app.py

import spaces
import torch
import gradio as gr
from gradio import processing_utils, utils
from PIL import Image
import random
from diffusers import (
    DiffusionPipeline,
    AutoencoderKL,
    StableDiffusionControlNetPipeline,
    ControlNetModel,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionControlNetImg2ImgPipeline,
    DPMSolverMultistepScheduler,  # <-- Added import
    EulerDiscreteScheduler  # <-- Added import
)
import tempfile
import time
from share_btn import community_icon_html, loading_icon_html, share_js
import user_history
from illusion_style import css


BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"


if torch.cuda.is_available():
    device='gpu'
else:
    device='cpu'

# Initialize both pipelines
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
#init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)#, torch_dtype=torch.float16)
main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
    BASE_MODEL,
    controlnet=controlnet,
    vae=vae,
    safety_checker=None,
    torch_dtype=torch.float16,
).to(device)

def greet(name):
    return "Hello " + name + "!!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()