# Additional Environment Configuration for ZeroGPU

Add this to your Hugging Face Space's **Settings** → **Variables**:

## Environment Variables

### Required:
```
ZEROGPU_OFFLOAD_DIR=/tmp/zerogpu-offload
```

### Recommended:
```
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
CUDA_LAUNCH_BLOCKING=0
HF_HUB_ENABLE_HF_TRANSFER=1
```

## Alternative: Direct Folder Creation

If the above doesn't work, you can also try creating a startup script in your Space.

## Space Configuration File

Create or modify your Space's `README.md` to include:

```yaml
---
title: Wan2.2-Fast-I2I
emoji: 💻
colorFrom: purple
colorTo: gray
sdk: gradio
sdk_version: 5.44.1
app_file: app.py
pinned: false
hardware: a10g-large
```

The `hardware: a10g-large` ensures you get a ZeroGPU instance with sufficient memory.

## Dockerfile Alternative

If you need more control, create a `Dockerfile`:

```dockerfile
FROM python:3.10

# Create offload directory
RUN mkdir -p /data-nvme/zerogpu-offload && chmod 755 /data-nvme/zerogpu-offload

# Set environment variables
ENV ZEROGPU_OFFLOAD_DIR=/data-nvme/zerogpu-offload
ENV PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

# Install your requirements
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy your app
COPY . /app
WORKDIR /app

CMD ["python", "app.py"]
```

## Testing the Fix

The modifications I made to `app.py` should handle:

1. ✅ **Automatic directory creation** - Creates `/data-nvme/zerogpu-offload` or falls back to `/tmp/zerogpu-offload`
2. ✅ **Permission handling** - Gracefully handles cases where NVMe isn't writable
3. ✅ **Environment variables** - Sets proper PyTorch memory configuration
4. ✅ **ZeroGPU decorators restored** - Keeps `@spaces.GPU()` for proper GPU allocation
5. ✅ **Memory optimization** - Added garbage collection and CUDA cache clearing

The error should be resolved and your Space should run on ZeroGPU infrastructure properly.