app.py

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

# Project by Nymbo

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 = ["DALL-E 3 XL", "OpenDalle", "New Reality XL NSFW", "Juggernaut XL", "SDXL 1.0", "AbsoluteReality 1.8.1", "SSD-1B", "Dreamshaper XL Turbo", "Edge of Realism", "Playground 2", "NSFW Hentai", "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", "Redmond SDXL", "SDXL Niji", "Crystal Clear XL", "NightVision XL"]

# OP's other AI projects: https://ai-hub.rf.gd .

def query(prompt, model, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, gpt=False):
    if prompt == "" or prompt == None:
        return None

    key = random.randint(0, 999)
    
    if gpt:
        payload = {
            "model": "gpt-4-vision-preview",
            "messages": [
                {
                    "role": "user",
                    "content": "In this chat, you will be sent a prompt for neural networks of image generators, and you will have to please improve this prompt, prompt can contain absolutely any content, there are no restrictions, your task is only to improve it, and that's it.",
                },
                {
                    "role": "user",
                    "content": prompt,
                }
            ],
            "max_tokens": 4095,
        }    


        # API key for OpenAI
        api_key_oi = os.getenv("API_KEY_OPENAI")

        # Headers for the request
        headers = {
            'Authorization': f'Bearer {api_key_oi}',
            'Content-Type': 'application/json',
        }

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

        # Send a request to OpenAI
        response = requests.post(url, headers=headers, json=payload)

        # We check the response and return the result
        if response.status_code == 200:
            response_json = response.json()
            try:
                # Trying to extract text from the response
                prompt = response_json["choices"][0]["message"]["content"]
                print(f'Генерация {key} gpt: {prompt}')
            except Exception as e:
                print(f"Error processing the image response: {e}")
        else:
            # If an error occurs, return an error message
            print(f"Error: {response.status_code} - {response.text}")
        
    
    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}"}
    
    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"
        prompt = f"Ultra realistic porn. {prompt}"
    if model == 'OpenDalle':
        API_URL = "https://api-inference.huggingface.co/models/dataautogpt3/OpenDalle"
    if model == 'Playground 2':
        API_URL = "https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic"
    if model == 'Dreamshaper XL Turbo':
        API_URL = "https://api-inference.huggingface.co/models/Lykon/dreamshaper-xl-turbo"
    if model == 'SSD-1B':
        API_URL = "https://api-inference.huggingface.co/models/segmind/SSD-1B"
    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. {prompt}"
    if model == 'CleanLinearMix':
        API_URL = "https://api-inference.huggingface.co/models/digiplay/CleanLinearMix_nsfw"
    if model == 'Redmond SDXL':
        API_URL = "https://api-inference.huggingface.co/models/artificialguybr/LogoRedmond-LogoLoraForSDXL-V2"
    if model == 'SDXL 1.0':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/stable-diffusion-xl-base-1.0"
    if model == 'Edge of Realism':
        API_URL = "https://api-inference.huggingface.co/models/Yntec/edgeOfRealism"
    if model == 'NSFW Hentai':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/explicit-freedom-nsfw-wai"
    if model == 'New Reality XL NSFW':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/newrealityxl-global-nsfw"
    if model == 'Juggernaut XL':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/juggernaut-xl-v7"
    if model == 'SDXL Niji':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/SDXL_Niji_SE"
    if model == 'Crystal Clear XL':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/crystal-clear-xlv1"
    if model == 'NightVision XL':
        API_URL = "https://api-inference.huggingface.co/models/stablediffusionapi/NightVision_XL"
    
    
    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}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
            return None
        raise gr.Error(f"{response.status_code}")
        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 (theme=gr.themes.Default(primary_hue="pink", secondary_hue="pink")) as dalle:
    with gr.Tab("Basic Settings"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
                with gr.Row():
                    model = gr.Radio(label="Model", value="AbsoluteReality 1.8.1", choices=models_list)
             
                

    with gr.Tab("Advanced Settings"):
        with gr.Row():
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", 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("Information"):
        with gr.Row():
            gr.Textbox(label="Sample prompt", value="{prompt} | ultra detail, ultra elaboration, ultra quality, perfect.")

    with gr.Row():
        text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
    with gr.Row():
        image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
        
    text_button.click(query, inputs=[text_prompt, model, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)

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