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| import os, cv2, numpy as np, onnxruntime as ort, gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| # --- MODEL DIRECTORY --- | |
| # Friendly labels for "noobs" mapped to technical repos | |
| MODEL_OPTIONS = { | |
| "💎 Pro Detail (APISR RRDB)": { | |
| "repo": "Xenova/2x_APISR_RRDB_GAN_generator-onnx", | |
| "desc": "Best for AI art and textures. High-quality reconstruction.", | |
| "scale": 2 | |
| }, | |
| "⚡ Fast & Sharp (APISR GRL)": { | |
| "repo": "Xenova/4x_APISR_GRL_GAN_generator-onnx", | |
| "desc": "Best for architecture and sharp lines. Very clean results.", | |
| "scale": 4 | |
| }, | |
| "📸 Realistic Photos (Swin2SR)": { | |
| "repo": "Xenova/swin2SR-realworld-sr-x4-64-bsrgan-psnr", | |
| "desc": "Best for real-life photography. Keeps skin and nature natural.", | |
| "scale": 4 | |
| }, | |
| "🧹 Noise Cleanup (Swin2SR Compressed)": { | |
| "repo": "Xenova/swin2SR-compressed-sr-x4-48", | |
| "desc": "Best for blurry, low-quality internet images with 'blocks'.", | |
| "scale": 4 | |
| } | |
| } | |
| # Global dictionary to cache loaded models | |
| loaded_sessions = {} | |
| def get_session(model_key): | |
| if model_key not in loaded_sessions: | |
| repo = MODEL_OPTIONS[model_key]["repo"] | |
| path = hf_hub_download(repo_id=repo, filename="onnx/model.onnx") | |
| opts = ort.SessionOptions() | |
| opts.intra_op_num_threads = 2 | |
| loaded_sessions[model_key] = ort.InferenceSession(path, opts, providers=['CPUExecutionProvider']) | |
| return loaded_sessions[model_key] | |
| def upscale_image_tiled(frame, model_key, tile_size=128, overlap=16): | |
| h, w, c = frame.shape | |
| scale = MODEL_OPTIONS[model_key]["scale"] | |
| session = get_session(model_key) | |
| output_h, output_w = h * scale, w * scale | |
| upscaled_img = np.zeros((output_h, output_w, c), dtype=np.uint8) | |
| stride = tile_size - (overlap * 2) | |
| for y in range(0, h, stride): | |
| for x in range(0, w, stride): | |
| y1, y2 = max(0, y - overlap), min(h, y + stride + overlap) | |
| x1, x2 = max(0, x - overlap), min(w, x + stride + overlap) | |
| tile = frame[y1:y2, x1:x2] | |
| # Pad to multiple of 8 for APISR/Swin2SR compatibility | |
| th, tw = tile.shape[:2] | |
| pad_h = (8 - (th % 8)) % 8 | |
| pad_w = (8 - (tw % 8)) % 8 | |
| if pad_h > 0 or pad_w > 0: | |
| tile = cv2.copyMakeBorder(tile, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT) | |
| # AI Inference | |
| img_input = cv2.cvtColor(tile, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0 | |
| img_input = np.transpose(img_input, (2, 0, 1))[np.newaxis, :] | |
| output = session.run(None, {session.get_inputs()[0].name: img_input})[0] | |
| # Post-process tile | |
| tile_out = np.clip(np.squeeze(output), 0, 1).transpose(1, 2, 0) | |
| tile_out = cv2.cvtColor((tile_out * 255.0).astype(np.uint8), cv2.COLOR_RGB2BGR) | |
| # Remove padding and overlap | |
| tile_out = tile_out[:(th*scale), :(tw*scale)] | |
| oy1, ox1 = (y - y1) * scale, (x - x1) * scale | |
| py1, py2 = y * scale, min(output_h, (y + stride) * scale) | |
| px1, px2 = x * scale, min(output_w, (x + stride) * scale) | |
| upscaled_img[py1:py2, px1:px2] = tile_out[oy1 : oy1 + (py2-py1), ox1 : ox1 + (px2-px1)] | |
| return upscaled_img | |
| def run_universal(img_data, model_choice, sharpen): | |
| if img_data is None: return None | |
| img = img_data["composite"] | |
| if img.shape[2] == 4: img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB) | |
| res = upscale_image_tiled(img, model_choice) | |
| if sharpen > 0: | |
| blurred = cv2.GaussianBlur(res, (0, 0), 3) | |
| res = cv2.addWeighted(res, 1 + sharpen, blurred, -sharpen, 0) | |
| return res | |
| # --- UI DESIGN --- | |
| with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo: | |
| gr.Markdown("# 🌌 Universal AI Image Enhancer") | |
| gr.Markdown("Pick a goal for your image and let the AI handle the rest.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_in = gr.ImageEditor(label="Upload Image", type="numpy") | |
| # Friendly Selector | |
| model_dropdown = gr.Dropdown( | |
| choices=list(MODEL_OPTIONS.keys()), | |
| value="💎 Pro Detail (APISR RRDB)", | |
| label="What is your goal?" | |
| ) | |
| sharp_slider = gr.Slider(0, 0.5, value=0.15, label="Sharpness Boost") | |
| submit_btn = gr.Button("🚀 ENHANCE IMAGE", variant="primary") | |
| with gr.Column(): | |
| image_out = gr.Image(label="Upscaled Result") | |
| submit_btn.click(run_universal, [image_in, model_dropdown, sharp_slider], image_out) | |
| if __name__ == "__main__": | |
| demo.queue().launch() |