app.py

import gradio as gr
import requests
import io
import random
import os
from PIL import Image
from deep_translator import GoogleTranslator
from langdetect import detect

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 = ["AbsoluteReality 1.8.1", "DALL-E 3 XL", "Playground 2", "Openjourney 4", "Lyriel 1.6", "Animagine XL 2.0", "Counterfeit 2.5", "Realistic Vision 5.1", "Incursios 1.6", "Anime Detailer XL", "Vector Art XL", "epiCRealism", "PixelArt XL", "NewReality XL", "Anything 5.0", "Disney", "CleanLinearMix", "Custom"]

# PLEASE ❤ like ❤ this space. Please like me. I am 12 years old, one of my projects is: https://ai-hub.rf.gd . I live in Russia, I don't know English very well. Therefore, I apologize that there is only Russian here, but I think it will not be difficult to translate all this. (For example, using gpt)

def query(prompt, model, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1):
    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}"}
    language = detect(prompt)
    key = random.randint(0, 999)

    print(f'\033[1mГенерация {key}:\033[0m {prompt}')
    if language == 'ru':
        prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
        print(f'\033[1mГенерация {key} перевод:\033[0m {prompt}')
    
    if model == 'DALL-E 3 XL':
        API_URL = "https://api-inference.huggingface.co/models/openskyml/dalle-3-xl"
    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"
    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"
    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"
    if model == 'CleanLinearMix':
        API_URL = "https://api-inference.huggingface.co/models/digiplay/CleanLinearMix_nsfw"
    
    payload = {
        "inputs": prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000)
        }

    image_bytes = requests.post(API_URL, headers=headers, json=payload, timeout=timeout).content
    image = Image.open(io.BytesIO(image_bytes))
    print(f'\033[1mГенерация {key} завершена!\033[0m ({prompt})')
    return image


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

with gr.Blocks(css=css) as dalle:
    with gr.Tab("Базовые настройки"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Описание изображения", lines=3, elem_id="prompt-text-input")
                with gr.Row():
                    model = gr.Radio(label="Модель", value="DALL-E 3 XL", choices=models_list)
             
                

    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():
            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)


    with gr.Row():
        text_button = gr.Button("Генерация", 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=[text_prompt, model, negative_prompt, steps, cfg, method, seed], outputs=image_output)

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