import gradio as gr
from PIL import Image
from io import BytesIO
import torch
import os
from diffusers import DiffusionPipeline, DDIMScheduler
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
has_cuda = torch.cuda.is_available()
device = torch.device('cpu' if not has_cuda else 'cuda')

pipe = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
        safety_checker=None,
    use_auth_token=MY_SECRET_TOKEN,
    custom_pipeline="imagic_stable_diffusion",
    scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
).to(device)

#generator = torch.Generator("cuda").manual_seed(0)

def infer(prompt, init_image):
    res = pipe.train(
        prompt,
        init_image,
        guidance_scale=7.5,
        num_inference_steps=50)
    
    res = pipe(alpha=1)

    return res.images[0]

title = """
    <div style="text-align: center; max-width: 650px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px;">
            Imagic Stable Diffusion • Community Pipeline
        </h1>
        </div>
        <p style="margin-bottom: 10px; font-size: 94%">
        Text-Based Real Image Editing with Diffusion Models
        </p>
    </div>
"""

article = """

"""

prompt_input = gr.Textbox()
image_init = gr.Image(source="upload", type="filepath")
image_output = gr.Image()

demo = gr.Interface(fn=infer, inputs=[prompt_input, image_init], outputs=image_output, title=title)
demo.launch()