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import gradio as gr
import torch
#from torch import autocast // only for GPU
from PIL import Image
import numpy as np
from io import BytesIO
import os
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
#from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline
print("hello sylvain")
YOUR_TOKEN=MY_SECRET_TOKEN
device="cpu"
#prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
#prompt_pipe.to(device)
img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=YOUR_TOKEN)
img_pipe.to(device)
source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px")
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
def resize(value,img):
#baseheight = value
img = Image.open(img)
#hpercent = (baseheight/float(img.size[1]))
#wsize = int((float(img.size[0])*float(hpercent)))
#img = img.resize((wsize,baseheight), 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(512, source_img)
source_image.save('source.png')
images_list = img_pipe([prompt] * 1, 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(images_list["images"]):
if(images_list["nsfw_content_detected"][i]):
images.append(safe_image)
else:
images.append(image)
return images
print("Great sylvain ! Everything is working fine !")
title="Img2Img Stable Diffusion CPU"
description="<p style='text-align: center;'>Img2Img Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled. <br /> <img id='visitor-badge' alt='visitor badge' src='https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.stable-diffusion-img2img' style='display: inline-block'/></b></p>"
gr.Interface(fn=infer, inputs=[source_img,
"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 = .75)],
outputs=gallery,title=title,description=description, allow_flagging="manual", flagging_dir="flagged").queue(max_size=100).launch(enable_queue=True) |