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import spaces
import gradio as gr
import time
import torch
import tempfile
import os
import gc
from loading_utils import load_image
from segment_utils import(
segment_image,
restore_result,
)
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"
def image_to_image(
input_image_path: str,
input_image_prompt: str,
edit_prompt: str,
seed: int,
w1: float,
num_steps: int,
start_step: int,
guidance_scale: float,
generate_size: int,
mask_expansion: int = 50,
mask_dilation: int = 2,
save_quality: int = 95,
enable_segment: bool = True,
):
segment_category = "face"
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)
input_image = load_image(input_image_path)
icc_profile = input_image.info.get('icc_profile')
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'load_image done')
if enable_segment:
target_area_image, croper = segment_image(
input_image,
segment_category,
generate_size,
mask_expansion,
mask_dilation,
)
else:
target_area_image = resize_image(input_image, generate_size)
croper = None
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'segment_image done')
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')
if enable_segment:
restored_image = restore_result(croper, segment_category, enhanced_image)
else:
restored_image = enhanced_image.resize(input_image.size)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'restore_result 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')
extension = 'png'
if restored_image.mode == 'RGBA':
extension = 'png'
else:
extension = 'webp'
output_path = tempfile.mktemp(suffix=f".{extension}")
restored_image.save(output_path, format=extension, quality=save_quality, icc_profile=icc_profile)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str, 'save_image done')
return output_path, restored_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 infer(
input_image_path: str,
input_image_prompt: str,
edit_prompt: str,
seed: int,
w1: float,
num_steps: int,
start_step: int,
guidance_scale: float,
generate_size: int,
mask_expansion: int = 50,
mask_dilation: int = 2,
save_quality: int = 95,
enable_segment: bool = True,
):
return image_to_image(
input_image_path,
input_image_prompt,
edit_prompt,
seed,
w1,
num_steps,
start_step,
guidance_scale,
generate_size,
mask_expansion,
mask_dilation,
save_quality,
enable_segment
)
infer = spaces.GPU(infer)
def create_demo() -> gr.Blocks:
with gr.Blocks() as demo:
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.Accordion("Advanced Options", open=False):
enable_segment = gr.Checkbox(label="Enable Segment", value=True)
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")
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():
input_image_path = gr.Image(label="Input Image", type="filepath", interactive=True)
with gr.Column():
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=infer,
inputs=[input_image_path, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, generate_size, mask_expansion, mask_dilation, save_quality, enable_segment],
outputs=[download_path, restored_image, generated_cost],
)
return demo |