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"""
Image Upscaler App
------------------
Aplikasi AI berbasis Gradio yang memanfaatkan Stable Diffusion Upscaler untuk meningkatkan resolusi gambar.
Tersedia juga fitur segmentasi & restorasi area tertentu pada gambar (misal: wajah).
Aplikasi mendukung input prompt teks untuk conditioning hasil upscaling.
Created by _drat | 2025
"""
# Import library eksternal yang diperlukan
import requests
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionUpscalePipeline # Pipeline Stable Diffusion untuk upscaling gambar
import torch
import gradio as gr
import time
import spaces
# Import fungsi segmentasi dan restorasi (definisi di segment_utils.py)
from segment_utils import(
segment_image, # Untuk segmentasi area penting pada gambar (misal: wajah)
restore_result, # Untuk menggabungkan hasil upscaling dengan gambar asli
)
# Setup device: gunakan CUDA (GPU) jika tersedia, jika tidak fallback ke CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f'{device} is available') # Debug: print device yang digunakan
# Load model Stable Diffusion Upscaler dari HuggingFace
model_id = "stabilityai/stable-diffusion-x4-upscaler"
upscale_pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
upscale_pipe = upscale_pipe.to(device)
# Default prompt dan kategori (untuk input Gradio)
DEFAULT_SRC_PROMPT = "a person with pefect face"
DEFAULT_CATEGORY = "face"
# Fungsi utama untuk membuat UI aplikasi Gradio
def create_demo() -> gr.Blocks:
# --- [ Function Definitions Tetap Seperti Asli Anda ] ---
@spaces.GPU(duration=30)
def upscale_image(
input_image: Image,
prompt: str,
num_inference_steps: int = 10,
):
time_cost_str = ''
run_task_time = 0
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
upscaled_image = upscale_pipe(
prompt=prompt,
image=input_image,
num_inference_steps=num_inference_steps,
).images[0]
run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
return upscaled_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
# --- [ UI Section ] ---
with gr.Blocks(css="creative_enhance.css") as demo:
gr.HTML("""
<div style='display:flex; justify-content:center;'>
<img src='https://i.ibb.co/pvWSfsMJ/feature-image-upscaler.jpg' alt='Feature Image' style='height:200px; width:auto; border-radius:24px; box-shadow:0 4px 18px #ffa07430; border:2.5px solid #ffe0b3;'/>
</div>
<div style='text-align:center;margin-top:6px;'>
<span style='font-size:2.0rem;font-weight:800;color:#d84040;letter-spacing:0.04em;font-family:Quicksand,sans-serif;'>
🔬 <b>AI Image Upscaler</b> <span style='font-size:1.2em;vertical-align:middle;'>🚀</span>
</span>
<br>
<span style='font-size:1.1rem;color:#ff914d;font-family:Inter,sans-serif;'>Perbesar gambar <b>HD</b> otomatis, detail makin nyata!</span>
</div>
""")
# Input prompt & parameter di satu card
with gr.Row():
with gr.Group(elem_id="control-card"):
with gr.Row():
input_image_prompt = gr.Textbox(
lines=1, label="🎯 Prompt AI (opsional)",
value=DEFAULT_SRC_PROMPT, elem_id="input-image-prompt"
)
num_inference_steps = gr.Number(
label="⚙️ Steps (Quality)", value=5, elem_id="num-inference"
)
generate_size = gr.Number(
label="📐 Size (px)", value=512, elem_id="generate-size"
)
g_btn = gr.Button("🪄 Upscale Sekarang", elem_id="upscale-btn")
# Input & Output Gambar
with gr.Row():
with gr.Column(scale=1):
with gr.Group(elem_id="input-card"):
gr.Markdown("<div class='card-title'><span style='font-size:1.2em;'>🖼️</span> Gambar Asli</div>")
input_image = gr.Image(label="", type="pil", elem_id="input-image", show_label=False)
gr.Markdown("<div class='hint'><span style='font-size:1.1em;'>⬆️</span> JPG/PNG max 5MB</div>")
with gr.Column(scale=1):
with gr.Group(elem_id="output-card"):
gr.Markdown("<div class='card-title'><span style='font-size:1.2em;'>💡</span> Upscale Preview</div>")
restored_image = gr.Image(label="Hasil Akhir", format="png", type="pil", interactive=False)
origin_area_image = gr.Image(label="", format="png", type="pil", interactive=False, visible=False)
upscaled_image = gr.Image(label="Upscaled", format="png", type="pil", interactive=False)
download_path = gr.File(label="⬇️ Download Image", interactive=False, elem_id="download-btn")
gr.Markdown("<div class='hint'><span style='font-size:1.1em;'>💾</span> Download hasil upscale PNG</div>")
generated_cost = gr.Textbox(label="⏱️ Time (ms)", visible=True, interactive=False)
# Hidden inputs untuk workflow
category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
mask_expansion = gr.Number(label="Mask Expansion", value=20, visible=False)
mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation", visible=False)
croper = gr.State()
# Workflow chaining
g_btn.click(
fn=segment_image,
inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
outputs=[origin_area_image, croper],
).success(
fn=upscale_image,
inputs=[origin_area_image, input_image_prompt, num_inference_steps],
outputs=[upscaled_image, generated_cost],
).success(
fn=restore_result,
inputs=[croper, category, upscaled_image],
outputs=[restored_image, download_path],
)
gr.Markdown("""
<div style='text-align:center;color:#aaa;font-size:0.98rem;margin-top:14px;'>
© 2025 <b>AI Image Upscaler</b> • Powered by <b>_drat</b> 🚀
</div>
""")
return demo
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