Upload examples_hf_api.py with huggingface_hub

examples_hf_api.py

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+"""
+Byte Dream - Hugging Face API Examples
+Complete examples for using Byte Dream with Hugging Face Hub
+"""
+
+# ============================================================================
+# Example 1: Use Model from Hugging Face Hub (Download Locally)
+# ============================================================================
+
+print("=" * 70)
+print("Example 1: Download and run model locally on CPU")
+print("=" * 70)
+
+from bytedream import ByteDreamGenerator
+
+# Load model directly from Hugging Face
+generator = ByteDreamGenerator(
+    hf_repo_id="Enzo8930302/ByteDream",  # Replace with your repo
+    config_path="config.yaml",
+    device="cpu",
+)
+
+# Generate single image
+image = generator.generate(
+    prompt="A beautiful sunset over mountains, digital art, vibrant colors",
+    negative_prompt="ugly, blurry, low quality, distorted",
+    width=512,
+    height=512,
+    num_inference_steps=50,
+    guidance_scale=7.5,
+    seed=42,
+)
+
+image.save("example_1_output.png")
+print("✓ Image saved to example_1_output.png\n")
+
+
+# ============================================================================
+# Example 2: Use Hugging Face Inference API (Cloud)
+# ============================================================================
+
+print("=" * 70)
+print("Example 2: Use Hugging Face Inference API (No local computation)")
+print("=" * 70)
+
+from bytedream import ByteDreamHFClient
+
+# Initialize client for API usage
+api_client = ByteDreamHFClient(
+    repo_id="Enzo8930302/ByteDream",
+    token=None,  # Add your HF token here for private models
+    use_api=True,  # Use cloud API instead of local inference
+)
+
+# Generate using cloud API
+image_api = api_client.generate(
+    prompt="Futuristic city with flying cars, cyberpunk style, night",
+    negative_prompt="daylight, sunny, calm",
+    width=512,
+    height=512,
+    num_inference_steps=50,
+    guidance_scale=7.5,
+)
+
+image_api.save("example_2_api_output.png")
+print("✓ Image generated via API and saved to example_2_api_output.png\n")
+
+
+# ============================================================================
+# Example 3: Batch Generation
+# ============================================================================
+
+print("=" * 70)
+print("Example 3: Generate multiple images in batch")
+print("=" * 70)
+
+prompts = [
+    "Majestic dragon flying over medieval castle, fantasy art",
+    "Peaceful Japanese garden with cherry blossoms, serene",
+    "Underwater coral reef with tropical fish, vibrant colors",
+    "Mountain landscape at sunrise, dramatic lighting, epic",
+]
+
+# Generate all images
+images = generator.generate_batch(
+    prompts=prompts,
+    negative_prompt="ugly, deformed, low quality",
+    width=512,
+    height=512,
+    num_inference_steps=40,
+    guidance_scale=7.5,
+)
+
+# Save all images
+for i, img in enumerate(images):
+    img.save(f"example_3_batch_{i+1}.png")
+    print(f"  ✓ Saved example_3_batch_{i+1}.png")
+
+print()
+
+
+# ============================================================================
+# Example 4: Upload Your Trained Model to Hugging Face
+# ============================================================================
+
+print("=" * 70)
+print("Example 4: Upload trained model to Hugging Face Hub")
+print("=" * 70)
+
+# After training your model:
+# python train.py --config config.yaml --train_data dataset
+
+# Load your trained model
+trained_generator = ByteDreamGenerator(
+    model_path="./models/bytedream",
+    config_path="config.yaml",
+    device="cpu",
+)
+
+# Upload to Hugging Face
+hf_token = "hf_xxxxxxxxxxxxx"  # Get from https://huggingface.co/settings/tokens
+
+trained_generator.push_to_hub(
+    repo_id="your_username/ByteDream",  # Replace with your username
+    token=hf_token,
+    private=False,  # Set True for private model
+    commit_message="Upload Byte Dream model v1.0",
+)
+
+print("✓ Model uploaded to Hugging Face!\n")
+
+
+# ============================================================================
+# Example 5: Deploy to Hugging Face Spaces
+# ============================================================================
+
+print("=" * 70)
+print("Example 5: Deploy interactive web app to Hugging Face Spaces")
+print("=" * 70)
+
+# Run this command in terminal:
+# python deploy_to_spaces.py --repo_id your_username/ByteDream-Space
+
+# Or programmatically:
+from huggingface_hub import create_repo, HfApi
+
+api = HfApi()
+
+# Create space
+create_repo(
+    repo_id="your_username/ByteDream-Space",
+    repo_type="space",
+    sdk="gradio",
+    token=hf_token,
+    exist_ok=True,
+)
+
+# Upload files
+api.upload_folder(
+    folder_path=".",
+    repo_id="your_username/ByteDream-Space",
+    repo_type="space",
+    token=hf_token,
+    ignore_patterns=["*.git/*", "outputs/*", "logs/*"],
+)
+
+print("✓ Space deployed! Visit: https://huggingface.co/spaces/your_username/ByteDream-Space\n")
+
+
+# ============================================================================
+# Example 6: Advanced API Usage with Custom Parameters
+# ============================================================================
+
+print("=" * 70)
+print("Example 6: Advanced generation with custom parameters")
+print("=" * 70)
+
+from bytedream import ByteDreamHFClient
+import torch
+
+client = ByteDreamHFClient(
+    repo_id="Enzo8930302/ByteDream",
+    use_api=False,  # Run locally
+    device="cpu",
+)
+
+# Generate with different resolutions
+resolutions = [(256, 256), (512, 512), (768, 768)]
+
+for width, height in resolutions:
+    print(f"\nGenerating {width}x{height} image...")
+    
+    img = client.generate(
+        prompt="Abstract geometric patterns, colorful, modern art",
+        width=width,
+        height=height,
+        num_inference_steps=30,  # Fewer steps for faster generation
+        guidance_scale=9.0,  # Higher guidance for more detail
+        seed=torch.randint(0, 1000000, (1,)).item(),
+    )
+    
+    img.save(f"example_6_{width}x{height}.png")
+    print(f"  ✓ Saved example_6_{width}x{height}.png")
+
+
+# ============================================================================
+# Example 7: Compare Local vs API Inference
+# ============================================================================
+
+print("\n" + "=" * 70)
+print("Example 7: Compare local inference vs cloud API")
+print("=" * 70)
+
+import time
+
+prompt = "Serene lake surrounded by pine trees, mountain reflection, sunset"
+
+# Time local inference
+print("\n⏱️ Testing LOCAL inference...")
+start_local = time.time()
+img_local = generator.generate(
+    prompt=prompt,
+    num_inference_steps=30,
+    seed=123,
+)
+time_local = time.time() - start_local
+img_local.save("comparison_local.png")
+print(f"Local: {time_local:.2f}s")
+
+# Time API inference
+print("\n⏱️ Testing CLOUD API inference...")
+start_api = time.time()
+img_api = api_client.generate(
+    prompt=prompt,
+    num_inference_steps=30,
+    seed=123,
+)
+time_api = time.time() - start_api
+img_api.save("comparison_api.png")
+print(f"API: {time_api:.2f}s")
+
+print(f"\nSpeed comparison:")
+print(f"  Local: {time_local:.2f}s (CPU)")
+print(f"  API:   {time_api:.2f}s (Cloud GPU/CPU)")
+print(f"  Winner: {'API' if time_api < time_local else 'Local'} 🏆")
+
+
+print("\n" + "=" * 70)
+print("All examples completed successfully! 🎉")
+print("=" * 70)
+print("\nNext steps:")
+print("1. Train your own model: python train.py")
+print("2. Upload to HF: generator.push_to_hub(repo_id='username/Model')")
+print("3. Deploy to Spaces: python deploy_to_spaces.py")
+print("4. Share with the community!")