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import spaces
import gradio as gr
import time
import torch
import os
import gc
from PIL import Image, ImageEnhance, ImageFilter
from segment_utils import(
segment_image,
restore_result_and_save,
)
from enhance_utils import enhance_sd_image
from inversion_run_base import run as base_run
DEFAULT_SRC_PROMPT = "a person"
DEFAULT_EDIT_PROMPT = "a person with perfect face"
DEFAULT_CATEGORY = "face"
filter_names = [
"NONE",
"DETAIL",
"SMOOTH",
"SMOOTH_MORE",
"SHARPEN",
"EDGE_ENHANCE",
"EDGE_ENHANCE_MORE",
]
@spaces.GPU(duration=10)
@torch.inference_mode()
@torch.no_grad()
def image_to_image(
input_image: Image,
input_image_prompt: str,
edit_prompt: str,
seed: int,
w1: float,
num_steps: int,
start_step: int,
guidance_scale: float,
brightness: float = 1.0,
color: float = 1.0,
contrast: float = 1.0,
sharpness: float = 1.0,
filter: str = "NONE",
):
w2 = 1.0
run_task_time = 0
time_cost_str = ''
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
target_area_image = input_image
run_model = base_run
try:
res_image = run_model(
target_area_image,
input_image_prompt,
edit_prompt ,
seed,
w1,
w2,
num_steps,
start_step,
guidance_scale,
)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'run_sd_model done')
finally:
torch.cuda.empty_cache()
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'cuda_empty_cache done')
enhanced_image = res_image
enhanced_image = enhance_sd_image(res_image)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'enhance_image done')
torch.cuda.empty_cache()
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'cuda_empty_cache done')
if os.getenv('ENABLE_GC', False):
gc.collect()
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'gc_collect done')
enhancer = ImageEnhance.Brightness(enhanced_image)
enhanced_image = enhancer.enhance(brightness)
enhancer = ImageEnhance.Color(enhanced_image)
enhanced_image = enhancer.enhance(color)
enhancer = ImageEnhance.Contrast(enhanced_image)
enhanced_image = enhancer.enhance(contrast)
enhancer = ImageEnhance.Sharpness(enhanced_image)
enhanced_image = enhancer.enhance(sharpness)
if filter == "NONE":
pass
elif filter == "DETAIL":
enhanced_image = enhanced_image.filter(ImageFilter.DETAIL)
elif filter == "SMOOTH":
enhanced_image = enhanced_image.filter(ImageFilter.SMOOTH)
elif filter == "SMOOTH_MORE":
enhanced_image = enhanced_image.filter(ImageFilter.SMOOTH_MORE)
elif filter == "SHARPEN":
enhanced_image = enhanced_image.filter(ImageFilter.SHARPEN)
elif filter == "EDGE_ENHANCE":
enhanced_image = enhanced_image.filter(ImageFilter.EDGE_ENHANCE)
elif filter == "EDGE_ENHANCE_MORE":
enhanced_image = enhanced_image.filter(ImageFilter.EDGE_ENHANCE_MORE)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'image_enhance done')
return enhanced_image, time_cost_str
def get_time_cost(
run_task_time,
time_cost_str,
step: str = ''
):
now_time = int(time.time()*1000)
if run_task_time == 0:
time_cost_str = 'start'
else:
if time_cost_str != '':
time_cost_str += f'-->'
time_cost_str += f'{now_time - run_task_time}'
if step != '':
time_cost_str += f'-->{step}'
run_task_time = now_time
return run_task_time, time_cost_str
def resize_image(image, target_size = 1024):
h, w = image.size
if h >= w:
w = int(w * target_size / h)
h = target_size
else:
h = int(h * target_size / w)
w = target_size
return image.resize((w, h))
def create_demo() -> gr.Blocks:
with gr.Blocks() as demo:
cropper = gr.State()
with gr.Row():
with gr.Column():
input_image_prompt = gr.Textbox(lines=1, label="Input Image Prompt", value=DEFAULT_SRC_PROMPT)
edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
with gr.Row():
brightness = gr.Number(label="Brightness", value=1.0, minimum=0.0, maximum=2.0, step=0.01)
color = gr.Number(label="Color", value=1.0, minimum=0.0, maximum=2.0, step=0.01)
contrast = gr.Number(label="Contrast", value=1.0, minimum=0.0, maximum=2.0, step=0.01)
sharpness = gr.Number(label="Sharpness", value=1.0, minimum=0.0, maximum=2.0, step=0.01)
with gr.Accordion("Advanced Options", open=False):
category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
mask_expansion = gr.Number(label="Mask Expansion", value=50, visible=True)
mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
save_quality = gr.Slider(minimum=1, maximum=100, value=95, step=1, label="Save Quality")
with gr.Column():
num_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Num Steps")
start_step = gr.Slider(minimum=1, maximum=100, value=15, step=1, label="Start Step")
filter = gr.Dropdown(choices=filter_names, label="Filter", value="NONE")
g_btn = gr.Button("Edit Image")
with gr.Accordion("Advanced Options", open=False):
guidance_scale = gr.Slider(minimum=0, maximum=20, value=0, step=0.5, label="Guidance Scale")
seed = gr.Number(label="Seed", value=8)
w1 = gr.Number(label="W1", value=1.5)
generate_size = gr.Number(label="Generate Size", value=1024)
with gr.Row():
with gr.Column():
origin_area_image = gr.Image(label="Origin Area Image", format="png", type="pil", interactive=False)
input_image = gr.Image(label="Input Image", type="pil", interactive=True)
with gr.Column():
enhanced_image = gr.Image(label="Enhanced Image", format="png", type="pil", interactive=False)
restored_image = gr.Image(label="Restored Image", format="png", type="pil", interactive=False)
download_path = gr.File(label="Download the output image", interactive=False)
generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
g_btn.click(
fn=segment_image,
inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
outputs=[origin_area_image, cropper],
).success(
fn=image_to_image,
inputs=[origin_area_image, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, brightness, color, contrast, sharpness, filter],
outputs=[enhanced_image, generated_cost],
).success(
fn=restore_result_and_save,
inputs=[cropper, category, enhanced_image, save_quality],
outputs=[restored_image, download_path],
)
return demo |