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import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "2"
import tensorflow as tf
import gradio as gr
import tensorflow_hub as hub
import random
import time
import PIL.Image
from PIL import Image
import numpy as np
import requests
from io import BytesIO
# from diffusers import StableDiffusionUpscalePipeline
from simple_lama_inpainting import SimpleLama
import torch
from shutil import copyfile
from PowerPaint import app
import argparse
def pil_to_binary_mask(pil_image, threshold=0):
np_image = np.array(pil_image)
grayscale_image = Image.fromarray(np_image).convert("L")
binary_mask = np.array(grayscale_image) > threshold
mask = np.zeros(binary_mask.shape, dtype=np.uint8)
for i in range(binary_mask.shape[0]):
for j in range(binary_mask.shape[1]):
if binary_mask[i,j] == True :
mask[i,j] = 1
mask = (mask*255).astype(np.uint8)
output_mask = Image.fromarray(mask)
return output_mask
def tensor_to_image(tensor):
tensor = tensor*255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor)>3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
# Do main logic (simple version)
def start_stylize_simple(img, style_img):
# global hub_model
hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
# Save to disk, put random number as a ID to avoid collision
ID = int(time.time())
img.save(filepath + f'/tmp/tmp_image-{ID}.jpg')
style_img.save(filepath + f'/tmp/tmp_style_image-{ID}.jpg')
# Load the input images.
content_image = load_img(filepath + f'/tmp/tmp_image-{ID}.jpg')
style_image = load_img(filepath + f'/tmp/tmp_style_image-{ID}.jpg')
stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
tensor_to_image(stylized_image).save(filepath + f'/tmp/result-{ID}.jpg')
return filepath + f'/tmp/result-{ID}.jpg'
def background_remove(img):
from rembg import new_session
from rembg import remove
session = new_session('isnet-general-use')
# Save to disk, put random number as a ID to avoid collision
ID = int(time.time())
img.save(filepath + f'/tmp/tmp_image-{ID}.jpg')
with open(filepath + f'/tmp/tmp_image-{ID}.jpg', 'rb') as i:
with open(filepath + f'/tmp/tmp_result-{ID}.jpg', 'wb') as o:
input = i.read()
output = remove(input, session = session)
o.write(output)
return filepath + f'/tmp/tmp_result-{ID}.jpg'
def object_remove(imgs):
ts = int(time.time())
os.mkdir(filepath + f'/tmp/tmp_image-{ts}')
os.mkdir(filepath + f'/tmp/tmp_mask-{ts}')
os.mkdir(filepath + f'/tmp/tmp_output-{ts}')
img = imgs["background"].convert("RGB")
mask = pil_to_binary_mask(imgs['layers'][-1].convert("RGB"))
img.save(filepath + f'/tmp/tmp_image-{ts}/image.png')
mask.save(filepath + f'/tmp/tmp_mask-{ts}/image.png')
simple_lama = SimpleLama()
img_path = filepath + f'/tmp/tmp_image-{ts}/image.png'
mask_path = filepath + f'/tmp/tmp_mask-{ts}/image.png'
image = Image.open(img_path)
mask = Image.open(mask_path).convert('L')
result = simple_lama(image, mask)
result.save(f"{filepath}/tmp/tmp_output-{ts}/image.png")
# os.system(f'simple_lama {filepath}/tmp/tmp_image-{ts}/image.png {filepath}/tmp/tmp_mask-{ts}/image.png {filepath}/tmp/tmp_output-{ts}/image.png')
# os.system(f'iopaint run --model=lama --device=cuda --image={filepath}/tmp/tmp_image-{ts} --mask={filepath}/tmp/tmp_mask-{ts} --output={filepath}/tmp/tmp_output-{ts}')
# filename = os.listdir(filepath + f'/tmp/tmp_output-{ts}')[0]
return filepath + f'/tmp/tmp_output-{ts}/image.png'
def upscale(img): #, prompt, upscale_radio):
# Save to disk, put random number as a ID to avoid collision
ID = int(time.time())
img.save(filepath + f'/tmp/tmp_image-{ID}.jpg')
if False: #upscale_radio == 'Stable Diffusion x4 upscaler':
# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
# let's download an image
#url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
#response = requests.get(url)
low_res_img = Image.open(filepath + f'/tmp/tmp_image-{ID}.jpg')
width, height = low_res_img.size
low_res_img = low_res_img.resize((128, 128))
# prompt = "a white cat"
upscaled_image = pipeline(prompt = prompt, image=low_res_img).images[0]
upscaled_image.resize((width, height)).save(filepath + f'/tmp/tmp_result-{ID}.jpg')
# Image.open(filepath + f'/tmp/tmp_result-{ID}.jpg').resize((width, height))
else:
os.system(f'python3 {filepath}/Real-ESRGAN/inference_realesrgan.py -n RealESRGAN_x4plus -i {filepath}/tmp/tmp_image-{ID}.jpg')
copyfile(f'{filepath}/results/tmp_image-{ID}_out.jpg', f'{filepath}/tmp/tmp_result-{ID}.jpg')
return filepath + f'/tmp/tmp_result-{ID}.jpg'
def in_painting(*args):
ID = int(time.time())
global flag
global controller
if flag == 0:
try:
controller = app.PowerPaintController(weight_dtype, "./checkpoints/ppt-v1", True, "ppt-v1")
flag += 1
except:
controller = app.PowerPaintController(weight_dtype, "./checkpoints/ppt-v1", False, "ppt-v1")
result = controller.infer(*args)[0][0]
result.save(f'{filepath}/tmp/tmp_result-{ID}.jpg')
return f'{filepath}/tmp/tmp_result-{ID}.jpg'
def radio_click(choice):
if choice == "Art style transfer":
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)]
elif choice == "Object erasing":
return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)]
elif choice == "In painting":
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
elif choice == "Background removal":
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
elif choice == "Image upscaling":
return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
else:
return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)]
if __name__ == '__main__':
args = argparse.ArgumentParser()
args.add_argument("--weight_dtype", type=str, default="float16")
args.add_argument("--checkpoint_dir", type=str, default="./checkpoints/ppt-v1")
args.add_argument("--version", type=str, default="ppt-v1")
args.add_argument("--share", action="store_true")
args.add_argument(
"--local_files_only", action="store_true", help="enable it to use cached files without requesting from the hub"
)
args.add_argument("--port", type=int, default=7860)
args = args.parse_args()
# initialize the pipeline controller
weight_dtype = torch.float16 if args.weight_dtype == "float16" else torch.float32
flag = 0
filepath = os.path.dirname(os.path.abspath(__file__))
physical_devices = tf.config.experimental.list_physical_devices('GPU')
for i in physical_devices:
tf.config.experimental.set_memory_growth(i, True)
os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
# hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
os.environ['GRADIO_TEMP_DIR']="/home/gradio_demos/tmp"
with gr.Blocks() as demo:
gr.Markdown("# Image2Image Demos")
#radio = gr.Radio(["Art style transfer", "Object erasing", "In painting", "Background removal", "Image upscaling"], value="Art style transfer", label = "Choose functionality")
radio = gr.Radio(["Art style transfer", "Object erasing", "In painting", "Background removal", "Image upscaling"], value="Art style transfer", label = "Choose functionality")
with gr.Column(visible = True) as art_style_transfer_block:
gr.Markdown("## Art style transfer")
gr.Markdown("### Using [arbitrary-image-stylization-v1](https://www.kaggle.com/models/google/arbitrary-image-stylization-v1/tensorFlow1/256/2) model")
with gr.Row():
with gr.Column():
img = gr.Image(sources='upload', type="pil", label='Image to apply art style')
img_list = os.listdir(filepath + "/images")
img_list_path = [os.path.join(filepath + "/images", image) for image in img_list]
example = gr.Examples(
inputs=img,
examples_per_page=6,
examples=img_list_path
)
with gr.Column():
style_img = gr.Image(label="Art syle image", sources='upload', type="pil")
style_list = os.listdir(filepath + "/style_images")
style_list_path = [os.path.join(filepath + "/style_images", style_image) for style_image in style_list]
example = gr.Examples(
inputs=style_img,
examples_per_page=6,
examples=style_list_path
)
with gr.Column():
# image_out = gr.Image(label="Output", elem_id="output-img", height=400)
image_out = gr.Image(label="Stylized image", elem_id="output-img" ,show_share_button=False, type = 'filepath')
stylize_button = gr.Button(value="Stylize")
with gr.Column(visible = False) as object_erasing_block:
gr.Markdown("## Object erasing")
gr.Markdown("### Using [lama](https://github.com/enesmsahin/simple-lama-inpainting) model")
with gr.Row():
with gr.Column():
imgs4 = gr.ImageEditor(sources='upload', type="pil", label='Image to erase object', interactive=True)
img_list = os.listdir(filepath + "/images4")
img_list_path = [os.path.join(filepath + "/images4", image) for image in img_list]
example = gr.Examples(
inputs=imgs4,
examples_per_page=6,
examples=img_list_path
)
with gr.Column():
image_out4 = gr.Image(label="Object removed image" ,show_share_button=False, type = 'filepath')
object_remove_button = gr.Button(value="Remove object")
with gr.Column(visible = False) as in_painting_block:
gr.Markdown("## In painting")
gr.Markdown("### Using [Powerpaint](https://github.com/open-mmlab/PowerPaint) model")
with gr.Row():
with gr.Column():
#gr.Markdown("### Input image and draw mask")
input_image = gr.ImageEditor(sources="upload", type="pil", label='Image to in-paint', interactive=True)
img_list = os.listdir(filepath + "/images4")
img_list_path = [os.path.join(filepath + "/images4", image) for image in img_list]
example = gr.Examples(
inputs=input_image,
examples_per_page=6,
examples=img_list_path
)
task = gr.Radio(
["text-guided", "object-removal", "shape-guided", "image-outpainting"],
show_label=False,
visible=False,
)
# Text-guided object inpainting
with gr.Tab("Text-guided object inpainting") as tab_text_guided:
enable_text_guided = gr.Checkbox(
label="Enable text-guided object inpainting", value=True, interactive=False, visible = False
)
text_guided_prompt = gr.Textbox(label="Prompt")
text_guided_negative_prompt = gr.Textbox(label="negative_prompt")
tab_text_guided.select(fn=app.select_tab_text_guided, inputs=None, outputs=task)
# currently, we only support controlnet in PowerPaint-v1
if args.version == "ppt-v1":
# gr.Markdown("### Controlnet setting")
enable_control = gr.Checkbox(
label="Enable controlnet", info="Enable this if you want to use controlnet", visible = False
)
controlnet_conditioning_scale = gr.Slider(
label="controlnet conditioning scale",
minimum=0,
maximum=1,
step=0.05,
value=0.5,
visible = False
)
control_type = gr.Radio(["canny", "pose", "depth", "hed"], label="Control type", visible = False)
input_control_image = gr.ImageEditor(sources="upload", type="pil", visible = False)
# Object removal inpainting
with gr.Tab("Object removal inpainting", visible = False) as tab_object_removal:
enable_object_removal = gr.Checkbox(
label="Enable object removal inpainting",
value=True,
info="The recommended configuration for the Guidance Scale is 10 or higher. \
If undesired objects appear in the masked area, \
you can address this by specifically increasing the Guidance Scale.",
interactive=False,
)
removal_prompt = gr.Textbox(label="Prompt")
removal_negative_prompt = gr.Textbox(label="negative_prompt")
tab_object_removal.select(fn=app.select_tab_object_removal, inputs=None, outputs=task)
# Object image outpainting
with gr.Tab("Image outpainting", visible = False) as tab_image_outpainting:
enable_object_removal = gr.Checkbox(
label="Enable image outpainting",
value=True,
info="The recommended configuration for the Guidance Scale is 10 or higher. \
If unwanted random objects appear in the extended image region, \
you can enhance the cleanliness of the extension area by increasing the Guidance Scale.",
interactive=False,
)
outpaint_prompt = gr.Textbox(label="Outpainting_prompt")
outpaint_negative_prompt = gr.Textbox(label="Outpainting_negative_prompt")
horizontal_expansion_ratio = gr.Slider(
label="horizontal expansion ratio",
minimum=1,
maximum=4,
step=0.05,
value=1,
)
vertical_expansion_ratio = gr.Slider(
label="vertical expansion ratio",
minimum=1,
maximum=4,
step=0.05,
value=1,
)
tab_image_outpainting.select(fn=app.select_tab_image_outpainting, inputs=None, outputs=task)
# Shape-guided object inpainting
with gr.Tab("Shape-guided object inpainting", visible = False) as tab_shape_guided:
enable_shape_guided = gr.Checkbox(
label="Enable shape-guided object inpainting", value=True, interactive=False
)
shape_guided_prompt = gr.Textbox(label="shape_guided_prompt")
shape_guided_negative_prompt = gr.Textbox(label="shape_guided_negative_prompt")
fitting_degree = gr.Slider(
label="fitting degree",
minimum=0,
maximum=1,
step=0.05,
value=1,
)
tab_shape_guided.select(fn=app.select_tab_shape_guided, inputs=None, outputs=task)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=2147483647,
step=1,
randomize=True,
)
with gr.Accordion("Advanced options", open=False, visible = False):
ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
scale = gr.Slider(
label="Guidance Scale",
info="For object removal and image outpainting, it is recommended to set the value at 10 or above.",
minimum=0.1,
maximum=30.0,
value=7.5,
step=0.1,
)
with gr.Column():
# gr.Markdown("### Inpainting result")
# inpaint_result = gr.Gallery(label="Generated image", show_label=True, columns=1)
inpaint_result = gr.Image(label="Generated image", elem_id="output-img" ,show_share_button=False, type = 'filepath')
#gr.Markdown("### Mask")
gallery = gr.Gallery(label="Generated masks", show_label=False, columns=2, visible = False)
run_button = gr.Button(value="In-paint")
run_button.click(
fn=in_painting, #controller.infer,
inputs=[
input_image,
text_guided_prompt,
text_guided_negative_prompt,
shape_guided_prompt,
shape_guided_negative_prompt,
fitting_degree,
ddim_steps,
scale,
seed,
task,
vertical_expansion_ratio,
horizontal_expansion_ratio,
outpaint_prompt,
outpaint_negative_prompt,
removal_prompt,
removal_negative_prompt,
enable_control,
input_control_image,
control_type,
controlnet_conditioning_scale,
],
outputs=[inpaint_result]#, gallery],
)
with gr.Column(visible = False) as background_removal_block:
gr.Markdown("## Background removal")
gr.Markdown("### Using [rembg](https://pypi.org/project/rembg/) model")
with gr.Row():
with gr.Column():
img2 = gr.Image(sources='upload', type="pil", label='Image to remove background')
img_list = os.listdir(filepath + "/images2")
img_list_path = [os.path.join(filepath + "/images2", image) for image in img_list]
example = gr.Examples(
inputs=img2,
examples_per_page=6,
examples=img_list_path
)
with gr.Column():
# image_out = gr.Image(label="Output", elem_id="output-img", height=400)
image_out2 = gr.Image(label="Background removed image", elem_id="output-img" ,show_share_button=False, type = 'filepath')
background_remove_button = gr.Button(value="Remove background")
with gr.Column(visible = False) as image_upscaling_block:
gr.Markdown("## Image upscaling")
# gr.Markdown("### Using [Stable Diffusion x4 upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) or [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) model")
gr.Markdown("### Using [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) model")
with gr.Row():
with gr.Column():
img3 = gr.Image(sources='upload', type="pil", label='Image to upscale')
img_list = os.listdir(filepath + "/images3")
img_list_path = [os.path.join(filepath + "/images3", image) for image in img_list]
example = gr.Examples(
inputs=img3,
examples_per_page=6,
examples=img_list_path
)
# prompt = gr.Textbox(label="Prompt")
# upscale_radio = gr.Radio(["Stable Diffusion x4 upscaler", "Real-ESRGAN"], value="Stable Diffusion x4 upscaler", label = "Choose a model")
with gr.Column():
# image_out = gr.Image(label="Output", elem_id="output-img", height=400)
image_out3 = gr.Image(label="Upscaled image", elem_id="output-img" ,show_share_button=False, type = 'filepath')
upscale_button = gr.Button(value="Upscale")
stylize_button.click(fn=start_stylize_simple, inputs=[img, style_img], outputs=[image_out], api_name='stylize')
background_remove_button.click(fn=background_remove, inputs=[img2], outputs=[image_out2], api_name='background_removal')
object_remove_button.click(fn=object_remove, inputs=[imgs4], outputs=[image_out4], api_name='object_removal')
upscale_button.click(fn=upscale, inputs=[img3], outputs=[image_out3], api_name='upscale')
radio.change(radio_click, radio, [art_style_transfer_block, object_erasing_block, in_painting_block, background_removal_block, image_upscaling_block])
demo.launch(share=False, server_name="0.0.0.0", ssl_verify=False)
# demo.launch(share=True) |