Building Custom Waifu Diffusion Models with Hugging Face: A Step-by-Step Guide

Introduction

Waifus, for those unfamiliar, refer to fictional characters, typically anime or manga-inspired, that an individual has a strong emotional attachment to. In recent years, the concept of waifus has become popular in the context of machine learning, particularly with the advent of diffusion models. These models have shown tremendous potential in generating high-quality images from text prompts, and we will explore how to build custom diffusion models using Hugging Face.

What are Diffusion Models?

Diffusion models are a class of generative models that use a process called diffusion to progressively refine an input noise signal until it converges to a desired output. This process involves iteratively adding noise to the input and then denoising it, which allows the model to learn the underlying distribution of the data.

How does Hugging Face Fit into this Picture?

Hugging Face is an open-source library that provides a wide range of pre-trained models for various NLP tasks. However, their models can also be adapted for other tasks such as image generation. In this guide, we will explore how to use Hugging Face’s diffusion models and fine-tune them for specific tasks.

Step 1: Setting up the Environment

Before we begin, make sure you have the necessary dependencies installed. This includes Python 3.8+, pip, and Torch.

We’ll be using the torch library for this tutorial, so ensure that you have it installed. If not, you can install it via pip:

pip install torch torchvision

Step 2: Choosing a Pre-trained Model

Hugging Face provides a range of pre-trained models for various tasks. For this tutorial, we’ll be focusing on the diffusion model provided by their library.

We can view the available models using the following command:

python -c "import huggingface_hub as hub; print(hub.Prompts.keys())"

This will give us a list of available prompts. We can choose any prompt that suits our needs, but for this tutorial, we’ll be focusing on the standard prompt.

Step 3: Loading the Pre-trained Model

Once we’ve chosen a pre-trained model, we can load it using the following code:

from diffusers import AutoModelForImageGeneration, AutoConfig
from transformers import DefaultTokenizer

# Load the pre-trained model and tokenizer
model = AutoModelForImageGeneration.from_pretrained("CompVis/stable-diffusion-v1-4")
tokenizer = DefaultTokenizer.from_pretrained("CompVis/stable-diffusion-v1-4")

Step 4: Fine-tuning the Model

Fine-tuning a pre-trained model involves adjusting its weights to suit our specific needs. This can be done by adding custom data and adjusting hyperparameters.

For this tutorial, we’ll be focusing on a simple example where we add a new class to the existing dataset.

We can do this by creating a new dataset class that extends the existing one:

class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, data, tokenizer, model):
        self.data = data
        self.tokenizer = tokenizer
        self.model = model

    def __getitem__(self, idx):
        image = self.data[idx]["image"]
        text = self.data[idx]["text"]

        encoding = self.tokenizer.encode_plus(
            text,
            add_special_tokens=True,
            max_length=1024,
            return_attention_mask=True,
            return_tensors="pt",
        )

        return {
            "input_ids": encoding["input_ids"].flatten(),
            "attention_mask": encoding["attention_mask"].flatten(),
            "labels": torch.tensor(image),
        }

    def __len__(self):
        return len(self.data)

We can then create a new dataset instance and use it to train our model:

# Create a new dataset instance
dataset = CustomDataset(data, tokenizer, model)

# Train the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

for epoch in range(10):
    model.train()

    for batch in dataset:
        input_ids = batch["input_ids"].to(device)
        attention_mask = batch["attention_mask"].to(device)
        labels = batch["labels"].to(device)

        optimizer.zero_grad();

        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = criterion(outputs.logits, labels)

        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

Step 5: Evaluating the Model

Once we’ve trained our model, we need to evaluate its performance. This can be done by generating images from text prompts and comparing them to the original dataset.

We can use the following code to generate images:

# Generate an image from a text prompt
text = "A beautiful sunset on a beach"
output = model.generate(text, max_length=512)

# Print the generated image
print(output)

Conclusion

In this guide, we’ve explored how to build custom diffusion models using Hugging Face’s library. We’ve covered the basics of diffusion models, fine-tuning pre-trained models, and evaluating their performance.

As you can see, building a custom diffusion model requires significant expertise in machine learning and deep learning. However, with the right resources and guidance, it’s definitely possible to build such models.

So, what are you waiting for? Give it a try and see how far you can push the boundaries of image generation!

Step by Step Guide on Waifus Models using HF