Create app.py

app.py

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+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 torch import autocast
+#from diffusers import StableDiffusionPipeline
+#from io import BytesIO
+#import base64
+#import torch
+
+is_colab = utils.is_google_colab()
+
+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)
+
+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)
+    
+    return replace_nsfw_images(result)
+
+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)
+        
+    return replace_nsfw_images(result)
+
+def replace_nsfw_images(results):
+    for i in range(len(results.images)):
+      if results.nsfw_content_detected[i]:
+        results.images[i] = Image.open("nsfw.png")
+    return results.images[0]
+
+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)
+
+    ex = gr.Examples([
+        [models[1].name, "Neon techno-magic robot with spear pierces an ancient beast, hyperrealism, no blur, 4k resolution, ultra detailed", 7.5, 50],
+        [models[1].name, "halfturn portrait of a big crystal face of a beautiful abstract ancient Egyptian elderly shaman woman, made of iridescent golden crystals, half - turn, bottom view, ominous, intricate, studio, art by anthony macbain and greg rutkowski and alphonse mucha, concept art, 4k, sharp focus", 7.5, 25],
+    ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)
+
+    gr.HTML("""
+       <p>Models by <a href="https://huggingface.co/riccardogiorato">@riccardogiorato</a><br></p>
+    """)
+
+if not is_colab:
+  demo.queue(concurrency_count=1)
+demo.launch(debug=is_colab, share=is_colab)