Upload folder using huggingface_hub

app.py

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+import os
+import sys
+
+from huggingface_hub import hf_hub_download
+import spaces
+from facenet_pytorch import MTCNN
+from torchvision import transforms
+import torch
+import PIL
+from PIL import Image
+import gradio as gr
+
+# Download models
+modelarcanev4 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.4", filename="ArcaneGANv0.4.jit")
+modelarcanev3 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.3", filename="ArcaneGANv0.3.jit")
+modelarcanev2 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.2", filename="ArcaneGANv0.2.jit")
+
+mtcnn = MTCNN(image_size=256, margin=80)
+
+# Face detection
+def detect(img):
+    batch_boxes, batch_probs, batch_points = mtcnn.detect(img, landmarks=True)
+    if not mtcnn.keep_all:
+        batch_boxes, batch_probs, batch_points = mtcnn.select_boxes(
+            batch_boxes, batch_probs, batch_points, img, method=mtcnn.selection_method
+        )
+    return batch_boxes, batch_points
+
+def makeEven(_x):
+    return _x if (_x % 2 == 0) else _x+1
+
+def scale(boxes, _img, max_res=1_500_000, target_face=256, fixed_ratio=0, max_upscale=2, VERBOSE=False):
+    x, y = _img.size
+    ratio = 2
+    
+    if (boxes is not None):
+        if len(boxes)>0:
+            ratio = target_face/max(boxes[0][2:]-boxes[0][:2])
+            ratio = min(ratio, max_upscale)
+    
+    if fixed_ratio>0:
+        ratio = fixed_ratio
+    
+    x*=ratio
+    y*=ratio
+    
+    res = x*y
+    if res > max_res:
+        ratio = pow(res/max_res,1/2)
+        x=int(x/ratio)
+        y=int(y/ratio)
+    
+    x = makeEven(int(x))
+    y = makeEven(int(y))
+    size = (x, y)
+    return _img.resize(size)
+
+def scale_by_face_size(_img, max_res=1_500_000, target_face=256, fix_ratio=0, max_upscale=2, VERBOSE=False):
+    boxes = None
+    boxes, _ = detect(_img)
+    img_resized = scale(boxes, _img, max_res, target_face, fix_ratio, max_upscale, VERBOSE)
+    return img_resized
+
+# Image processing setup
+size = 256
+means = [0.485, 0.456, 0.406]
+stds = [0.229, 0.224, 0.225]
+
+img_transforms = transforms.Compose([                        
+    transforms.ToTensor(),
+    transforms.Normalize(means,stds)
+])
+
+# Load models globally on CPU
+modelv4_cpu = torch.jit.load(modelarcanev4, map_location='cpu').eval()
+modelv3_cpu = torch.jit.load(modelarcanev3, map_location='cpu').eval()
+modelv2_cpu = torch.jit.load(modelarcanev2, map_location='cpu').eval()
+
+@spaces.GPU
+def proc_pil_img(input_image, model_path):
+    """GPU-accelerated image processing"""
+    # Load model fresh on GPU to avoid device mismatch
+    model = torch.jit.load(model_path, map_location='cuda').eval()
+    
+    # Create tensors on GPU
+    t_stds = torch.tensor(stds).cuda().view(3, 1, 1)
+    t_means = torch.tensor(means).cuda().view(3, 1, 1)
+    
+    # Transform and move to GPU
+    transformed_image = img_transforms(input_image).unsqueeze(0).cuda()
+    
+    with torch.no_grad():
+        result_image = model(transformed_image)[0]
+        output_image = result_image.mul(t_stds).add(t_means).mul(255.).clamp(0, 255).permute(1, 2, 0)
+        output_image = output_image.cpu().numpy().astype('uint8')
+        output_image = PIL.Image.fromarray(output_image)
+    
+    # Clean up
+    del model
+    torch.cuda.empty_cache()
+    
+    return output_image
+
+@spaces.GPU
+def process(im, version):
+    """Main processing function with GPU acceleration"""
+    if im is None:
+        raise gr.Error("Please upload an image first!")
+    
+    try:
+        # Ensure image is PIL Image
+        if not isinstance(im, Image.Image):
+            im = Image.fromarray(im)
+        
+        # Convert to RGB if needed
+        if im.mode != 'RGB':
+            im = im.convert('RGB')
+        
+        # Scale image (CPU operation)
+        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
+        
+        # Select model based on version
+        if version == 'v0.4 (Recommended)':
+            res = proc_pil_img(im, modelarcanev4)
+        elif version == 'v0.3':
+            res = proc_pil_img(im, modelarcanev3)
+        else:
+            res = proc_pil_img(im, modelarcanev2)
+        
+        return res
+    
+    except Exception as e:
+        raise gr.Error(f"Error processing image: {str(e)}")
+
+# Custom theme
+custom_theme = gr.themes.Soft(
+    primary_hue="blue",
+    secondary_hue="indigo",
+    neutral_hue="slate",
+    font=gr.themes.GoogleFont("Inter"),
+    text_size="lg",
+    spacing_size="md",
+    radius_size="lg"
+).set(
+    button_primary_background_fill="*primary_600",
+    button_primary_background_fill_hover="*primary_700",
+    block_title_text_weight="600",
+    block_border_width="2px",
+    block_shadow="*shadow_drop_lg",
+)
+
+# Custom CSS for mobile-friendly design
+custom_css = """
+.gradio-container {
+    max-width: 1200px !important;
+    margin: auto !important;
+}
+
+#header {
+    text-align: center;
+    margin-bottom: 2rem;
+}
+
+#header h1 {
+    font-size: 2.5rem;
+    font-weight: 700;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin-bottom: 0.5rem;
+}
+
+#description {
+    font-size: 1.1rem;
+    color: #64748b;
+    margin-bottom: 1rem;
+}
+
+.input-column, .output-column {
+    border-radius: 16px;
+    padding: 1.5rem;
+    background: linear-gradient(135deg, rgba(102, 126, 234, 0.05) 0%, rgba(118, 75, 162, 0.05) 100%);
+}
+
+@media (max-width: 768px) {
+    #header h1 {
+        font-size: 2rem;
+    }
+    
+    #description {
+        font-size: 1rem;
+    }
+    
+    .input-column, .output-column {
+        padding: 1rem;
+    }
+}
+
+#footer {
+    text-align: center;
+    margin-top: 2rem;
+    padding: 1.5rem;
+    border-top: 2px solid #e2e8f0;
+}
+
+#footer a {
+    color: #667eea;
+    text-decoration: none;
+    font-weight: 600;
+}
+
+#footer a:hover {
+    color: #764ba2;
+    text-decoration: underline;
+}
+
+.example-container {
+    margin-top: 1rem;
+}
+
+.gpu-badge {
+    display: inline-block;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    color: white;
+    padding: 0.5rem 1rem;
+    border-radius: 20px;
+    font-weight: 600;
+    margin-top: 0.5rem;
+}
+
+#anycoder-link {
+    text-align: center;
+    margin-top: 0.5rem;
+}
+
+#anycoder-link a {
+    color: #667eea;
+    text-decoration: none;
+    font-weight: 600;
+    font-size: 0.9rem;
+}
+
+#anycoder-link a:hover {
+    color: #764ba2;
+    text-decoration: underline;
+}
+"""
+
+# Build the interface
+with gr.Blocks() as demo:
+    
+    # Header
+    with gr.Column(elem_id="header"):
+        gr.Markdown(
+            """
+            # 🎨 ArcaneGAN
+            ### Transform Your Photos into Arcane-Style Art
+            Upload a portrait and watch it transform into the stunning visual style of Netflix's Arcane series.
+            
+            <span class="gpu-badge">⚡ Powered by Zero-GPU</span>
+            """
+        )
+        gr.Markdown(
+            "[Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)",
+            elem_id="anycoder-link"
+        )
+    
+    # Main content
+    with gr.Row(equal_height=True):
+        # Input column
+        with gr.Column(scale=1, elem_classes="input-column"):
+            gr.Markdown("### 📤 Upload Your Photo")
+            input_image = gr.Image(
+                type="pil",
+                label="Input Image",
+                sources=["upload", "webcam", "clipboard"],
+                height=400
+            )
+            
+            version_selector = gr.Radio(
+                choices=['v0.4 (Recommended)', 'v0.3', 'v0.2'],
+                value='v0.4 (Recommended)',
+                label="Model Version",
+                info="v0.4 offers the best quality"
+            )
+            
+            transform_btn = gr.Button(
+                "✨ Transform to Arcane Style",
+                variant="primary",
+                size="lg"
+            )
+        
+        # Output column
+        with gr.Column(scale=1, elem_classes="output-column"):
+            gr.Markdown("### 🎭 Arcane-Style Result")
+            output_image = gr.Image(
+                type="pil",
+                label="Transformed Image",
+                height=400,
+                buttons=["download", "share"]
+            )
+    
+    # Tips section
+    with gr.Row():
+        gr.Markdown(
+            """
+            ### 💡 Tips for Best Results
+            - Use clear, well-lit portrait photos
+            - Face should be clearly visible and not too small
+            - Works best with frontal or slightly angled faces
+            - Try different model versions for varied artistic styles
+            """
+        )
+    
+    # Footer
+    with gr.Column(elem_id="footer"):
+        gr.Markdown(
+            """
+            ---
+            **ArcaneGAN** by [Alexander S](https://twitter.com/devdef) | 
+            [GitHub Repository](https://github.com/Sxela/ArcaneGAN) | 
+            [Original Space](https://huggingface.co/spaces/akhaliq/ArcaneGAN)
+            
+            **⚡ Zero-GPU Optimization**: This Space uses Hugging Face's Zero-GPU infrastructure for efficient GPU allocation.
+            
+            **Model Versions:**
+            - **v0.4**: Latest and recommended - best quality and style accuracy
+            - **v0.3**: Alternative style interpretation
+            - **v0.2**: Original version with unique characteristics
+            
+            <div style='margin-top: 1rem;'>
+                <img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_arcanegan' alt='visitor badge'>
+            </div>
+            """
+        )
+    
+    # Event handlers
+    transform_btn.click(
+        fn=process,
+        inputs=[input_image, version_selector],
+        outputs=output_image,
+        api_visibility="public"
+    )
+    
+    input_image.upload(
+        fn=process,
+        inputs=[input_image, version_selector],
+        outputs=output_image
+    )
+
+# Launch with Gradio 6 syntax
+demo.launch(
+    theme=custom_theme,
+    css=custom_css,
+    footer_links=[
+        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
+    ]
+)

requirements.txt

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+torch
+torchvision
+torchaudio
+facenet-pytorch
+Pillow
+huggingface-hub
+gradio>=6.0
+spaces
+numpy
+requests
+tqdm
+filelock
+pandas
+accelerate
+transformers
+sentencepiece