Fine Tuning Replicate Train

Integrator submits POST /v1/trainings with version=<sdxl-trainer-version>, input={training_data: <zip-url>, ...}, destination='myorg/sdxl-brand-lora'.

Specify destination as an existing model owner/name the token has write access to. On success, the training writes a new version into that model (immutable). Retain the training_id for audit. Choose destination model before training, not after — the model must exist.

Integrator passes training_data='https://my-private-bucket.s3.amazonaws.com/dataset.zip' with no auth in the URL.

training_data URLs must be publicly fetchable from Replicate workers (or use a presigned URL). Bake authentication into the URL, not headers — the training worker cannot send custom headers. Do not host on internal IPs. For sensitive datasets, prefer Replicate-hosted Files API uploads.

Training created with status=starting. Polling cadence 5 s.

Poll GET /v1/trainings/{id} with exponential backoff (start 30 s, cap 5 min) — trainings run for minutes to hours. Prefer webhooks for terminal delivery. Do not poll faster than 30 s — training progress evolves slowly.