I ran fast iterations & tests using an NVIDIA Tesla V100 GPU for the training process on Google Cloud Platform (GCP). I prefered Tensorflow because the large user base and knowledge base gave me confidence that I can easily find answers in case I run into an issue. His code uses Tensorflow, as opposed to the original version, which is based on Torch, and has proven to be easy to deploy. I used Christopher Hesse’s implementation of pix2pix. As an example, just showing our model the shape of a parcel and its associated building footprint yields a model able to create typical building footprints given a parcel’s shape. We control the type of information that the model learns by formatting images. We use this ability to learn image mappings which lets our models learn topological features and space organization directly from floor plan images. The two parts of the network challenge each other resulting in higher quality outputs which are difficult to differentiate from the original images. The Generator transforms the input image to an output image the Discriminator tries to guess if the image was produced by the generator or if it is the original image. The network consists of two main pieces, the Generator and the Discriminator. Pix2Pix uses a conditional generative adversarial network (cGAN) to learn a mapping from an input image to an output image. Additionally, by tackling multi-apartment processing, this project scales beyond the simplicity of single-family houses.īeyond the mere development of a generation pipeline, this attempt aims at demonstrating the potential of GANs for any design process, whereby nesting GAN models, and allowing user input between them, I try to achieve a back and forth between humans and machines, between disciplinarian intuition and technical innovation. By nesting these models one after the other, I create an entire apartment building “ generation stack” while allowing for user input at each step. Let’s unpack floor plan design into 3 distinct steps:Įach step corresponds to a Pix2Pix GAN-model trained to perform one of the 3 tasks above. Rather than using machines to optimize a set of variables, relying on them to extract significant qualities and mimicking them all along the design process represents a paradigm shift. This approach is less deterministic and more holistic in character. I believe a statistical approach to design conception will shape AI’s potential for Architecture.
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