Generating stunning images arts paintings with your creativities using Artificial Intelligent (Stable Diffusion, DALL E)

The recent AI news, which catches social media attention is absolutely those AI (Artificial Intelligent) models generating stunning images based on your given text FOR FREE. And as soon as those models open to public, creative individuals have been experimenting with the different text input to generate creative art images, paintings, and all other different sorts of interesting images.

 

I've joined Stability AI as CIO. Check out why in my latest article where I cover the transformative power of generative AI, the rise of open foundation models, + the battle for a future that's truly open versus open in name only.#stablediffusion https://t.co/9ZADY9YqsZ

— Daniel Jeffries (@Dan_Jeffries1) September 28, 2022

Atompunk psychedelic series.
Generated in #stablediffusion pic.twitter.com/agHsVYciAY

— doppiad_ML (@Doppiad_ML) September 25, 2022

One such kind of model is Stable Diffusion, which has been made to be freely accessible to the public on this August. The web application can be accessed on Hugging Face. 

Another popular model is DALL*E from OpenAI, which also has been made available to everyone on this September recently without to be added and queued in a waiting list. Once you sign up, you can immediately get 50 credits (1 credit can generate 1 image based on your given text), and every month you can get 15 credits for now.

In the following, I also share some images generated while I'm playing with the Stable Diffusion and DALL E models. First, I got the text from the Stable Diffusion application page which is "A pikachu fine dining with a view to the Eiffel Tower" and generated images using those two different AI models. Personally and my Kido also love the first ones generated by the DALL E model.

Text: A pikachu fine dining with a view to the Eiffel Tower

DALL E results

Stable Diffusion results

Next, we can look at the images generated with a text of "Wildflowers, grassy field, autumn rhythm, watercolor" from the DALL E website, again, using those two models mentioned above. Those generated images clearly look different based on the same text input.

Text: Wildflowers, grassy field, autumn rhythm, watercolor

DALL E results

Stable Diffusion results

It is interesting to see the creativity and new images that can be generated by those models and their potential for public usage. And there are many debates and worries going on regarding those AI generated arts. It would be interesting to see how those creative tools will impact our lives in the near future with more interesting and creative applications. 

Finally, close up with some more images while I'm playing with my Kido using those AI models.

DALL E: A cute pokemon eletric type 

A pikachu with armour detaild 4k, 8k high resolution

Stable Diffusion: A cute pokemon eletric type 

DALL E: A cute girl with big eyes and long hair, detailed, realistic, 4k, 8k, HD, high resolution

"A cute girl with big eyes and long hair, detailed, realistic, 4k, 8k, HD, high resolution" from the Stable Diffusion.

MLOps: From Model-centric to Data-centric AI

This post summarizes an interesting video with respect to MLOps: From Model-centric to Data-centric AI, from Andrew Ng from DeepLearningAI regarding MLOps, which I found really interesting where it stresses the shift of mindset from current model-centric AI to data-centric AI. 

Why? The motivation is that AI system is made by code (including models) and data, and the current focus has been heavily on developing/improving models (e.g., on benchmark datasets). However, we already know and hear often that normally 80% (or maybe much more) of time a data scienc project focuses on preparing high quality data and the rest for training a model due to the fact of "garbage in garbage out". And with many data science projects done for a wide range of industries, Andrew and his team have also noticed that fixing the model and working on data could have significant improvement compared to working on the model with data fixed (details can be found in the video). In this regard, having tools and processes for high quality data (even small) is critical and better than having (relatively higher volume of) noisy data.

So what Data-centric AI refers to? It means we need to have tools and processes to in a systematic way to improve the data quality. The following figure shows a clear difference between Model-centric and Data-centric views in the context of speech recognition after error analysis.

More specifically, making the tools and process systematic indicates iteratively improving data

• Train a model
• Do error analysis to identify types of data that the algorithm struggles
• Either get more of that data via data augmentation, data generation, or data collection (change inputs X); or give a consistent definition for labels if they found to be ambiguous (change labels Y).

After deployment, monitor model performance, and collect flow new data back to refine and update the model

• Systematically check concept drift/data drift (performance degradation)
• Flow new data back to retrain/update model regularly

The following figure summarizes the MLOps really well with its analogy to SE and DevOps.