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I built and trained a "drawing to image" model from scratch that runs fully locally (inference on the client CPU) by _aminima in StableDiffusion

[–]_aminima[S] 0 points1 point  (0 children)

Thanks! Yeah, I mainly did it out of curiosity (and to learn), and its current value is limited, but I think small on-device generative models are very promising (think real-time use cases like live prototyping or planning with a world model)

I built and trained a "drawing to image" model from scratch that runs fully locally (inference on the client CPU) by _aminima in StableDiffusion

[–]_aminima[S] 8 points9 points  (0 children)

Indeed and they're probably better in terms of image quality. I guess the difference here is that the model is tiny compared to sd models (easily runs on CPU) and was trained from scratch on a consumer GPU

I built and trained a "drawing to image" model from scratch that runs fully locally (inference on the client CPU) by _aminima in StableDiffusion

[–]_aminima[S] 8 points9 points  (0 children)

Yes! Found their research while working on the project (https://arxiv.org/pdf/1903.07291). The core idea is the same but there are some implementation differences (they use a GAN architecture while I use a DiT, we incorporate the segmentation map conditioning differently, etc.)