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test.py
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test.py
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#@title Prepare the Concepts Library to be used
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 gradio as gr
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
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)
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")
stable_inversion = "user/my-stable-inversion" #@param {type:"string"}
inversion_path = hf_hub_download(repo_id=stable_inversion, filename="token_embeddings.pt")
text_encoder.text_model.embeddings.token_embedding.weight = torch.load(inversion_path)
subprocess.run(["make", "build-all"], shell=False)
img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
stable_diffusion = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion")
is_colab = utils.is_google_colab()
MODE = os.environ.get('FLASK_ENV', 'production')
IS_DEV = MODE == 'development'
app = Flask(__name__, static_url_path='/static')
app.config['JSONIFY_PRETTYPRINT_REGULAR'] = False
schema = {
"type": "object",
"properties": {
"prompt": {"type": "string"},
"images": {
"type": "array",
"items": {
"type": "object",
"minProperties": 2,
"maxProperties": 2,
"properties": {
"colors": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 5,
"minItems": 5
},
"imgURL": {"type": "string"}}
}
}
},
"minProperties": 2,
"maxProperties": 2
}
CORS(app)
DB_FILE = Path("./data.db")
TOKEN = os.environ.get('HUGGING_FACE_HUB_TOKEN')
repo = Repository(
local_dir="data",
repo_type="dataset",
clone_from="huggingface-projects/color-palettes-sd",
use_auth_token=TOKEN
)
repo.git_pull()
# copy db on db to local path
shutil.copyfile("./data/data.db", DB_FILE)
db = sqlite3.connect(DB_FILE)
try:
data = db.execute("SELECT * FROM palettes").fetchall()
if IS_DEV:
print(f"Loaded {len(data)} palettes from local db")
db.close()
except sqlite3.OperationalError:
db.execute(
'CREATE TABLE palettes (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, data json, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL)')
db.commit()
api = HfApi()
models_list = api.list_models(author="sd-concepts-library", sort="likes", direction=-1)
models = []
if torch.cuda.is_available():
torchfloat = torch.float16
else:
torchfloat = torch.float32
class Model:
def __init__(self, name, path, prefix):
self.name = name
self.path = path
self.prefix = prefix
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", ""),
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", ""),
]
last_mode = "txt2img"
current_model = models[1]
current_model_path = current_model.path
models = [
"DGSpitzer/Cyberpunk-Anime-Diffusion"
]
prompt_prefixes = {
models[0]: "dgs illustration style "
}
current_model = models[0]
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda"
#If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co/settings/tokens into the use_auth_token field below.
#pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True, revision="fp16", torch_dtype=torch.float16)
#pipe = pipe.to(device)
#torch.backends.cudnn.benchmark = True
#auth_token = os.environ.get("test") or True
#pipe = StableDiffusionPipeline.from_pretrained(current_model, use_auth_token=auth_token, torch_dtype=torchfloat, revision="fp16")
#model_id = "hakurei/waifu-diffusion"
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")
# pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
with open("ideas.txt", "r") as f:
line = f.readlines()
if torch.cuda.is_available():
pipe = pipe.to("cuda")
# pipe_i2i = pipe_i2i.to("cuda")
else:
pipe = pipe.to("cpu")
device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
#torch.backends.cudnn.benchmark = True
num_samples = 2
is_gpu_busy = False
def on_model_change(model):
global current_model
global pipe
if model != current_model:
current_model = model
pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torchfloat)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
def inference(prompt, guidance, steps):
promptPrev = prompt
prompt = prompt_prefixes[current_model] + prompt
image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
return image, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(placeholder=promptPrev)
def inference_example(prompt, guidance, steps):
prompt = prompt_prefixes[current_model] + prompt
image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0]
return image
def infer(prompt):
images = pipe([prompt] * num_samples, guidance_scale=7.5)["sample"]
global is_gpu_busy
samples = 4
steps = 50
scale = 7.5
#generator = torch.Generator(device=device).manual_seed(seed)
#print("Is GPU busy? ", is_gpu_busy)
images = []
#if(not is_gpu_busy):
# is_gpu_busy = True
# images_list = pipe(
# [prompt] * samples,
# num_inference_steps=steps,
# guidance_scale=scale,
#generator=generator,
# )
# is_gpu_busy = False
# for i, image in enumerate(images_list["sample"]):
# images.append(image)
#else:
url = os.getenv('JAX_BACKEND_URL')
payload = {'prompt': prompt}
images_request = requests.post(url, json = payload)
for image in images_request.json()["images"]:
image_b64 = (f"data:image/jpeg;base64,{image}")
images.append(image_b64)
return images
def generate(starting_text):
seed = random.randint(100, 1000000)
set_seed(seed)
if starting_text == "":
starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
response_list = []
for x in response:
resp = x['generated_text'].strip()
if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
response_list.append(resp+'\n')
response_end = "\n".join(response_list)
response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
response_end = response_end.replace("<", "").replace(">", "")
if response_end != "":
return response_end
def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
# separate token and the embeds
trained_token = list(loaded_learned_embeds.keys())[0]
embeds = loaded_learned_embeds[trained_token]
# cast to dtype of text_encoder
dtype = text_encoder.get_input_embeddings().weight.dtype
# add the token in tokenizer
token = token if token is not None else trained_token
num_added_tokens = tokenizer.add_tokens(token)
i = 1
while(num_added_tokens == 0):
print(f"The tokenizer already contains the token {token}.")
token = f"{token[:-1]}-{i}>"
print(f"Attempting to add the token {token}.")
num_added_tokens = tokenizer.add_tokens(token)
i+=1
# resize the token embeddings
text_encoder.resize_token_embeddings(len(tokenizer))
# get the id for the token and assign the embeds
token_id = tokenizer.convert_tokens_to_ids(token)
text_encoder.get_input_embeddings().weight.data[token_id] = embeds
return token
print("Setting up the public library")
for model in models_list:
model_content = {}
model_id = model.modelId
model_content["id"] = model_id
embeds_url = f"https://huggingface.co/{model_id}/resolve/main/learned_embeds.bin"
os.makedirs(model_id,exist_ok = True)
if not os.path.exists(f"{model_id}/learned_embeds.bin"):
try:
wget.download(embeds_url, out=model_id)
except:
continue
token_identifier = f"https://huggingface.co/{model_id}/raw/main/token_identifier.txt"
response = requests.get(token_identifier)
token_name = response.text
concept_type = f"https://huggingface.co/{model_id}/raw/main/type_of_concept.txt"
response = requests.get(concept_type)
concept_name = response.text
model_content["concept_type"] = concept_name
images = []
for i in range(4):
url = f"https://huggingface.co/{model_id}/resolve/main/concept_images/{i}.jpeg"
image_download = requests.get(url)
url_code = image_download.status_code
if(url_code == 200):
file = open(f"{model_id}/{i}.jpeg", "wb") ## Creates the file for image
file.write(image_download.content) ## Saves file content
file.close()
images.append(f"{model_id}/{i}.jpeg")
model_content["images"] = images
#if token cannot be loaded, skip it
try:
learned_token = load_learned_embed_in_clip(f"{model_id}/learned_embeds.bin", pipe.text_encoder, pipe.tokenizer, token_name)
except:
continue
model_content["token"] = learned_token
models.append(model_content)
#@title Run the app to navigate around [the Library](https://huggingface.co/sd-concepts-library)
#@markdown Click the `Running on public URL:` result to run the Gradio app
SELECT_LABEL = "Select concept"
def assembleHTML(model):
html_gallery = ''
html_gallery = html_gallery+'''
<div class="flex gr-gap gr-form-gap row gap-4 w-full flex-wrap" id="main_row">
'''
cap = 0
for model in models:
html_gallery = html_gallery+f'''
<div class="gr-block gr-box relative w-full overflow-hidden border-solid border border-gray-200 gr-panel">
<div class="output-markdown gr-prose" style="max-width: 100%;">
<h3>
<a href="https://huggingface.co/{model["id"]}" target="_blank">
<code>{html.escape(model["token"])}</code>
</a>
</h3>
</div>
<div id="gallery" class="gr-block gr-box relative w-full overflow-hidden border-solid border border-gray-200">
<div class="wrap svelte-17ttdjv opacity-0"></div>
<div class="absolute left-0 top-0 py-1 px-2 rounded-br-lg shadow-sm text-xs text-gray-500 flex items-center pointer-events-none bg-white z-20 border-b border-r border-gray-100 dark:bg-gray-900">
<span class="mr-2 h-[12px] w-[12px] opacity-80">
<svg xmlns="http://www.w3.org/2000/svg" width="100%" height="100%" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather feather-image">
<rect x="3" y="3" width="18" height="18" rx="2" ry="2"></rect>
<circle cx="8.5" cy="8.5" r="1.5"></circle>
<polyline points="21 15 16 10 5 21"></polyline>
</svg>
</span> {model["concept_type"]}
</div>
<div class="overflow-y-auto h-full p-2" style="position: relative;">
<div class="grid gap-2 grid-cols-2 sm:grid-cols-2 md:grid-cols-2 lg:grid-cols-2 xl:grid-cols-2 2xl:grid-cols-2 svelte-1g9btlg pt-6">
'''
for image in model["images"]:
html_gallery = html_gallery + f'''
<button class="gallery-item svelte-1g9btlg">
<img alt="" loading="lazy" class="h-full w-full overflow-hidden object-contain" src="file/{image}">
</button>
'''
html_gallery = html_gallery+'''
</div>
<iframe style="display: block; position: absolute; top: 0; left: 0; width: 100%; height: 100%; overflow: hidden; border: 0; opacity: 0; pointer-events: none; z-index: -1;" aria-hidden="true" tabindex="-1" src="about:blank"></iframe>
</div>
</div>
</div>
'''
cap += 1
if(cap == 99):
break
html_gallery = html_gallery+'''
</div>
'''
return html_gallery
def title_block(title, id):
return gr.Markdown(f"### [`{title}`](https://huggingface.co/{id})")
def image_block(image_list, concept_type):
return gr.Gallery(
label=concept_type, value=image_list, elem_id="gallery"
).style(grid=[2], height="auto")
def checkbox_block():
checkbox = gr.Checkbox(label=SELECT_LABEL).style(container=False)
return checkbox
def infer(text):
with autocast("cuda"):
images_list = pipe(
[text]*2,
num_inference_steps=50,
guidance_scale=7.5
)
output_images = []
for i, image in enumerate(images_list["sample"]):
output_images.append(image)
return output_images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
# idetnical to `infer` function without gradio state updates for share btn
def infer_examples(text):
with autocast("cuda"):
images_list = pipe(
[text]*2,
num_inference_steps=50,
guidance_scale=7.5
)
output_images = []
for i, image in enumerate(images_list["sample"]):
output_images.append(image)
return output_images
txt = grad.Textbox(lines=1, label="Initial Text", placeholder="English Text here")
out = grad.Textbox(lines=4, label="Generated Prompts")
examples = []
for x in range(8):
examples.append(line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize())
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
pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
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)
def get_images(prompt):
gallery_dir = stable_diffusion(prompt, fn_index=2)
sd_output = [os.path.join(gallery_dir, image) for image in os.listdir(gallery_dir)]
return sd_output, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
def get_prompts(uploaded_image):
return img_to_text(uploaded_image, fn_index=1)[0]
def get_db():
db = getattr(g, '_database', None)
if db is None:
db = g._database = sqlite3.connect(DB_FILE)
db.row_factory = sqlite3.Row
return db
@app.teardown_appcontext
def close_connection(exception):
db = getattr(g, '_database', None)
if db is not None:
db.close()
def update_repository():
repo.git_pull()
# copy db on db to local path
shutil.copyfile(DB_FILE, "./data/data.db")
with sqlite3.connect("./data/data.db") as db:
db.row_factory = sqlite3.Row
palettes = db.execute("SELECT * FROM palettes").fetchall()
data = [{'id': row['id'], 'data': json.loads(
row['data']), 'created_at': row['created_at']} for row in palettes]
with open('./data/data.json', 'w') as f:
json.dump(data, f, separators=(',', ':'))
print("Updating repository")
subprocess.Popen(
"git add . && git commit --amend -m 'update' && git push --force", cwd="./data", shell=True)
repo.push_to_hub(blocking=False)
@app.route('/')
def index():
return app.send_static_file('index.html')
@app.route('/force_push')
def push():
if (request.headers['token'] == TOKEN):
update_repository()
return jsonify({'success': True})
else:
return "Error", 401
def getAllData():
palettes = get_db().execute("SELECT * FROM palettes").fetchall()
data = [{'id': row['id'], 'data': json.loads(
row['data']), 'created_at': row['created_at']} for row in palettes]
return data
@app.route('/data')
def getdata():
return jsonify(getAllData())
@app.route('/new_palette', methods=['POST'])
@expects_json(schema)
def create():
data = g.data
db = get_db()
cursor = db.cursor()
cursor.execute("INSERT INTO palettes(data) VALUES (?)", [json.dumps(data)])
db.commit()
return jsonify(getAllData())
@app.errorhandler(400)
def bad_request(error):
if isinstance(error.description, ValidationError):
original_error = error.description
return jsonify({'error': original_error.message}), 400
return error
if __name__ == '__main__':
if not IS_DEV:
print("Starting scheduler -- Running Production")
scheduler = APScheduler()
scheduler.add_job(id='Update Dataset Repository',
func=update_repository, trigger='interval', hours=1)
scheduler.start()
else:
print("Not Starting scheduler -- Running Development")
app.run(host='0.0.0.0', port=int(
os.environ.get('PORT', 7860)), debug=True, use_reloader=IS_DEV)
title = "Stable Diffusion Prompt Generator"
description = 'This is a demo of the model series: "MagicPrompt", in this case, aimed at: "Stable Diffusion". To use it, simply submit your text or click on one of the examples. To learn more about the model, [click here](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).<br>'
grad.Interface(fn=generate,
inputs=txt,
outputs=out,
examples=examples,
title=title,
description=description,
article='',
allow_flagging='never',
cache_examples=False,
theme="default").launch(enable_queue=True, debug=True)
css = """
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
a {text-decoration-line: underline;}
<style>
.finetuned-diffusion-div {
text-align: center;
max-width: 700px;
margin: 0 auto;
}
.finetuned-diffusion-div div {
display: inline-flex;
align-items: center;
gap: 0.8rem;
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%;
}
.finetuned-diffusion-div p a {
text-decoration: underline;
}
.tabs {
margin-top: 0px;
margin-bottom: 0px;
}
#gallery {
min-height: 20rem;
}
</style>
.gradio-container {font-family: 'IBM Plex Sans', sans-serif}
#top_title{margin-bottom: .5em}
#top_title h2{margin-bottom: 0; text-align: center}
/*#main_row{flex-wrap: wrap; gap: 1em; max-height: 550px; overflow-y: scroll; flex-direction: row}*/
#component-3{height: 760px; overflow: auto}
#component-9{position: sticky;top: 0;align-self: flex-start;}
@media (min-width: 768px){#main_row > div{flex: 1 1 32%; margin-left: 0 !important}}
.gr-prose code::before, .gr-prose code::after {content: "" !important}
::-webkit-scrollbar {width: 10px}
::-webkit-scrollbar-track {background: #f1f1f1}
::-webkit-scrollbar-thumb {background: #888}
::-webkit-scrollbar-thumb:hover {background: #555}
.gr-button {white-space: nowrap}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
#prompt_input{flex: 1 3 auto; width: auto !important;}
#prompt_area{margin-bottom: .75em}
#prompt_area > div:first-child{flex: 1 3 auto}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
all: unset;
}
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
.gradio-container
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: black;
background: black;
}
input[type='range'] {
accent-color: black;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 730px;
margin: auto;
padding-top: 1.5rem;
}
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
#gallery>div>.h-full {
min-height: 20rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
#advanced-btn {
font-size: .7rem !important;
line-height: 19px;
margin-top: 12px;
margin-bottom: 12px;
padding: 2px 8px;
border-radius: 14px !important;
}
#advanced-options {
display: none;
margin-bottom: 20px;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.acknowledgments h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;