Hmm… I think the approach would be quite different depending on whether you use an existing model or design and train an entirely new architecture from scratch…
If the goal is **a text-to-image (T2I) system that generates images on a CPU and can be incorporated into an app**, there is already open-source code to work from. [FastSD CPU](https://github.com/rupeshs/fastsdcpu) provides application-oriented interfaces and CPU-capable inference routes; [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) provides native C/C++ inference. Both are useful places to look for code and integration patterns. Neither requires training a new model simply to test CPU image generation.
If by **“from scratch” you mean designing and training a new generator**, that is a different project, and there are code examples for it too: [TinyDiT](https://github.com/ivanmikhnenkov/tinydit) and [DiffSynth-Studio’s training-from-scratch tutorial](https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Research_Tutorial/train_from_scratch.md). Both illustrate a useful middle ground: **train a newly initialized image-generation network while reusing a pretrained text encoder and image autoencoder**. The generator is genuinely new even though the surrounding components are not.
If existing weights are acceptable, a practical default is to **run one small CPU inference baseline first**, even if you eventually want to build your own architecture. It gives you a reference for time, RAM, resolution, output quality and the app interface. If it meets your needs, the integration route is available; if not, you have concrete targets for a new model. That baseline need not limit the research direction.
I also ran a small CPU-only check with an existing, distilled model, SDXS-512-DreamShaper. In one Google Colab session with two logical CPU cores, float32 PyTorch CPU, one inference step and guidance scale 0, a 512 x 512 image took 12.010 seconds for the measured generation stage, including PNG saving. It ran after a 256 x 256 smoke test; the highest sampled Python-process RSS was about 3.45 GB. This is one successful run, not a hardware-independent speed benchmark, a RAM requirement or a test of an integrated app. Importantly, it says nothing about the cost of training a model from scratch.
That leaves three useful paths open:
The two details that would change which route makes sense are whether pretrained weights or supporting components are acceptable, and the intended CPU/RAM, resolution and acceptable time per image. There is no need to settle them before exploring the code. I would not assume that “to CPU” requires CPU-only training: training on a GPU and deploying for CPU inference is also a coherent approach.
I have put the implementation, new-model training and measurement details in collapsible sections below, so you can follow whichever route is relevant.
If the immediate goal is a CPU text-to-image app The projects above address different meanings of “creating a T2I AI for CPU.” If your main goal is an app, the existing inference implementations offer code to start from. If the goal is an original generator, the training projects show ways to start that research while keeping CPU deployment in mind. Neither path has to exclude the other.