How I trained and ran a Latent Diffusion AI Model on a Gaming PC
Training and inference of generative image models with latent diffusion from scratch on a gaming PC — I trained a generative image model end-to-end on a single consumer GPU in about a week, leveraging the Latent Diffusion framework (Rombach et al., 2022) to make this feasible without cloud compute. By applying the diffusion process in a compressed latent space rather than pixel space, and targeting the LSUN Churches dataset for its single-domain convergence properties, I brought training costs down from hundreds of GPU-days to just 5 on desktop hardware. The trained model generates novel architectural scenes from text prompts via CLIP-conditioned sampling with classifier-free guidance — a full text-to-image pipeline running locally, no cloud required. GitHub
I trained a https://arxiv.org/abs/2112.10752 (Rombach et al., 2022) from scratch on a single consumer GPU — the kind you'd find in a gaming desktop and used it to generate novel images via text-conditioned sampling with CLIP embeddings. The key insight that makes this feasible is in the name: latent diffusion. Traditional diffusion models operate directly in pixel space, making training prohibitively expensive — on the order of hundreds of GPU-days even for modest resolutions. LDM sidesteps this by first encoding images into a compressed latent representation using a pretrained KL-regularized autoencoder (f=8, yielding 32x32x4 latent maps from 256x256 images), then applying the forward and reverse diffusion processes entirely in that lower-dimensional space. This reduces the computational cost by orders of magnitude without sacrificing perceptual quality.
To further keep training times practical on a single GPU, I chose the LSUN Churches dataset rather than ImageNet. Churches is a single-domain dataset with strong structural consistency, which means the model converges faster than it would on ImageNet's 1000-class distribution. With this setup, end-to-end training completed in roughly one week — well within reach for anyone with a modern gaming card and some patience. For inference, I use DDIM sampling with classifier-free guidance, conditioning on CLIP text embeddings to steer generation. The result: a locally-trained generative model that synthesizes novel architectural scenes from text prompts, running entirely on desktop hardware.
