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import spaces
import gradio as gr
import os
import random
import json
import time
import uuid
from PIL import Image
from huggingface_hub import snapshot_download
from diffusers import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler, AutoPipelineForText2Image, DiffusionPipeline
from diffusers import EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, DPMSolverSDEScheduler
from diffusers.models.attention_processor import AttnProcessor2_0
import torch
from typing import Tuple
from datetime import datetime
import requests
import torch
from diffusers import DiffusionPipeline
import importlib
import re
from urllib.parse import urlparse
random.seed(time.time())
MAX_SEED = 12211231
CACHE_EXAMPLES = "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4192"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
NUM_IMAGES_PER_PROMPT = 1
# Define the regular expression
child_related_regex = re.compile(
r'(child|children|kid|kids|baby|babies|toddler|infant|juvenile|minor|underage|preteen|adolescent|youngster|youth|son|daughter|young|kindergarten|preschool|'
r'([1-9]|1[0-7])[\s_\-|\.\,]*year(s)?[\s_\-|\.\,]*old|' # Matches 1 to 17 years old with various separators
r'little|small|tiny|short|young|new[\s_\-|\.\,]*born[\s_\-|\.\,]*(boy|girl|male|man|bro|brother|sis|sister))',
re.IGNORECASE
)
# Function to remove child-related content from a prompt
def remove_child_related_content(prompt):
cleaned_prompt = re.sub(child_related_regex, '', prompt)
return cleaned_prompt.strip()
# Function to check if a prompt contains child-related content
def contains_child_related_content(prompt):
if child_related_regex.search(prompt):
return True
return False
cfg = json.load(open("app.conf"))
def load_pipeline_and_scheduler():
clip_skip = cfg.get("clip_skip", 0)
# Download the model files
ckpt_dir = snapshot_download(repo_id=cfg["model_id"])
# Load the models
vae = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir, "vae"), torch_dtype=torch.float16)
pipe = StableDiffusionXLPipeline.from_pretrained(
ckpt_dir,
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16"
)
pipe = pipe.to("cuda")
pipe.unet.set_attn_processor(AttnProcessor2_0())
# Define samplers
samplers = {
"Euler a": EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config),
"DPM++ SDE Karras": DPMSolverSDEScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
}
# Set the scheduler based on the selected sampler
pipe.scheduler = samplers[cfg.get("sampler","DPM++ SDE Karras")]
# Set clip skip
pipe.text_encoder.config.num_hidden_layers -= (clip_skip - 1)
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
print("Model Compiled!")
return pipe
pipe = load_pipeline_and_scheduler()
css = '''
.gradio-container{max-width: 560px !important}
body {
background-color: rgb(3, 7, 18);
color: white;
}
.gradio-container {
background-color: rgb(3, 7, 18) !important;
border: none !important;
}
.gradio-container footer {
display: block !important;
}
'''
js = '''
<script src="https://huggingface.co/spaces/nsfwalex/sd_card/resolve/main/'''+cfg.get("prompt_generator", "psv1.js")+'''"></script>
<script>
function getEnvInfo() {
const result = {};
// Get URL parameters
const urlParams = new URLSearchParams(window.location.search);
for (const [key, value] of urlParams) {
result[key] = value;
}
// Get current domain and convert to lowercase
result["__domain"] = window.location.hostname.toLowerCase();
// Get iframe parent domain, if any, and convert to lowercase
try {
if (window.self !== window.top) {
result["__iframe_domain"] = document.referrer
? new URL(document.referrer).hostname.toLowerCase()
: "unable to get iframe parent domain";
}else{
result["__iframe_domain"] = "";
}
} catch (e) {
result["__iframe_domain"] = "unable to access iframe parent domain";
}
return result;
}
function isValidEnv(){
envInfo = getEnvInfo();
return envInfo["e"] == "1" ||
envInfo["__domain"].indexOf("nsfwais.io") != -1 ||
envInfo["__iframe_domain"].indexOf("nsfwais.io") != -1 ||
envInfo["__domain"].indexOf("127.0.0.1") != -1 ||
envInfo["__iframe_domain"].indexOf("127.0.0.1") != -1;
}
window.g=function(p){
params = getEnvInfo();
if (!isValidEnv()){
return "";
}
const conditions = {
"tag": ["normal", "sexy"],
"exclude_category": ["Clothing"],
"count_per_tag": 1
};
prompt = generateSexyPrompt()
console.log(prompt);
return prompt
}
window.postMessageToParent = function(prompt, event, source, value) {
// Construct the message object with the provided parameters
console.log("post start",event, source, value);
const message = {
event: event,
source: source,
value: value
};
if (!prompt){
prompt = window.g();
// Find the prompt input element
const promptContainer = document.getElementById('prompt_input_box');
if (promptContainer) {
const promptInput = promptContainer.querySelector('input') || promptContainer.querySelector('textarea');
if (promptInput) {
promptInput.value = prompt;
// Trigger an input event to ensure Gradio recognizes the change
promptInput.dispatchEvent(new Event('input', { bubbles: true }));
}
}
}
if (window.self !== window.top) {
// Post the message to the parent window
window.parent.postMessage(message, '*');
}else if(isValidEnv()){
try{
sendCustomEventToDataLayer({},event,source,value)
} catch (error) {
console.error("Error in sendCustomEventToDataLayer:", error);
}
}else{
console.log("Not in an iframe, can't post to parent");
}
console.log("post finish");
return prompt;
}
function uploadImage(prompt, images, event, source, value) {
// Ensure we're in an iframe
console.log("uploadImage", prompt, images && images.length > 0 ? images[0].image.url : null, event, source, value);
// Get the first image from the gallery (assuming it's an array)
let imageUrl = images && images.length > 0 ? images[0].image.url : null;
if (window.self !== window.top) {
// Post the message to the parent window
// Prepare the data to send
let data = {
event: event,
source: source,
value:{
prompt: prompt,
image: imageUrl
}
};
window.parent.postMessage(JSON.stringify(data), '*');
} else if (isValidEnv()){
try{
sendCustomEventToDataLayer({},event,source,{"prompt": prompt, "image":imageUrl, "model": value})
} catch (error) {
console.error("Error in sendCustomEventToDataLayer:", error);
}
}else{
console.log("Not in an iframe, can't post to parent");
}
return prompt, images
}
function onDemoLoad(){
let envInfo = getEnvInfo();
console.log(envInfo);
if (isValidEnv()){
var element = document.getElementById("desc_html_code");
if (element) {
element.parentNode.removeChild(element);
}
}
return;
//return envInfo["__domain"], envInfo["__iframe_domain"]
}
</script>
'''
desc_html='''
<div style="background-color: #f0f0f0; padding: 10px; border-radius: 5px; text-align: center; margin-top: 20px;">
<p style="font-size: 16px; color: #333;">
For the full version and more exciting NSFW AI apps, visit
<a href="https://nsfwais.io?utm_source=hf_'''+cfg["model_id"].replace("/","_")+'''&utm_medium=referral" style="color: #0066cc; text-decoration: none; font-weight: bold;" rel="dofollow">nsfwais.io</a>!
</p>
</div>
'''
def save_image(img):
# Generate a unique filename
unique_name = str(uuid.uuid4()) + ".webp"
# Convert the image to WebP format
webp_img = img.convert("RGB") # Ensure the image is in RGB mode
# Save the image in WebP format with high quality
webp_img.save(unique_name, "WEBP", quality=90)
# Open the saved WebP file and return it as a PIL Image object
with Image.open(unique_name) as webp_file:
webp_image = webp_file.copy()
return webp_image, unique_name
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@spaces.GPU(duration=60)
def generate(p, progress=gr.Progress(track_tqdm=True)):
negative_prompt = cfg.get("negative_prompt", "")
style_selection = ""
use_negative_prompt = True
seed = 0
width = cfg.get("width", 1024)
height = cfg.get("width", 768)
inference_steps = cfg.get("inference_steps", 30)
randomize_seed = True
guidance_scale = cfg.get("guidance_scale", 7.5)
p = remove_child_related_content(p)
prompt_str = cfg.get("prompt", "{prompt}").replace("{prompt}", p)
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator(pipe.device).manual_seed(seed)
images = pipe(
prompt=prompt_str,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=inference_steps,
generator=generator,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
output_type="pil",
).images
images = [save_image(img) for img in images]
image_paths = [i[1] for i in images]
print(prompt_str, image_paths)
return [i[0] for i in images]
default_image = cfg.get("cover_path", None)
if default_image:
if isinstance(default_image, list):
# Filter out non-existent paths
existing_images = [img for img in default_image if os.path.exists(img)]
#print(f"found cover files: {existing_images}")
if existing_images:
default_image = existing_images[int(time.time()*1000)%len(existing_images)]
else:
default_image = None
elif not os.path.exists(default_image):
print(f"cover file not existed, {default_image}")
default_image = None
else:
default_image = None
with gr.Blocks(css=css,head=js,fill_height=True) as demo:
with gr.Row(equal_height=False):
with gr.Group():
gr.HTML(value=desc_html, elem_id='desc_html_code')
result = gr.Gallery(value=[default_image],
label="Result", show_label=False, columns=1, rows=1, show_share_button=True,elem_id=cfg["model_id"].replace("/", "-"),
show_download_button=True,allow_preview=False,interactive=False, min_width=cfg.get("window_min_width", 340),height=360
)
with gr.Row():
prompt = gr.Text(
show_label=False,
max_lines=2,
lines=2,
placeholder="Enter your fantasy or click ->",
container=False,
scale=5,
min_width=100,
elem_id="prompt_input_box"
)
random_button = gr.Button("Surprise Me", scale=1, min_width=10)
run_button = gr.Button( "GO!", scale=1, min_width=20, variant="primary",icon="https://huggingface.co/spaces/nsfwalex/sd_card/resolve/main/hot.svg")
def on_demo_load(request: gr.Request):
current_domain = request.request.headers.get("Host", "")
# Get the potential iframe parent domain from the Referer header
referer = request.request.headers.get("Referer", "")
iframe_parent_domain = ""
if referer:
try:
parsed_referer = urlparse(referer)
iframe_parent_domain = parsed_referer.netloc
except:
iframe_parent_domain = "Unable to parse referer"
params = dict(request.query_params)
print(f"load_demo, urlparams={params},cover={default_image},domain={current_domain},iframe={iframe_parent_domain}")
session_data = {
"params": params,
"client_ip": request.client.host,
"refer": referer,
"host": current_domain
}
if params.get("e", "0") == "1" or "nsfwais.io" in current_domain or "nsfwais.io" in iframe_parent_domain or "127.0.0.1" in current_domain or "127.0.0.1" in iframe_parent_domain:
#update the image
#bind events
#return [Image.open(default_image)], session_data
return session_data
return session_data
#return [], session_data
session_state = gr.State()
result.change(fn=lambda x,y:None , inputs=[prompt,result], outputs=[], js=f'''(p,img)=>window.uploadImage(p, img,"process_finished","demo_hf_{cfg.get("name")}_card", "{cfg["model_id"]}")''')
run_button.click(generate, inputs=[prompt], outputs=[result],trigger_mode="once",js=f'''(p)=>window.postMessageToParent(p,"process_started","demo_hf_{cfg.get("name")}_card", "click_go")''')
random_button.click(fn=lambda x:x, inputs=[prompt], outputs=[prompt], js='''(p)=>window.g(p)''')
demo.load(fn=on_demo_load, inputs=[], outputs=[session_state], js='''()=>onDemoLoad()''')
if __name__ == "__main__":
demo.queue(max_size=100).launch(show_api=True,show_error=False)