app.py

import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import base64

api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"

def generate_image(model="Deliberate", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
    data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
    response = requests.post(api_url + data, timeout=400)
    if response.status_code == 200:
        img_base64 = response.json()["url"]
        img_bytes = base64.b64decode(img_base64)
        img = Image.open(BytesIO(img_bytes))
        return img
    else:
        return None

inputs = [
     gr.inputs.Dropdown(['Analog Diffusion', 'Anything Diffusion', 'Anything v3', 'ChilloutMix', 'Counterfeit', 'CyriousMix', 'Deliberate', 'Dreamshaper', 'Dreamlike Diffusion', 'Dreamlike Photoreal',  'Experience', 'FaeTastic', 'Hassanblend', 'Mega Merge Diffusion',  'Midjourney Diffusion', 'ModernArt Diffusion', 'Movie Diffusion', 'NeverEnding Dream', 'Perfect World', 'PortraitPlus', 'ProtoGen',  'Protogen Anime', 'Protogen Infinity', 'RealBiter', 'Realism Engine', 'Realistic Vision', 'Rev Animated',  'RPG', 'Seek.art MEGA', 'stable_diffusion', 'stable_diffusion_2.1' , 'Unstable Ink Dream'], label="Model", default="Deliberate"),
    gr.inputs.Textbox(label="Prompt", default=""),
    gr.inputs.Number(label="Seed", default=0),
    gr.inputs.Textbox(label="Negative Prompt", default=""),
    gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
    gr.inputs.Number(label="Steps", default=50)
]

outputs = gr.outputs.Image(label="Generated Image", type="pil")

interface = gr.Interface(generate_image, inputs, outputs, title="Diffusion 50", 
                         description="<center>Live access to the most popular Diffusion models</center>", 
                         examples=[])

interface.launch()