import torch
from torch import autocast
from diffusers import StableDiffusionPipeline, DDIMScheduler
from IPython.display import display

model_path = "../800/"             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive

pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
g_cuda = None

import gradio as gr

def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50, guidance_scale=7.5):
    with torch.autocast("cuda"), torch.inference_mode():
        return pipe(
                prompt, height=int(height), width=int(width),
                negative_prompt=negative_prompt,
                num_images_per_prompt=int(num_samples),
                num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale,
                generator=g_cuda
            ).images

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", value="photo of aaabbbccc man")
            negative_prompt = gr.Textbox(label="Negative Prompt", value="")
            run = gr.Button(value="Generate")
            with gr.Row():
                num_samples = gr.Number(label="Number of Samples", value=4)
                guidance_scale = gr.Number(label="Guidance Scale", value=7.5)
            with gr.Row():
                height = gr.Number(label="Height", value=512)
                width = gr.Number(label="Width", value=512)
            num_inference_steps = gr.Slider(label="Steps", value=24)
        with gr.Column():
            gallery = gr.Gallery()

    run.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale], outputs=gallery)

demo.launch(debug=True)