runpod

Original：🇺🇸 English

Translated

Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).

4installs

Sourcedigitalsamba/claude-code-video-toolkit

Added on2026-02-28

NPX Install

npx skill4agent add digitalsamba/claude-code-video-toolkit runpod

SKILL.md Content

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RunPod Cloud GPU

Run open-source AI models on cloud GPUs via RunPod serverless. Pay-per-second, no minimums.

Setup

bash

# 1. Create account at https://runpod.io
# 2. Add API key to .env
echo "RUNPOD_API_KEY=your_key_here" >> .env

# 3. Deploy any tool with --setup
python tools/image_edit.py --setup
python tools/upscale.py --setup
python tools/dewatermark.py --setup
python tools/sadtalker.py --setup
python tools/qwen3_tts.py --setup

Each

--setup

command:

Creates a RunPod template from the Docker image
Creates a serverless endpoint with appropriate GPU
Saves the endpoint ID to
```
.env
```
(e.g.
```
RUNPOD_QWEN_EDIT_ENDPOINT_ID
```
)

Available Images

All images are public on GHCR — no authentication needed.

Tool	Docker Image	GPU	VRAM	Typical Cost
image_edit	`ghcr.io/conalmullan/video-toolkit-qwen-edit:latest`	A6000/L40S	48GB+	~$0.05-0.15/job
upscale	`ghcr.io/conalmullan/video-toolkit-realesrgan:latest`	RTX 3090/4090	24GB	~$0.01-0.05/job
dewatermark	`ghcr.io/conalmullan/video-toolkit-propainter:latest`	RTX 3090/4090	24GB	~$0.05-0.30/job
sadtalker	`ghcr.io/conalmullan/video-toolkit-sadtalker:latest`	RTX 4090	24GB	~$0.05-0.15/job
qwen3_tts	`ghcr.io/conalmullan/video-toolkit-qwen3-tts:latest`	ADA 24GB	24GB	~$0.01-0.05/job

Total monthly cost: Rarely exceeds $10 even with heavy use.

How It Works

All tools follow the same pattern:

Local CLI → Upload input to cloud storage → RunPod API → Poll for result → Download output

File transfer: Tools use Cloudflare R2 when configured (
```
R2_ACCOUNT_ID
```
,
```
R2_ACCESS_KEY_ID
```
,
```
R2_SECRET_ACCESS_KEY
```
,
```
R2_BUCKET_NAME
```
), falling back to free upload services
RunPod API: Tools call the
```
/run
```
endpoint, then poll
```
/status/{job_id}
```
until complete
Cold vs warm start: First request after idle spins up a worker (~30-90s). Subsequent requests are fast (~5-15s)

Endpoint Management

Workers

workersMin: 0    — Scale to zero when idle (no cost)
workersMax: 1    — Max concurrent jobs (increase for throughput)
idleTimeout: 5   — Seconds before worker scales down

Across all endpoints, you share a total worker pool based on your RunPod plan. If you hit limits, reduce

workersMax

on endpoints you're not actively using.

Checking Endpoint Status

Each tool stores its endpoint ID in

.env

:

Tool	Env Var
image_edit	`RUNPOD_QWEN_EDIT_ENDPOINT_ID`
upscale	`RUNPOD_UPSCALE_ENDPOINT_ID`
dewatermark	`RUNPOD_DEWATERMARK_ENDPOINT_ID`
sadtalker	`RUNPOD_SADTALKER_ENDPOINT_ID`
qwen3_tts	`RUNPOD_QWEN3_TTS_ENDPOINT_ID`

Disabling an Endpoint

To free worker slots without deleting the endpoint, set

workersMax=0

via the RunPod dashboard or GraphQL API.

Troubleshooting

Force Image Pull

When you push a new Docker image version, RunPod may still use the cached old one. To force a pull:

Update the template's
```
imageName
```
to use
```
@sha256:DIGEST
```
notation
Wait for the worker to restart
Revert to
```
:latest
```
tag after confirming

Cold Start Too Slow

qwen3-tts: ~70s cold start, ~7s warm
sadtalker: ~60s cold start, ~10s warm
image_edit: ~90s cold start, ~15s warm

If cold starts are a problem, set

workersMin: 1

(costs money when idle).

Job Fails with OOM

The model needs more VRAM than the GPU provides. Options:

Use a larger GPU tier
For dewatermark: reduce
```
--resize-ratio
```
(default 0.5 for safety)
For image_edit: reduce
```
--steps
```

"No workers available"

You've hit your plan's concurrent worker limit. Either:

Wait for a running job to finish
Set
```
workersMax=0
```
on endpoints you're not using
Upgrade your RunPod plan

Docker Images

All Dockerfiles live in

docker/runpod-*/

. Images use

runpod/pytorch

as the base to share layers across tools.

Building for RunPod (from Apple Silicon Mac):