Image generation

How to deploy stable-diffusion-xl-base-1.0 on a GPU cloud

A 2.6B-parameter text-to-image model. Full specs, license and use cases.

stable-diffusion-xl-base-1.0 size and hardware requirements

2.6B
Total parameters
Dense (no MoE)
Architecture
F32
Published precision
11.5 GB
Min VRAM (native)
PrecisionWeight size on diskRequired VRAMCheapest live fitGPUs neededEst. $/hr (full fit)
FP329.6 GB11.5 GBV1001$0.060/hr
FP8 (quantized)2.4 GB2.9 GBRTX 4070 Super1$0.110/hr
INT4 (quantized)1.2 GB1.4 GBA161$0.059/hr

How to run stable-diffusion-xl-base-1.0

Run stable-diffusion-xl-base-1.0 with Diffusers (Python)

Generic example using Hugging Face's diffusers library, not from the model's own docs.

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe("a description of the scene").images[0]
image.save("output.png")

Run stable-diffusion-xl-base-1.0 with ComfyUI

Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download stabilityai/stable-diffusion-xl-base-1.0's checkpoint into the models folder and load it in a workflow; this is a real Aquanode template, but loading this specific checkpoint is a manual step, not a one-click deploy.

Deploy stable-diffusion-xl-base-1.0 on Aquanode

Aquanode has no one-click deploy template for stable-diffusion-xl-base-1.0; it comes with ComfyUI preinstalled, so you only need to load the checkpoint, not install anything. Aquanode sells GPU pods billed per second, not a hosted inference API.

  1. Launch the ComfyUI template sized to the requirement above (1× V100 or larger).
  2. Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
  3. Run the command and connect to the resulting endpoint.

Submit the job. Everything after that is ours.

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