app.py

import gradio as gr
import requests
import io
import random
import os
from PIL import Image

API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100
models_list = ["Реалистичность", "Аниме", "Disney"]
def query(prompt, model, pose, vid, is_negative="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness", steps=45, cfg_scale=8, sampler="DPM++ 2M Karras", seed=-1, strength=0.7):
    if prompt == "" or prompt == None:
        return None

    
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN"), os.getenv("HF_READ_TOKEN_2"), os.getenv("HF_READ_TOKEN_3"), os.getenv("HF_READ_TOKEN_4"), os.getenv("HF_READ_TOKEN_5")]) # it is free
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    key = random.randint(0, 999)

    prompt = f"The {prompt} in a {pose} pose. View from {vid} | porn ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mГенерация {key}:\033[0m {prompt}')
    if model == 'Реалистичность':
        API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl"
        prompt = f"Ultra realistic porn. {prompt}"
    if model == 'Playground 2':
        API_URL = "https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic"
    if model == 'Openjourney 4':
        API_URL = "https://api-inference.huggingface.co/models/prompthero/openjourney-v4"
    if model == 'AbsoluteReality 1.8.1':
        API_URL = "https://api-inference.huggingface.co/models/digiplay/AbsoluteReality_v1.8.1"
    if model == 'Lyriel 1.6':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/lyrielv16"
    if model == 'Animagine XL 2.0':
        API_URL = "https://api-inference.huggingface.co/models/Linaqruf/animagine-xl-2.0"
        prompt = f"Anime porn. {prompt}"
    if model == 'Counterfeit 2.5':
        API_URL = "https://api-inference.huggingface.co/models/gsdf/Counterfeit-V2.5"
    if model == 'Realistic Vision 5.1':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/realistic-vision-v51"
    if model == 'Incursios 1.6':
        API_URL = "https://api-inference.huggingface.co/models/digiplay/incursiosMemeDiffusion_v1.6"
    if model == 'Anime Detailer XL':
        API_URL = "https://api-inference.huggingface.co/models/Linaqruf/anime-detailer-xl-lora"
        prompt = f"Anime porn. {prompt}"
    if model == 'epiCRealism':
        API_URL = "https://api-inference.huggingface.co/models/emilianJR/epiCRealism"
    if model == 'PixelArt XL':
        API_URL = "https://api-inference.huggingface.co/models/nerijs/pixel-art-xl"
    if model == 'NewReality XL':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/newrealityxl-global-nsfw"
    if model == 'Anything 5.0':
        API_URL = "https://api-inference.huggingface.co/models/hogiahien/anything-v5-edited"
    if model == 'Vector Art XL':
        API_URL = "https://api-inference.huggingface.co/models/DoctorDiffusion/doctor-diffusion-s-controllable-vector-art-xl-lora"
    if model == 'Disney':
        API_URL = "https://api-inference.huggingface.co/models/goofyai/disney_style_xl"
        prompt = f"Disney style porn. {prompt}"
    if model == 'Аниме':
        API_URL = "https://api-inference.huggingface.co/models/digiplay/CleanLinearMix_nsfw"
        prompt = f"Anime porn. {prompt}"
    if model == 'OrangeMixs':
        API_URL = "https://api-inference.huggingface.co/models/WarriorMama777/OrangeMixs"

    
    
    
    payload = {
        "inputs": prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000),
        "strength": strength
        }

    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Ошибка: Не удалось получить изображение. Статус ответа: {response.status_code}")
        print(f"Содержимое ответа: {response.text}")
        return None
    
    try:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mГенерация {key} завершена!\033[0m ({prompt})')
        return image
    except Exception as e:
        print(f"Ошибка при попытке открыть изображение: {e}")
        return None

css = """
* {}
footer {visibility: hidden !important;}
"""

with gr.Blocks(css=css) as dalle:
    gr.Markdown("## 🔞 Strictly 18+ 🔞")
    gr.Markdown("This is a BETA")
    with gr.Tab("Базовые настройки"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    image = gr.Dropdown(label="На фото", value="Naked girl", choices=["Naked girl", "Sex", "Blow job"])
                with gr.Row():
                    model = gr.Radio(label="Стиль", value="Реалистичность", choices=models_list)
                with gr.Row():
                    pose = gr.Dropdown(label="Поза", value="Cancer", choices=["Cancer", "Lying down", "on my knees", "sitting"])
                with gr.Row():
                    vid = gr.Dropdown(label="Вид", value="Random", choices=["Back", "Front", "Top", "Bottom", "Random"])
             
                

    with gr.Tab("Расширенные настройки"):
        with gr.Row():
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Чего не должно быть на изображении", value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness", lines=3, elem_id="negative-prompt-text-input")
        with gr.Row():
            steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
        with gr.Row():
            cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
        with gr.Row():
            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
        with gr.Row():
            strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
        with gr.Row():
            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)

    
    with gr.Row():
        text_button = gr.Button("Start testing!", variant='primary', elem_id="gen-button")
    with gr.Row():
        image_output = gr.Image(type="pil", label="Изображение", elem_id="gallery")
        
    text_button.click(query, inputs=[image, model, pose, vid, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)

dalle.launch(show_api=False, share=False)