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

stable_model_list = [
    "runwayml/stable-diffusion-v1-5",
    "stabilityai/stable-diffusion-2",
    "stabilityai/stable-diffusion-2-base",
    "stabilityai/stable-diffusion-2-1",
    "stabilityai/stable-diffusion-2-1-base"
]

stable_prompt_list = [
        "a photo of a man.",
        "a photo of a girl."
    ]

stable_negative_prompt_list = [
        "bad, ugly",
        "deformed"
    ]

def stable_diffusion_text2img(
    model_path:str,
    prompt:str,
    negative_prompt:str,
    guidance_scale:int,
    num_inference_step:int,
    height:int,
    width:int,
    ):

    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()

    images = pipe(
        prompt,
        height=height,
        width=width,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_step,
        guidance_scale=guidance_scale,
    ).images

    return images[0]

def stable_diffusion_text2img_app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                text2image_model_path = gr.Dropdown(
                    choices=stable_model_list, 
                    value=stable_model_list[0], 
                    label='Text-Image Model Id'
                )

                text2image_prompt = gr.Textbox(
                    lines=1, 
                    value=stable_prompt_list[0], 
                    label='Prompt'
                )

                text2image_negative_prompt = gr.Textbox(
                    lines=1, 
                    value=stable_negative_prompt_list[0], 
                    label='Negative Prompt'
                )

                with gr.Accordion("Advanced Options", open=False):
                    text2image_guidance_scale = gr.Slider(
                        minimum=0.1, 
                        maximum=15, 
                        step=0.1, 
                        value=7.5, 
                        label='Guidance Scale'
                    )

                    text2image_num_inference_step = gr.Slider(
                        minimum=1, 
                        maximum=100, 
                        step=1, 
                        value=50, 
                        label='Num Inference Step'
                    )

                    text2image_height = gr.Slider(
                        minimum=128, 
                        maximum=1280, 
                        step=32, 
                        value=512, 
                        label='Image Height'
                    )

                    text2image_width = gr.Slider(
                        minimum=128, 
                        maximum=1280, 
                        step=32, 
                        value=768, 
                        label='Image Height'
                    )

                text2image_predict = gr.Button(value='Generator')
        
            with gr.Column():
                output_image = gr.Image(label='Output')
                    
        
        text2image_predict.click(
            fn=stable_diffusion_text2img,
            inputs=[
                text2image_model_path,
                text2image_prompt,
                text2image_negative_prompt,
                text2image_guidance_scale,
                text2image_num_inference_step,
                text2image_height,
                text2image_width,
            ],
            outputs=output_image
        )