:
                        sample = torch.cat([frames_already_processed, sample], dim=1)
                    frames_already_processed = sample

                time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss")
                save_prompt = original_prompts[0]
                if os.name == 'nt':
                    file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(save_prompt[:50]).strip()}.mp4"
                else:
                    file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(save_prompt[:100]).strip()}.mp4"
                video_path = os.path.join(save_path, file_name)

                if audio_guide == None:
                    cache_video( tensor=sample[None], save_file=video_path, fps=output_fps, nrow=1, normalize=True, value_range=(-1, 1))
                else:
                    save_path_tmp = video_path[:-4] + "_tmp.mp4"
                    cache_video( tensor=sample[None], save_file=save_path_tmp, fps=output_fps, nrow=1, normalize=True, value_range=(-1, 1))
                    final_command = [ "ffmpeg", "-y", "-i", save_path_tmp, "-i", audio_guide, "-c:v", "libx264", "-c:a", "aac", "-shortest", "-loglevel", "warning", "-nostats", video_path, ]
                    import subprocess
                    subprocess.run(final_command, check=True)
                    os.remove(save_path_tmp)

                end_time = time.time()

                inputs = get_function_arguments(generate_video, locals())
                inputs.pop("send_cmd")
                inputs.pop("task")
                configs = prepare_inputs_dict("metadata", inputs)
                configs["prompt"] = "\n".join(original_prompts)
                if prompt_enhancer_image_caption_model != None and prompt_enhancer !=None and len(prompt_enhancer)>0:
                    configs["enhanced_prompt"] = "\n".join(prompts)
                configs["generation_time"] = round(end_time-start_time)
                metadata_choice = server_config.get("metadata_type","metadata")
                if metadata_choice == "json":
                    with open(video_path.replace('.mp4', '.json'), 'w') as f:
                        json.dump(configs, f, indent=4)
                elif metadata_choice == "metadata":
                    from mutagen.mp4 import MP4
                    file = MP4(video_path)
                    file.tags['©cmt'] = [json.dumps(configs)]
                    file.save()

                print(f"New video saved to Path: "+video_path)
                file_list.append(video_path)
                file_settings_list.append(configs)

                send_cmd("output")

        seed += 1
    clear_status(state)
    if temp_filename!= None and  os.path.isfile(temp_filename):
        os.remove(temp_filename)
    offload.unload_loras_from_model(trans)

def prepare_generate_video(state):    
    if state.get("validate_success",0) != 1:
        return gr.Button(visible= True), gr.Button(visible= False), gr.Column(visible= False)
    else:
        return gr.Button(visible= False), gr.Button(visible= True), gr.Column(visible= True)

def generate_preview(latents):
    import einops

    model_family = get_model_family(transformer_filename)
    if model_family == "wan":
        latent_channels = 16
        latent_dimensions = 3
        latent_rgb_factors = [
                [-0.1299, -0.1692,  0.2932],
                [ 0.0671,  0.0406,  0.0442],
                [ 0.3568,  0.2548,  0.1747],
                [ 0.0372,  0.2344,  0.1420],
                [ 0.0313,  0.0189, -0.0328],
                [ 0.0296, -0.0956, -0.0665],
                [-0.3477, -0.4059, -0.2925],
                [ 0.0166,  0.1902,  0.1975],
                [-0.0412,  0.0267, -0.1364],
                [-0.1293,  0.0740,  0.1636],
                [ 0.0680,  0.3019,  0.1128],
                [ 0.0032,  0.0581,  0.0639],
                [-0.1251,  0.0927,  0.1699],
                [ 0.0060, -0.0633,  0.0005],
                [ 0.3477,  0.2275,  0.2950],
                [ 0.1984,  0.0913,  0.1861]
            ]
    
        # credits for the rgb factors to ComfyUI ?

        latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360]

        # latent_rgb_factors_bias = [0.0259, -0.0192, -0.0761]
    elif model_family == "ltxv":
        latent_channels = 128
        latent_dimensions = 3

        latent_rgb_factors = [
            [ 1.1202e-02, -6.3815e-04, -1.0021e-02],
            [ 8.6031e-02,  6.5813e-02,  9.5409e-04],
            [-1.2576e-02, -7.5734e-03, -4.0528e-03],
            [ 9.4063e-03, -2.1688e-03,  2.6093e-03],
            [ 3.7636e-03,  1.2765e-02,  9.1548e-03],
            [ 2.1024e-02, -5.2973e-03,  3.4373e-03],
            [-8.8896e-03, -1.9703e-02, -1.8761e-02],
            [-1.3160e-02, -1.0523e-02,  1.9709e-03],
            [-1.5152e-03, -6.9891e-03, -7.5810e-03],
            [-1.7247e-03,  4.6560e-04, -3.3839e-03],
            [ 1.3617e-02,  4.7077e-03, -2.0045e-03],
            [ 1.0256e-02,  7.7318e-03,  1.3948e-02],
            [-1.6108e-02, -6.2151e-03,  1.1561e-03],
            [ 7.3407e-03,  1.5628e-02,  4.4865e-04],
            [ 9.5357e-04, -2.9518e-03, -1.4760e-02],
            [ 1.9143e-02,  1.0868e-02,  1.2264e-02],
            [ 4.4575e-03,  3.6682e-05, -6.8508e-03],
            [-4.5681e-04,  3.2570e-03,  7.7929e-03],
            [ 3.3902e-02,  3.3405e-02,  3.7454e-02],
            [-2.3001e-02, -2.4877e-03, -3.1033e-03],
            [ 5.0265e-02,  3.8841e-02,  3.3539e-02],
            [-4.1018e-03, -1.1095e-03,  1.5859e-03],
            [-1.2689e-01, -1.3107e-01, -2.1005e-01],
            [ 2.6276e-02,  1.4189e-02, -3.5963e-03],
            [-4.8679e-03,  8.8486e-03,  7.8029e-03],
            [-1.6610e-03, -4.8597e-03, -5.2060e-03],
            [-2.1010e-03,  2.3610e-03,  9.3796e-03],
            [-2.2482e-02, -2.1305e-02, -1.5087e-02],
            [-1.5753e-02, -1.0646e-02, -6.5083e-03],
            [-4.6975e-03,  5.0288e-03, -6.7390e-03],
            [ 1.1951e-02,  2.0712e-02,  1.6191e-02],
            [-6.3704e-03, -8.4827e-03, -9.5483e-03],
            [ 7.2610e-03, -9.9326e-03, -2.2978e-02],
            [-9.1904e-04,  6.2882e-03,  9.5720e-03],
            [-3.7178e-02, -3.7123e-02, -5.6713e-02],
            [-1.3373e-01, -1.0720e-01, -5.3801e-02],
            [-5.3702e-03,  8.1256e-03,  8.8397e-03],
            [-1.5247e-01, -2.1437e-01, -2.1843e-01],
            [ 3.1441e-02,  7.0335e-03, -9.7541e-03],
            [ 2.1528e-03, -8.9817e-03, -2.1023e-02],
            [ 3.8461e-03, -5.8957e-03, -1.5014e-02],
            [-4.3470e-03, -1.2940e-02, -1.5972e-02],
            [-5.4781e-03, -1.0842e-02, -3.0204e-03],
            [-6.5347e-03,  3.0806e-03, -1.0163e-02],
            [-5.0414e-03, -7.1503e-03, -8.9686e-04],
            [-8.5851e-03, -2.4351e-03,  1.0674e-03],
            [-9.0016e-03, -9.6493e-03,  1.5692e-03],
            [ 5.0914e-03,  1.2099e-02,  1.9968e-02],
            [ 1.3758e-02,  1.1669e-02,  8.1958e-03],
            [-1.0518e-02, -1.1575e-02, -4.1307e-03],
            [-2.8410e-02, -3.1266e-02, -2.2149e-02],
            [ 2.9336e-03,  3.6511e-02,  1.8717e-02],
            [-1.6703e-02, -1.6696e-02, -4.4529e-03],
            [ 4.8818e-02,  4.0063e-02,  8.7410e-03],
            [-1.5066e-02, -5.7328e-04,  2.9785e-03],
            [-1.7613e-02, -8.1034e-03,  1.3086e-02],
            [-9.2633e-03,  1.0803e-02, -6.3489e-03],
            [ 3.0851e-03,  4.7750e-04,  1.2347e-02],
            [-2.2785e-02, -2.3043e-02, -2.6005e-02],
            [-2.4787e-02, -1.5389e-02, -2.2104e-02],
            [-2.3572e-02,  1.0544e-03,  1.2361e-02],
            [-7.8915e-03, -1.2271e-03, -6.0968e-03],
            [-1.1478e-02, -1.2543e-03,  6.2679e-03],
            [-5.4229e-02,  2.6644e-02,  6.3394e-03],
            [ 4.4216e-03, -7.3338e-03, -1.0464e-02],
            [-4.5013e-03,  1.6082e-03,  1.4420e-02],
            [ 1.3673e-02,  8.8877e-03,  4.1253e-03],
            [-1.0145e-02,  9.0072e-03,  1.5695e-02],
            [-5.6234e-03,  1.1847e-03,  8.1261e-03],
            [-3.7171e-03, -5.3538e-03,  1.2590e-03],
            [ 2.9476e-02,  2.1424e-02,  3.0424e-02],
            [-3.4925e-02, -2.4340e-02, -2.5316e-02],
            [-3.4127e-02, -2.2406e-02, -1.0589e-02],
            [-1.7342e-02, -1.3249e-02, -1.0719e-02],
            [-2.1478e-03, -8.6051e-03, -2.9878e-03],
            [ 1.2089e-03, -4.2391e-03, -6.8569e-03],
            [ 9.0411e-04, -6.6886e-03, -6.7547e-05],
            [ 1.6048e-02, -1.0057e-02, -2.8929e-02],
            [ 1.2290e-03,  1.0163e-02,  1.8861e-02],
            [ 1.7264e-02,  2.7257e-04,  1.3785e-02],
            [-1.3482e-02, -3.6427e-03,  6.7481e-04],
            [ 4.6782e-03, -5.2423e-03,  2.4467e-03],
            [-5.9113e-03, -6.2244e-03, -1.8162e-03],
            [ 1.5496e-02,  1.4582e-02,  1.9514e-03],
            [ 7.4958e-03,  1.5886e-03, -8.2305e-03],
            [ 1.9086e-02,  1.6360e-03, -3.9674e-03],
            [-5.7021e-03, -2.7307e-03, -4.1066e-03],
            [ 1.7450e-03,  1.4602e-02,  2.5794e-02],
            [-8.2788e-04,  2.2902e-03,  4.5161e-03],
            [ 1.1632e-02,  8.9193e-03, -7.2813e-03],
            [ 7.5721e-03,  2.6784e-03,  1.1393e-02],
            [ 5.1939e-03,  3.6903e-03,  1.4049e-02],
            [-1.8383e-02, -2.2529e-02, -2.4477e-02],
            [ 5.8842e-04, -5.7874e-03, -1.4770e-02],
            [-1.6125e-02, -8.6101e-03, -1.4533e-02],
            [ 2.0540e-02,  2.0729e-02,  6.4338e-03],
            [ 3.3587e-03, -1.1226e-02, -1.6444e-02],
            [-1.4742e-03, -1.0489e-02,  1.7097e-03],
            [ 2.8130e-02,  2.3546e-02,  3.2791e-02],
            [-1.8532e-02, -1.2842e-02, -8.7756e-03],
            [-8.0533e-03, -1.0771e-02, -1.7536e-02],
            [-3.9009e-03,  1.6150e-02,  3.3359e-02],
            [-7.4554e-03, -1.4154e-02, -6.1910e-03],
            [ 3.4734e-03, -1.1370e-02, -1.0581e-02],
            [ 1.1476e-02,  3.9281e-03,  2.8231e-03],
            [ 7.1639e-03, -1.4741e-03, -3.8066e-03],
            [ 2.2250e-03, -8.7552e-03, -9.5719e-03],
            [ 2.4146e-02,  2.1696e-02,  2.8056e-02],
            [-5.4365e-03, -2.4291e-02, -1.7802e-02],
            [ 7.4263e-03,  1.0510e-02,  1.2705e-02],
            [ 6.2669e-03,  6.2658e-03,  1.9211e-02],
            [ 1.6378e-02,  9.4933e-03,  6.6971e-03],
            [ 1.7173e-02,  2.3601e-02,  2.3296e-02],
            [-1.4568e-02, -9.8279e-03, -1.1556e-02],
            [ 1.4431e-02,  1.4430e-02,  6.6362e-03],
            [-6.8230e-03,  1.8863e-02,  1.4555e-02],
            [ 6.1156e-03,  3.4700e-03, -2.6662e-03],
            [-2.6983e-03, -5.9402e-03, -9.2276e-03],
            [ 1.0235e-02,  7.4173e-03, -7.6243e-03],
            [-1.3255e-02,  1.9322e-02, -9.2153e-04],
            [ 2.4222e-03, -4.8039e-03, -1.5759e-02],
            [ 2.6244e-02,  2.5951e-02,  2.0249e-02],
            [ 1.5711e-02,  1.8498e-02,  2.7407e-03],
            [-2.1714e-03,  4.7214e-03, -2.2443e-02],
            [-7.4747e-03,  7.4166e-03,  1.4430e-02],
            [-8.3906e-03, -7.9776e-03,  9.7927e-03],
            [ 3.8321e-02,  9.6622e-03, -1.9268e-02],
            [-1.4605e-02, -6.7032e-03,  3.9675e-03]
        ]
        latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]    

    elif model_family == "hunyuan":
        latent_channels = 16
        latent_dimensions = 3
        scale_factor = 0.476986
        latent_rgb_factors = [
            [-0.0395, -0.0331,  0.0445],
            [ 0.0696,  0.0795,  0.0518],
            [ 0.0135, -0.0945, -0.0282],
            [ 0.0108, -0.0250, -0.0765],
            [-0.0209,  0.0032,  0.0224],
            [-0.0804, -0.0254, -0.0639],
            [-0.0991,  0.0271, -0.0669],
            [-0.0646, -0.0422, -0.0400],
            [-0.0696, -0.0595, -0.0894],
            [-0.0799, -0.0208, -0.0375],
            [ 0.1166,  0.1627,  0.0962],
            [ 0.1165,  0.0432,  0.0407],
            [-0.2315, -0.1920, -0.1355],
            [-0.0270,  0.0401, -0.0821],
            [-0.0616, -0.0997, -0.0727],
            [ 0.0249, -0.0469, -0.1703]
        ]

        latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]        
    else:
        raise Exception("preview not supported")
    latents = latents.unsqueeze(0) 
    nb_latents = latents.shape[2]
    latents_to_preview = 4
    latents_to_preview = min(nb_latents, latents_to_preview)
    skip_latent =  nb_latents / latents_to_preview
    latent_no = 0
    selected_latents = []
    while latent_no < nb_latents:
        selected_latents.append( latents[:, : , int(latent_no): int(latent_no)+1])
        latent_no += skip_latent 

    latents = torch.cat(selected_latents, dim = 2)
    weight = torch.tensor(latent_rgb_factors, device=latents.device, dtype=latents.dtype).transpose(0, 1)[:, :, None, None, None]
    bias = torch.tensor(latent_rgb_factors_bias, device=latents.device, dtype=latents.dtype)

    images = torch.nn.functional.conv3d(latents, weight, bias=bias, stride=1, padding=0, dilation=1, groups=1)
    images = images.add_(1.0).mul_(127.5)
    images = images.detach().cpu().numpy().clip(0, 255).astype(np.uint8)
    images = einops.rearrange(images, 'b c t h w -> (b h) (t w) c')
    h, w, _ = images.shape
    scale = 200 / h
    images= Image.fromarray(images)
    images = images.resize(( int(w*scale),int(h*scale)), resample=Image.Resampling.BILINEAR) 
    return images


def process_tasks(state):
    from wan.utils.thread_utils import AsyncStream, async_run

    gen = get_gen_info(state)
    queue = gen.get("queue", [])
    progress = None

    if len(queue) == 0:
        gen["status_display"] =  False
        return
    gen = get_gen_info(state)
    clear_file_list = server_config.get("clear_file_list", 0)    
    file_list = gen.get("file_list", [])
    file_settings_list = gen.get("file_settings_list", [])
    if clear_file_list > 0:
        file_list_current_size = len(file_list)
        keep_file_from = max(file_list_current_size - clear_file_list, 0)
        files_removed = keep_file_from
        choice = gen.get("selected",0)
        choice = max(choice- files_removed, 0)
        file_list = file_list[ keep_file_from: ]
        file_settings_list = file_settings_list[ keep_file_from: ]
    else:
        file_list = []
        choice = 0
    gen["selected"] = choice         
    gen["file_list"] = file_list    
    gen["file_settings_list"] = file_settings_list    

    start_time = time.time()

    global gen_in_progress
    gen_in_progress = True
    gen["in_progress"] = True
    gen["preview"] = None
    gen["status"] = "Generating Video"
    yield time.time(), time.time() 
    prompt_no = 0
    while len(queue) > 0:
        prompt_no += 1
        gen["prompt_no"] = prompt_no
        task = queue[0]
        task_id = task["id"] 
        params = task['params']

        com_stream = AsyncStream()
        send_cmd = com_stream.output_queue.push
        def generate_video_error_handler():
            try:
                generate_video(task, send_cmd,  **params)
            except Exception as e:
                tb = traceback.format_exc().split('\n')[:-1] 
                print('\n'.join(tb))
                send_cmd("error",str(e))
            finally:
                send_cmd("exit", None)


        async_run(generate_video_error_handler)

        while True:
            cmd, data = com_stream.output_queue.next()               
            if cmd == "exit":
                break
            elif cmd == "info":
                gr.Info(data)
            elif cmd == "error": 
                queue.clear()
                gen["prompts_max"] = 0
                gen["prompt"] = ""
                gen["status_display"] =  False

                raise gr.Error(data, print_exception= False)
            elif cmd == "status":
                gen["status"] = data
            elif cmd == "output":
                gen["preview"] = None
                yield time.time() , time.time() 
            elif cmd == "progress":
                gen["progress_args"] = data
                # progress(*data)
            elif cmd == "preview":
                torch.cuda.current_stream().synchronize()
                preview= None if data== None else generate_preview(data) 
                gen["preview"] = preview
                yield time.time() , gr.Text()
            else:
                raise Exception(f"unknown command {cmd}")

        abort = gen.get("abort", False)
        if abort:
            gen["abort"] = False
            status = "Video Generation Aborted", "Video Generation Aborted"
            yield  gr.Text(), gr.Text()
            gen["status"] = status

        queue[:] = [item for item in queue if item['id'] != task['id']]
        update_global_queue_ref(queue)

    gen["prompts_max"] = 0
    gen["prompt"] = ""
    end_time = time.time()
    if abort:
        status = f"Video generation was aborted. Total Generation Time: {end_time-start_time:.1f}s" 
    else:
        status = f"Total Generation Time: {end_time-start_time:.1f}s" 
    gen["status"] = status
    gen["status_display"] =  False



def get_generation_status(prompt_no, prompts_max, repeat_no, repeat_max, window_no, total_windows):
    if prompts_max == 1:        
        if repeat_max == 1:
            status = ""
        else:
            status = f"Sample {repeat_no}/{repeat_max}"
    else:
        if repeat_max == 1:
            status = f"Prompt {prompt_no}/{prompts_max}"
        else:
            status = f"Prompt {prompt_no}/{prompts_max}, Sample {repeat_no}/{repeat_max}"
    if total_windows > 1:
        if len(status) > 0:
            status += ", "
        status += f"Sliding Window {window_no}/{total_windows}"

    return status

refresh_id = 0

def get_new_refresh_id():
    global refresh_id
    refresh_id += 1
    return refresh_id

def merge_status_context(status="", context=""):
    if len(status) == 0:
        return context
    elif len(context) == 0:
        return status
    else:
        return status + " - " + context

def clear_status(state):
    gen = get_gen_info(state)
    gen["extra_windows"] = 0
    gen["total_windows"] = 1
    gen["window_no"] = 1
    gen["extra_orders"] = 0
    gen["repeat_no"] = 0
    gen["total_generation"] = 0

def get_latest_status(state, context=""):
    gen = get_gen_info(state)
    prompt_no = gen["prompt_no"] 
    prompts_max = gen.get("prompts_max",0)
    total_generation = gen.get("total_generation", 1)
    repeat_no = gen.get("repeat_no",0)
    total_generation += gen.get("extra_orders", 0)
    total_windows = gen.get("total_windows", 0)
    total_windows += gen.get("extra_windows", 0)
    window_no = gen.get("window_no", 0)
    status = get_generation_status(prompt_no, prompts_max, repeat_no, total_generation, window_no, total_windows)
    return merge_status_context(status, context)

def update_status(state): 
    gen = get_gen_info(state)
    gen["progress_status"] = get_latest_status(state)
    gen["refresh"] = get_new_refresh_id()


def one_more_sample(state):
    gen = get_gen_info(state)
    extra_orders = gen.get("extra_orders", 0)
    extra_orders += 1
    gen["extra_orders"]  = extra_orders
    in_progress = gen.get("in_progress", False)
    if not in_progress :
        return state
    total_generation = gen.get("total_generation", 0) + extra_orders
    gen["progress_status"] = get_latest_status(state)
    gen["refresh"] = get_new_refresh_id()
    gr.Info(f"An extra sample generation is planned for a total of {total_generation} videos for this prompt")

    return state 

def one_more_window(state):
    gen = get_gen_info(state)
    extra_windows = gen.get("extra_windows", 0)
    extra_windows += 1
    gen["extra_windows"]= extra_windows
    in_progress = gen.get("in_progress", False)
    if not in_progress :
        return state
    total_windows = gen.get("total_windows", 0) + extra_windows
    gen["progress_status"] = get_latest_status(state)
    gen["refresh"] = get_new_refresh_id()
    gr.Info(f"An extra window generation is planned for a total of {total_windows} videos for this sample")

    return state 

def get_new_preset_msg(advanced = True):
    if advanced:
        return "Enter here a Name for a Lora Preset or Choose one in the List"
    else:
        return "Choose a Lora Preset in this List to Apply a Special Effect"


def validate_delete_lset(lset_name):
    if len(lset_name) == 0 or lset_name == get_new_preset_msg(True) or lset_name == get_new_preset_msg(False):
        gr.Info(f"Choose a Preset to delete")
        return  gr.Button(visible= True), gr.Checkbox(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False) 
    else:
        return  gr.Button(visible= False), gr.Checkbox(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= True), gr.Button(visible= True) 
    
def validate_save_lset(lset_name):
    if len(lset_name) == 0 or lset_name == get_new_preset_msg(True) or lset_name == get_new_preset_msg(False):
        gr.Info("Please enter a name for the preset")
        return  gr.Button(visible= True), gr.Checkbox(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False),gr.Checkbox(visible= False) 
    else:
        return  gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= True), gr.Button(visible= True),gr.Checkbox(visible= True)

def cancel_lset():
    return gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False), gr.Button(visible= False), gr.Checkbox(visible= False)



def save_lset(state, lset_name, loras_choices, loras_mult_choices, prompt, save_lset_prompt_cbox):    
    loras_presets = state["loras_presets"] 
    loras = state["loras"]
    if state.get("validate_success",0) == 0:
        pass
    if len(lset_name) == 0 or lset_name == get_new_preset_msg(True) or lset_name == get_new_preset_msg(False):
        gr.Info("Please enter a name for the preset")
        lset_choices =[("Please enter a name for a Lora Preset","")]
    else:
        lset_name = sanitize_file_name(lset_name)

        loras_choices_files = [ Path(loras[int(choice_no)]).parts[-1] for choice_no in loras_choices  ]
        lset  = {"loras" : loras_choices_files, "loras_mult" : loras_mult_choices}
        if save_lset_prompt_cbox!=1:
            prompts = prompt.replace("\r", "").split("\n")
            prompts = [prompt for prompt in prompts if len(prompt)> 0 and prompt.startswith("#")]
            prompt = "\n".join(prompts)

        if len(prompt) > 0:
            lset["prompt"] = prompt
        lset["full_prompt"] = save_lset_prompt_cbox ==1
        

        lset_name_filename = lset_name + ".lset" 
        full_lset_name_filename = os.path.join(get_lora_dir(state["model_filename"]), lset_name_filename) 

        with open(full_lset_name_filename, "w", encoding="utf-8") as writer:
            writer.write(json.dumps(lset, indent=4))

        if lset_name in loras_presets:
            gr.Info(f"Lora Preset '{lset_name}' has been updated")
        else:
            gr.Info(f"Lora Preset '{lset_name}' has been created")
            loras_presets.append(Path(Path(lset_name_filename).parts[-1]).stem )
        lset_choices = [ ( preset, preset) for preset in loras_presets ]
        lset_choices.append( (get_new_preset_msg(), ""))
        state["loras_presets"] = loras_presets
    return gr.Dropdown(choices=lset_choices, value= lset_name), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Button(visible= False), gr.Checkbox(visible= False)

def delete_lset(state, lset_name):
    loras_presets = state["loras_presets"]
    lset_name_filename = os.path.join( get_lora_dir(state["model_filename"]),  sanitize_file_name(lset_name) + ".lset" )
    if len(lset_name) > 0 and lset_name != get_new_preset_msg(True) and  lset_name != get_new_preset_msg(False):
        if not os.path.isfile(lset_name_filename):
            raise gr.Error(f"Preset '{lset_name}' not found ")
        os.remove(lset_name_filename)
        pos = loras_presets.index(lset_name) 
        gr.Info(f"Lora Preset '{lset_name}' has been deleted")
        loras_presets.remove(lset_name)
    else:
        pos = len(loras_presets) 
        gr.Info(f"Choose a Preset to delete")

    state["loras_presets"] = loras_presets

    lset_choices = [ (preset, preset) for preset in loras_presets]
    lset_choices.append((get_new_preset_msg(), ""))
    return  gr.Dropdown(choices=lset_choices, value= lset_choices[pos][1]), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Checkbox(visible= False)

def refresh_lora_list(state, lset_name, loras_choices):
    loras_names = state["loras_names"]
    prev_lora_names_selected = [ loras_names[int(i)] for i in loras_choices]
    model_filename= state["model_filename"]
    loras, loras_names, loras_presets, _, _, _, _  = setup_loras(model_filename, None,  get_lora_dir(model_filename), lora_preselected_preset, None)
    state["loras"] = loras
    state["loras_names"] = loras_names
    state["loras_presets"] = loras_presets

    gc.collect()
    new_loras_choices = [ (loras_name, str(i)) for i,loras_name in enumerate(loras_names)]
    new_loras_dict = { loras_name: str(i) for i,loras_name in enumerate(loras_names) }
    lora_names_selected = []
    for lora in prev_lora_names_selected:
        lora_id = new_loras_dict.get(lora, None)
        if lora_id!= None:
            lora_names_selected.append(lora_id)

    lset_choices = [ (preset, preset) for preset in loras_presets]
    lset_choices.append((get_new_preset_msg( state["advanced"]), "")) 
    if lset_name in loras_presets:
        pos = loras_presets.index(lset_name) 
    else:
        pos = len(loras_presets)
        lset_name =""
    
    if wan_model != None:
        errors = getattr(get_transformer_model(wan_model), "_loras_errors", "")
        if errors !=None and len(errors) > 0:
            error_files = [path for path, _ in errors]
            gr.Info("Error while refreshing Lora List, invalid Lora files: " + ", ".join(error_files))
        else:
            gr.Info("Lora List has been refreshed")


    return gr.Dropdown(choices=lset_choices, value= lset_choices[pos][1]), gr.Dropdown(choices=new_loras_choices, value= lora_names_selected) 

def apply_lset(state, wizard_prompt_activated, lset_name, loras_choices, loras_mult_choices, prompt):

    state["apply_success"] = 0

    if len(lset_name) == 0 or lset_name== get_new_preset_msg(True) or lset_name== get_new_preset_msg(False):
        gr.Info("Please choose a preset in the list or create one")
    else:
        loras = state["loras"]
        loras_choices, loras_mult_choices, preset_prompt, full_prompt, error = extract_preset(state["model_filename"],  lset_name, loras)
        if len(error) > 0:
            gr.Info(error)
        else:
            if full_prompt:
                prompt = preset_prompt
            elif len(preset_prompt) > 0:
                prompts = prompt.replace("\r", "").split("\n")
                prompts = [prompt for prompt in prompts if len(prompt)>0 and not prompt.startswith("#")]
                prompt = "\n".join(prompts) 
                prompt = preset_prompt + '\n' + prompt
            gr.Info(f"Lora Preset '{lset_name}' has been applied")
            state["apply_success"] = 1
            wizard_prompt_activated = "on"

    return wizard_prompt_activated, loras_choices, loras_mult_choices, prompt


def extract_prompt_from_wizard(state, variables_names, prompt, wizard_prompt, allow_null_values, *args):

    prompts = wizard_prompt.replace("\r" ,"").split("\n")

    new_prompts = [] 
    macro_already_written = False
    for prompt in prompts:
        if not macro_already_written and not prompt.startswith("#") and "{"  in prompt and "}"  in prompt:
            variables =  variables_names.split("\n")   
            values = args[:len(variables)]
            macro = "! "
            for i, (variable, value) in enumerate(zip(variables, values)):
                if len(value) == 0 and not allow_null_values:
                    return prompt, "You need to provide a value for '" + variable + "'" 
                sub_values= [ "\"" + sub_value + "\"" for sub_value in value.split("\n") ]
                value = ",".join(sub_values)
                if i>0:
                    macro += " : "    
                macro += "{" + variable + "}"+ f"={value}"
            if len(variables) > 0:
                macro_already_written = True
                new_prompts.append(macro)
            new_prompts.append(prompt)
        else:
            new_prompts.append(prompt)

    prompt = "\n".join(new_prompts)
    return prompt, ""

def validate_wizard_prompt(state, wizard_prompt_activated, wizard_variables_names, prompt, wizard_prompt, *args):
    state["validate_success"] = 0

    if wizard_prompt_activated != "on":
        state["validate_success"] = 1
        return prompt

    prompt, errors = extract_prompt_from_wizard(state, wizard_variables_names, prompt, wizard_prompt, False, *args)
    if len(errors) > 0:
        gr.Info(errors)
        return prompt

    state["validate_success"] = 1

    return prompt

def fill_prompt_from_wizard(state, wizard_prompt_activated, wizard_variables_names, prompt, wizard_prompt, *args):

    if wizard_prompt_activated == "on":
        prompt, errors = extract_prompt_from_wizard(state, wizard_variables_names, prompt,  wizard_prompt, True, *args)
        if len(errors) > 0:
            gr.Info(errors)

        wizard_prompt_activated = "off"

    return wizard_prompt_activated, "", gr.Textbox(visible= True, value =prompt) , gr.Textbox(visible= False), gr.Column(visible = True), *[gr.Column(visible = False)] * 2,  *[gr.Textbox(visible= False)] * PROMPT_VARS_MAX

def extract_wizard_prompt(prompt):
    variables = []
    values = {}
    prompts = prompt.replace("\r" ,"").split("\n")
    if sum(prompt.startswith("!") for prompt in prompts) > 1:
        return "", variables, values, "Prompt is too complex for basic Prompt editor, switching to Advanced Prompt"

    new_prompts = [] 
    errors = ""
    for prompt in prompts:
        if prompt.startswith("!"):
            variables, errors = prompt_parser.extract_variable_names(prompt)
            if len(errors) > 0:
                return "", variables, values, "Error parsing Prompt templace: " + errors
            if len(variables) > PROMPT_VARS_MAX:
                return "", variables, values, "Prompt is too complex for basic Prompt editor, switching to Advanced Prompt"
            values, errors = prompt_parser.extract_variable_values(prompt)
            if len(errors) > 0:
                return "", variables, values, "Error parsing Prompt templace: " + errors
        else:
            variables_extra, errors = prompt_parser.extract_variable_names(prompt)
            if len(errors) > 0:
                return "", variables, values, "Error parsing Prompt templace: " + errors
            variables += variables_extra
            variables = [var for pos, var in enumerate(variables) if var not in variables[:pos]]
            if len(variables) > PROMPT_VARS_MAX:
                return "", variables, values, "Prompt is too complex for basic Prompt editor, switching to Advanced Prompt"

            new_prompts.append(prompt)
    wizard_prompt = "\n".join(new_prompts)
    return  wizard_prompt, variables, values, errors

def fill_wizard_prompt(state, wizard_prompt_activated, prompt, wizard_prompt):
    def get_hidden_textboxes(num = PROMPT_VARS_MAX ):
        return [gr.Textbox(value="", visible=False)] * num

    hidden_column =  gr.Column(visible = False)
    visible_column =  gr.Column(visible = True)

    wizard_prompt_activated  = "off"  
    if state["advanced"] or state.get("apply_success") != 1:
        return wizard_prompt_activated, gr.Text(), prompt, wizard_prompt, gr.Column(), gr.Column(), hidden_column,  *get_hidden_textboxes() 
    prompt_parts= []

    wizard_prompt, variables, values, errors =  extract_wizard_prompt(prompt)
    if len(errors) > 0:
        gr.Info( errors )
        return wizard_prompt_activated, "", gr.Textbox(prompt, visible=True), gr.Textbox(wizard_prompt, visible=False), visible_column, *[hidden_column] * 2, *get_hidden_textboxes()

    for variable in variables:
        value = values.get(variable, "")
        prompt_parts.append(gr.Textbox( placeholder=variable, info= variable, visible= True, value= "\n".join(value) ))
    any_macro = len(variables) > 0

    prompt_parts += get_hidden_textboxes(PROMPT_VARS_MAX-len(prompt_parts))

    variables_names= "\n".join(variables)
    wizard_prompt_activated  = "on"

    return wizard_prompt_activated, variables_names,  gr.Textbox(prompt, visible = False),  gr.Textbox(wizard_prompt, visible = True),   hidden_column, visible_column, visible_column if any_macro else hidden_column, *prompt_parts

def switch_prompt_type(state, wizard_prompt_activated_var, wizard_variables_names, prompt, wizard_prompt, *prompt_vars):
    if state["advanced"]:
        return fill_prompt_from_wizard(state, wizard_prompt_activated_var, wizard_variables_names, prompt, wizard_prompt, *prompt_vars)
    else:
        state["apply_success"] = 1
        return fill_wizard_prompt(state, wizard_prompt_activated_var, prompt, wizard_prompt)

visible= False
def switch_advanced(state, new_advanced, lset_name):
    state["advanced"] = new_advanced
    loras_presets = state["loras_presets"]
    lset_choices = [ (preset, preset) for preset in loras_presets]
    lset_choices.append((get_new_preset_msg(new_advanced), ""))
    if lset_name== get_new_preset_msg(True) or lset_name== get_new_preset_msg(False) or lset_name=="":
        lset_name =  get_new_preset_msg(new_advanced)

    if only_allow_edit_in_advanced:
        return  gr.Row(visible=new_advanced), gr.Row(visible=new_advanced), gr.Button(visible=new_advanced), gr.Row(visible= not new_advanced), gr.Dropdown(choices=lset_choices, value= lset_name)
    else:
        return  gr.Row(visible=new_advanced), gr.Row(visible=True), gr.Button(visible=True), gr.Row(visible= False), gr.Dropdown(choices=lset_choices, value= lset_name)


def prepare_inputs_dict(target, inputs ):
    
    state = inputs.pop("state")
    loras = state["loras"]
    if "loras_choices" in inputs:
        loras_choices = inputs.pop("loras_choices")
        inputs.pop("model_filename", None)
        activated_loras = [Path( loras[int(no)]).parts[-1]  for no in loras_choices ]
        inputs["activated_loras"] = activated_loras

    if target == "state":
        return inputs
    unsaved_params = ["image_start", "image_end", "image_refs", "video_guide", "video_source", "video_mask", "audio_guide"]
    for k in unsaved_params:
        inputs.pop(k)

    model_filename = state["model_filename"]
    inputs["type"] = f"WanGP v{WanGP_version} by DeepBeepMeep - " +  get_model_name(model_filename)

    if target == "settings":
        return inputs
    
    if not test_class_i2v(model_filename):
        inputs.pop("image_prompt_type")

    if not server_config.get("enhancer_enabled", 0) == 1:
        inputs.pop("prompt_enhancer")

    if not "recam" in model_filename and not "diffusion_forcing" in model_filename:
        inputs.pop("model_mode")

    if not "Vace" in model_filename and not "phantom" in model_filename and not "hunyuan_video_custom" in model_filename:
        unsaved_params = ["keep_frames_video_guide", "video_prompt_type",  "remove_background_images_ref"]
        for k in unsaved_params:
            inputs.pop(k)

    if not ("diffusion_forcing" in model_filename or "ltxv" in model_filename):
        unsaved_params = ["keep_frames_video_source"]
        for k in unsaved_params:
            inputs.pop(k)


    if not "Vace" in model_filename and not "diffusion_forcing" in model_filename and not "ltxv" in model_filename:
        unsaved_params = [ "sliding_window_size", "sliding_window_overlap", "sliding_window_overlap_noise", "sliding_window_discard_last_frames"]
        for k in unsaved_params:
            inputs.pop(k)

    if not "fantasy" in model_filename:
        inputs.pop("audio_guidance_scale")

    if not "hunyuan" in model_filename:
        inputs.pop("embedded_guidance_scale")

    if target == "metadata":
        inputs = {k: v for k,v in inputs.items() if v != None  }

    return inputs

def get_function_arguments(func, locals):
    args_names = list(inspect.signature(func).parameters)
    kwargs = typing.OrderedDict()
    for k in args_names:
        kwargs[k] = locals[k]
    return kwargs

def export_settings(state):
    model_filename = state["model_filename"]
    model_type = get_model_type(model_filename)
    settings = state[model_type]
    settings["state"] = state
    settings = prepare_inputs_dict("metadata", settings)
    settings["model_filename"] = model_filename 
    text = json.dumps(settings, indent=4)
    text_base64 = base64.b64encode(text.encode('utf8')).decode('utf-8')
    return text_base64

def use_video_settings(state, files):
    gen = get_gen_info(state)
    choice = gen.get("selected",-1)
    file_list = gen.get("file_list", None)
    if file_list !=None and choice >=0 and len(file_list)>0:
        file_settings_list = gen["file_settings_list"]
        configs = file_settings_list[choice]
        model_filename = configs["model_filename"]
        model_type = get_model_type(model_filename)
        defaults = state.get(model_type, None) 
        defaults = get_default_settings(model_filename) if defaults == None else defaults
        defaults.update(configs)
        current_model_filename = state["model_filename"]
        prompt = configs.get("prompt", "")
        state[model_type] = defaults
        gr.Info(f"Settings Loaded from Video with prompt '{prompt[:100]}'")
        if model_type == get_model_type(current_model_filename):
            return gr.update(), str(time.time())
        else:
            return generate_dropdown_model_list(model_filename), gr.update()
    else:
        gr.Info(f"No Video is Selected")

    return gr.update(), gr.update()

def load_settings_from_file(state, file_path):
    gen = get_gen_info(state)
    if file_path==None:
        return gr.update(), gr.update(), None

    configs = None
    if file_path.endswith(".json"):
        try:
            with open(file_path, 'r', encoding='utf-8') as f:
                configs = json.load(f)
        except:
            pass
    else:
        from mutagen.mp4 import MP4
        tags = None
        try:
            file = MP4(file_path)
            tags = file.tags['©cmt'][0] 
        except:
            pass
        if tags != None:    
            configs = json.loads(tags)
    if configs == None:
        gr.Info("File not supported")
        return gr.update(), gr.update(), None

    prompt = configs.get("prompt", "")
    current_model_filename = state["model_filename"]
    model_filename = configs["model_filename"]
    model_type = get_model_type(model_filename)
    defaults = state.get(model_type, None) 
    defaults = get_default_settings(model_filename) if defaults == None else defaults
    defaults.update(configs)
    state[model_type]= defaults
    if tags != None:    
        gr.Info(f"Settings Loaded from Video generated with prompt '{prompt[:100]}'")
    else:
        gr.Info(f"Settings Loaded from Settings file with prompt '{prompt[:100]}'")
    if model_type == get_model_type(current_model_filename):
        return gr.update(), str(time.time()), None
    else:
        return generate_dropdown_model_list(model_filename), gr.update(), None

def save_inputs(
            target,
            lset_name,
            prompt,
            negative_prompt,
            resolution,
            video_length,
            seed,
            num_inference_steps,
            guidance_scale,
            audio_guidance_scale,
            flow_shift,
            embedded_guidance_scale,
            repeat_generation,
            multi_images_gen_type,
            tea_cache_setting,
            tea_cache_start_step_perc,
            loras_choices,
            loras_multipliers,
            image_prompt_type,
            image_start,
            image_end,
            model_mode,
            video_source,
            keep_frames_video_source,
            video_prompt_type,
            image_refs,
            video_guide,
            keep_frames_video_guide,
            video_mask,
            audio_guide,
            sliding_window_size,
            sliding_window_overlap,
            sliding_window_overlap_noise,
            sliding_window_discard_last_frames,            
            remove_background_images_ref,
            temporal_upsampling,
            spatial_upsampling,
            RIFLEx_setting,
            slg_switch, 
            slg_layers,
            slg_start_perc,
            slg_end_perc,
            cfg_star_switch,
            cfg_zero_step,
            prompt_enhancer,
            state,
):

  
    # if state.get("validate_success",0) != 1:
    #     return
    model_filename = state["model_filename"]
    inputs = get_function_arguments(save_inputs, locals())
    inputs.pop("target")
    cleaned_inputs = prepare_inputs_dict(target, inputs)
    if target == "settings":
        defaults_filename = get_settings_file_name(model_filename)

        with open(defaults_filename, "w", encoding="utf-8") as f:
            json.dump(cleaned_inputs, f, indent=4)

        gr.Info("New Default Settings saved")
    elif target == "state":
        state[get_model_type(model_filename)] = cleaned_inputs

def download_loras():
    from huggingface_hub import  snapshot_download    
    yield gr.Row(visible=True), "<B><FONT SIZE=3>Please wait while the Loras are being downloaded</B></FONT>", *[gr.Column(visible=False)] * 2
    lora_dir = get_lora_dir(get_model_filename("i2v"))
    log_path = os.path.join(lora_dir, "log.txt")
    if not os.path.isfile(log_path):
        tmp_path = os.path.join(lora_dir, "tmp_lora_dowload")
        import glob
        snapshot_download(repo_id="DeepBeepMeep/Wan2.1",  allow_patterns="loras_i2v/*", local_dir= tmp_path)
        for f in glob.glob(os.path.join(tmp_path, "loras_i2v", "*.*")):
            target_file = os.path.join(lora_dir,  Path(f).parts[-1] )
            if os.path.isfile(target_file):
                os.remove(f)
            else:
                shutil.move(f, lora_dir) 
    try:
        os.remove(tmp_path)
    except:
        pass
    yield gr.Row(visible=True), "<B><FONT SIZE=3>Loras have been completely downloaded</B></FONT>", *[gr.Column(visible=True)] * 2

    from datetime import datetime
    dt = datetime.today().strftime('%Y-%m-%d')
    with open( log_path, "w", encoding="utf-8") as writer:
        writer.write(f"Loras downloaded on the {dt} at {time.time()} on the {time.time()}")
    return

def refresh_image_prompt_type(state, image_prompt_type):
    return gr.update(visible = "S" in image_prompt_type ), gr.update(visible = "E" in image_prompt_type ), gr.update(visible = "V" in image_prompt_type) , gr.update(visible = "V" in image_prompt_type ) 

def refresh_video_prompt_type(state, video_prompt_type):
    return gr.Gallery(visible = "I" in video_prompt_type), gr.Video(visible= "V" in video_prompt_type),gr.Video(visible= "M" in video_prompt_type ), gr.Text(visible= "V" in video_prompt_type) , gr.Checkbox(visible= "I" in video_prompt_type)


def handle_celll_selection(state, evt: gr.SelectData):
    gen = get_gen_info(state)
    queue = gen.get("queue", [])

    if evt.index is None:
        return gr.update(), gr.update(), gr.update(visible=False)
    row_index, col_index = evt.index
    cell_value = None
    if col_index in [6, 7, 8]:
        if col_index == 6: cell_value = "↑"
        elif col_index == 7: cell_value = "↓"
        elif col_index == 8: cell_value = "✖"
    if col_index == 6:
        new_df_data = move_up(queue, [row_index])
        return new_df_data, gr.update(), gr.update(visible=False)
    elif col_index == 7:
        new_df_data = move_down(queue, [row_index])
        return new_df_data, gr.update(), gr.update(visible=False)
    elif col_index == 8:
        new_df_data = remove_task(queue, [row_index])
        gen["prompts_max"] = gen.get("prompts_max",0) - 1
        update_status(state)
        return new_df_data, gr.update(), gr.update(visible=False)
    start_img_col_idx = 4
    end_img_col_idx = 5
    image_data_to_show = None
    if col_index == start_img_col_idx:
        with lock:
            row_index += 1
            if row_index < len(queue):
                image_data_to_show = queue[row_index].get('start_image_data')
    elif col_index == end_img_col_idx:
        with lock:
            row_index += 1
            if row_index < len(queue):
                image_data_to_show = queue[row_index].get('end_image_data')

    if image_data_to_show:
        return gr.update(), gr.update(value=image_data_to_show[0]), gr.update(visible=True)
    else:
        return gr.update(), gr.update(), gr.update(visible=False)


def change_model(state, model_choice):
    if model_choice == None:
        return
    model_filename = get_model_filename(model_choice, transformer_quantization, transformer_dtype_policy)
    state["model_filename"] = model_filename
    header = generate_header(model_filename, compile=compile, attention_mode=attention_mode)
    return header

def fill_inputs(state):
    model_filename = state["model_filename"]
    prefix = get_model_type(model_filename)
    ui_defaults = state.get(prefix, None)
    if ui_defaults == None:
        ui_defaults = get_default_settings(model_filename)
 
    return generate_video_tab(update_form = True, state_dict = state, ui_defaults = ui_defaults)

def preload_model_when_switching(state):
    global reload_needed, wan_model, offloadobj
    if "S" in preload_model_policy:
        model_filename = state["model_filename"] 
        if  state["model_filename"] !=  transformer_filename:
            wan_model = None
            if offloadobj is not None:
                offloadobj.release()
                offloadobj = None
            gc.collect()
            yield f"Loading model {get_model_name(model_filename)}..."
            wan_model, offloadobj, _ = load_models(model_filename)
            yield f"Model loaded"
            reload_needed=  False 
        return   
    return gr.Text()

def unload_model_if_needed(state):
    global reload_needed, wan_model, offloadobj
    if "U" in preload_model_policy:
        if wan_model != None:
            wan_model = None
            if offloadobj is not None:
                offloadobj.release()
                offloadobj = None
            gc.collect()
            reload_needed=  True

def filter_letters(source_str, letters):
    ret = ""
    for letter in letters:
        if letter in source_str:
            ret += letter
    return ret    

def add_to_sequence(source_str, letters):
    ret = source_str
    for letter in letters:
        if not letter in source_str:
            ret += letter
    return ret    

def del_in_sequence(source_str, letters):
    ret = source_str
    for letter in letters:
        if letter in source_str:
            ret = ret.replace(letter, "")
    return ret    


def refresh_video_prompt_type_image_refs(video_prompt_type, video_prompt_type_image_refs):
    # video_prompt_type = add_to_sequence(video_prompt_type, "I") if video_prompt_type_image_refs else  del_in_sequence(video_prompt_type, "I")
    video_prompt_type_image_refs = "I" in video_prompt_type_image_refs
    video_prompt_type = add_to_sequence(video_prompt_type, "I") if video_prompt_type_image_refs else  del_in_sequence(video_prompt_type, "I")
    return video_prompt_type, gr.update(visible = video_prompt_type_image_refs),gr.update(visible = video_prompt_type_image_refs)
                
def refresh_video_prompt_type_video_guide(video_prompt_type, video_prompt_type_video_guide):
    video_prompt_type = del_in_sequence(video_prompt_type, "ODPCMV")
    video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)
    visible = "V" in video_prompt_type
    return video_prompt_type, gr.update(visible = visible), gr.update(visible = visible), gr.update(visible= "M" in video_prompt_type )

def refresh_video_prompt_video_guide_trigger(video_prompt_type, video_prompt_type_video_guide):
    video_prompt_type_video_guide = video_prompt_type_video_guide.split("#")[0]
    video_prompt_type = del_in_sequence(video_prompt_type, "ODPCMV")
    video_prompt_type = add_to_sequence(video_prompt_type, video_prompt_type_video_guide)

    return video_prompt_type, video_prompt_type_video_guide, gr.update(visible= "V" in video_prompt_type ), gr.update(visible= "M" in video_prompt_type) , gr.update(visible= "V" in video_prompt_type )

def refresh_preview(state):
    gen = get_gen_info(state)
    preview = gen.get("preview", None)
    return preview

def init_process_queue_if_any(state):                
    gen = get_gen_info(state)
    if bool(gen.get("queue",[])):
        state["validate_success"] = 1
        return gr.Button(visible=False), gr.Button(visible=True), gr.Column(visible=True)                   
    else:
        return gr.Button(visible=True), gr.Button(visible=False), gr.Column(visible=False)



def generate_video_tab(update_form = False, state_dict = None, ui_defaults = None, model_choice = None, header = None, main = None):
    global inputs_names #, advanced

    if update_form:
        model_filename = state_dict["model_filename"]
        advanced_ui = state_dict["advanced"]  
    else:
        model_filename = transformer_filename
        advanced_ui = advanced
        ui_defaults=  get_default_settings(model_filename)
        state_dict = {}
        state_dict["model_filename"] = model_filename
        state_dict["advanced"] = advanced_ui
        gen = dict()
        gen["queue"] = []
        state_dict["gen"] = gen

    preset_to_load = lora_preselected_preset if lora_preset_model == model_filename else "" 

    loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset = setup_loras(model_filename,  None,  get_lora_dir(model_filename), preset_to_load, None)

    state_dict["loras"] = loras
    state_dict["loras_presets"] = loras_presets
    state_dict["loras_names"] = loras_names

    launch_prompt = ""
    launch_preset = ""
    launch_loras = []
    launch_multis_str = ""

    if update_form:
        pass
    if len(default_lora_preset) > 0 and lora_preset_model == model_filename:
        launch_preset = default_lora_preset
        launch_prompt = default_lora_preset_prompt 
        launch_loras = default_loras_choices
        launch_multis_str = default_loras_multis_str

    if len(launch_preset) == 0:
        launch_preset = ui_defaults.get("lset_name","")
    if len(launch_prompt) == 0:
        launch_prompt = ui_defaults.get("prompt","")
    if len(launch_loras) == 0:
        launch_multis_str = ui_defaults.get("loras_multipliers","")
        activated_loras = ui_defaults.get("activated_loras",[])
        if len(activated_loras) > 0:
            lora_filenames = [os.path.basename(lora_path) for lora_path in loras]
            activated_indices = []
            for lora_file in ui_defaults["activated_loras"]:
                try:
                    idx = lora_filenames.index(lora_file)
                    activated_indices.append(str(idx))
                except ValueError:
                    print(f"Warning: Lora file {lora_file} from config not found in loras directory")
            launch_loras = activated_indices

    with gr.Row():
        with gr.Column():
            with gr.Column(visible=False, elem_id="image-modal-container") as modal_container:
                with gr.Row(elem_id="image-modal-close-button-row"):
                     close_modal_button = gr.Button("❌", size="sm")
                modal_image_display = gr.Image(label="Full Resolution Image", interactive=False, show_label=False)
            with gr.Row(visible= True): #len(loras)>0) as presets_column:
                lset_choices = [ (preset, preset) for preset in loras_presets ] + [(get_new_preset_msg(advanced_ui), "")]
                with gr.Column(scale=6):
                    lset_name = gr.Dropdown(show_label=False, allow_custom_value= True, scale=5, filterable=True, choices= lset_choices, value=launch_preset)
                with gr.Column(scale=1):
                    with gr.Row(height=17):
                        apply_lset_btn = gr.Button("Apply Lora Preset", size="sm", min_width= 1)
                        refresh_lora_btn = gr.Button("Refresh", size="sm", min_width= 1, visible=advanced_ui or not only_allow_edit_in_advanced)
                        save_lset_prompt_drop= gr.Dropdown(
                            choices=[
                                ("Save Prompt Comments Only", 0),
                                ("Save Full Prompt", 1)
                            ],  show_label= False, container=False, value =1, visible= False
                        ) 
                    with gr.Row(height=17, visible=False) as refresh2_row:
                        refresh_lora_btn2 = gr.Button("Refresh", size="sm", min_width= 1)

                    with gr.Row(height=17, visible=advanced_ui or not only_allow_edit_in_advanced) as preset_buttons_rows:
                        confirm_save_lset_btn = gr.Button("Go Ahead Save it !", size="sm", min_width= 1, visible=False) 
                        confirm_delete_lset_btn = gr.Button("Go Ahead Delete it !", size="sm", min_width= 1, visible=False) 
                        save_lset_btn = gr.Button("Save", size="sm", min_width= 1)
                        delete_lset_btn = gr.Button("Delete", size="sm", min_width= 1)
                        cancel_lset_btn = gr.Button("Don't do it !", size="sm", min_width= 1 , visible=False)  

            if not update_form:
                state = gr.State(state_dict)     
            trigger_refresh_input_type = gr.Text(interactive= False, visible= False)
            diffusion_forcing = "diffusion_forcing" in model_filename 
            ltxv = "ltxv" in model_filename 
            ltxv_distilled = "ltxv" in model_filename and "distilled" in model_filename 
            recammaster = "recam" in model_filename
            vace = "Vace" in model_filename
            phantom = "phantom" in model_filename
            fantasy = "fantasy" in model_filename
            hunyuan_t2v = "hunyuan_video_720" in model_filename
            hunyuan_i2v = "hunyuan_video_i2v" in model_filename
            hunyuan_video_custom = "hunyuan_video_custom" in model_filename
            sliding_window_enabled = vace or diffusion_forcing or ltxv
            new_line_text = "each new line of prompt will be used for a window" if sliding_window_enabled else "each new line of prompt will generate a new video"

            with gr.Column(visible= test_class_i2v(model_filename) or diffusion_forcing or ltxv or recammaster) as image_prompt_column: 
                if diffusion_forcing or ltxv:
                    image_prompt_type_value= ui_defaults.get("image_prompt_type","S")
                    # image_prompt_type = gr.Radio( [("Start Video with Image", "S"),("Start and End Video with Images", "SE"), ("Continue Video", "V"),("Text Prompt Only", "T")], value =image_prompt_type_value, label="Location", show_label= False, visible= True, scale= 3)
                    image_prompt_type = gr.Radio( [("Start Video with Image", "S"),("Continue Video", "V"),("Text Prompt Only", "T")], value =image_prompt_type_value, label="Location", show_label= False, visible= True , scale= 3)

                    # image_start = gr.Image(label= "Image as a starting point for a new video", type ="pil",value= ui_defaults.get("image_start", None), visible= "S" in image_prompt_type_value )
                    image_start = gr.Gallery(
                            label="Images as starting points for new videos", type ="pil", #file_types= "image", 
                            columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, value= ui_defaults.get("image_start", None), visible= "S" in image_prompt_type_value) 
                    image_end  = gr.Gallery(
                            label="Images as ending points for new videos", type ="pil", #file_types= "image", 
                            columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible="E" in image_prompt_type_value, value= ui_defaults.get("image_end", None))
                    video_source = gr.Video(label= "Video to Continue", visible= "V" in image_prompt_type_value, value= ui_defaults.get("video_source", None),)
                    if ltxv:
                        model_mode = gr.Dropdown(
                            choices=[
                            ], value=None, 
                            visible= False
                        )
                    else:
                        model_mode = gr.Dropdown(
                            choices=[
                                ("Synchronous", 0),
                                ("Asynchronous (better quality but around 50% extra steps added)", 5),
                            ],
                            value=ui_defaults.get("model_mode", 0),
                            label="Generation Type", scale = 3,
                            visible= True
                        )
                    keep_frames_video_source = gr.Text(value=ui_defaults.get("keep_frames_video_source","") , visible= "V" in image_prompt_type_value, scale = 2, label= "Truncate Video beyond this number of Frames of Video (empty=Keep All)" ) 
                elif recammaster:
                    image_prompt_type = gr.Radio(visible= False)
                    image_start = gr.Gallery(visible = False)
                    image_end  = gr.Gallery(visible=False)
                    video_source = gr.Video(label= "Video Source", visible = True, value= ui_defaults.get("video_source", None),)
                    model_mode = gr.Dropdown(
                        choices=[
                            ("Pan Right", 1),
                            ("Pan Left", 2),
                            ("Tilt Up", 3),
                            ("Tilt Down", 4),
                            ("Zoom In", 5),
                            ("Zoom Out", 6),
                            ("Translate Up (with rotation)", 7),
                            ("Translate Down (with rotation)", 8),
                            ("Arc Left (with rotation)", 9),
                            ("Arc Right (with rotation)", 10),
                        ],
                        value=ui_defaults.get("model_mode", 1),
                        label="Camera Movement Type", scale = 3,
                        visible= True
                    )
                    keep_frames_video_source = gr.Text(visible=False)
                else:
                    image_prompt_type_value= ui_defaults.get("image_prompt_type","S")
                    image_prompt_type = gr.Radio( [("Use only a Start Image", "S"),("Use both a Start and an End Image", "SE")], value =image_prompt_type_value, label="Location", show_label= False, visible= not hunyuan_i2v, scale= 3)

                    image_start = gr.Gallery(
                            label="Images as starting points for new videos", type ="pil", #file_types= "image", 
                            columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, value= ui_defaults.get("image_start", None), visible= "S" in image_prompt_type_value) 

                    image_end  = gr.Gallery(
                            label="Images as ending points for new videos", type ="pil", #file_types= "image", 
                            columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible="E" in image_prompt_type_value, value= ui_defaults.get("image_end", None))

                    video_source = gr.Video(visible=False)
                    model_mode = gr.Dropdown(value=None, visible=False)
                    keep_frames_video_source = gr.Text(visible=False)

            with gr.Column(visible= vace or phantom or hunyuan_video_custom) as video_prompt_column: 
                video_prompt_type_value= ui_defaults.get("video_prompt_type","")
                video_prompt_type = gr.Text(value= video_prompt_type_value, visible= False)
                with gr.Row():
                    if vace:
                        video_prompt_type_video_guide = gr.Dropdown(
                            choices=[
                                ("None", ""),
                                ("Transfer Human Motion from the Control Video", "PV"),
                                ("Transfer Depth from the Control Video", "DV"),
                                ("Recolorize the Control Video", "CV"),
                                ("Extend Video", "OV"),
                                ("Video contains Open Pose, Depth, Black & White, Inpainting ", "V"),
                                ("Control Video and Mask video for Inpainting ", "MV"),
                            ],
                            value=filter_letters(video_prompt_type_value, "ODPCMV"),
                            label="Video to Video", scale = 3, visible= True
                        )
                    else:
                        video_prompt_type_video_guide = gr.Dropdown(visible= False)

                    video_prompt_video_guide_trigger = gr.Text(visible=False, value="")

                    video_prompt_type_image_refs = gr.Dropdown(
                        choices=[
                            ("None", ""),
                            ("Inject custom Faces / Objects", "I"),
                        ],
                        value="I" if "I" in video_prompt_type_value  else "",
                        label="Reference Images", scale = 2
                    )

                # video_prompt_type_image_refs = gr.Checkbox(value="I" in video_prompt_type_value , label= "Use References Images (Faces, Objects) to customize New Video",  scale =1 ) 
                video_guide = gr.Video(label= "Control Video", visible= "V" in video_prompt_type_value, value= ui_defaults.get("video_guide", None),)
                keep_frames_video_guide = gr.Text(value=ui_defaults.get("keep_frames_video_guide","") , visible= "V" in video_prompt_type_value, scale = 2, label= "Frames to keep in Control Video (empty=All, 1=first, a:b for a range, space to separate values)" ) #, -1=last
                image_refs = gr.Gallery( label ="Reference Images",
                        type ="pil",   show_label= True,
                        columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible= "I" in video_prompt_type_value, 
                        value= ui_defaults.get("image_refs", None),
                 )

                # with gr.Row():
                remove_background_images_ref = gr.Dropdown(
                    choices=[
                        ("Keep Backgrounds of All Images (landscape)", 0),
                        ("Remove Backgrounds of All Images (objects / faces)", 1),
                        ("Keep it for first Image (landscape) and remove it for other Images (objects / faces)", 2),
                    ],
                    value=ui_defaults.get("remove_background_images_ref",1),
                    label="Remove Background of Images References", scale = 3, visible= "I" in video_prompt_type_value
                )

                # remove_background_images_ref = gr.Checkbox(value=ui_defaults.get("remove_background_images_ref",1), label= "Remove Background of Images References", visible= "I" in video_prompt_type_value, scale =1 ) 


                video_mask = gr.Video(label= "Video Mask (for Inpainting or Outpaing, white pixels = Mask)", visible= "M" in video_prompt_type_value, value= ui_defaults.get("video_mask", None)) 
            audio_guide = gr.Audio(value= ui_defaults.get("audio_guide", None), type="filepath", label="Voice to follow", show_download_button= True, visible= fantasy )

            advanced_prompt = advanced_ui
            prompt_vars=[]

            if advanced_prompt:
                default_wizard_prompt, variables, values= None, None, None
            else:                 
                default_wizard_prompt, variables, values, errors =  extract_wizard_prompt(launch_prompt)
                advanced_prompt  = len(errors) > 0
            with gr.Column(visible= advanced_prompt) as prompt_column_advanced:
                prompt = gr.Textbox( visible= advanced_prompt, label="Prompts (" + new_line_text + ", # lines = comments, ! lines = macros)", value=launch_prompt, lines=3)

            with gr.Column(visible=not advanced_prompt and len(variables) > 0) as prompt_column_wizard_vars:
                gr.Markdown("<B>Please fill the following input fields to adapt automatically the Prompt:</B>")
                wizard_prompt_activated = "off"
                wizard_variables = ""
                with gr.Row():
                    if not advanced_prompt:
                        for variable in variables:
                            value = values.get(variable, "")
                            prompt_vars.append(gr.Textbox( placeholder=variable, min_width=80, show_label= False, info= variable, visible= True, value= "\n".join(value) ))
                        wizard_prompt_activated = "on"
                        if len(variables) > 0:
                            wizard_variables = "\n".join(variables)
                    for _ in range( PROMPT_VARS_MAX - len(prompt_vars)):
                        prompt_vars.append(gr.Textbox(visible= False, min_width=80, show_label= False))
            with gr.Column(not advanced_prompt) as prompt_column_wizard:
                wizard_prompt = gr.Textbox(visible = not advanced_prompt, label="Prompts (" + new_line_text + ", # lines = comments)", value=default_wizard_prompt, lines=3)
                wizard_prompt_activated_var = gr.Text(wizard_prompt_activated, visible= False)
                wizard_variables_var = gr.Text(wizard_variables, visible = False)
            with gr.Row(visible= server_config.get("enhancer_enabled", 0) == 1  ) as prompt_enhancer_row:
                prompt_enhancer = gr.Dropdown(
                    choices=[
                        ("Disabled", ""),
                        ("Based on Text Prompts", "T"),
                        ("Based on Image Prompts (such as Start Image and Reference Images)", "I"),
                        ("Based on both Text Prompts and Image Prompts", "TI"),
                    ],
                    value=ui_defaults.get("prompt_enhancer", ""),
                    label="Enhance Prompt using a LLM", scale = 3,
                    visible= True
                )
            with gr.Row():
                if test_class_i2v(model_filename):
                    if server_config.get("fit_canvas", 0) == 1:
                        label = "Max Resolution (as it maybe less depending on video width / height ratio)"
                    else:
                        label = "Max Resolution (as it maybe less depending on video width / height ratio)" 
                else:
                    label = "Max Resolution (as it maybe less depending on video width / height ratio)" 
                resolution = gr.Dropdown(
                    choices=[
                        # 1080p
                        ("1920x832 (21:9, 1080p)", "1920x832"),
                        ("832x1920 (9:21, 1080p)", "832x1920"),
                        # 720p
                        ("1280x720 (16:9, 720p)", "1280x720"),
                        ("720x1280 (9:16, 720p)", "720x1280"), 
                        ("1024x1024 (1:1, 720p)", "1024x024"),
                        ("1280x544 (21:9, 720p)", "1280x544"),
                        ("544x1280 (9:21, 720p)", "544x1280"),
                        ("1104x832 (4:3, 720p)", "1104x832"),
                        ("832x1104 (3:4, 720p)", "832x1104"),
                        ("960x960 (1:1, 720p)", "960x960"),
                        # 480p
                        ("960x544 (16:9, 540p)", "960x544"),
                        ("544x960 (9:16, 540p)", "544x960"),
                        ("832x480 (16:9, 480p)", "832x480"),
                        ("480x832 (9:16, 480p)", "480x832"),
                        ("832x624 (4:3, 480p)", "832x624"), 
                        ("624x832 (3:4, 480p)", "624x832"),
                        ("720x720 (1:1, 480p)", "720x720"),
                        ("512x512 (1:1, 480p)", "512x512"),
                    ],
                    value=ui_defaults.get("resolution","832x480"),
                    label= label 
                )
            with gr.Row():
                if recammaster:
                    video_length = gr.Slider(5, 193, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (16 = 1s), locked", interactive= False)
                elif diffusion_forcing:
                    video_length = gr.Slider(17, 737, value=ui_defaults.get("video_length", 97), step=20, label="Number of frames (24 = 1s)", interactive= True)
                elif ltxv:
                    video_length = gr.Slider(17, 737, value=ui_defaults.get("video_length", 97), step=8, label="Number of frames (30 = 1s)", interactive= True)
                elif vace:
                    video_length = gr.Slider(17, 737, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (16 = 1s)", interactive= True)
                elif fantasy:
                    video_length = gr.Slider(5, 233, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (23 = 1s)", interactive= True)
                elif hunyuan_t2v or hunyuan_i2v:
                    video_length = gr.Slider(5, 337, value=ui_defaults.get("video_length", 97), step=4, label="Number of frames (24 = 1s)", interactive= True)
                else:
                    video_length = gr.Slider(5, 193, value=ui_defaults.get("video_length", 81), step=4, label="Number of frames (16 = 1s)", interactive= True)
            with gr.Row(visible = not ltxv_distilled) as inference_steps_row:                                       
                num_inference_steps = gr.Slider(1, 100, value=ui_defaults.get("num_inference_steps",30), step=1, label="Number of Inference Steps")



            show_advanced = gr.Checkbox(label="Advanced Mode", value=advanced_ui)
            with gr.Tabs(visible=advanced_ui) as advanced_row:
                # with gr.Row(visible=advanced_ui) as advanced_row:
                with gr.Tab("Generation"):
                    with gr.Column():
                        seed = gr.Slider(-1, 999999999, value=ui_defaults["seed"], step=1, label="Seed (-1 for random)") 
                        with gr.Row():
                            repeat_generation = gr.Slider(1, 25.0, value=ui_defaults.get("repeat_generation",1), step=1, label="Default Number of Generated Videos per Prompt") 
                            multi_images_gen_type = gr.Dropdown( value=ui_defaults.get("multi_images_gen_type",0), 
                                choices=[
                                    ("Generate every combination of images and texts", 0),
                                    ("Match images and text prompts", 1),
                                ], visible= test_class_i2v(model_filename), label= "Multiple Images as Texts Prompts"
                            )
                        with gr.Row(visible = not ltxv):
                            guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults.get("guidance_scale",5), step=0.5, label="Guidance Scale", visible=not (hunyuan_t2v or hunyuan_i2v))
                            audio_guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults.get("audio_guidance_scale",5), step=0.5, label="Audio Guidance", visible=fantasy)
                            embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale", visible=(hunyuan_t2v or hunyuan_i2v))
                            flow_shift = gr.Slider(0.0, 25.0, value=ui_defaults.get("flow_shift",3), step=0.1, label="Shift Scale") 
                        with gr.Row():
                            negative_prompt = gr.Textbox(label="Negative Prompt", value=ui_defaults.get("negative_prompt", "") )
                with gr.Tab("Loras"):
                    with gr.Column(visible = True): #as loras_column:
                        gr.Markdown("<B>Loras can be used to create special effects on the video by mentioning a trigger word in the Prompt. You can save Loras combinations in presets.</B>")
                        loras_choices = gr.Dropdown(
                            choices=[
                                (lora_name, str(i) ) for i, lora_name in enumerate(loras_names)
                            ],
                            value= launch_loras,
                            multiselect= True,
                            label="Activated Loras"
                        )
                        loras_multipliers = gr.Textbox(label="Loras Multipliers (1.0 by default) separated by space characters or carriage returns, line that starts with # are ignored", value=launch_multis_str)
                with gr.Tab("Speed", visible = not ltxv) as speed_tab:
                    with gr.Column():
                        gr.Markdown("<B>Tea Cache accelerates by skipping intelligently some steps, the more steps are skipped the lower the quality of the video (Tea Cache consumes also VRAM)</B>")

                        tea_cache_setting = gr.Dropdown(
                            choices=[
                                ("Tea Cache Disabled", 0),
                                ("around x1.5 speed up", 1.5), 
                                ("around x1.75 speed up", 1.75), 
                                ("around x2 speed up", 2.0), 
                                ("around x2.25 speed up", 2.25), 
                                ("around x2.5 speed up", 2.5), 
                            ],
                            value=float(ui_defaults.get("tea_cache_setting",0)),
                            visible=True,
                            label="Tea Cache Global Acceleration"
                        )
                        tea_cache_start_step_perc = gr.Slider(0, 100, value=ui_defaults.get("tea_cache_start_step_perc",0), step=1, label="Tea Cache starting moment in % of generation") 

                with gr.Tab("Upsampling"):

                    with gr.Column():
                        gr.Markdown("<B>Upsampling - postprocessing that may improve fluidity and the size of the video</B>")
                        temporal_upsampling = gr.Dropdown(
                            choices=[
                                ("Disabled", ""),
                                ("Rife x2 (32 frames/s)", "rife2"), 
                                ("Rife x4 (64 frames/s)", "rife4"), 
                            ],
                            value=ui_defaults.get("temporal_upsampling", ""),
                            visible=True,
                            scale = 1,
                            label="Temporal Upsampling"
                        )
                        spatial_upsampling = gr.Dropdown(
                            choices=[
                                ("Disabled", ""),
                                ("Lanczos x1.5", "lanczos1.5"), 
                                ("Lanczos x2.0", "lanczos2"), 
                            ],
                            value=ui_defaults.get("spatial_upsampling", ""),
                            visible=True,
                            scale = 1,
                            label="Spatial Upsampling"
                        )

                with gr.Tab("Quality", visible = not ltxv) as quality_tab:
                        with gr.Column(visible = not (hunyuan_i2v or hunyuan_t2v or hunyuan_video_custom) ) as skip_layer_guidance_row:
                            gr.Markdown("<B>Skip Layer Guidance (improves video quality)</B>")
                            with gr.Row():
                                slg_switch = gr.Dropdown(
                                    choices=[
                                        ("OFF", 0),
                                        ("ON", 1), 
                                    ],
                                    value=ui_defaults.get("slg_switch",0),
                                    visible=True,
                                    scale = 1,
                                    label="Skip Layer guidance"
                                )
                                slg_layers = gr.Dropdown(
                                    choices=[
                                        (str(i), i ) for i in range(40)
                                    ],
                                    value=ui_defaults.get("slg_layers", ["9"]),
                                    multiselect= True,
                                    label="Skip Layers",
                                    scale= 3
                                )
                            with gr.Row():
                                slg_start_perc = gr.Slider(0, 100, value=ui_defaults.get("slg_start_perc",10), step=1, label="Denoising Steps % start") 
                                slg_end_perc = gr.Slider(0, 100, value=ui_defaults.get("slg_end_perc",90), step=1, label="Denoising Steps % end") 

                        with gr.Row():
                            gr.Markdown("<B>Experimental: Classifier-Free Guidance Zero Star, better adherence to Text Prompt")
                        with gr.Row():
                            cfg_star_switch = gr.Dropdown(
                                choices=[
                                    ("OFF", 0),
                                    ("ON", 1), 
                                ],
                                value=ui_defaults.get("cfg_star_switch",0),
                                visible=True,
                                scale = 1,
                                label="CFG Star"
                            )
                            with gr.Row():
                                cfg_zero_step = gr.Slider(-1, 39, value=ui_defaults.get("cfg_zero_step",-1), step=1, label="CFG Zero below this Layer (Extra Process)") 
                with gr.Tab("Sliding Window", visible= sliding_window_enabled) as sliding_window_tab:

                    with gr.Column():  
                        gr.Markdown("<B>A Sliding Window allows you to generate video with a duration not limited by the Model</B>")
                        gr.Markdown("<B>It is automatically turned on if the number of frames to generate is higher than the Window Size</B>")
                        if diffusion_forcing:
                            sliding_window_size = gr.Slider(37, 137, value=ui_defaults.get("sliding_window_size", 97), step=20, label="Sliding Window Size (recommended to keep it at 97)")
                            sliding_window_overlap = gr.Slider(17, 97, value=ui_defaults.get("sliding_window_overlap",17), step=20, label="Windows Frames Overlap (needed to maintain continuity between windows, a higher value will require more windows)")
                            sliding_window_overlap_noise = gr.Slider(0, 100, value=ui_defaults.get("sliding_window_overlap_noise",20), step=1, label="Noise to be added to overlapped frames to reduce blur effect")
                            sliding_window_discard_last_frames = gr.Slider(0, 20, value=ui_defaults.get("sliding_window_discard_last_frames", 0), step=4, visible = False)
                        elif ltxv:
                            sliding_window_size = gr.Slider(41, 257, value=ui_defaults.get("sliding_window_size", 129), step=8, label="Sliding Window Size")
                            sliding_window_overlap = gr.Slider(9, 97, value=ui_defaults.get("sliding_window_overlap",9), step=8, label="Windows Frames Overlap (needed to maintain continuity between windows, a higher value will require more windows)")
                            sliding_window_overlap_noise = gr.Slider(0, 100, value=ui_defaults.get("sliding_window_overlap_noise",20), step=1, label="Noise to be added to overlapped frames to reduce blur effect")
                            sliding_window_discard_last_frames = gr.Slider(0, 20, value=ui_defaults.get("sliding_window_discard_last_frames", 0), step=4, visible = False)
                        else:
                            sliding_window_size = gr.Slider(5, 137, value=ui_defaults.get("sliding_window_size", 81), step=4, label="Sliding Window Size")
                            sliding_window_overlap = gr.Slider(1, 97, value=ui_defaults.get("sliding_window_overlap",5), step=4, label="Windows Frames Overlap (needed to maintain continuity between windows, a higher value will require more windows)")
                            sliding_window_overlap_noise = gr.Slider(0, 150, value=ui_defaults.get("sliding_window_overlap_noise",20), step=1, label="Noise to be added to overlapped frames to reduce blur effect")
                            sliding_window_discard_last_frames = gr.Slider(0, 20, value=ui_defaults.get("sliding_window_discard_last_frames", 8), step=4, label="Discard Last Frames of a Window (that may have bad quality)", visible = True)


                with gr.Tab("Miscellaneous", visible= not (recammaster or ltxv or diffusion_forcing)) as misc_tab:
                    gr.Markdown("<B>With Riflex you can generate videos longer than 5s which is the default duration of videos used to train the model</B>")
                    RIFLEx_setting = gr.Dropdown(
                        choices=[
                            ("Auto (ON if Video longer than 5s)", 0),
                            ("Always ON", 1), 
                            ("Always OFF", 2), 
                        ],
                        value=ui_defaults.get("RIFLEx_setting",0),
                        label="RIFLEx positional embedding to generate long video"
                    )

            with gr.Row():
                save_settings_btn = gr.Button("Set Settings as Default", visible = not args.lock_config)
                export_settings_from_file_btn = gr.Button("Export Settings to File", visible = not args.lock_config)
                use_video_settings_btn = gr.Button("Use Selected Video Settings", visible = not args.lock_config)
            with gr.Row():
                settings_file = gr.File(height=41,label="Load Settings From Video / Json")
                settings_base64_output = gr.Text(interactive= False, visible=False, value = "")
        if not update_form:
            with gr.Column():
                gen_status = gr.Text(interactive= False, label = "Status")
                status_trigger = gr.Text(interactive= False, visible=False)
                output = gr.Gallery( label="Generated videos", show_label=False, elem_id="gallery" , columns=[3], rows=[1], object_fit="contain", height=450, selected_index=0, interactive= False)
                output_trigger = gr.Text(interactive= False, visible=False)
                refresh_form_trigger = gr.Text(interactive= False, visible=False)

                generate_btn = gr.Button("Generate")
                add_to_queue_btn = gr.Button("Add New Prompt To Queue", visible = False)

                with gr.Column(visible= False) as current_gen_column:
                    with gr.Accordion("Preview", open=False) as queue_accordion:
                        preview = gr.Image(label="Preview", height=200, show_label= False)
                        preview_trigger = gr.Text(visible= False)
                    gen_info = gr.HTML(visible=False, min_height=1) 
                    with gr.Row():
                        onemoresample_btn = gr.Button("One More Sample Please !")
                        onemorewindow_btn = gr.Button("Extend this Sample Please !", visible = False)
                        abort_btn = gr.Button("Abort")
                with gr.Accordion("Queue Management", open=False) as queue_accordion:
                    with gr.Row( ): 
                        queue_df = gr.DataFrame(
                            headers=["Qty","Prompt", "Length","Steps","", "", "", "", ""],
                            datatype=[ "str","markdown","str", "markdown", "markdown", "markdown", "str", "str", "str"],
                            column_widths= ["5%", None, "7%", "7%", "10%", "10%", "3%", "3%", "34"],
                            interactive=False,
                            col_count=(9, "fixed"),
                            wrap=True,
                            value=[],
                            line_breaks= True,
                            visible= True,
                            elem_id="queue_df",
                            max_height= 1000

                        )
                    with gr.Row(visible= True):
                        queue_zip_base64_output = gr.Text(visible=False)
                        save_queue_btn = gr.DownloadButton("Save Queue", size="sm")
                        load_queue_btn = gr.UploadButton("Load Queue", file_types=[".zip"], size="sm")
                        clear_queue_btn = gr.Button("Clear Queue", size="sm", variant="stop")
                        quit_button = gr.Button("Save and Quit", size="sm", variant="secondary")
                        with gr.Row(visible=False) as quit_confirmation_row:
                            confirm_quit_button = gr.Button("Confirm", elem_id="comfirm_quit_btn_hidden", size="sm", variant="stop")
                            cancel_quit_button = gr.Button("Cancel", size="sm", variant="secondary")
                        hidden_force_quit_trigger = gr.Button("force_quit", visible=False, elem_id="force_quit_btn_hidden")
                        hidden_countdown_state = gr.Number(value=-1, visible=False, elem_id="hidden_countdown_state_num")
                        single_hidden_trigger_btn = gr.Button("trigger_countdown", visible=False, elem_id="trigger_info_single_btn")

        extra_inputs = prompt_vars + [wizard_prompt, wizard_variables_var, wizard_prompt_activated_var, video_prompt_column, image_prompt_column,
                                      prompt_column_advanced, prompt_column_wizard_vars, prompt_column_wizard, lset_name, advanced_row, speed_tab, quality_tab,
                                      sliding_window_tab, misc_tab, prompt_enhancer_row, inference_steps_row, skip_layer_guidance_row,
                                      video_prompt_type_video_guide, video_prompt_type_image_refs] # show_advanced presets_column,
        if update_form:
            locals_dict = locals()
            gen_inputs = [state_dict if k=="state" else locals_dict[k]  for k in inputs_names] + [state_dict] + extra_inputs
            return gen_inputs
        else:
            target_state = gr.Text(value = "state", interactive= False, visible= False)
            target_settings = gr.Text(value = "settings", interactive= False, visible= False)

            image_prompt_type.change(fn=refresh_image_prompt_type, inputs=[state, image_prompt_type], outputs=[image_start, image_end, video_source, keep_frames_video_source] ) 
            video_prompt_video_guide_trigger.change(fn=refresh_video_prompt_video_guide_trigger, inputs=[video_prompt_type, video_prompt_video_guide_trigger], outputs=[video_prompt_type, video_prompt_type_video_guide, video_guide, video_mask, keep_frames_video_guide])
            video_prompt_type_image_refs.input(fn=refresh_video_prompt_type_image_refs, inputs = [video_prompt_type, video_prompt_type_image_refs], outputs = [video_prompt_type, image_refs, remove_background_images_ref ])
            video_prompt_type_video_guide.input(fn=refresh_video_prompt_type_video_guide, inputs = [video_prompt_type, video_prompt_type_video_guide], outputs = [video_prompt_type, video_guide, keep_frames_video_guide, video_mask])

            show_advanced.change(fn=switch_advanced, inputs=[state, show_advanced, lset_name], outputs=[advanced_row, preset_buttons_rows, refresh_lora_btn, refresh2_row ,lset_name ]).then(
                fn=switch_prompt_type, inputs = [state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars], outputs = [wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars])
            queue_df.select( fn=handle_celll_selection, inputs=state, outputs=[queue_df, modal_image_display, modal_container])
            save_lset_btn.click(validate_save_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop])
            confirm_save_lset_btn.click(fn=validate_wizard_prompt, inputs =[state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then(
            save_lset, inputs=[state, lset_name, loras_choices, loras_multipliers, prompt, save_lset_prompt_drop], outputs=[lset_name, apply_lset_btn,refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop])
            delete_lset_btn.click(validate_delete_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
            confirm_delete_lset_btn.click(delete_lset, inputs=[state, lset_name], outputs=[lset_name, apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
            cancel_lset_btn.click(cancel_lset, inputs=[], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_delete_lset_btn,confirm_save_lset_btn, cancel_lset_btn,save_lset_prompt_drop ])
            apply_lset_btn.click(apply_lset, inputs=[state, wizard_prompt_activated_var, lset_name,loras_choices, loras_multipliers, prompt], outputs=[wizard_prompt_activated_var, loras_choices, loras_multipliers, prompt]).then(
                fn = fill_wizard_prompt, inputs = [state, wizard_prompt_activated_var, prompt, wizard_prompt], outputs = [ wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars]
            )
            refresh_lora_btn.click(refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices])
            refresh_lora_btn2.click(refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices])
            output.select(select_video, state, None )
            preview_trigger.change(refresh_preview, inputs= [state], outputs= [preview])

            def refresh_status_async(state, progress=gr.Progress()):
                gen = get_gen_info(state)
                gen["progress"] = progress

                while True: 
                    progress_args= gen.get("progress_args", None)
                    if progress_args != None:
                        progress(*progress_args)
                        gen["progress_args"] = None
                    status= gen.get("status","")
                    if status == None or len(status) > 0:
                        yield status
                        gen["status"]= ""
                    if not gen.get("status_display", False):
                        return
                    time.sleep(0.5)

            def activate_status(state):
                if state.get("validate_success",0) != 1:
                    return
                gen = get_gen_info(state)
                gen["status_display"] = True
                return time.time()

            start_quit_timer_js, cancel_quit_timer_js, trigger_zip_download_js, trigger_settings_download_js = get_js()

            status_trigger.change(refresh_status_async, inputs= [state] , outputs= [gen_status], show_progress_on= [gen_status])

            output_trigger.change(refresh_gallery,
                inputs = [state], 
                outputs = [output, gen_info, generate_btn, add_to_queue_btn, current_gen_column,  queue_df, abort_btn, onemorewindow_btn])



            abort_btn.click(abort_generation, [state], [ abort_btn] ) #.then(refresh_gallery, inputs = [state, gen_info], outputs = [output, gen_info, queue_df] )
            onemoresample_btn.click(fn=one_more_sample,inputs=[state], outputs= [state])
            onemorewindow_btn.click(fn=one_more_window,inputs=[state], outputs= [state])

            inputs_names= list(inspect.signature(save_inputs).parameters)[1:-1]
            locals_dict = locals()
            gen_inputs = [locals_dict[k] for k in inputs_names] + [state]
            save_settings_btn.click( fn=validate_wizard_prompt, inputs =[state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then(
                save_inputs, inputs =[target_settings] + gen_inputs, outputs = [])

            use_video_settings_btn.click(fn=validate_wizard_prompt,
                inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                outputs= [prompt]
            ).then(fn=save_inputs,
                inputs =[target_state] + gen_inputs,
                outputs= None
            ).then( fn=use_video_settings, inputs =[state, output] , outputs= [model_choice, refresh_form_trigger])

            export_settings_from_file_btn.click(fn=validate_wizard_prompt,
                inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                outputs= [prompt]
            ).then(fn=save_inputs,
                inputs =[target_state] + gen_inputs,
                outputs= None
            ).then(fn=export_settings, 
                inputs =[state], 
                outputs= [settings_base64_output]
            ).then(
                fn=None,
                inputs=[settings_base64_output],
                outputs=None,
                js=trigger_settings_download_js
            )
            

            settings_file.upload(fn=validate_wizard_prompt,
                inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                outputs= [prompt]
            ).then(fn=save_inputs,
                inputs =[target_state] + gen_inputs,
                outputs= None
            ).then(fn=load_settings_from_file, inputs =[state, settings_file] , outputs= [model_choice, refresh_form_trigger, settings_file])


            refresh_form_trigger.change(fn= fill_inputs, 
                inputs=[state],
                outputs=gen_inputs + extra_inputs
            ).then(fn=validate_wizard_prompt,
                inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars],
                outputs= [prompt]
            )                

            model_choice.change(fn=validate_wizard_prompt,
                inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                outputs= [prompt]
            ).then(fn=save_inputs,
                inputs =[target_state] + gen_inputs,
                outputs= None
            ).then(fn= change_model,
                inputs=[state, model_choice],
                outputs= [header]
            ).then(fn= fill_inputs, 
                inputs=[state],
                outputs=gen_inputs + extra_inputs
            ).then(fn= preload_model_when_switching, 
                inputs=[state],
                outputs=[gen_status])

            generate_btn.click(fn=validate_wizard_prompt,
                inputs= [state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                outputs= [prompt]
            ).then(fn=save_inputs,
                inputs =[target_state] + gen_inputs,
                outputs= None
            ).then(fn=process_prompt_and_add_tasks,
                inputs = [state, model_choice],
                outputs= queue_df
            ).then(fn=prepare_generate_video,
                inputs= [state],
                outputs= [generate_btn, add_to_queue_btn, current_gen_column]
            ).then(fn=activate_status,
                inputs= [state],
                outputs= [status_trigger],             
            ).then(fn=process_tasks,
                inputs= [state],
                outputs= [preview_trigger, output_trigger], 
            ).then(finalize_generation,
                inputs= [state], 
                outputs= [output, abort_btn, generate_btn, add_to_queue_btn, current_gen_column, gen_info]
            ).then(
                fn=lambda s: gr.Accordion(open=False) if len(get_gen_info(s).get("queue", [])) <= 1 else gr.update(),
                inputs=[state],
                outputs=[queue_accordion]
            ).then(unload_model_if_needed,
                inputs= [state], 
                outputs= []
            )

            gr.on(triggers=[load_queue_btn.upload, main.load],
                fn=load_queue_action,
                inputs=[load_queue_btn, state],
                outputs=[queue_df]
            ).then(
                 fn=lambda s: (gr.update(visible=bool(get_gen_info(s).get("queue",[]))), gr.Accordion(open=True)) if bool(get_gen_info(s).get("queue",[])) else (gr.update(visible=False), gr.update()),
                 inputs=[state],
                 outputs=[current_gen_column, queue_accordion]
            ).then(
                fn=init_process_queue_if_any,
                inputs=[state],
                outputs=[generate_btn, add_to_queue_btn, current_gen_column, ]
            ).then(fn=activate_status,
                inputs= [state],
                outputs= [status_trigger],             
            ).then(
                fn=process_tasks,
                inputs=[state],
                outputs=[preview_trigger, output_trigger],
                trigger_mode="once"
            ).then(
                fn=finalize_generation_with_state,
                inputs=[state],
                outputs=[output, abort_btn, generate_btn, add_to_queue_btn, current_gen_column, gen_info, queue_accordion, state],
                trigger_mode="always_last"
            ).then(
                unload_model_if_needed,
                 inputs= [state],
                 outputs= []
            )



            single_hidden_trigger_btn.click(
                fn=show_countdown_info_from_state,
                inputs=[hidden_countdown_state],
                outputs=[hidden_countdown_state]
            )
            quit_button.click(
                fn=start_quit_process,
                inputs=[],
                outputs=[hidden_countdown_state, quit_button, quit_confirmation_row]
            ).then(
                fn=None, inputs=None, outputs=None, js=start_quit_timer_js
            )

            confirm_quit_button.click(
                fn=quit_application,
                inputs=[],
                outputs=[]
            ).then(
                fn=None, inputs=None, outputs=None, js=cancel_quit_timer_js
            )

            cancel_quit_button.click(
                fn=cancel_quit_process,
                inputs=[],
                outputs=[hidden_countdown_state, quit_button, quit_confirmation_row]
            ).then(
                fn=None, inputs=None, outputs=None, js=cancel_quit_timer_js
            )

            hidden_force_quit_trigger.click(
                fn=quit_application,
                inputs=[],
                outputs=[]
            )

            save_queue_btn.click(
                fn=save_queue_action,
                inputs=[state],
                outputs=[queue_zip_base64_output]
            ).then(
                fn=None,
                inputs=[queue_zip_base64_output],
                outputs=None,
                js=trigger_zip_download_js
            )

            clear_queue_btn.click(
                fn=clear_queue_action,
                inputs=[state],
                outputs=[queue_df]
            ).then(
                 fn=lambda: (gr.update(visible=False), gr.Accordion(open=False)),
                 inputs=None,
                 outputs=[current_gen_column, queue_accordion]
            )


            add_to_queue_btn.click(fn=validate_wizard_prompt, 
                inputs =[state, wizard_prompt_activated_var, wizard_variables_var,  prompt, wizard_prompt, *prompt_vars] ,
                outputs= [prompt]
            ).then(fn=save_inputs,
                inputs =[target_state] + gen_inputs,
                outputs= None
            ).then(fn=process_prompt_and_add_tasks,
                inputs = [state, model_choice],
                outputs=queue_df
            ).then(
                fn=lambda s: gr.Accordion(open=True) if len(get_gen_info(s).get("queue", [])) > 1 else gr.update(),
                inputs=[state],
                outputs=[queue_accordion]
            ).then(
                fn=update_status,
                inputs = [state],
            )

            close_modal_button.click(
                lambda: gr.update(visible=False),
                inputs=[],
                outputs=[modal_container]
            )

    return ( state, loras_choices, lset_name, state,
             video_guide, video_mask, image_refs, video_prompt_video_guide_trigger, prompt_enhancer    
        ) 
 

def generate_download_tab(lset_name,loras_choices, state):
    with gr.Row():
        with gr.Row(scale =2):
            gr.Markdown("<I>WanGP's Lora Festival ! Press the following button to download i2v <B>Remade_AI</B> Loras collection (and bonuses Loras).")
        with gr.Row(scale =1):
            download_loras_btn = gr.Button("---> Let the Lora's Festival Start !", scale =1)
        with gr.Row(scale =1):
            gr.Markdown("")
    with gr.Row() as download_status_row: 
        download_status = gr.Markdown()

    download_loras_btn.click(fn=download_loras, inputs=[], outputs=[download_status_row, download_status]).then(fn=refresh_lora_list, inputs=[state, lset_name,loras_choices], outputs=[lset_name, loras_choices])

    
def generate_configuration_tab(state, blocks, header, model_choice, prompt_enhancer_row):
    gr.Markdown("Please click Apply Changes at the bottom so that the changes are effective. Some choices below may be locked if the app has been launched by specifying a config preset.")
    with gr.Column():
        model_list = []


        with gr.Tabs():
            # with gr.Row(visible=advanced_ui) as advanced_row:
            with gr.Tab("General"):
                for model_type in model_types:
                    choice = get_model_filename(model_type, transformer_quantization, transformer_dtype_policy)
                    model_list.append(choice)
                dropdown_choices = [ ( get_model_name(choice),  get_model_type(choice) ) for choice in model_list]
                transformer_types_choices = gr.Dropdown(
                    choices= dropdown_choices,
                    value= transformer_types,
                    label= "Selectable Wan Transformer Models (keep empty to get All of them)",
                    scale= 2,
                    multiselect= True
                    )

                fit_canvas_choice = gr.Dropdown(
                    choices=[
                        ("Dimensions correspond to the Pixels Budget (as the Prompt Image/Video will be resized to match this pixels budget, output video height or width may exceed the requested dimensions )", 0),
                        ("Dimensions correspond to the Maximum Width and Height (as the Prompt Image/Video will be resized to fit into these dimensions, the output video may be smaller)", 1),
                    ],
                    value= server_config.get("fit_canvas", 0),
                    label="Generated Video Dimensions when Prompt contains an Image or a Video",
                    interactive= not lock_ui_attention
                 )


                def check(mode): 
                    if not mode in attention_modes_installed:
                        return " (NOT INSTALLED)"
                    elif not mode in attention_modes_supported:
                        return " (NOT SUPPORTED)"
                    else:
                        return ""
                attention_choice = gr.Dropdown(
                    choices=[
                        ("Auto : pick sage2 > sage > sdpa depending on what is installed", "auto"),
                        ("Scale Dot Product Attention: default, always available", "sdpa"),
                        ("Flash" + check("flash")+ ": good quality - requires additional install (usually complex to set up on Windows without WSL)", "flash"),
                        ("Xformers" + check("xformers")+ ": good quality - requires additional install (usually complex, may consume less VRAM to set up on Windows without WSL)", "xformers"),
                        ("Sage" + check("sage")+ ": 30% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage"),
                        ("Sage2" + check("sage2")+ ": 40% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage2"),
                    ],
                    value= attention_mode,
                    label="Attention Type",
                    interactive= not lock_ui_attention
                 )


                metadata_choice = gr.Dropdown(
                    choices=[
                        ("Export JSON files", "json"),
                        ("Add metadata to video", "metadata"),
                        ("Neither", "none")
                    ],
                    value=server_config.get("metadata_type", "metadata"),
                    label="Metadata Handling"
                )
                preload_model_policy_choice = gr.CheckboxGroup([("Preload Model while Launching the App","P"), ("Preload Model while Switching Model", "S"), ("Unload Model when Queue is Done", "U")],
                    value=server_config.get("preload_model_policy",[]),
                    label="RAM Loading / Unloading Model Policy (in any case VRAM will be freed once the queue has been processed)"
                )

                clear_file_list_choice = gr.Dropdown(
                    choices=[
                        ("None", 0),
                        ("Keep the last video", 1),
                        ("Keep the last 5 videos", 5),
                        ("Keep the last 10 videos", 10),
                        ("Keep the last 20 videos", 20),
                        ("Keep the last 30 videos", 30),
                    ],
                    value=server_config.get("clear_file_list", 5),
                    label="Keep Previously Generated Videos when starting a new Generation Batch"
                )

                enhancer_enabled_choice = gr.Dropdown(
                    choices=[
                        ("On", 1),
                        ("Off", 0),
                    ],
                    value=server_config.get("enhancer_enabled", 0),
                    label="Prompt Enhancer (if enabled, 8 GB of extra models will be downloaded)"
                )

                UI_theme_choice = gr.Dropdown(
                    choices=[
                        ("Blue Sky", "default"),
                        ("Classic Gradio", "gradio"),
                    ],
                    value=server_config.get("UI_theme", "default"),
                    label="User Interface Theme. You will need to restart the App the see new Theme."
                )

                save_path_choice = gr.Textbox(
                    label="Output Folder for Generated Videos",
                    value=server_config.get("save_path", save_path)
                )

            with gr.Tab("Performance"):

                quantization_choice = gr.Dropdown(
                    choices=[
                        ("Scaled Int8 Quantization (recommended)", "int8"),
                        ("16 bits (no quantization)", "bf16"),
                    ],
                    value= transformer_quantization,
                    label="Transformer Model Quantization Type (if available)",
                )                

                transformer_dtype_policy_choice = gr.Dropdown(
                    choices=[
                        ("Best Supported Data Type by Hardware", ""),
                        ("FP16", "fp16"),
                        ("BF16", "bf16"),
                    ],
                    value= server_config.get("transformer_dtype_policy", ""),
                    label="Transformer Data Type (if available)"
                )

                mixed_precision_choice = gr.Dropdown(
                    choices=[
                        ("16 bits only, requires less VRAM", "0"),
                        ("Mixed 16 / 32 bits, slightly more VRAM needed but better Quality mainly for 1.3B models", "1"),
                    ],
                    value= server_config.get("mixed_precision", "0"),
                    label="Transformer Engine Calculation"
                )


                text_encoder_quantization_choice = gr.Dropdown(
                    choices=[
                        ("16 bits - unquantized text encoder, better quality uses more RAM", "bf16"),
                        ("8 bits - quantized text encoder, slightly worse quality but uses less RAM", "int8"),
                    ],
                    value= text_encoder_quantization,
                    label="Text Encoder model"
                )

                VAE_precision_choice = gr.Dropdown(
                    choices=[
                        ("16 bits, requires less VRAM and faster", "16"),
                        ("32 bits, requires twice more VRAM and slower but recommended with Window Sliding", "32"),
                    ],
                    value= server_config.get("vae_precision", "16"),
                    label="VAE Encoding / Decoding precision"
                )

                gr.Text("Beware: when restarting the server or changing a resolution or video duration, the first step of generation for a duration / resolution may last a few minutes due to recompilation", interactive= False, show_label= False )
                compile_choice = gr.Dropdown(
                    choices=[
                        ("On (requires to have Triton installed)", "transformer"),
                        ("Off", "" ),
                    ],
                    value= compile,
                    label="Compile Transformer (up to 50% faster and 30% more frames but requires Linux / WSL and Flash or Sage attention)",
                    interactive= not lock_ui_compile
                )              

                vae_config_choice = gr.Dropdown(
                    choices=[
                ("Auto", 0),
                ("Disabled (faster but may require up to 22 GB of VRAM)", 1),
                ("256 x 256 : If at least 8 GB of VRAM", 2),
                ("128 x 128 : If at least 6 GB of VRAM", 3),
                    ],
                    value= vae_config,
                    label="VAE Tiling - reduce the high VRAM requirements for VAE decoding and VAE encoding (if enabled it will be slower)"
                )

                boost_choice = gr.Dropdown(
                    choices=[
                        # ("Auto (ON if Video longer than 5s)", 0),
                        ("ON", 1), 
                        ("OFF", 2), 
                    ],
                    value=boost,
                    label="Boost: Give a 10% speedup without losing quality at the cost of a litle VRAM (up to 1GB at max frames and resolution)"
                )

                profile_choice = gr.Dropdown(
                    choices=[
                ("HighRAM_HighVRAM, profile 1: at least 48 GB of RAM and 24 GB of VRAM, the fastest for short videos a RTX 3090 / RTX 4090", 1),
                ("HighRAM_LowVRAM, profile 2 (Recommended): at least 48 GB of RAM and 12 GB of VRAM, the most versatile profile with high RAM, better suited for RTX 3070/3080/4070/4080 or for RTX 3090 / RTX 4090 with large pictures batches or long videos", 2),
                ("LowRAM_HighVRAM, profile 3: at least 32 GB of RAM and 24 GB of VRAM, adapted for RTX 3090 / RTX 4090 with limited RAM for good speed short video",3),
                ("LowRAM_LowVRAM, profile 4 (Default): at least 32 GB of RAM and 12 GB of VRAM, if you have little VRAM or want to generate longer videos",4),
                ("VerylowRAM_LowVRAM, profile 5: (Fail safe): at least 16 GB of RAM and 10 GB of VRAM, if you don't have much it won't be fast but maybe it will work",5)
                    ],
                    value= profile,
                    label="Profile (for power users only, not needed to change it)"
                )
                preload_in_VRAM_choice = gr.Slider(0, 40000, value=server_config.get("preload_in_VRAM", 0), step=100, label="Number of MB of Models that are Preloaded in VRAM (0 will use Profile default)")



        
        msg = gr.Markdown()            
        apply_btn  = gr.Button("Apply Changes")
        apply_btn.click(
                fn=apply_changes,
                inputs=[
                    state,
                    transformer_types_choices,
                    transformer_dtype_policy_choice,
                    text_encoder_quantization_choice,
                    VAE_precision_choice,
                    mixed_precision_choice,
                    save_path_choice,
                    attention_choice,
                    compile_choice,                            
                    profile_choice,
                    vae_config_choice,
                    metadata_choice,
                    quantization_choice,
                    boost_choice,
                    clear_file_list_choice,
                    preload_model_policy_choice,
                    UI_theme_choice,
                    enhancer_enabled_choice,
                    fit_canvas_choice,
                    preload_in_VRAM_choice
                ],
                outputs= [msg , header, model_choice, prompt_enhancer_row]
        )

def generate_about_tab():
    gr.Markdown("<H2>WanGP - Wan 2.1 model for the GPU Poor by <B>DeepBeepMeep</B> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>GitHub</A>)</H2>")
    gr.Markdown("Original Wan 2.1 Model by <B>Alibaba</B> (<A HREF='https://github.com/Wan-Video/Wan2.1'>GitHub</A>)")
    gr.Markdown("Many thanks to:")
    gr.Markdown("- <B>Alibaba Wan team for the best open source video generator")
    gr.Markdown("- <B>Alibaba Vace and Fun Teams for their incredible control net models")
    gr.Markdown("- <B>Cocktail Peanuts</B> : QA and simple installation via Pinokio.computer")
    gr.Markdown("- <B>Tophness</B> : created (former) multi tabs and queuing frameworks")
    gr.Markdown("- <B>AmericanPresidentJimmyCarter</B> : added original support for Skip Layer Guidance")
    gr.Markdown("- <B>Remade_AI</B> : for their awesome Loras collection")
    gr.Markdown("<BR>Huge acknowlegments to these great open source projects used in WanGP:")
    gr.Markdown("- <B>Rife</B>: temporal upsampler (https://github.com/hzwer/ECCV2022-RIFE)")
    gr.Markdown("- <B>DwPose</B>: Open Pose extractor (https://github.com/IDEA-Research/DWPose)")
    gr.Markdown("- <B>Midas</B>: Depth extractor (https://github.com/isl-org/MiDaS")
    gr.Markdown("- <B>Matanyone</B> and <B>SAM2</B>: Mask Generation (https://github.com/pq-yang/MatAnyone) and (https://github.com/facebookresearch/sam2)")


def generate_info_tab():
    gr.Markdown("<FONT SIZE=3>Welcome to WanGP a super fast and low VRAM AI Video Generator !</FONT>")
    
    gr.Markdown("The VRAM requirements will depend greatly of the resolution and the duration of the video, for instance :")
    gr.Markdown("- 848 x 480 with a 14B model: 80 frames (5s) : 8 GB of VRAM")
    gr.Markdown("- 848 x 480 with the 1.3B model: 80 frames (5s) : 5 GB of VRAM")
    gr.Markdown("- 1280 x 720 with a 14B model: 80 frames (5s): 11 GB of VRAM")
    gr.Markdown("It is not recommmended to generate a video longer than 8s (128 frames) even if there is still some VRAM left as some artifacts may appear")
    gr.Markdown("Please note that if your turn on compilation, the first denoising step of the first video generation will be slow due to the compilation. Therefore all your tests should be done with compilation turned off.")


def generate_dropdown_model_list(model_filename):
    dropdown_types= transformer_types if len(transformer_types) > 0 else model_types 
    current_model_type = get_model_type(model_filename)
    if current_model_type not in dropdown_types:
        dropdown_types.append(current_model_type)
    model_list = []
    for model_type in dropdown_types:
        choice = get_model_filename(model_type, transformer_quantization, transformer_dtype_policy)
        model_list.append(choice)
    dropdown_choices = [ ( get_model_name(choice),  get_model_type(choice) ) for choice in model_list]
    return gr.Dropdown(
        choices= dropdown_choices,
        value= current_model_type,
        show_label= False,
        scale= 2,
        elem_id="model_list",
        elem_classes="model_list_class",
        )


def select_tab(tab_state, evt:gr.SelectData):
    tab_video_mask_creator = 2

    old_tab_no = tab_state.get("tab_no",0)
    new_tab_no = evt.index 
    if old_tab_no == tab_video_mask_creator:
        vmc_event_handler(False)
    elif new_tab_no == tab_video_mask_creator:
        if gen_in_progress:
            gr.Info("Unable to access this Tab while a Generation is in Progress. Please come back later")
            tab_state["tab_no"] = 0
            return gr.Tabs(selected="video_gen") 
        else:
            vmc_event_handler(True)
    tab_state["tab_no"] = new_tab_no
    return gr.Tabs() 

def get_js():
    start_quit_timer_js = """
    () => {
        function findAndClickGradioButton(elemId) {
            const gradioApp = document.querySelector('gradio-app') || document;
            const button = gradioApp.querySelector(`#${elemId}`);
            if (button) { button.click(); }
        }

        if (window.quitCountdownTimeoutId) clearTimeout(window.quitCountdownTimeoutId);

        let js_click_count = 0;
        const max_clicks = 5;

        function countdownStep() {
            if (js_click_count < max_clicks) {
                findAndClickGradioButton('trigger_info_single_btn');
                js_click_count++;
                window.quitCountdownTimeoutId = setTimeout(countdownStep, 1000);
            } else {
                findAndClickGradioButton('force_quit_btn_hidden');
            }
        }

        countdownStep();
    }
    """

    cancel_quit_timer_js = """
    () => {
        if (window.quitCountdownTimeoutId) {
            clearTimeout(window.quitCountdownTimeoutId);
            window.quitCountdownTimeoutId = null;
            console.log("Quit countdown cancelled (single trigger).");
        }
    }
    """

    trigger_zip_download_js = """
    (base64String) => {
        if (!base64String) {
        console.log("No base64 zip data received, skipping download.");
        return;
        }
        try {
        const byteCharacters = atob(base64String);
        const byteNumbers = new Array(byteCharacters.length);
        for (let i = 0; i < byteCharacters.length; i++) {
            byteNumbers[i] = byteCharacters.charCodeAt(i);
        }
        const byteArray = new Uint8Array(byteNumbers);
        const blob = new Blob([byteArray], { type: 'application/zip' });

        const url = URL.createObjectURL(blob);
        const a = document.createElement('a');
        a.style.display = 'none';
        a.href = url;
        a.download = 'queue.zip';
        document.body.appendChild(a);
        a.click();

        window.URL.revokeObjectURL(url);
        document.body.removeChild(a);
        console.log("Zip download triggered.");
        } catch (e) {
        console.error("Error processing base64 data or triggering download:", e);
        }
    }
    """

    trigger_settings_download_js = """
    (base64String) => {
        if (!base64String) {
        console.log("No base64 settings data received, skipping download.");
        return;
        }
        try {
        const byteCharacters = atob(base64String);
        const byteNumbers = new Array(byteCharacters.length);
        for (let i = 0; i < byteCharacters.length; i++) {
            byteNumbers[i] = byteCharacters.charCodeAt(i);
        }
        const byteArray = new Uint8Array(byteNumbers);
        const blob = new Blob([byteArray], { type: 'application/text' });

        const url = URL.createObjectURL(blob);
        const a = document.createElement('a');
        a.style.display = 'none';
        a.href = url;
        a.download = 'settings.json';
        document.body.appendChild(a);
        a.click();

        window.URL.revokeObjectURL(url);
        document.body.removeChild(a);
        console.log("settings download triggered.");
        } catch (e) {
        console.error("Error processing base64 data or triggering download:", e);
        }
    }
    """
    return start_quit_timer_js, cancel_quit_timer_js, trigger_zip_download_js, trigger_settings_download_js

def create_ui():
    global vmc_event_handler    
    css = """
        #model_list{
        background-color:black;
        padding:1px}

        #model_list input {
        font-size:25px}

        .title-with-lines {
            display: flex;
            align-items: center;
            margin: 25px 0;
        }
        .line {
            flex-grow: 1;
            height: 1px;
            background-color: #333;
        }
        h2 {
            margin: 0 20px;
            white-space: nowrap;
        }
        .queue-item {
            border: 1px solid #ccc;
            padding: 10px;
            margin: 5px 0;
            border-radius: 5px;
        }
        .current {
            background: #f8f9fa;
            border-left: 4px solid #007bff;
        }
        .task-header {
            display: flex;
            justify-content: space-between;
            margin-bottom: 5px;
        }
        .progress-container {
            height: 10px;
            background: #e9ecef;
            border-radius: 5px;
            overflow: hidden;
        }
        .progress-bar {
            height: 100%;
            background: #007bff;
            transition: width 0.3s ease;
        }
        .task-details {
            display: flex;
            justify-content: space-between;
            font-size: 0.9em;
            color: #6c757d;
            margin-top: 5px;
        }
        .task-prompt {
            font-size: 0.8em;
            color: #868e96;
            margin-top: 5px;
            white-space: nowrap;
            overflow: hidden;
            text-overflow: ellipsis;
        }
        #queue_df th {
            pointer-events: none;
            text-align: center;
            vertical-align: middle;
            font-size:11px;
        }
        #xqueue_df table {
            width: 100%;
            overflow: hidden !important;
        }
        #xqueue_df::-webkit-scrollbar {
            display: none !important;
        }
        #xqueue_df {
            scrollbar-width: none !important;
            -ms-overflow-style: none !important;
        }
        .selection-button {
            display: none;
        }
        .cell-selected {
            --ring-color: none;
        }
        #queue_df th:nth-child(1),
        #queue_df td:nth-child(1) {
            width: 60px;
            text-align: center;
            vertical-align: middle;
            cursor: default !important;
            pointer-events: none;
        }
        #xqueue_df th:nth-child(2),
        #queue_df td:nth-child(2) {
            text-align: center;
            vertical-align: middle;
            white-space: normal;
        }
        #queue_df td:nth-child(2) {
            cursor: default !important;
        }
        #queue_df th:nth-child(3),
        #queue_df td:nth-child(3) {
            width: 60px;
            text-align: center;
            vertical-align: middle;
            cursor: default !important;
            pointer-events: none;
        }
        #queue_df th:nth-child(4),
        #queue_df td:nth-child(4) {
            width: 60px;
            text-align: center;
            white-space: nowrap;
            cursor: default !important;
            pointer-events: none;
        }
        #queue_df th:nth-child(5), #queue_df td:nth-child(7),
        #queue_df th:nth-child(6), #queue_df td:nth-child(8) {
            width: 60px;
            text-align: center;
            vertical-align: middle;
        }
        #queue_df td:nth-child(5) img,
        #queue_df td:nth-child(6) img {
            max-width: 50px;
            max-height: 50px;
            object-fit: contain;
            display: block;
            margin: auto;
            cursor: pointer;
        }
        #queue_df th:nth-child(7), #queue_df td:nth-child(9),
        #queue_df th:nth-child(8), #queue_df td:nth-child(10),
        #queue_df th:nth-child(9), #queue_df td:nth-child(11) {
            width: 20px;
            padding: 2px !important;
            cursor: pointer;
            text-align: center;
            font-weight: bold;
            vertical-align: middle;
        }
        #queue_df td:nth-child(5):hover,
        #queue_df td:nth-child(6):hover,
        #queue_df td:nth-child(7):hover,
        #queue_df td:nth-child(8):hover,
        #queue_df td:nth-child(9):hover {
            background-color: #e0e0e0;
        }
        #image-modal-container {
            position: fixed;
            top: 0;
            left: 0;
            width: 100%;
            height: 100%;
            background-color: rgba(0, 0, 0, 0.7);
            justify-content: center;
            align-items: center;
            z-index: 1000;
            padding: 20px;
            box-sizing: border-box;
        }
        #image-modal-container > div {
             background-color: white;
             padding: 15px;
             border-radius: 8px;
             max-width: 90%;
             max-height: 90%;
             overflow: auto;
             position: relative;
             display: flex;
             flex-direction: column;
        }
         #image-modal-container img {
             max-width: 100%;
             max-height: 80vh;
             object-fit: contain;
             margin-top: 10px;
         }
         #image-modal-close-button-row {
             display: flex;
             justify-content: flex-end;
         }
         #image-modal-close-button-row button {
            cursor: pointer;
         }
        .progress-container-custom {
            width: 100%;
            background-color: #e9ecef;
            border-radius: 0.375rem;
            overflow: hidden;
            height: 25px;
            position: relative;
            margin-top: 5px;
            margin-bottom: 5px;
        }
        .progress-bar-custom {
            height: 100%;
            background-color: #0d6efd;
            transition: width 0.3s ease-in-out;
            display: flex;
            align-items: center;
            justify-content: center;
            color: white;
            font-size: 0.9em;
            font-weight: bold;
            white-space: nowrap;
            overflow: hidden;
        }
        .progress-bar-custom.idle {
            background-color: #6c757d;
        }
        .progress-bar-text {
            position: absolute;
            top: 0;
            left: 0;
            width: 100%;
            height: 100%;
            display: flex;
            align-items: center;
            justify-content: center;
            color: white;
            mix-blend-mode: difference;
            font-size: 0.9em;
            font-weight: bold;
            white-space: nowrap;
            z-index: 2;
            pointer-events: none;
        }

    """
    UI_theme = server_config.get("UI_theme", "default")
    UI_theme  = args.theme if len(args.theme) > 0 else UI_theme
    if UI_theme == "gradio":
        theme = None
    else:
        theme = gr.themes.Soft(font=["Verdana"], primary_hue="sky", neutral_hue="slate", text_size="md")

    with gr.Blocks(css=css, theme=theme, title= "WanGP") as main:
        gr.Markdown(f"<div align=center><H1>Wan<SUP>GP</SUP> v{WanGP_version} <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3>") # (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A>)</FONT SIZE=3></H1></div>")
        global model_list

        tab_state = gr.State({ "tab_no":0 }) 

        with gr.Tabs(selected="video_gen", ) as main_tabs:
            with gr.Tab("Video Generator", id="video_gen"):
                with gr.Row():
                    if args.lock_model:    
                        gr.Markdown("<div class='title-with-lines'><div class=line></div><h2>" + get_model_name(transformer_filename) + "</h2><div class=line></div>")
                        model_choice = gr.Dropdown(visible=False, value= get_model_type(transformer_filename))
                    else:
                        gr.Markdown("<div class='title-with-lines'><div class=line width=100%></div></div>")
                        model_choice = generate_dropdown_model_list(transformer_filename)
                        gr.Markdown("<div class='title-with-lines'><div class=line width=100%></div></div>")
                with gr.Row():
                    header = gr.Markdown(generate_header(transformer_filename, compile, attention_mode), visible= True)
                with gr.Row():
                    (   state, loras_choices, lset_name, state,
                        video_guide, video_mask, image_refs, video_prompt_type_video_trigger, prompt_enhancer_row
                    ) = generate_video_tab(model_choice=model_choice, header=header, main = main)
            with gr.Tab("Informations", id="info"):
                generate_info_tab()
            with gr.Tab("Video Mask Creator", id="video_mask_creator") as video_mask_creator:
                from preprocessing.matanyone  import app as matanyone_app
                vmc_event_handler = matanyone_app.get_vmc_event_handler()

                matanyone_app.display(main_tabs, model_choice, video_guide, video_mask, image_refs, video_prompt_type_video_trigger)
            if not args.lock_config:
                with gr.Tab("Downloads", id="downloads") as downloads_tab:
                    generate_download_tab(lset_name, loras_choices, state)
                with gr.Tab("Configuration", id="configuration"):
                    generate_configuration_tab(state, main, header, model_choice, prompt_enhancer_row)
            with gr.Tab("About"):
                generate_about_tab()

        main_tabs.select(fn=select_tab, inputs= [tab_state], outputs= main_tabs)
        return main

if __name__ == "__main__":
    atexit.register(autosave_queue)
    download_ffmpeg()
    # threading.Thread(target=runner, daemon=True).start()
    os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
    server_port = int(args.server_port)
    if os.name == "nt":
        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
    if server_port == 0:
        server_port = int(os.getenv("SERVER_PORT", "7860"))
    server_name = args.server_name
    if args.listen:
        server_name = "0.0.0.0"
    if len(server_name) == 0:
        server_name = os.getenv("SERVER_NAME", "localhost")      
    demo = create_ui()
    if args.open_browser:
        import webbrowser 
        if server_name.startswith("http"):
            url = server_name 
        else:
            url = "http://" + server_name 
        webbrowser.open(url + ":" + str(server_port), new = 0, autoraise = True)
    demo.launch(server_name=server_name, server_port=server_port, share=args.share, allowed_paths=[save_path])