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"""

Playground Diffusion

Demo for multiple fine-tuned Stable Diffusion models, trained on different styles:
Avatar,
Beeple,
Beksinski,
Diffusers 🧨 SD model hosted on HuggingFace 🤗. Running on {device}{(" in a Google Colab." if is_colab else "")}

""" ) 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("
Custom models have to be downloaded first, so give it some time.
") 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)