Diffusion models are a powerful new tool for generating creative content, but they also have some limitations. Here are some of the key limitations of diffusion models:
Computational cost: Diffusion models can be computationally expensive to train and run, especially for high-resolution images and videos.
Sampling time: Diffusion models can be slow to generate samples, especially for complex or high-quality samples.
Mode collapse: Diffusion models can sometimes collapse into a single mode, generating samples that are all very similar.
Bias: Diffusion models can be biased, reflecting the biases in the data they are trained on. This can lead to the generation of harmful or offensive content.
Explainability: It can be difficult to explain how diffusion models work and why they generate the outputs that they do. This can make it difficult to trust these models and use them in critical applications.
Researchers are working on addressing all of these limitations. For example, there is ongoing research into developing new training and inference algorithms that are more efficient and less prone to mode collapse. There is also research into developing new techniques for mitigating bias and improving explainability.
Despite their limitations, diffusion models are a powerful new tool with the potential to revolutionize many industries and applications. I am excited to see how diffusion models continue to develop and evolve in the years to come.