app.py

import gradio as gr
import torch

from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
import diffusers
from PIL import Image

import os
from io import BytesIO
import base64
import re

SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')

# Regex pattern to match data URI scheme
data_uri_pattern = re.compile(r'data:image/(png|jpeg|jpg|webp);base64,')

def readb64(b64):
    # Remove any data URI scheme prefix with regex
    b64 = data_uri_pattern.sub("", b64)
    # Decode and open the image with PIL
    img = Image.open(BytesIO(base64.b64decode(b64)))
    return img
    
# convert from PIL to base64
def writeb64(image):
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    b64image = base64.b64encode(buffered.getvalue())
    b64image_str = b64image.decode("utf-8")
    return b64image_str
    
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to(device)

def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

def predict(secret_token, input_image_b64, input_mask_b64, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
    if secret_token != SECRET_TOKEN:
        raise gr.Error(
            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')

    if negative_prompt == "":
        negative_prompt = None
    scheduler_class_name = scheduler.split("-")[0]

    add_kwargs = {}
    if len(scheduler.split("-")) > 1:
        add_kwargs["use_karras"] = True
    if len(scheduler.split("-")) > 2:
        add_kwargs["algorithm_type"] = "sde-dpmsolver++"

    scheduler = getattr(diffusers, scheduler_class_name)
    pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
    
    init_image = readb64(input_image_b64).convert("RGB").resize((1024, 1024))
    mask = readb64(input_mask_b64).convert("RGB").resize((1024, 1024))
    
    output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)

    
    return writeb64(output.images[0])



inpainter = gr.Blocks()
with inpainter as demo:
    gr.HTML("""
        <div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
        <div style="text-align: center; color: black;">
        <p style="color: black;">This space is a REST API to programmatically inpaint an image.</p>
        <p style="color: black;">Interested in using it? Please use the <a href="https://huggingface.co/spaces/diffusers/stable-diffusion-xl-inpainting" target="_blank">original space</a>, thank you!</p>
        </div>
        </div>""")
    
    secret_token = gr.Textbox()
    input_image_b64 = gr.Textbox()
    input_mask_b64 = gr.Textbox()
    prompt = gr.Textbox()
    guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
    steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
    strength = gr.Number(value=0.99, minimum=0.01, maximum=1.0, step=0.01, label="strength")
    negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
    schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
    scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")             
    output_image_b64 = gr.Textbox()
    btn = gr.Button("Inpaint")
    btn.click(fn=predict, inputs=[secret_token, input_image_b64, input_mask_b64, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=output_image_b64)

inpainter.queue(max_size=25).launch()