Initial upload of files

.gitattributes

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-*.npy filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
+demo.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Ignore Python bytecode files
+__pycache__/
+*.py[cod]
+
+# Ignore virtual environments
+venv/
+.venv/
+
+# Ignore API keys and configuration files
+.env
+
+# Ignore model files and other large files
+model/

README.md

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 ---
-title: SAT ELITE
-emoji: 🐢
-colorFrom: green
-colorTo: purple
+title: Real-ESRGAN Dual-Mode Image Upscaler
+emoji: 🖼️
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 5.39.0
+sdk_version: 5.31.0
 app_file: app.py
 pinned: false
+license: mit
 ---
+# 🖼️ SpectraGAN Dual-Mode Image Upscaler
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+A lightweight Gradio web app to upscale any image using the Real-ESRGAN model. Simply upload your photo, choose either **Standard Upscale** (×4) or **Premium Upscale** (×8), and download the upscaled image.
+
+---
+
+## 📑 Table of Contents
+
+1. [Features](#features)  
+2. [Project Structure](#project-structure)  
+3. [Prerequisites](#prerequisites)  
+4. [Installation](#installation)  
+5. [Running Locally](#running-locally)  
+6. [Usage](#usage)  
+7. [Contributing](#contributing)  
+8. [License](#license)  
+9. [Author & Credits](#author--credits)
+
+---
+
+## ✨ Features
+
+- **Standard Upscale (×4)**  
+  Enhance image resolution by 4x for clearer and larger images.
+
+- **Premium Upscale (×8)**  
+  Upscales first to 4x and then resizes using bicubic interpolation for even higher resolution (8x).
+
+- **Live Preview**  
+  See your original and upscaled images side by side before downloading.
+
+- **Instant Download**  
+  Export the upscaled image as a PNG and use it immediately.
+
+---
+
+## 📁 Project Structure
+
+```
+upscale-project/
+├── model/
+│ └── Real-ESRGAN-x4plus.onnx # ONNX model for upscaling
+├── app.py # Main application file
+├── requirements.txt # List of Python dependencies
+├── .gitignore # Git ignore file to exclude unnecessary files
+├── LICENSE # License file for the project
+└── README.md # Project documentation
+```
+
+---
+
+---
+
+## ⚙️ Prerequisites
+
+- Python 3.10 or higher  
+- `git`  
+- A terminal / command prompt  
+
+---
+
+## 🔧 Installation
+
+1. Clone this repository:
+
+   ```bash
+   git clone https://github.com/salmanalfarisi11/Upscaler_images.git
+   cd Upscaler_images
+   ```
+
+2. Create and activate a virtual environment:
+
+   ```bash
+    python -m venv .venv
+    source .venv/bin/activate   # Linux/macOS
+    .venv\Scripts\activate      # Windows
+   ```
+
+3. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## 🚀 Running Locally
+
+Launch the app on your machine:
+
+   ```bash
+   python app.py
+   ```
+
+By default, it will start on <http://127.0.0.1:7860/>. Open that URL in your browser to access the interface.
+
+## 🎯 Usage
+
+1. **Upload Photo** via the left panel.  
+2. **Choose a Mode**:  
+   - Click **Standard Upscale (×4)** for a 4x resolution increase.
+   - Click **Premium Upscale (×8)** for an 8x resolution increase.
+3. Preview your result on the right side.
+4. Click **Download PNG** to save the upscaled image.
+
+## Acknowledgements
+
+This project uses the Real-ESRGAN model developed by Xintao Wang.
+The model is available under the BSD 3-Clause License.

app.py

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+import os
+import math
+import uuid
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+import gradio as gr
+import tempfile
+import requests
+
+
+MODEL_DIR     = "model"
+MODEL_X2_PATH = os.path.join(MODEL_DIR, "Real-ESRGAN_x2plus.onnx")
+MODEL_X4_PATH = os.path.join(MODEL_DIR, "Real-ESRGAN-x4plus.onnx")
+
+
+FILE_ID_X2 = "15xmXXZNH2wMyeQv4ie5hagT7eWK9MgP6"
+FILE_ID_X4 = "1wDBHad9RCJgJDGsPdapLYl3cr8j-PMJ6"
+
+def download_from_drive(file_id: str, dest_path: str):
+  
+    URL = "https://drive.google.com/uc?export=download"
+    session = requests.Session()
+
+    
+    response = session.get(URL, params={"id": file_id}, stream=True)
+    token = None
+    for key, value in response.cookies.items():
+        if key.startswith('download_warning'):
+            token = value
+            break
+
+    
+    if token:
+        params = {"id": file_id, "confirm": token}
+        response = session.get(URL, params=params, stream=True)
+
+    
+    os.makedirs(os.path.dirname(dest_path), exist_ok=True)
+    with open(dest_path, "wb") as f:
+        for chunk in response.iter_content(chunk_size=32768):
+            if chunk:
+                f.write(chunk)
+    print(f"Model telah diunduh dan disimpan di {dest_path}")
+    return dest_path
+
+
+if not os.path.isfile(MODEL_X2_PATH):
+    download_from_drive(FILE_ID_X2, MODEL_X2_PATH)
+
+# Unduh model ×4
+if not os.path.isfile(MODEL_X4_PATH):
+    download_from_drive(FILE_ID_X4, MODEL_X4_PATH)
+
+
+sess_opts = ort.SessionOptions()
+sess_opts.intra_op_num_threads = 2
+sess_opts.inter_op_num_threads = 2
+
+session_x2 = ort.InferenceSession(MODEL_X2_PATH, sess_options=sess_opts, providers=["CPUExecutionProvider"])
+session_x4 = ort.InferenceSession(MODEL_X4_PATH, sess_options=sess_opts, providers=["CPUExecutionProvider"])
+
+
+input_meta_x2 = session_x2.get_inputs()[0]
+_, _, H_in_x2, W_in_x2 = tuple(input_meta_x2.shape)
+H_in_x2, W_in_x2 = int(H_in_x2), int(W_in_x2)
+
+input_meta_x4 = session_x4.get_inputs()[0]
+_, _, H_in_x4, W_in_x4 = tuple(input_meta_x4.shape)
+H_in_x4, W_in_x4 = int(H_in_x4), int(W_in_x4)
+
+
+dummy_x2 = np.zeros((1, 3, H_in_x2, W_in_x2), dtype=np.float32)
+dummy_out_x2 = session_x2.run(None, {input_meta_x2.name: dummy_x2})[0]
+_, _, H_out_x2, W_out_x2 = dummy_out_x2.shape
+SCALE_X2 = H_out_x2 // H_in_x2
+if SCALE_X2 != 2:
+    raise RuntimeError(f"Model ×2 menghasilkan scale = {SCALE_X2}, bukan 2")
+
+dummy_x4 = np.zeros((1, 3, H_in_x4, W_in_x4), dtype=np.float32)
+dummy_out_x4 = session_x4.run(None, {input_meta_x4.name: dummy_x4})[0]
+_, _, H_out_x4, W_out_x4 = dummy_out_x4.shape
+SCALE_X4 = H_out_x4 // H_in_x4
+if SCALE_X4 != 4:
+    raise RuntimeError(f"Model ×4 menghasilkan scale = {SCALE_X4}, bukan 4")
+
+
+def run_tile_x2(tile_np: np.ndarray) -> np.ndarray:
+    patch_nchw = np.transpose(tile_np, (2, 0, 1))[None, ...]
+    out_nchw = session_x2.run(None, {input_meta_x2.name: patch_nchw})[0]
+    out_nchw = np.squeeze(out_nchw, axis=0)
+    out_hwc  = np.transpose(out_nchw, (1, 2, 0))
+    return out_hwc
+
+def run_tile_x4(tile_np: np.ndarray) -> np.ndarray:
+    patch_nchw = np.transpose(tile_np, (2, 0, 1))[None, ...]
+    out_nchw = session_x4.run(None, {input_meta_x4.name: patch_nchw})[0]
+    out_nchw = np.squeeze(out_nchw, axis=0)
+    out_hwc  = np.transpose(out_nchw, (1, 2, 0))
+    return out_hwc
+
+
+def tile_upscale(input_img: Image.Image, scale: int, max_dim=1024):
+    if scale == 2:
+        H_in, W_in, run_tile, SCALE = H_in_x2, W_in_x2, run_tile_x2, SCALE_X2
+    else:
+        H_in, W_in, run_tile, SCALE = H_in_x4, W_in_x4, run_tile_x4, SCALE_X4
+
+    
+    w, h = input_img.size
+    if w > max_dim or h > max_dim:
+        scale_factor = max_dim / float(max(w, h))
+        new_w = int(w * scale_factor)
+        new_h = int(h * scale_factor)
+        input_img = input_img.resize((new_w, new_h), Image.LANCZOS)
+
+    img_rgb = input_img.convert("RGB")
+    arr = np.array(img_rgb).astype(np.float32) / 255.0
+    h_orig, w_orig, _ = arr.shape
+
+    tiles_h = math.ceil(h_orig / H_in)
+    tiles_w = math.ceil(w_orig / W_in)
+    pad_h = tiles_h * H_in - h_orig
+    pad_w = tiles_w * W_in - w_orig
+
+    arr_padded = np.pad(arr, ((0, pad_h), (0, pad_w), (0, 0)), mode="reflect")
+    out_h = tiles_h * H_in * SCALE
+    out_w = tiles_w * W_in * SCALE
+    out_arr = np.zeros((out_h, out_w, 3), dtype=np.float32)
+
+    for i in range(tiles_h):
+        for j in range(tiles_w):
+            y0, x0 = i * H_in, j * W_in
+            tile = arr_padded[y0:y0+H_in, x0:x0+W_in, :]
+            up_tile = run_tile(tile)
+            oy0, ox0 = i * H_in * SCALE, j * W_in * SCALE
+            out_arr[oy0:oy0 + H_in * SCALE, ox0:ox0 + W_in * SCALE, :] = up_tile
+
+    final_arr = out_arr[0:h_orig * SCALE, 0:w_orig * SCALE, :]
+    final_arr = np.clip(final_arr, 0.0, 1.0)
+    final_uint8 = (final_arr * 255.0).round().astype(np.uint8)
+    final_pil = Image.fromarray(final_uint8)
+
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    final_pil.save(tmp.name, format="PNG")
+    tmp.close()
+    return final_pil, tmp.name
+
+
+def upscale_x2(input_img: Image.Image):
+    return tile_upscale(input_img, scale=2)
+
+def standard_upscale(input_img: Image.Image):
+    return tile_upscale(input_img, scale=4)
+
+def premium_upscale(input_img: Image.Image):
+    final_4x, _ = tile_upscale(input_img, scale=4)
+    w_orig, h_orig = input_img.size
+    target_size = (w_orig * 8, h_orig * 8)
+    final_8x = final_4x.resize(target_size, resample=Image.LANCZOS)
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    final_8x.save(tmp.name, format="PNG")
+    tmp.close()
+    return final_8x, tmp.name
+
+
+css = """
+#x2-btn {
+    background-color: lightgreen !important;
+    color: black !important;
+}
+#premium-btn {
+    background-color: gold !important;
+    color: black !important;
+}
+"""
+
+
+with gr.Blocks(css=css, title="SpectraGAN Triple-Mode Upscaler") as demo:
+    gr.Markdown(
+        """
+        # SpectraGAN Upscaler  
+        **Upscale (×2)**, **Standard Upscale (×4)** atau **Premium Upscale 🚀 (×8)**.  
+        
+        """
+    )
+
+    with gr.Row():
+        inp_image = gr.Image(type="pil", label="Upload Source Image")
+
+    with gr.Row():
+        btn_x2   = gr.Button("Upscale (×2)", elem_id="x2-btn")
+        btn_std  = gr.Button("Standard Upscale (×4)", variant="primary", elem_id="std-btn")
+        btn_prem = gr.Button("Premium Upscale 🚀 (×8)", elem_id="premium-btn")
+
+    out_preview  = gr.Image(type="pil", label="Upscaled Preview")
+    out_download = gr.DownloadButton("⬇️ Download PNG", visible=True)
+
+    btn_x2.click(fn=upscale_x2, inputs=inp_image, outputs=[out_preview, out_download])
+    btn_std.click(fn=standard_upscale, inputs=inp_image, outputs=[out_preview, out_download])
+    btn_prem.click(fn=premium_upscale, inputs=inp_image, outputs=[out_preview, out_download])
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+onnxruntime             # ONNX inference engine (CPU)
+numpy                   # Array manipulation
+Pillow                  # Image I/O
+gradio>=3.0             # Web UI
+requests