app.py

# -*- coding: utf-8 -*-
"""in_painting_app

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1E_8Pn-aZGSq6Sf1DzYqSkcahkZu9AyDq

# In-painting pipeline for Stable Diffusion using 🧨 Diffusers 

This notebook shows how to do text-guided in-painting with Stable Diffusion model using  🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). 

For a general introduction to the Stable Diffusion model please refer to this [colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb).
"""

!pip install -qq -U diffusers==0.6.0 transformers ftfy gradio
!pip install git+https://github.com/huggingface/diffusers.git
!pip install diffusers[torch]

"""First , in order to use the model, you need to accept the model license before downloading or using the weights. In this post we'll use `runwayml/stable-diffusion-inpainting` model released by Runwayml so you'll need to  visit [its card](https://huggingface.co/runwayml/stable-diffusion-inpainting), read the license and tick the checkbox if you agree. 

You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
"""

# from huggingface_hub import notebook_login

# notebook_login()

import inspect
from typing import List, Optional, Union

import numpy as np
import torch

import PIL
import gradio as gr
from diffusers import StableDiffusionInpaintPipeline

device = "cuda"
model_path = "runwayml/stable-diffusion-inpainting"

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    model_path,
    revision="fp16", 
    torch_dtype=torch.float16,
    use_auth_token=True,
    safety_checker=None
).to(device)

import requests
from io import BytesIO

def image_grid(imgs, rows, cols):
    assert len(imgs) == rows*cols

    w, h = imgs[0].size
    grid = PIL.Image.new('RGB', size=(cols*w, rows*h))
    grid_w, grid_h = grid.size
    
    for i, img in enumerate(imgs):
        grid.paste(img, box=(i%cols*w, i//cols*h))
    return grid


def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")

img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

"""### Gradio Demo"""

def predict(dict, prompt):
  image =  dict['image'].convert("RGB").resize((512, 512))
  mask_image = dict['mask'].convert("RGB").resize((512, 512))
  images = pipe(prompt=prompt, image=image, mask_image=mask_image).images
  return(images[0])

gr.Interface(
    predict,
    title = 'Stable Diffusion In-Painting',
    inputs=[
        gr.Image(source = 'upload', tool = 'sketch', type = 'pil'),
        gr.Textbox(label = 'prompt')
    ],
    outputs = [
        gr.Image()
        ]
).launch(debug=True)