Add macOS Compatibility with CPU Support and Interactive CLI

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+DepthCrafter is a deep learning project for generating temporally consistent long depth sequences from open-world videos. It uses a diffusion-based model built on Stable Video Diffusion to estimate depth maps without requiring camera poses or optical flow.
+
+## Architecture
+
+### Core Components
+
+1. **Main Pipeline (`depthcrafter/depth_crafter_ppl.py`)**: Implements the DepthCrafterPipeline extending diffusers for depth estimation
+2. **UNet Model (`depthcrafter/unet.py`)**: Custom spatio-temporal UNet for depth prediction
+3. **Inference Scripts**:
+   - `run.py`: Main CLI for single video inference
+   - `app.py`: Gradio web interface
+   - `benchmark/infer/infer_batch.py`: Batch processing for benchmarks
+
+### Key Directories
+
+- `depthcrafter/`: Core model implementation
+- `benchmark/`: Dataset evaluation scripts and CSV metadata
+- `examples/`: Sample video files for testing
+- `visualization/`: Point cloud visualization tools
+
+## Common Commands
+
+### Installation
+```bash
+pip install -r requirements.txt
+```
+
+### Single Video Inference
+
+High-resolution (requires ~26GB GPU memory):
+```bash
+python run.py --video-path examples/example_01.mp4
+```
+
+Low-resolution (requires ~9GB GPU memory):
+```bash
+python run.py --video-path examples/example_01.mp4 --max-res 512
+```
+
+### Gradio Demo
+```bash
+gradio app.py
+```
+
+### Benchmark Evaluation
+
+Run inference on all datasets:
+```bash
+bash benchmark/infer/infer.sh
+```
+
+Evaluate results:
+```bash
+bash benchmark/eval/eval.sh
+```
+
+### Key Parameters
+
+- `--process-length`: Number of frames to process (default: 195)
+- `--window-size`: Sliding window size (default: 110)
+- `--overlap`: Frame overlap between windows (default: 25)
+- `--max-res`: Maximum resolution (default: 1024)
+- `--num-denoising-steps`: Denoising steps (default: 5)
+- `--guidance-scale`: Guidance scale for inference (default: 1.0)
+- `--save-npz`: Save depth as NPZ file
+- `--save-exr`: Save depth as EXR file
+
+## Model Loading
+
+The model uses two key components from Hugging Face:
+1. DepthCrafter UNet: `tencent/DepthCrafter`
+2. Base diffusion model: `stabilityai/stable-video-diffusion-img2vid-xt`
+
+## Dependencies
+
+Key dependencies:
+- PyTorch 2.0.1
+- Diffusers 0.29.1
+- Transformers 4.41.2
+- XFormers 0.0.20 (for memory efficient attention)
+- OpenEXR 3.2.4 (for EXR output)
+
+## Performance Notes
+
+- v1.0.1 improvements: ~4x faster inference (465ms/frame vs 1914ms/frame at 1024x576)
+- Memory optimization options via `--cpu-offload` parameter:
+  - `"model"`: Standard CPU offloading
+  - `"sequential"`: Sequential offloading (slower but saves more memory)