Image generation
How to deploy RealVisXL_V5.0 on a GPU cloud
A 2.6B-parameter text-to-image model. Full specs, license and use cases.
RealVisXL_V5.0 size and hardware requirements
2.6B
Total parameters
Dense (no MoE)
Architecture
F32
Published precision
11.5 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 9.6 GB | 11.5 GB | V100 | 1 | $0.060/hr |
| FP8 (quantized) | 2.4 GB | 2.9 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 1.2 GB | 1.4 GB | A16 | 1 | $0.059/hr |
How to run RealVisXL_V5.0
Run RealVisXL_V5.0 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("SG161222/RealVisXL_V5.0", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe("a description of the scene").images[0]
image.save("output.png")Run RealVisXL_V5.0 with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download SG161222/RealVisXL_V5.0'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 RealVisXL_V5.0 on Aquanode
Aquanode has no one-click deploy template for RealVisXL_V5.0; 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.
- Launch the ComfyUI template sized to the requirement above (1× V100 or larger).
- 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.
- Run the command and connect to the resulting endpoint.
Launch the ComfyUI template