Runpod shows how to build your own Vlad Diffusion template for cloud GPUs

Runpod has published a step-by-step guide on its blog for building a custom template for Vlad Diffusion. This is a fork of Stable Diffusion WebUI that Vladimir Mandic developed in parallel with AUTOMATIC1111 and later renamed SD.Next. The idea is simple: describe the environment once, then spin it up on any suitable cloud GPU in a couple of clicks. The material itself is short and practical, so what follows is mostly a look at the mechanics and limitations of this approach.

Why you need your own template

Runpod already offers ready-made Stable Diffusion templates, but they are built for the average user. The interface version in them may lag behind, the set of extensions is fixed, and you have to download the models you need by hand on every launch. Vlad Diffusion also differs from the classic WebUI: it has a different backend architecture, its own set of options and more frequent updates. If you need a specific branch, PyTorch version or set of plugins, it is easier to build an image for yourself.

In Runpod terms, a template describes how a pod starts, meaning a rented container with a GPU. The platform pulls the image from a registry, attaches disks, forwards ports and runs the start command. Everything else happens inside the container.

What a template consists of

As the platform is designed, the configuration sets the following parameters:

• the template name and launch type (a pod with a GPU);
• the container image: either a ready-made one from Docker Hub or your own, with Vlad Diffusion and all its dependencies already installed;
• the size of the container disk for the system and temporary files;
• the size of the persistent volume for checkpoints, LoRAs and generation results;
• the HTTP port of the web interface, which for SD.Next and similar projects is usually 7860;
• environment variables and a start command with launch flags.

The key practical decision here is how to split the disks. The container disk disappears along with the pod, while the volume survives a stop. Stable Diffusion models weigh from a few gigabytes to tens of gigabytes, so they belong on the volume. Otherwise you will download them again on every launch and pay for the idle GPU.

What the user gets

The main gain is reproducibility. An environment that has been built and tested once does not break because of dependency updates, and you can pin the interface version. For teams, it is also a way to hand the same environment to several people without explaining which packages to install.

The economics are straightforward too. Runpod charges hourly for the GPU, and while a pod is stopped you mostly pay for volume storage. For people who generate images only occasionally, this is cheaper than buying their own card. For those who work every day for many hours, the math may come out differently, and it is worth running the numbers in advance using the actual rates for the chosen GPU.

The limitations need a separate mention. The original post contains no comparative speed measurements, no specific GPU models and no prices, so you cannot judge performance from it. Generation speed depends on the card, the resolution, the number of steps and the chosen backend, not on the template as such.

Where to start

If you want to replicate the setup, here is the sequence:

1. Start from a ready-made image with PyTorch and CUDA, or build your own Dockerfile that clones the SD.Next repository. 2. Publish the image to a registry that Runpod can access. 3. Create a template and specify the image, the interface port, the disk sizes and a start command with the flags you need. 4. Launch a pod on an inexpensive card and check that the interface opens through the Runpod proxy address. 5. Put the models on the persistent volume and make sure the path to them is set in the settings. 6. After the test, stop the pod so you do not pay for idle time.

A useful habit: keep the Dockerfile and the list of flags in a repository. Then you can recreate the template in a few minutes if you have to update the image or move to another provider.