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README.md

@@ -1,9 +1,13 @@
 ---
+title: Finetuned Diffusion
+emoji: 🪄🖼️
+colorFrom: red
+colorTo: pink
+sdk: gradio
+sdk_version: 3.18.0
+app_file: app.py
+pinned: true
 license: mit
-inference: true
-datasets:
-- Gustavosta/Stable-Diffusion-Prompts
-- Fazzie/Teyvat
-- Guizmus/AnimeChanStyle
-- poloclub/diffusiondb
----
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,164 +1,21 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
 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
+from PIL import Image
 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 datetime
 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")
+import psutil
+import random
 
-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")
+start_time = time.time()
+is_colab = utils.is_google_colab()
+state = None
+current_steps = 25
 
 class Model:
-    def __init__(self, name, path, prefix):
+    def __init__(self, name, path="", prefix=""):
         self.name = name
         self.path = path
         self.prefix = prefix
@@ -166,179 +23,36 @@ class Model:
         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("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
+     Model("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "),
+     Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
+     Model("Anything V3", "Linaqruf/anything-v3.0", ""),
+     Model("Modern Disney", "nitrosocke/mo-di-diffusion", "modern disney style "),
+     Model("Classic Disney", "nitrosocke/classic-anim-diffusion", "classic disney style "),
+     Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "),
+     Model("Wavyfusion", "wavymulder/wavyfusion", "wa-vy style "),
+     Model("Analog Diffusion", "wavymulder/Analog-Diffusion", "analog style "),
+     Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "),
+     Model("Midjourney v4 style", "prompthero/midjourney-v4-diffusion", "mdjrny-v4 style "),
+     Model("Waifu", "hakurei/waifu-diffusion"),
+     Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
+     Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
+     Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"),
+     Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
+     Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
+     Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy "),
+     Model("Pokémon", "lambdalabs/sd-pokemon-diffusers"),
+     Model("Pony Diffusion", "AstraliteHeart/pony-diffusion"),
+     Model("Robo Diffusion", "nousr/robo-diffusion"),
+     Model("Epic Diffusion", "johnslegers/epic-diffusion"),
+     Model("Modern Era TARDIS Interior", "Guizmus/Tardisfusion", "Modern Tardis style"),
+     Model("Classic Era TARDIS Interior", "Guizmus/Tardisfusion", "Classic Tardis style"),
+     Model("Cyber-Genshin", "AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters", "Teyvat, Teyvat Style, cyberware_style, m_cyberware, Cyberware, Cyberware style, m_cyberware style, cyberware style, dgs illustration style, genshin impact 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", "", ""))
+  models.insert(0, Model("Custom model"))
   custom_model = models[0]
 
 last_mode = "txt2img"
@@ -346,41 +60,61 @@ 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
-  
+  pipe = StableDiffusionPipeline.from_pretrained(
+      current_model.path,
+      torch_dtype=torch.float16,
+      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+      safety_checker=lambda images, clip_input: (images, False)
+      )
+
+else:
+  pipe = StableDiffusionPipeline.from_pretrained(
+      current_model.path,
+      torch_dtype=torch.float16,
+      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+      )
+    
 if torch.cuda.is_available():
   pipe = pipe.to("cuda")
+  pipe.enable_xformers_memory_efficient_attention()
 
 device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
 
+def error_str(error, title="Error"):
+    return f"""#### {title}
+            {error}"""  if error else ""
+
+def update_state(new_state):
+  global state
+  state = new_state
+
+def update_state_info(old_state):
+  if state and state != old_state:
+    return gr.update(value=state)
+
 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=""):
+def on_model_change(model_name):
+  
+  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
+
+  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
+
+def on_steps_change(steps):
+  global current_steps
+  current_steps = steps
+
+def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):
+    update_state(f"{step}/{current_steps} steps")#\nTime left, sec: {timestep/100:.0f}")
+
+def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
+
+  update_state(" ")
+
+  print(psutil.virtual_memory()) # print memory usage
 
   global current_model
   for model in models:
@@ -388,14 +122,23 @@ def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0
       current_model = model
       model_path = current_model.path
 
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+  # generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+  if seed == 0:
+    seed = random.randint(0, 2147483647)
+
+  generator = torch.Generator('cuda').manual_seed(seed)
 
-  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)
+  try:
+    if img is not None:
+      return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
+    else:
+      return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
+  except Exception as e:
+    return None, error_str(e)
 
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None):
+def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed):
+
+    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
 
     global last_mode
     global pipe
@@ -403,29 +146,48 @@ def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, g
     if model_path != current_model_path or last_mode != "txt2img":
         current_model_path = model_path
 
+        update_state(f"Loading {current_model.name} text-to-image model...")
+
         if is_colab or current_model == custom_model:
-          pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
+          pipe = StableDiffusionPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+              safety_checker=lambda images, clip_input: (images, False)
+              )
         else:
-          pipe.to("cpu")
-          pipe = current_model.pipe_t2i
+          pipe = StableDiffusionPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+              )
+          # pipe = pipe.to("cpu")
+          # pipe = current_model.pipe_t2i
 
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
+          pipe.enable_xformers_memory_efficient_attention()
         last_mode = "txt2img"
 
     prompt = current_model.prefix + prompt  
     result = pipe(
       prompt,
       negative_prompt = neg_prompt,
-      # num_images_per_prompt=n_images,
+      num_images_per_prompt=n_images,
       num_inference_steps = int(steps),
       guidance_scale = guidance,
       width = width,
       height = height,
-      generator = generator)
- 
+      generator = generator,
+      callback=pipe_callback)
 
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator=None):
+    # update_state(f"Done. Seed: {seed}")
+    
+    return replace_nsfw_images(result)
+
+def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed):
+
+    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
 
     global last_mode
     global pipe
@@ -433,14 +195,27 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
     if model_path != current_model_path or last_mode != "img2img":
         current_model_path = model_path
 
+        update_state(f"Loading {current_model.name} image-to-image model...")
+
         if is_colab or current_model == custom_model:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler)
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
+              safety_checker=lambda images, clip_input: (images, False)
+              )
         else:
-          pipe.to("cpu")
-          pipe = current_model.pipe_i2i
+          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+              current_model_path,
+              torch_dtype=torch.float16,
+              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
+              )
+          # pipe = pipe.to("cpu")
+          # pipe = current_model.pipe_i2i
         
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
+          pipe.enable_xformers_memory_efficient_attention()
         last_mode = "img2img"
 
     prompt = current_model.prefix + prompt
@@ -449,32 +224,48 @@ def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, w
     result = pipe(
         prompt,
         negative_prompt = neg_prompt,
-        # num_images_per_prompt=n_images,
-        init_image = img,
+        num_images_per_prompt=n_images,
+        image = img,
         num_inference_steps = int(steps),
         strength = strength,
         guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
+        # width = width,
+        # height = height,
+        generator = generator,
+        callback=pipe_callback)
+
+    # update_state(f"Done. Seed: {seed}")
         
-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:
+    return replace_nsfw_images(result)
+
+def replace_nsfw_images(results):
+
+    if is_colab:
+      return results.images
+      
+    for i in range(len(results.images)):
+      if results.nsfw_content_detected[i]:
+        results.images[i] = Image.open("nsfw.png")
+    return results.images
+
+# 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="style.css") as demo:
     gr.HTML(
         f"""
             <div class="finetuned-diffusion-div">
               <div>
-                <h1>Playground Diffusion</h1>
+                <h1>Finetuned 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 🤗.
+               <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/mo-di-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/dallinmackay/Van-Gogh-diffusion">Loving Vincent (Van Gogh)</a>, <a href="https://huggingface.co/nitrosocke/redshift-diffusion">Redshift renderer (Cinema4D)</a>, <a href="https://huggingface.co/prompthero/midjourney-v4-diffusion">Midjourney v4 style</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a>, <a href="https://huggingface.co/Fictiverse/Stable_Diffusion_BalloonArt_Model">Balloon Art</a> + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗.
+              </p>
+              <p>You can skip the queue and load custom models in the colab: <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
                Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
               </p>
+              <p>You can also duplicate this space and upgrade to gpu by going to settings:<br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/anzorq/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
             </div>
         """
     )
@@ -492,25 +283,26 @@ with gr.Blocks(css=css) as demo:
                 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")
+              # image_out = gr.Image(height=512)
+              gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
+          
+          state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False)
+          error_output = gr.Markdown()
 
         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)
+              n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=16, 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)
+                steps = gr.Slider(label="Steps", value=current_steps, minimum=2, maximum=100, 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)
+                width = gr.Slider(label="Width", value=512, minimum=64, maximum=2048, step=8)
+                height = gr.Slider(label="Height", value=512, minimum=64, maximum=2048, step=8)
 
               seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
 
@@ -520,14 +312,41 @@ with gr.Blocks(css=css) as demo:
                 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)
+        model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
+        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)
+    steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False)
+
+    inputs = [model_name, prompt, guidance, steps, n_images, width, height, seed, image, strength, neg_prompt]
+    outputs = [gallery, error_output]
+    prompt.submit(inference, inputs=inputs, outputs=outputs)
+    generate.click(inference, inputs=inputs, outputs=outputs)
+
+    ex = gr.Examples([
+        [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 7.5, 25],
+        [models[4].name, "portrait of dwayne johnson", 7.0, 35],
+        [models[5].name, "portrait of a beautiful alyx vance half life", 10, 25],
+        [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 30],
+        [models[5].name, "fantasy portrait painting, digital art", 4.0, 20],
+    ], inputs=[model_name, prompt, guidance, steps], outputs=outputs, fn=inference, cache_examples=False)
+
+    gr.HTML("""
+    <div style="border-top: 1px solid #303030;">
+      <br>
+      <p>Models by <a href="https://huggingface.co/nitrosocke">@nitrosocke</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others. ❤️</p>
+      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p>
+      <p>Space by:<br>
+      <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br>
+      <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br><br>
+      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
+      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p>
+    </div>
+    """)
+
+    demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False)
+
+print(f"Space built in {time.time() - start_time:.2f} seconds")
+
+# if not is_colab:
+demo.queue(concurrency_count=1)
+demo.launch(debug=is_colab, share=is_colab)

requirements (1).txt

@@ -0,0 +1,27 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+numpy
+torch
+torchvision
+diffusers
+#diffusers
+#git+https://github.com/huggingface/diffusers.git
+#git+https://github.com/huggingface/diffusers
+git+https://github.com/huggingface/diffusers.git
+git+https://github.com/huggingface/diffusers
+transformers
+#transformers
+#git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/transformers
+scipy
+ftfy
+psutil
+accelerate
+OmegaConf
+pytorch_lightning
+#OmegaConf
+#pytorch_lightning
+triton
+xformers
+#xformers
+#https://github.com/apolinario/xformers/releases/download/0.0.3/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl
+https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.16/xformers-0.0.16+bc08bbc.d20230123-cp38-cp38-linux_x86_64.whl

requirements (2).txt

@@ -0,0 +1,27 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+numpy
+torch
+torchvision
+accelerate
+transformers
+#transformers
+#git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/transformers
+diffusers
+#diffusers
+#git+https://github.com/huggingface/diffusers.git
+#git+https://github.com/huggingface/diffusers
+git+https://github.com/huggingface/diffusers.git
+git+https://github.com/huggingface/diffusers
+scipy
+ftfy
+psutil
+OmegaConf
+pytorch_lightning
+#OmegaConf
+#pytorch_lightning
+triton
+xformers
+#xformers
+#https://github.com/apolinario/xformers/releases/download/0.0.3/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl
+https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.16/xformers-0.0.16+bc08bbc.d20230123-cp38-cp38-linux_x86_64.whl

requirements.txt

@@ -0,0 +1,19 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+numpy
+torch
+torchvision
+#diffusers
+git+https://github.com/huggingface/diffusers.git
+#transformers
+git+https://github.com/huggingface/transformers
+scipy
+ftfy
+psutil
+accelerate
+#OmegaConf
+#pytorch_lightning
+triton
+xformers
+#https://github.com/apolinario/xformers/releases/download/0.0.3/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl
+https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.16/xformers-0.0.16+bc08bbc.d20230123-cp38-cp38-linux_x86_64.whl
+https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.16/xformers-0.0.16+814314d.d20230119.A10G-cp310-cp310-linux_x86_64.whl