app.py

import PIL
import requests
import torch
import gradio as gr
import random
import datetime
from PIL import Image
import os
import time
from diffusers import (
    StableDiffusionInstructPix2PixPipeline,
    EulerAncestralDiscreteScheduler,
)

# Loading from Diffusers Library
model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
    model_id, torch_dtype=torch.float16, revision="fp16"
)  # , safety_checker=None)
pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention()
pipe.unet.to(memory_format=torch.channels_last)

counter = 0


help_text = """ Note: Functionality to revert your changes to previous/original image can be released in future versions. For now only forward editing is available.

Some notes from the official [instruct-pix2pix](https://huggingface.co/spaces/timbrooks/instruct-pix2pix) Space by the authors  
and from the official [Diffusers docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix) -

If you're not getting what you want, there may be a few reasons:
1. Is the image not changing enough? Your guidance_scale may be too low. It should be >1. Higher guidance scale encourages to generate images 
that are closely linked to the text `prompt`, usually at the expense of lower image quality. This value dictates how similar the output should 
be to the input. This pipeline requires a value of at least `1`. It's possible your edit requires larger changes from the original image. 
                
2. Alternatively, you can toggle image_guidance_scale. Image guidance scale is to push the generated image towards the inital image. Image guidance 
                scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to generate images that are closely 
                linked to the source image `image`, usually at the expense of lower image quality.  

3. I have observed that rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").

4. Increasing the number of steps sometimes improves results.

5. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
    * Cropping the image so the face takes up a larger portion of the frame.
"""


def chat(
    image_in,
    in_steps,
    in_guidance_scale,
    in_img_guidance_scale,
    image_hid,
    img_name,
    counter_out,
    prompt,
    history,
    progress=gr.Progress(track_tqdm=True),
):
    start = datetime.datetime.now()
    progress(0, desc="Starting...")
    # if message == "revert": --to add revert functionality later
    if counter_out > 0:
        edited_image = pipe(
            prompt,
            image=image_hid,
            num_inference_steps=int(in_steps),
            guidance_scale=float(in_guidance_scale),
            image_guidance_scale=float(in_img_guidance_scale),
        ).images[0]
        if os.path.exists(img_name):
            os.remove(img_name)
        temp_img_name = img_name[:-4] + str(int(time.time())) + ".png"
        # Create a file-like object
        with open(temp_img_name, "wb") as fp:
            # Save the image to the file-like object
            edited_image.save(fp)
        # Get the name of the saved image
        saved_image_name = fp.name
        # edited_image.save(temp_img_name) #, overwrite=True)
        counter_out += 1
    else:
        seed = random.randint(0, 1000000)
        img_name = f"./edited_image_{seed}.png"
        edited_image = pipe(
            prompt,
            image=image_in,
            num_inference_steps=int(in_steps),
            guidance_scale=float(in_guidance_scale),
            image_guidance_scale=float(in_img_guidance_scale),
        ).images[0]
        if os.path.exists(img_name):
            os.remove(img_name)
        with open(img_name, "wb") as fp:
            # Save the image to the file-like object
            edited_image.save(fp)
        # Get the name of the saved image
        saved_image_name2 = fp.name
    print(f"Ran in {datetime.datetime.now() - start}")
    history = history or []
    # Resizing (or not) the image for better display and adding supportive sample text
    add_text_list = [
        "There you go",
        "Enjoy your image!",
        "Nice work! Wonder what you gonna do next!",
        "Way to go!",
        "Does this work for you?",
        "Something like this?",
    ]
    if counter_out > 0:
        response = (
            random.choice(add_text_list) + '<img src="/file=' + saved_image_name + '">'
        )
        history.append((prompt, response))
        return history, history, edited_image, temp_img_name, counter_out
    else:
        response = (
            random.choice(add_text_list) + '<img src="/file=' + saved_image_name2 + '">'
        )  # IMG_NAME
        history.append((prompt, response))
        counter_out += 1
        return history, history, edited_image, img_name, counter_out


with gr.Blocks() as demo:
    gr.Markdown(
        """<h1><center> Chat Interface with InstructPix2Pix: Give Image Editing Instructions</h1></center>
    <p>For faster inference without waiting in the queue, you may duplicate the space and upgrade to GPU in settings.<br/>
    <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true">
    <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
    **Note: Please be advised that a safety checker has been implemented in this public space. 
    Any attempts to generate inappropriate or NSFW images will result in the display of a black screen 
    as a precautionary measure for the protection of all users. We appreciate your cooperation in 
    maintaining a safe and appropriate environment for all members of our community.**
    <p/>"""
    )
    with gr.Row():
        with gr.Column():
            image_in = gr.Image(type="pil", label="Original Image")
            text_in = gr.Textbox()
            state_in = gr.State()
            b1 = gr.Button("Edit the image!")
            with gr.Accordion(
                "Advance settings for Training and Inference", open=False
            ):
                gr.Markdown(
                    "Advance settings for - Number of Inference steps, Guidanace scale, and Image guidance scale."
                )
                in_steps = gr.Number(
                    label="Enter the number of Inference steps", value=20
                )
                in_guidance_scale = gr.Slider(
                    1, 10, step=0.5, label="Set Guidance scale", value=7.5
                )
                in_img_guidance_scale = gr.Slider(
                    1, 10, step=0.5, label="Set Image Guidance scale", value=1.5
                )
                image_hid = gr.Image(type="pil", visible=False)
                img_name_temp_out = gr.Textbox(visible=False)
                counter_out = gr.Number(visible=False, value=0, precision=0)
        chatbot = gr.Chatbot()
    b1.click(
        chat,
        [
            image_in,
            in_steps,
            in_guidance_scale,
            in_img_guidance_scale,
            image_hid,
            img_name_temp_out,
            counter_out,
            text_in,
            state_in,
        ],
        [chatbot, state_in, image_hid, img_name_temp_out, counter_out],
    )  # , queue=True)
    gr.Markdown(help_text)

demo.queue(concurrency_count=10)
demo.launch(debug=True, width="80%", height=2000)