File size: 3,248 Bytes
41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 41a10e6 dcc0102 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # 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": "..."}}}'
```
|