--- title: "Run Kohya LoRA training on a cloud GPU from $0.054/hr | PowerGPU" description: "Kohya's GUI on a fixed-price cloud GPU: one-click template, running in ~30 s, from $0.054/hr on RTX 3090. Best GPUs, volumes, per-second billing." url: https://powergpu.ai/templates/kohya-s-gui last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Template · Training & fine-tuning · CUDA 12.6 # Run Kohya LoRA training on a cloud GPU, in 30 seconds Train SDXL and Flux image LoRAs in a browser GUI — dataset in,.safetensors out. Kohya's GUI is the standard trainer for Stable Diffusion, SDXL and Flux LoRAs: dataset prep, captioning helpers, bucketed resolutions and a browser UI over the sd-scripts. Twenty to forty images in, a.safetensors LoRA out, usually within an hour on a 4090. From **$0.054** /hr on an interruptible RTX 3090. Kohya's GUI powergpu/kohyas-gui (CUDA 12.6) (SSH) (Jupyter) Pinned image, rebuilt on upstream releases. Override env, ports and disk at deploy. ### Running in ~30 s Pre-cached image, ports mapped, TLS terminated — the stack is working before you finish reading this. ### Template is free You pay the GPU price only: Kohya's GUI on a RTX 3090 is $0.108/hr on-demand, $0.054/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 Kohya's GUI 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 | | | --- | --- | --- | --- | --- | --- | --- | | (Good) | [RTX 3090](https://powergpu.ai/gpu/rtx-3090) | 24 GB | $0.108 | $0.054 | 24 GB at the lowest rate for SDXL LoRAs. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-3090) | | (Better) | [RTX 4090](https://powergpu.ai/gpu/rtx-4090) | 24 GB | $0.327 | $0.163 | The community default: SDXL and Flux LoRAs in 30–60 minutes. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-4090) | | (Best) | [RTX 5090](https://powergpu.ai/gpu/rtx-5090) | 32 GB | $0.439 | $0.219 | 32 GB for Flux full-precision training and larger batch sizes. | [Deploy](https://cloud.powergpu.ai/?gpu=rtx-5090) | Need more VRAM? The [full catalogue](https://powergpu.ai/gpus) lists all 80 models with live availability; the [VRAM guide](https://powergpu.ai/guides/llm-vram-requirements) sizes models to cards. ## Deploy Kohya's GUI from the console, CLI or API Pick the template in the [console](https://cloud.powergpu.ai/) deploy bar, or script it: - **Console** — filter by GPU, choose *Kohya's GUI* in the template picker, set disk and env, deploy. - **CLI** — pip install powergpu, then the command on the right. [CLI reference](https://powergpu.ai/cli). - **API** — POST /v1/instances with "template": "kohya-s-gui". [REST reference](https://powergpu.ai/api). - **Own image** — any OCI reference works too; we inject the NVIDIA runtime. [Template docs](https://powergpu.ai/docs/templates). *deploy — kohya-s-gui* ``` $ powergpu launch --gpu rtx-3090 --template kohya-s-gui \ --disk 100 --volume models:/workspace/models ✓ instance i-7a41c0e2 running (27.9s) # Kohya's GUI · RTX 3090 · $0.108/hr · per second # https://i-7a41c0e2.powergpu.ai:8888 (TLS) $ powergpu stop i-7a41c0e2 # billing ends this second ``` ## What is inside the Kohya's GUI template - **Image**: powergpu/kohyas-gui - **CUDA**: CUDA 12.6 - **Access**: SSH shell · JupyterLab on a mapped port - **Category**: [Training & fine-tuning](https://powergpu.ai/templates#tune) - **Storage**: Instance NVMe disk (sized at deploy) + optional [network volumes](https://powergpu.ai/products/volumes) - **Billing**: GPU price only, per second — no template fee, no setup fee ## Other training & fine-tuning templates - [Axolotl — Fine Tuning on a cloud GPU](https://powergpu.ai/templates/axolotl-fine-tuning) — Fine-tune Llama, Qwen or Mistral from one YAML — LoRA, QLoRA and multi-GPU FSDP. - [Unsloth Studio on a cloud GPU](https://powergpu.ai/templates/unsloth-studio) — 2× faster, lower-VRAM fine-tuning — Unsloth kernels with a notebook workflow. - [Flux Gym on a cloud GPU](https://powergpu.ai/templates/flux-gym) — A dead-simple UI for training Flux LoRAs, built on the Kohya scripts. - [Ostris AI Toolkit on a cloud GPU](https://powergpu.ai/templates/ostris-ai-toolkit) — The AI-Toolkit trainer for Flux and diffusion models, with a web UI. - [All 37 templates](https://powergpu.ai/templates) ## Kohya's GUI on a cloud GPU: FAQ Environment variables, ports and custom images are covered in the [template docs](https://powergpu.ai/docs/templates). **How much does it cost to run Kohya's GUI on a cloud GPU?** Only the GPU price — the template is free. From $0.054/hr on an interruptible RTX 3090, $0.108/hr on-demand on a RTX 3090. Billing is per second, so an hour of tinkering costs an hour, not a day. Storage is $0.08/GB/month. **How long does Kohya's GUI 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. **How much does a style LoRA cost to train?** A typical 30-image SDXL LoRA at 1,500 steps takes 30–45 minutes on an RTX 4090 — well under a dollar on interruptible pricing. --- *About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API. Source: https://powergpu.ai/templates/kohya-s-gui · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt