app.py

import gradio as gr
import json
import shutil
import sqlite3
import subprocess
import sys
sys.path.append('src/blip')
sys.path.append('src/clip')
import clip
import hashlib
import math
import numpy as np
import pickle
import torchvision.transforms as T
import torchvision.transforms.functional as TF
import requests
import wget
import gradio as grad, random, re
import torch
import os
import utils
import html
import re
import base64
import subprocess
import argparse
import logging
import streamlit as st
import pandas as pd
import datasets
import yaml
import textwrap
import tornado
import time
import cv2 as cv
from torch import autocast
from diffusers import StableDiffusionPipeline
from transformers import pipeline, set_seed
from huggingface_hub import HfApi
from huggingface_hub import hf_hub_download
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, UNet2DConditionModel
from diffusers import StableDiffusionImg2ImgPipeline 
from PIL import Image
from datasets import load_dataset
from share_btn import community_icon_html, loading_icon_html, share_js
from io import BytesIO
from models.blip import blip_decoder
from torch import nn
from torch.nn import functional as F
from tqdm import tqdm
from pathlib import Path
from flask import Flask, request, jsonify, g
from flask_expects_json import expects_json
from flask_cors import CORS
from huggingface_hub import Repository
from flask_apscheduler import APScheduler
from jsonschema import ValidationError
from os import mkdir
from os.path import isdir
from colorthief import ColorThief
from data_measurements.dataset_statistics import DatasetStatisticsCacheClass as dmt_cls
from utils import dataset_utils
from utils import streamlit_utils as st_utils
from dataclasses import asdict
from .transfer import transfer_color
from .utils import convert_bytes_to_pil
from diffusers import DiffusionPipeline
from huggingface_hub.inference_api import InferenceApi
from huggingface_hub import login
from datasets import load_dataset
#from torch import autocast
#from diffusers import StableDiffusionPipeline
#from io import BytesIO
#import base64
#import torch

is_colab = utils.is_google_colab()

from share_btn import community_icon_html, loading_icon_html, share_js

from huggingface_hub import login
login()

from huggingface_hub.inference_api import InferenceApi
inference = InferenceApi(repo_id="bert-base-uncased", token=API_TOKEN)

from datasets import load_dataset

dataset = load_dataset("Fazzie/Teyvat")

from datasets import load_dataset

dataset = load_dataset("Guizmus/AnimeChanStyle")

from datasets import load_dataset

dataset = load_dataset("poloclub/diffusiondb")

from datasets import load_dataset

dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")

from datasets import load_dataset
dataset = load_dataset("Fazzie/Teyvat")
from datasets import load_dataset
dataset = load_dataset("Guizmus/AnimeChanStyle")
from datasets import load_dataset
dataset = load_dataset("poloclub/diffusiondb")
from datasets import load_dataset
dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")

from datasets import load_dataset
dataset = load_dataset("Fazzie/Teyvat")

from datasets import load_dataset
dataset = load_dataset("Guizmus/AnimeChanStyle")

from datasets import load_dataset
dataset = load_dataset("poloclub/diffusiondb")

from datasets import load_dataset
dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")


dataset = load_dataset("Fazzie/Teyvat")


dataset = load_dataset("Guizmus/AnimeChanStyle")


dataset = load_dataset("poloclub/diffusiondb")


dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")

dataset = load_dataset("Fazzie/Teyvat")
dataset = load_dataset("Guizmus/AnimeChanStyle")
dataset = load_dataset("poloclub/diffusiondb")
dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")

dataset = load_dataset("Fazzie/Teyvat")

dataset = load_dataset("Guizmus/AnimeChanStyle")

dataset = load_dataset("poloclub/diffusiondb")

dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts")

sys.path.append('src/blip')
sys.path.append('src/clip')

pipeline = DiffusionPipeline.from_pretrained("flax/waifu-diffusion")
pipeline = DiffusionPipeline.from_pretrained("flax/Cyberpunk-Anime-Diffusion")
pipeline = DiffusionPipeline.from_pretrained("technillogue/waifu-diffusion")
pipeline = DiffusionPipeline.from_pretrained("svjack/Stable-Diffusion-Pokemon-en")
pipeline = DiffusionPipeline.from_pretrained("AdamOswald1/Idk")
pipeline = DiffusionPipeline.from_pretrained("katakana/2D-Mix")

class Model:
    def __init__(self, name, path, prefix):
        self.name = name
        self.path = path
        self.prefix = prefix
        self.pipe_t2i = None
        self.pipe_i2i = None

models = [
    Model("Custom model", "", ""),
    Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style"),
    Model("Archer", "nitrosocke/archer-diffusion", "archer style"),
    Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style"),
    Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style"),
    Model("Modern Disney", "nitrosocke/modern-disney-diffusion", "modern disney style"),
    Model("Classic Disney", "nitrosocke/classic-anim-diffusion", "classic disney style"),
    Model("Waifu", "hakurei/waifu-diffusion", ""),
    Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", "pokemon style"),
    Model("Pokémon", "svjack/Stable-Diffusion-Pokemon-en", "pokemon style"),
    Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", "pony style"),
    Model("Robo Diffusion", "nousr/robo-diffusion", "robo style"),
    Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion, flax/Cyberpunk-Anime-Diffusion", "cyberpunk style"),
    Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "cyberpunk style"),
    Model("Cyberpunk Anime", "flax/Cyberpunk-Anime-Diffusion", "cyberpunk style"),
    Model("Cyberware", "Eppinette/Cyberware", "cyberware"),
    Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy"),
    Model("Waifu", "flax/waifu-diffusion", ""),
    Model("Dark Souls", "Guizmus/DarkSoulsDiffusion", "dark souls style"),
    Model("Waifu", "technillogue/waifu-diffusion", ""),
    Model("Ouroborus", "Eppinette/Ouroboros", "m_ouroboros style"),
    Model("Ouroborus alt", "Eppinette/Ouroboros", "m_ouroboros"),
    Model("Waifu", "Eppinette/Mona", "Mona"),
    Model("Waifu", "Eppinette/Mona", "Mona Woman"),
    Model("Waifu", "Eppinette/Mona", "Mona Genshin"),
    Model("Genshin", "Eppinette/Mona", "Mona"),
    Model("Genshin", "Eppinette/Mona", "Mona Woman"),
    Model("Genshin", "Eppinette/Mona", "Mona Genshin"),
    Model("Space Machine", "rabidgremlin/sd-db-epic-space-machine", "EpicSpaceMachine"),
    Model("Spacecraft", "rabidgremlin/sd-db-epic-space-machine", "EpicSpaceMachine"),
    Model("TARDIS", "Guizmus/Tardisfusion", "Classic Tardis style"),
    Model("TARDIS", "Guizmus/Tardisfusion", "Modern Tardis style"),
    Model("TARDIS", "Guizmus/Tardisfusion", "Tardis Box style"),
    Model("Spacecraft", "Guizmus/Tardisfusion", "Classic Tardis style"),
    Model("Spacecraft", "Guizmus/Tardisfusion", "Modern Tardis style"),
    Model("Spacecraft", "Guizmus/Tardisfusion", "Tardis Box style"),
    Model("CLIP", "EleutherAI/clip-guided-diffusion", "CLIP"),
    Model("Face Swap", "felixrosberg/face-swap", "faceswap"),
    Model("Face Swap", "felixrosberg/face-swap", "faceswap with"),
    Model("Face Swap", "felixrosberg/face-swap", "faceswapped"),
    Model("Face Swap", "felixrosberg/face-swap", "faceswapped with"),
    Model("Face Swap", "felixrosberg/face-swap", "face on"),
    Model("Waifu", "Fampai/lumine_genshin_impact", "lumine_genshin"),
    Model("Waifu", "Fampai/lumine_genshin_impact", "lumine"),
    Model("Waifu", "Fampai/lumine_genshin_impact", "Lumine Genshin"),
    Model("Waifu", "Fampai/lumine_genshin_impact", "Lumine_genshin"),
    Model("Waifu", "Fampai/lumine_genshin_impact", "Lumine_Genshin"),
    Model("Waifu", "Fampai/lumine_genshin_impact", "Lumine"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Lumine_genshin"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Lumine_Genshin"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Lumine"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Lumine Genshin"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "lumine"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "Ganyu"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "Ganyu Woman"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "Ganyu Genshin"),
    Model("Waifu", "sd-concepts-library/ganyu-genshin-impact", "Ganyu"),
    Model("Waifu", "sd-concepts-library/ganyu-genshin-impact", "Ganyu Woman"),
    Model("Waifu", "sd-concepts-library/ganyu-genshin-impact", "Ganyu Genshin"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "raiden_ei"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Raiden Ei"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Ei Genshin"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Raiden Genshin"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Raiden_Genshin"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Ei_Genshin"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Raiden"),
    Model("Waifu", "Fampai/raiden_genshin_impact", "Ei"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Raiden Ei"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "raiden_ei"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Raiden"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Raiden Genshin"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Ei Genshin"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Raiden_Genshin"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Ei_Genshin"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Ei"),
    Model("Waifu", "Fampai/hutao_genshin_impact", "hutao_genshin"),
    Model("Waifu", "Fampai/hutao_genshin_impact", "HuTao_Genshin"),
    Model("Waifu", "Fampai/hutao_genshin_impact", "HuTao Genshin"),
    Model("Waifu", "Fampai/hutao_genshin_impact", "HuTao"),
    Model("Waifu", "Fampai/hutao_genshin_impact", "hutao_genshin"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "hutao_genshin"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "HuTao_Genshin"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "HuTao Genshin"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "HuTao"),
    Model("Genshin", "Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "Female"),
    Model("Genshin", "Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "female"),
    Model("Genshin", "Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "Woman"),
    Model("Genshin", "Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "woman"),
    Model("Genshin", "Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "Girl"),
    Model("Genshin", "Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "girl"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Female"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "female"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Woman"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "woman"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "Girl"),
    Model("Genshin", "Fampai/lumine_genshin_impact", "girl"),
    Model("Genshin", "Eppinette/Mona", "Female"),
    Model("Genshin", "Eppinette/Mona", "female"),
    Model("Genshin", "Eppinette/Mona", "Woman"),
    Model("Genshin", "Eppinette/Mona", "woman"),
    Model("Genshin", "Eppinette/Mona", "Girl"),
    Model("Genshin", "Eppinette/Mona", "girl"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "Female"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "female"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "Woman"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "woman"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "Girl"),
    Model("Genshin", "sd-concepts-library/ganyu-genshin-impact", "girl"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Female"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "female"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Woman"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "woman"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "Girl"),
    Model("Genshin", "Fampai/raiden_genshin_impact", "girl"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "Female"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "female"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "Woman"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "woman"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "Girl"),
    Model("Genshin", "Fampai/hutao_genshin_impact", "girl"),
    Model("Waifu", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "Genshin"),
    Model("Waifu", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", "Genshin Impact"),
    Model("Genshin", "crumb/genshin-stable-inversion, yuiqena/GenshinImpact, Fampai/lumine_genshin_impact, Eppinette/Mona, sd-concepts-library/ganyu-genshin-impact, Fampai/raiden_genshin_impact, Fampai/hutao_genshin_impact", ""),
    Model("Waifu", "crumb/genshin-stable-inversion", "Genshin"),
    Model("Waifu", "crumb/genshin-stable-inversion", "Genshin Impact"),
    Model("Genshin", "crumb/genshin-stable-inversion", ""),
    Model("Waifu", "yuiqena/GenshinImpact", "Genshin"),
    Model("Waifu", "yuiqena/GenshinImpact", "Genshin Impact"),
    Model("Genshin", "yuiqena/GenshinImpact", ""),
    Model("Waifu", "hakurei/waifu-diffusion, flax/waifu-diffusion, technillogue/waifu-diffusion, Guizmus/AnimeChanStyle, katakana/2D-Mix", ""),
    Model("Pokémon", "lambdalabs/sd-pokemon-diffusers, svjack/Stable-Diffusion-Pokemon-en", "pokemon style"),
    Model("Pokémon", "lambdalabs/sd-pokemon-diffusers, svjack/Stable-Diffusion-Pokemon-en", ""),
    Model("Test", "AdamoOswald1/Idk", ""),
    Model("Anime", "Guizmus/AnimeChanStyle", "AnimeChan Style"),
    Model("Genshin", "Guizmus/AnimeChanStyle", "AnimeChan Style"),
    Model("Waifu", "Guizmus/AnimeChanStyle", "AnimeChan Style"),
    Model("Waifu", "Guizmus/AnimeChanStyle", "Genshin"),
    Model("Waifu", "Guizmus/AnimeChanStyle", "Genshin Impact"),
    Model("Genshin", "Guizmus/AnimeChanStyle", ""),
    Model("Anime", "Guizmus/AnimeChanStyle", ""),
    Model("Waifu", "Guizmus/AnimeChanStyle", ""),
    Model("Anime", "Guizmus/AnimeChanStyle, katakana/2D-Mix", ""),
    Model("Anime", "katakana/2D-Mix", "2D-Mix"),
    Model("Genshin", "katakana/2D-Mix", "2D-Mix"),
    Model("Waifu", "katakana/2D-Mix", "2D-Mix"),
    Model("Waifu", "katakana/2D-Mix", "Genshin"),
    Model("Waifu", "katakana/2D-Mix", "Genshin Impact"),
    Model("Genshin", "katakana/2D-Mix", ""),
    Model("Anime", "katakana/2D-Mix", ""),
    Model("Waifu", "katakana/2D-Mix", ""),
    Model("Beeple", "riccardogiorato/beeple-diffusion", "beeple style "),
    Model("Avatar", "riccardogiorato/avatar-diffusion", "avatartwow style "),
    Model("Poolsuite", "prompthero/poolsuite", "poolsuite style ")
  ]
# Model("Beksinski", "s3nh/beksinski-style-stable-diffusion", "beksinski style "),
# Model("Guohua", "Langboat/Guohua-Diffusion", "guohua style ")
     
scheduler = DPMSolverMultistepScheduler(
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    num_train_timesteps=1000,
    trained_betas=None,
    predict_epsilon=True,
    thresholding=False,
    algorithm_type="dpmsolver++",
    solver_type="midpoint",
    lower_order_final=True,
)

custom_model = None
if is_colab:
  models.insert(0, Model("Custom model", "", ""))
  custom_model = models[0]

last_mode = "txt2img"
current_model = models[1] if is_colab else models[0]
current_model_path = current_model.path

if is_colab:
  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler)
  pipe = StableDiffusionPipeline.from_pretrained("hakurei/waifu-diffusion", torch_type=torch.float16, revision="fp16")
  pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torchfloat, revision="fp16")
  gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
  pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="fp16", torch_dtype=torch.float16).to("cuda")
  pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
  pipeline = DiffusionPipeline.from_pretrained("flax/waifu-diffusion")
  pipeline = DiffusionPipeline.from_pretrained("flax/Cyberpunk-Anime-Diffusion")
  pipeline = DiffusionPipeline.from_pretrained("technillogue/waifu-diffusion")
  pipeline = DiffusionPipeline.from_pretrained("svjack/Stable-Diffusion-Pokemon-en")
  pipeline = DiffusionPipeline.from_pretrained("AdamOswald1/Idk")
  pipeline = DiffusionPipeline.from_pretrained("katakana/2D-Mix")

else: # download all models
  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
  for model in models:
    try:
        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
    except:
        models.remove(model)
  pipe = models[0].pipe_t2i
  
if torch.cuda.is_available():
  pipe = pipe.to("cuda")

device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"

def custom_model_changed(path):
  models[0].path = path
  global current_model
  current_model = models[0]

def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):

  global current_model
  for model in models:
    if model.name == model_name:
      current_model = model
      model_path = current_model.path

  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None

  if img is not None:
    return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
  else:
    return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator)

def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None):

    global last_mode
    global pipe
    global current_model_path
    if model_path != current_model_path or last_mode != "txt2img":
        current_model_path = model_path

        if is_colab or current_model == custom_model:
          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
        else:
          pipe.to("cpu")
          pipe = current_model.pipe_t2i

        if torch.cuda.is_available():
          pipe = pipe.to("cuda")
        last_mode = "txt2img"

    prompt = current_model.prefix + prompt  
    result = pipe(
      prompt,
      negative_prompt = neg_prompt,
      # num_images_per_prompt=n_images,
      num_inference_steps = int(steps),
      guidance_scale = guidance,
      width = width,
      height = height,
      generator = generator)
 

def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator=None):

    global last_mode
    global pipe
    global current_model_path
    if model_path != current_model_path or last_mode != "img2img":
        current_model_path = model_path

        if is_colab or current_model == custom_model:
          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
        else:
          pipe.to("cpu")
          pipe = current_model.pipe_i2i
        
        if torch.cuda.is_available():
          pipe = pipe.to("cuda")
        last_mode = "img2img"

    prompt = current_model.prefix + prompt
    ratio = min(height / img.height, width / img.width)
    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
    result = pipe(
        prompt,
        negative_prompt = neg_prompt,
        # num_images_per_prompt=n_images,
        init_image = img,
        num_inference_steps = int(steps),
        strength = strength,
        guidance_scale = guidance,
        width = width,
        height = height,
        generator = generator)
        
css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
    gr.HTML(
        f"""
            <div class="finetuned-diffusion-div">
              <div>
                <h1>Playground Diffusion</h1>
              </div>
              <p>
               Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
                <a href="https://huggingface.co/riccardogiorato/avatar-diffusion">Avatar</a>,<br/>
               <a href="https://huggingface.co/riccardogiorato/beeple-diffusion">Beeple</a>,<br/>
               <a href="https://huggingface.co/s3nh/beksinski-style-stable-diffusion">Beksinski</a>,<br/>
               Diffusers 🧨 SD model hosted on HuggingFace 🤗.
               Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
              </p>
            </div>
        """
    )
    with gr.Row():
        
        with gr.Column(scale=55):
          with gr.Group():
              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
              with gr.Box(visible=False) as custom_model_group:
                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
              
              with gr.Row():
                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))


              image_out = gr.Image(height=512)
              # gallery = gr.Gallery(
              #     label="Generated images", show_label=False, elem_id="gallery"
              # ).style(grid=[1], height="auto")

        with gr.Column(scale=45):
          with gr.Tab("Options"):
            with gr.Group():
              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")

              # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)

              with gr.Row():
                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)

              with gr.Row():
                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)

              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)

          with gr.Tab("Image to image"):
              with gr.Group():
                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)

    if is_colab:
      model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_group)
      custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)

    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
    prompt.submit(inference, inputs=inputs, outputs=image_out)
    generate.click(inference, inputs=inputs, outputs=image_out)

if not is_colab:
  demo.queue(concurrency_count=1)
demo.launch(debug=is_colab, share=is_colab)