How to run stable diffusion on cloud gpu for cheap

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okay, let's dive into a detailed guide on running stable diffusion on a cloud gpu affordably. this tutorial focuses on using google colab pro/pro+/pay-as-you-go or a similar service, as it provides a relatively easy and accessible entry point. we'll cover setup, installation, running the model, and some optimization tips.

**i. understanding the landscape and choosing your service**

* **why cloud gpus?** stable diffusion, especially generating high-resolution images, requires significant computational power. your average laptop or desktop cpu will be painfully slow. gpus, designed for parallel processing, excel at the matrix multiplications that underpin deep learning. cloud gpus provide access to this power on demand.

* **google colab pro/pro+/pay-as-you-go:**
* **pros:** easy setup, free (limited), popular, supports basic and advanced usage.
* **cons:** can be restrictive (session timeouts, gpu availability varies), storage limitations.
* **suitable for:** experimentation, learning, moderate-sized projects, occasional use.

* **other options (if colab doesn't cut it):**
* **aws sagemaker studio lab:** free (limited) but may require a waitlist.
* **kaggle kernels:** free (limited) but may have stricter usage policies.
* **runpod, vast.ai, paperspace gradient:** pay-as-you-go, more control over the gpu and environment. these are great for larger projects where you need dedicated resources or specific configurations, but require more setup.
* **google cloud platform (gcp), amazon web services (aws), microsoft azure:** offer a wider range of instance types and services but require more in-depth cloud computing knowledge.

for this tutorial, we'll primarily focus on **google colab pro/pro+/pay-as-you-go** because of its accessibility. i will also give a simple example on how to setup automatic restarts of your script and models, in case of connection loss.

**ii. setting up google colab pro/pro+/pay-as-you-go** ...

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