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import spaces
import gradio as gr
import time
import torch
from PIL import Image
from segment_utils import(
segment_image,
restore_result,
)
from enhance_utils import enhance_image
from inversion_run_adapter import run as adapter_run
DEFAULT_SRC_PROMPT = "a woman"
DEFAULT_EDIT_PROMPT = "a woman, with red lips, 8k, high quality"
DEFAULT_CATEGORY = "face"
device = "cuda" if torch.cuda.is_available() else "cpu"
@spaces.GPU(duration=15)
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,
generate_size: int,
lineart_scale: float,
canny_scale: float,
lineart_detect: float,
canny_detect: float,
enhance_face: bool = True,
):
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)
run_model = adapter_run
generated_image = run_model(
input_image,
input_image_prompt,
edit_prompt,
generate_size,
seed,
w1,
w2,
num_steps,
start_step,
guidance_scale,
lineart_scale,
canny_scale,
lineart_detect,
canny_detect,
)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
enhanced_image = enhance_image(generated_image, enhance_face)
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
return enhanced_image, generated_image, time_cost_str
def get_time_cost(run_task_time, time_cost_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}'
run_task_time = now_time
return run_task_time, time_cost_str
def create_demo() -> gr.Blocks:
with gr.Blocks() as demo:
croper = 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)
category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
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")
with gr.Accordion("Advanced Options", open=False):
guidance_scale = gr.Slider(minimum=0, maximum=20, value=0, step=0.5, label="Guidance Scale", visible=True)
generate_size = gr.Number(label="Generate Size", value=1024)
mask_expansion = gr.Number(label="Mask Expansion", value=10, visible=True)
mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
enhance_face = gr.Checkbox(label="Enhance Face", value=False)
lineart_scale = gr.Slider(minimum=0, maximum=5, value=0.8, step=0.1, label="Lineart Weights", visible=True)
canny_scale = gr.Slider(minimum=0, maximum=5, value=0.4, step=0.1, label="Canny Weights", visible=True)
lineart_detect = gr.Number(label="Lineart Detect", value=0.375, visible=True)
canny_detect = gr.Number(label="Canny Detect", value=0.375, visible=True)
with gr.Column():
seed = gr.Number(label="Seed", value=8)
w1 = gr.Number(label="W1", value=2.5)
g_btn = gr.Button("Edit Image")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="pil")
with gr.Column():
restored_image = gr.Image(label="Restored Image", type="pil", interactive=False)
download_path = gr.File(label="Download the output image", interactive=False)
with gr.Column():
origin_area_image = gr.Image(label="Origin Area Image", type="pil", interactive=False)
enhanced_image = gr.Image(label="Enhanced Image", type="pil", interactive=False)
generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
generated_image = gr.Image(label="Generated Image", type="pil", interactive=False)
g_btn.click(
fn=segment_image,
inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
outputs=[origin_area_image, croper],
).success(
fn=image_to_image,
inputs=[origin_area_image, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, generate_size, lineart_scale, canny_scale, lineart_detect, canny_detect, enhance_face],
outputs=[enhanced_image, generated_image, generated_cost],
).success(
fn=restore_result,
inputs=[croper, category, enhanced_image],
outputs=[restored_image, download_path],
)
return demo |