app.py

import gradio as gr
import torch
import numpy as np
from PIL import Image
from diffusers import StableDiffusionInstructPix2PixPipeline

model_id = "timbrooks/instruct-pix2pix"
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", safety_checker=None) if torch.cuda.is_available() else StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, safety_checker=None)
pipe = pipe.to(device)

def resize(value,img):
    img = Image.open(img)
    img = img.resize((value,value))
    return img

def infer(source_img, instructions, guide, steps, seed, Strength):
    generator = torch.Generator(device).manual_seed(seed)     
    source_image = resize(512, source_img)
    source_image.save('source.png')
    image = pipe(instructions, image=source_image,
            guidance_scale=guide, image_guidance_scale=Strength,
            num_inference_steps=steps, generator=generator,).images[0]
    return image

gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"), 
    gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
    gr.Slider(2, 15, value = 7.5, label = 'Guidance Scale'),
    gr.Slider(1, 20, value = 5, step = 1, label = 'Number of Iterations'),
    gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), 
    gr.Slider(label='Strength', minimum = 1, maximum = 2, step = .25, value = 1.5)], 
    outputs = 'image', 
    title = "Instructions Picture to Picture",
    description = "Simply upload an image you want to edit, MUST Be .PNG and 512x512 or 768x768, then enter a Prompt telling the AI how to change the image, then click submit. This version runs on GPU or CPU and is currently running on the free CPU tier. 10 Iterations takes ~660 seconds currently. This version has no NSFW filter.", 
    article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch(debug=True)