app.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline, FluxPipeline

# Initialize models
sd_model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
sd_model.to("cuda")

flux_model = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.float16)
flux_model.enable_model_cpu_offload()

def generate_sd_image(prompt):
    return sd_model(prompt).images[0]

def generate_flux_image(prompt):
    return flux_model(prompt, guidance_scale=0.0, num_inference_steps=4).images[0]

def generate_image(prompt, model_choice):
    if model_choice == "Stable Diffusion":
        return generate_sd_image(prompt)
    else:
        return generate_flux_image(prompt)

# Create Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Enter your prompt"),
        gr.Radio(["Stable Diffusion", "Flux"], label="Choose Model")
    ],
    outputs=gr.Image(type="pil"),
    title="Image Generation with Stable Diffusion and Flux",
    description="Generate images using Stable Diffusion (Midjourney-like) or Flux models."
)

iface.launch()