Launch: Generative AI for Industrial Designers

July 5, 2024

ID AI, built between June 4 and July 4, 2024, is a toolkit of generative AI resources to support industrial designers.

The inspiration came from a conversation with my classmate, Saloni, who was preparing an IDSA conference talk on generative AI tools she used at work. She shared several problems with these products that seemed easily solvable. One tool claimed to create a moodboard from a prompt but instead produced a disjointed image with 'moodboard' in the prompt. While it superficially resembled a moodboard, upon closer inspection, it was nonsense. Another tool failed to accurately apply specific materials to the objects she was generating.

Inspired by these gaps, I created and launched four demos: 

Interface for Image Generator + Moodboard
Cat Planters generated with the moodboard above

1. Image Generator + Moodboard

The initial step in design is to create a moodboard to represent a direction and potential design language. In this demo, the moodboard on the left constrains and guides the generated image results.

Seamless Texture Generator output

2. Seamless Texture Generator

Designers often spend considerable time in Photoshop creating seamless patterns for Keyshot textures. This demo takes an image and generates an infinitely repeating texture..

In the 'After' image, the lighting is consistent and the render blends in with the photograph

3. In-Context Images

Another tedious Photoshop task is making renders look realistic within the context of photographs. The challenge here is ensuring every detail remains the same while only the lighting changes, as the lighting affects every pixel.

Generated image: Ghost made of rubber by a model fine-tuned on materials
Generated Image: Ghost made of oak and green velvet by a model fine-tuned on materials

4. Trained Model (Color, Material, Finish)

This model is fine-tuned on color, material (rubber, acrylic), and finish (gloss, matte). Creating it wasn't scalable, as I manually assembled a dataset of 100 images using resources like material sample catalogs. This ensured that the training images closely reflected the real-life materials users would be designing with.

These demos are designed to support existing workflows, not to build tools that can run autonomously. 

Results

2,388 images were generated by 271 users.

These results are just from word of mouth and sharing on Reddit. It averages out to each user generating ~9 images. In reality, that number is skewed towards a handful of returning users.

Feedback from users

Process

My work was split between user interviews and building or improving the demos. A cycle would look something like this:

  • User interview
  • Run quick experiments in Google Colab to show what’s possible 
  • Show the user interviewee these options
  • Build what they said was most useful
  • Launch  
  • Test and repeat

Resources I used to move quickly:

  • Gradio
  • HuggingFace
  • Replicate
  • Squarespace

And my favorite part, Saloni included it in her presentation!

ID AI is the logo in red :)

Interested in following along? 

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