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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
def empty_checker(images, **kwargs): return images, False
print("hello")
YOUR_TOKEN=MY_SECRET_TOKEN
device="cpu"
# img2img pipeline
img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("AkiKagura/mkgen-diffusion", duse_auth_token=YOUR_TOKEN)
img_pipe.safety_checker = empty_checker
img_pipe.to(device)
source_img = gr.Image(source="upload", type="filepath", label="init_img")
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
def resize(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)
hsize = img.size[1]
wsize = img.size[0]
if 6*wsize <= 5*hsize:
wsize = 512
hsize = 768
elif 4*wsize >= 5*hsize:
wsize = 768
hsize = 512
else:
wsize = 512
hsize = 512
img = img.resize((wsize,hsize), Image.Resampling.LANCZOS)
return img, wsize, hsize
def infer(source_img, prompt, guide, steps, seed, strength):
generator = torch.Generator('cpu').manual_seed(seed)
source_image, img_w, img_h = resize(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, width=img_w, height=img_h)
images = []
for i, image in enumerate(images_list["images"]):
images.append(image)
return images
print("done")
title="Marco Generation Img2img"
description="<p style='text-align: center;'>Upload your image and input 'mkmk woman' to get Marco image. <br />Warning: Slow process... about 10 min inference time.</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) |