app.py

import streamlit as st
import gradio as gr
import torch
from PIL import Image
import numpy as np
from io import BytesIO
from diffusers import StableDiffusionImg2ImgPipeline

device="cpu"

pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token = "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")
pipe.to(device)

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

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

gr.Interface(fn=infer,
    inputs= [
        gr.Image(source="upload", type="filepath", label="Raw Image"),
        gr.Textbox(label = 'Prompt Input Text'),
        gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
        gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'),
        gr.Slider(
            label = "Seed",
            minimum = 0,
            maximum = 2147483647,
            step = 1,
        randomize = True),
        gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)
    ],
    outputs='image', title = "Stable Diffusion Image to Image Pipeline CPU",
    description = "Upload an Image (must be .PNG and 512x512-2048x2048) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about 300 seconds currently.").queue(max_size=10).launch(enable_queue=True, debug=True)