In this blog, we will walk through the process of utilizing the Diffusers library for generating 3D shapes using a pre-trained model. Whether you’re an AI enthusiast or a developer, you will find this guide user-friendly and straightforward. Let’s dive in!

Prerequisites

Before we start, ensure you have the following:

• Python installed (preferably version 3.6 or higher)
• Pip for installing libraries
• A compatible GPU (optional, but recommended for performance)

Step 1: Install the Required Libraries

Start by installing the necessary packages. Open your command line interface and run the following command:

!pip install diffusers

Step 2: Import Required Modules

Next, you need to import the required modules in your Python script:

from diffusers import DiffusionPipeline
import torch
from PIL import Image

Step 3: Set Up the Device

Depending on your hardware, configure your device. If you have a GPU, we will use it; otherwise, it will fallback on the CPU:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

Step 4: Load the Pre-trained Model

Load the model and its scheduler. Here’s where you specify the model ID:

model_id = "eurecom-dsscoresdeve-conditional-ema-shapes3d-64"
pipe = DiffusionPipeline.from_pretrained(model_id, trust_remote_code=True)
pipe.to(device)

Understanding the Class Labels

Imagine you are at a bakery, selecting from different shaped pastries. Each pastry represents a class label:

• Cube: [0, 0, 0, 0, 1, 0]
• Cylinder: [0, 0, 0, 0, 2, 0]
• Sphere: [0, 0, 0, 0, 3, 0]
• Capsule: [0, 0, 0, 0, 4, 0]
• Unconditional: [0, 0, 0, 0, 0, 0]

The combination of these class labels determines the shape and conditions of the images generated. Thus, you can customize your output based on specific requirements!

class_labels = torch.tensor([[0, 0, 0, 0, 1, 0],  # Cube
                              [0, 0, 0, 0, 2, 0],  # Cylinder
                              [0, 0, 0, 0, 3, 0],  # Sphere
                              [0, 0, 0, 0, 4, 0],  # Capsule
                              [0, 0, 0, 0, 0, 0],  # Unconditional
                              ...]).to(device=pipe.device)

Step 5: Generate Images

Let’s generate the images using the pipeline. You can control the generation using a random seed and specify how many images you want:

generator = torch.Generator(device=device).manual_seed(46)
image = pipe(generator=generator, 
              batch_size=16, 
              class_labels=class_labels, 
              num_inference_steps=1000).images

Step 6: Create a Grid Image

Now, we will create a grid of images to visualize the results:

width, height = image[0].size
grid = Image.new("RGB", (width * 8, height * 2))
for index, img in enumerate(image):
    x = index % 8 * width  # Column index (0-7)
    y = index // 8 * height  # Row index (0-1)
    grid.paste(img, (x, y))
grid.save("sde_ve_conditional_generated_grid.png")

Troubleshooting

If you encounter issues while running the code, consider these troubleshooting tips:

• Ensure you have installed all packages without errors.
• Check if your GPU is correctly set up and available.
• If images refuse to load, verify the model ID and ensure it’s accessible.
• For model-specific issues, consult the Diffusers documentation for additional support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following these steps, you should be able to successfully generate 3D shapes using the Diffusers library. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.