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import gradio as gr
import torch
import streamlit as st
from PIL import Image
import numpy as np
from io import BytesIO
from diffusers import StableDiffusionImg2ImgPipeline
device="cpu"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=st.secrets['USER_TOKEN'])
pipe.to(device)
def resize(w_val,l_val,img):
img = Image.open(img)
img = img.resize((w_val,l_val), Image.Resampling.LANCZOS)
#img = img.resize((value,value), Image.Resampling.LANCZOS)
return img
def infer(source_img, prompt, guide, steps, seed, Strength):
generator = torch.Generator('cpu').manual_seed(seed)
source_image = resize(768, 512, source_img)
source_image.save('source.png')
image_list = pipe([prompt], init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps)
images = []
safe_image = Image.open(r"unsafe.png")
for i, image in enumerate(image_list["sample"]):
if(image_list["nsfw_content_detected"][i]):
images.append(safe_image)
else:
images.append(image)
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').queue(max_size=10).launch(enable_queue=True)
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