# Serverless client scripts

Standalone callers (Python 3.8+, stdlib only — no pip installs) for the
deployed RunPod endpoints. Each endpoint has a sync script (`/runsync`, blocks
until done) and an async one (`/run` — submit, poll, fire-and-forget with
`--no-wait`, collect with `--job-id`, `--cancel`).

| endpoint | id | sync | async | workflow copy |
|---|---|---|---|---|
| qwen-edit-turbo | `dom5lwr0o5wq6u` | `qwen_edit_sync.py` | `qwen_edit_async.py` | `qwen-edit-turbo-v4.json` |
| qwen-edit-nsfw | `tplcz9jtzihoxa` | `qwen_edit_nsfw_sync.py` | `qwen_edit_nsfw_async.py` | `qwen-edit-nsfw.json` |
| faceswap-lissie | `qkeibkeoefcrk3` | `faceswap_lissie_sync.py` | `faceswap_lissie_async.py` | `new-faceswap-lissie-v4.json` |

Endpoint ids are baked in as defaults; override with `--endpoint-id`. Auth via
`--api-key` or `RUNPOD_API_KEY` env. Results are saved into `--out` (default
`.`); the seed used is always printed so runs are reproducible with `--seed`.

## Examples

```bash
export RUNPOD_API_KEY=...

# turbo outfit transfer (two images)
python qwen_edit_sync.py --image person.jpg --ref-image outfit.png \
    --prompt "replace the outfit of the person in image 1 with the outfit in image 2" \
    --mode turbo-8 --out ./results

# nsfw single-image edit, 8 steps
python qwen_edit_nsfw_sync.py --image person.jpg \
    --prompt "replace the outfit with ..." --out ./results

# head swap: face from face.jpg onto scene.jpg
python faceswap_lissie_sync.py --body-image scene.jpg --face-image face.jpg \
    --out ./results

# any of them, fire-and-forget
job=$(python qwen_edit_nsfw_async.py --image a.jpg --prompt "..." --no-wait)
python qwen_edit_nsfw_async.py --job-id "$job" --out ./results
```

## Common arguments (all scripts)

`--api-key`, `--endpoint-id`, `--seed`, `--out`, `--timeout`,
`--set NODE.INPUT=VALUE` (raw graph override, repeatable),
`--workflow NAME` (another workflow baked in that image),
`--workflow-json FILE` (**full custom API-format graph** — the endpoints are
workflow-agnostic; uploaded images are matched to your graph's `LoadImage`
nodes by filename). Async extras: `--no-wait`, `--job-id`, `--poll`, `--cancel`.

Per-endpoint knobs are documented in each script's `--help`; the full
param-name → node mapping lives in `../endpoints/<endpoint>/params/`.

## Expectations (measured 2026-07-23/24, H100)

| endpoint | warm exec | notes |
|---|---|---|
| qwen-edit-turbo | turbo-4 ≈ 29-42 s, turbo-8 ≈ 60 s | 2048px, 2560px output upscale |
| qwen-edit-nsfw | ≈ 32 s @ 8 steps, 1 MP | webp output |
| faceswap-lissie | ≈ 11 s @ 5 steps | VL captioner kept in VRAM |

Cold starts: FlashBoot resume ≈ 0.5 s delay with models still in VRAM; a
brand-new host pays a one-time image pull (~10-15 min) then ~1-2 min of model
loading on the first job. Payload limits ~10 MB (`/run`) / ~20 MB (`/runsync`)
with base64 adding ~33% — keep inputs under ~7 MB combined or use async.

Raw HTTP shape (same for all endpoints):

```bash
curl -s -X POST "https://api.runpod.ai/v2/<ENDPOINT_ID>/runsync" \
  -H "Authorization: Bearer $RUNPOD_API_KEY" -H "Content-Type: application/json" \
  -d '{"input": {"images": [{"name": "a.jpg", "image": "<base64>"}],
       "params": {"prompt": "..."}}}'
```