optimize ComfyUI on serverless
I have ComfyUI deployed on runpod serverless, so I send the json workflows to runpod and receive the generated images in return. Right now, all my models are stored in a network volume. However, I read that loading the models from a network volume is not optimal.
In each workflow, I either use Stable Diffusion 1.5 or Stable Diffusion XL. My 1.5 and sdxl workflows always share some models (such as the checkpoint) but otherwise require different models with each request.
I am thinking about the following options to optimize further:
1. bake almost all the models, except the loras, into one docker image (about 30 GB)
2. build two different images, one for all the sdxl and one for all the 1.5 models
3. build two different images, one for 1.5 and one for sdxl. Only include the models into each image that I would use with every request (such as the 1.5 and sdxl checkpoint) and keep the rest in a network volume
Does someone have an idea what the best approach would be?
Thanks!
In each workflow, I either use Stable Diffusion 1.5 or Stable Diffusion XL. My 1.5 and sdxl workflows always share some models (such as the checkpoint) but otherwise require different models with each request.
I am thinking about the following options to optimize further:
1. bake almost all the models, except the loras, into one docker image (about 30 GB)
2. build two different images, one for all the sdxl and one for all the 1.5 models
3. build two different images, one for 1.5 and one for sdxl. Only include the models into each image that I would use with every request (such as the 1.5 and sdxl checkpoint) and keep the rest in a network volume
Does someone have an idea what the best approach would be?
Thanks!
