Running in ~30 s
Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this.
Template · Image generation · CUDA 12.1
Run Fooocus on a cloud GPU, in 30 seconds
Prompt-and-go image generation — Midjourney-style simplicity on SDXL. Fooocus wraps SDXL in a Midjourney-style interface: type a prompt, get great images, no sampler settings to learn. It is the template to hand to non-technical teammates — and per-second billing means an afternoon of prompting costs less than a coffee. From $0.020/hr on an interruptible RTX 3060.
Template is free
You pay the GPU price only: Fooocus on a RTX 3060 is $0.041/hr on-demand, $0.020/hr interruptible, billed per second.
Volumes for state
Models, datasets and outputs live on a $0.08/GB/mo volume; the instance stays disposable.
Private by default
Dedicated GPU, encrypted disk, crypto payments, no KYC, no stored IPs — Jupyter and SSH behind your own credentials.
Best GPUs for Fooocus
Three price points that run this template well — Good, Better, Best. Every model in the catalogue works; these are the value picks.
| Tier | GPU | VRAM | On-demand | Interruptible | Why this card | Action |
|---|---|---|---|---|---|---|
| Good | RTX 3060 | 12 GB | $0.041 | $0.020 | 12 GB runs Fooocus with its built-in low-VRAM mode. | Deploy |
| Better | RTX 4070 | 12 GB | $0.066 | $0.033 | 12 GB GDDR6X and Ada speed at a low rate. | Deploy |
| Best | RTX 4090 | 24 GB | $0.262 | $0.131 | 24 GB for fast, large-batch generation. | Deploy |
Need more VRAM? The full catalogue lists all 76 models with live availability; the VRAM guide sizes models to cards.
Deploy Fooocus from the console, CLI or API
Pick the template in the console deploy bar, or script it:
- Console — filter by GPU, choose Fooocus in the template picker, set disk and env, deploy.
- CLI — pip install powergpu, then the command on the right. CLI reference.
- API — POST /v1/instances with "template": "fooocus". REST reference.
- Own image — any OCI reference works too; we inject the NVIDIA runtime. Template docs.
$ powergpu launch --gpu rtx-3060 --template fooocus \
--disk 100 --volume models:/workspace/models
✓ instance i-7a41c0e2 running (27.9s)
# Fooocus · RTX 3060 · $0.041/hr · per second
# https://i-7a41c0e2.powergpu.io:8888 (TLS)
$ powergpu stop i-7a41c0e2 # billing ends this secondWhat is inside the Fooocus template
| Image | powergpu/fooocus |
|---|---|
| CUDA | CUDA 12.1 |
| Access | SSH shell · JupyterLab on a mapped port |
| Category | Image generation |
| Storage | Instance NVMe disk (sized at deploy) + optional network volumes |
| Billing | GPU price only, per second — no template fee, no setup fee |
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SD WebUI Forge on a cloud GPU — The Forge fork of the SD WebUI — faster attention, lower VRAM, same extensions.- SD WebUI A1111 on a cloud GPU — The classic AUTOMATIC1111 Stable Diffusion WebUI with the full extension ecosystem.
SwarmUI on a cloud GPU — A ComfyUI-backed UI that scales generation across multiple GPUs.- All 37 templates
Fooocus on a cloud GPU: FAQ
Environment variables, ports and custom images are covered in the template docs.
How much does it cost to run Fooocus on a cloud GPU?
Only the GPU price — the template is free. From $0.020/hr on an interruptible RTX 3060, $0.041/hr on-demand on a RTX 3060. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month.
How long does Fooocus take to start?
About 30 seconds from the deploy click: the image is pre-cached on hosts, ports are mapped and TLS is terminated for you. Restarting a stopped instance is faster, and your disk is exactly as you left it.
Can I keep my models and outputs between sessions?
Yes — attach a volume at deploy. Everything on the volume survives instance destruction and mounts on the next instance in the region in seconds, at $0.08/GB/month. The instance disk itself survives stop/start but not destroy.
Can I add my own SDXL checkpoints and LoRAs?
Drop them on a volume mounted at the models path; Fooocus lists them in the advanced panel on the next start.