app.py

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

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", "OrangeMixs"]

# 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, strength=0.7, gpt):
    if prompt == "" or prompt == None:
        return None

    if gpt:
        payload = {
            "model": "gpt-4-vision-preview",
            "messages": [
                {
                    "role": "user",
                    "content": "В этом чате тебе будут присылать prompt для нейросетей генераторов изображений. А ты должен будешь пожалуйста улучшить этот prompt.",
                },
                {
                    "role": "user",
                    "content": prompt,
                }
            ],
            "max_tokens": 4095,
        }    


        # API ключ для OpenAI
        api_key = os.getenv("API_KEY_OPENAI")

        # Заголовки для запроса
        headers = {
            'Authorization': f'Bearer {api_key}',
            'Content-Type': 'application/json',
        }

        # URL для запроса к API OpenAI
        url = "https://api.openai.com/v1/chat/completions"

        # Отправляем запрос в OpenAI
        response = requests.post(url, headers=headers, json=payload)

        # Проверяем ответ и возвращаем результат
        if response.status_code == 200:
            response_json = response.json()
            try:
                # Пытаемся извлечь текст из ответа
                prompt = response_json["choices"][0]["message"]["content"]
            except Exception as e:
                # Если есть ошибка в структуре JSON, выводим ее
        else:
            # Если произошла ошибка, возвращаем сообщение об ошибке
        
    
    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 = GoogleTranslator(source='ru', target='en').translate(prompt)
    print(f'\033[1mГенерация {key} перевод:\033[0m {prompt}')

    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mГенерация {key}:\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"
        prompt = f"Anime. {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. {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. {prompt}"
    if model == 'CleanLinearMix':
        API_URL = "https://api-inference.huggingface.co/models/digiplay/CleanLinearMix_nsfw"
    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:
    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():
            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():
            gpt = gr.Checkbox(label="ChatGPT")

    with gr.Tab("Информация"):
        with gr.Row():
            gr.Textbox(label="Шаблон prompt", value="{prompt} | ultra detail, ultra elaboration, ultra quality, perfect.")

    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, strength, gpt], outputs=image_output)

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