ml-for-3d-course/main/en/unit2/hands-on-2.md

# Hands-on (Part 2)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/dylanebert/ml-for-3d-course-notebooks/blob/main/multi_view_diffusion.ipynb)

Time to host your own demo! In this portion, you will:

1. Re-run the notebook with your own model.
2. Create a demo using Gradio.
3. (Optional) Deploy your demo.

## Re-Run the Notebook

In the notebook, replace the model name with your own model name:

```python
import torch
from diffusers import DiffusionPipeline

multi_view_diffusion_pipeline = DiffusionPipeline.from_pretrained(
    "/",
    custom_pipeline="dylanebert/multi-view-diffusion",
    torch_dtype=torch.float16,
    trust_remote_code=True,
).to("cuda")
```

Then, re-run the notebook. You should see the same results as before.

![Multi-view Cats](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ml-for-3d-course/multi-view-cats.png)

## Gradio Demo

Now, let's create a Gradio demo:

```python
import gradio as gr

def run(image):
  image = np.array(image, dtype=np.float32) / 255.0
  images = multi_view_diffusion_pipeline("", image, guidance_scale=5, num_inference_steps=30, elevation=0)

  images = [Image.fromarray((img * 255).astype("uint8")) for img in images]

  width, height = images[0].size
  grid_img = Image.new("RGB", (2 * width, 2 * height))

  grid_img.paste(images[0], (0, 0))
  grid_img.paste(images[1], (width, 0))
  grid_img.paste(images[2], (0, height))
  grid_img.paste(images[3], (width, height))

  return grid_img

demo = gr.Interface(fn=run, inputs="image", outputs="image")
demo.launch()
```

The `run` method combines all the code from earlier in a single function. The `gr.Interface` method then uses this function to create a demo with `image` inputs and `image` outputs.

Congratulations! You've created a Gradio demo for your model.

## (Optional) Deploy Your Demo

You probably want to run your demo outside of Colab.

There are many ways to do this:

### Option 1: Create a Space

Go to [Hugging Face Spaces](https://huggingface.co/spaces) and create a new Space. Choose the `Gradio Space SDK`. Create a new file in the Space called `app.py` and paste the code from the Gradio demo. Copy the demo [requirements.txt](https://huggingface.co/spaces/dylanebert/multi-view-diffusion/raw/main/requirements.txt) into the Space.

For a complete example, check out this [Space](https://huggingface.co/spaces/dylanebert/multi-view-diffusion), then click `Files` in the top right to view the source code.

> Note: This approach requires a GPU to host publicly, which costs money. However, you can run the demo locally for free, following the instructions in [Option 3](#option-3-run-locally).

### Option 2: Gradio Deploy

Gradio makes it easy to deploy your demo to a server using the `gradio deploy` command.

For more details, check out the [Gradio documentation](https://www.gradio.app/guides/sharing-your-app).

### Option 3: Run locally

To run locally, simply copy the code into a Python file and run it on your machine.

The full source file should look like this:

```python
import gradio as gr
import numpy as np
import torch
from diffusers import DiffusionPipeline
from PIL import Image

multi_view_diffusion_pipeline = DiffusionPipeline.from_pretrained(
    "dylanebert/multi-view-diffusion",
    custom_pipeline="dylanebert/multi-view-diffusion",
    torch_dtype=torch.float16,
    trust_remote_code=True,
).to("cuda")

def run(image):
    image = np.array(image, dtype=np.float32) / 255.0
    images = multi_view_diffusion_pipeline(
        "", image, guidance_scale=5, num_inference_steps=30, elevation=0
    )

    images = [Image.fromarray((img * 255).astype("uint8")) for img in images]

    width, height = images[0].size
    grid_img = Image.new("RGB", (2 * width, 2 * height))

    grid_img.paste(images[0], (0, 0))
    grid_img.paste(images[1], (width, 0))
    grid_img.paste(images[2], (0, height))
    grid_img.paste(images[3], (width, height))

    return grid_img

demo = gr.Interface(fn=run, inputs="image", outputs="image")
demo.launch()
```

To set up and run this demo in a virtual Python environment, run the following:

```bash
# Setup
python -m venv venv
source venv/bin/activate
pip install -r https://huggingface.co/spaces/dylanebert/multi-view-diffusion/raw/main/requirements.txt

# Run
python app.py
```

> Note: This was tested using Python 3.10.12 and CUDA 12.1 on an NVIDIA RTX 4090.