app.py

from fix_cache import remove_old_cache
import gradio as gr
from diffusers import StableDiffusionPipeline
import torch

remove_old_cache()
# Load different models
models = {
    "Stable Diffusion v1.5": "runwayml/stable-diffusion-v1-5",
    "Stable Diffusion v2.1": "stabilityai/stable-diffusion-2-1",
    "Anime Diffusion": "hakurei/waifu-diffusion-v1-4",
}

# Function to load the selected model
def load_model(model_name):
    model_id = models[model_name]
    pipe = StableDiffusionPipeline.from_pretrained(
        model_id, torch_dtype=torch.float16
    )
    pipe = pipe.to("cpu")  # Use GPU
    return pipe

# Load the default model
current_pipe = load_model("Stable Diffusion v1.5")

# Function to generate image
def generate_image(prompt, model_name):
    global current_pipe
    # Reload pipeline if the model changes
    if model_name not in current_pipe.config["_name_or_path"]:
        current_pipe = load_model(model_name)
    # Generate the image
    image = current_pipe(prompt).images[0]
    return image

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("### Multi-Model Text-to-Image Generator")
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(label="Enter a text prompt", placeholder="Describe the image you want...")
            model_selector = gr.Dropdown(
                label="Select Model", choices=list(models.keys()), value="Stable Diffusion v1.5"
            )
            generate_button = gr.Button("Generate Image")
        with gr.Column():
            output_image = gr.Image(label="Generated Image")
        with gr.Column():
            output_image2 = gr.Image(label= "Generated Image 2")

    generate_button.click(
        generate_image, inputs=[text_input, model_selector], outputs=output_image
    )

demo.launch(share=True)