RunComfy API Docs

These endpoints let you submit and monitor AI Toolkit training jobs (typically LoRA training), then download training artifacts (checkpoints, config yaml, samples) as hosted URLs.

Endpoints

Base URL: https://trainer-api.runcomfy.net

Endpoint	Method	Description
`/prod/v1/trainers/ai-toolkit/jobs`	`POST`	Submit a training job
`/prod/v1/trainers/ai-toolkit/jobs/{job_id}/status`	`GET`	Check status
`/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result`	`GET`	Retrieve training results (artifacts/checkpoints)
`/prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel`	`POST`	Cancel a queued/running job
`/prod/v1/trainers/ai-toolkit/jobs/{job_id}/resume`	`POST`	Resume from the latest checkpoint (if available)
`/prod/v1/trainers/ai-toolkit/jobs/{job_id}/edit`	`POST`	Edit config of a non-running job

Quickstart (minimum working flow)

Create + upload a dataset (Dataset API) until it becomes READY. See: Training Datasets API
Write an AI Toolkit YAML config that references dataset paths in the mounted dataset folder.
Submit a training job with your config_file and required gpu_type.
Poll status and fetch result artifacts.

Preparing your config file.

Before submit your request, you need to save your AI Toolkit config as config.yaml. Your config.yaml is the full AI Toolkit YAML configuration for the training job (model/quantization/train/sample settings, etc.). You should set the parameters you need for your training run in this file first.

Below is the dataset path portion inside config_file. This is how the dataset is mounted into the training job and referenced by the AI Toolkit config:

training_folder must be /app/ai-toolkit/output (fixed; do not change)
folder_path must be /app/ai-toolkit/datasets/{dataset_name} (fixed prefix; only {dataset_name} changes)

This is an example for part of YAML snippet:

job: extension
config:
  name: "trainingjobname1"
  process:
    - save:
      datasets:
        folder_path: "/app/ai-toolkit/datasets/{dataset_name}"
        # ... other datasets config ...

      training_folder: "/app/ai-toolkit/output"

      # ... your model/network/train/sample config ...
meta:
  name: "[name]"

Important details:

{dataset_name} is your dataset’s name from the Dataset API response (or from GET /prod/v1/trainers/datasets).

Submit a training job

Submit a new AI Toolkit training job (typically LoRA training) to the async queue. The job will mount your READY dataset and run the YAML config you provide.

POST /prod/v1/trainers/ai-toolkit/jobs

Request body (important fields)

Field	Type	Required	Description
`config_file_format`	string	✅	Must be `yaml`
`config_file`	string	✅	Full AI Toolkit YAML config file (as a JSON string) . Note: `config_file` is multiline YAML but the Trainer API request body is JSON, you must JSON-escape the YAML into a string before sending.
`gpu_type`	string	✅	Supported values: `ADA_80_PLUS` (RunComfy Trainer UI: H100) or `HOPPER_141` (RunComfy Trainer UI: H200)
`gpu_count`	integer	❌	Number of GPUs. `1` for single-GPU (default), `8` for multi-GPU. Multi-GPU (`8`) is currently only supported for `ADA_80_PLUS` (H100).

Request example

curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs" \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer <token>" \
  --data '{
    "config_file_format": "yaml",
    "config_file": "<YOUR_AI_TOOLKIT_YAML_AS_A_JSON_STRING>",
    "gpu_type": "ADA_80_PLUS",
    "gpu_id":"#1"
  }'

Response

{
  "id": "{job_id}",
  "name": "{job_name}",
  "status_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/status",
  "result_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result",
  "cancel_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel"
}

Monitor training job status

Use this endpoint to check a training job’s current status and progress. Poll it periodically to track the lifecycle and decide when to fetch results or take action. A typical lifecycle is: IN_QUEUE → RUNNING → STOPPED (or FAILED or CANCELED)

Training job status values

IN_QUEUE: Job is accepted and waiting in the queue
RUNNING: Training is currently running
STOPPED: Training has stopped without an error (typically completed successfully, or stopped due to preemption)
FAILED: Training has stopped due to an error; the response includes an error field describing what went wrong (for example, AI Toolkit training errors, or the job stopping due to insufficient account balance)
CANCELED: Job was canceled by the user

GET /prod/v1/trainers/ai-toolkit/jobs/{job_id}/status

Request example

curl --request GET \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/status" \
  --header "Authorization: Bearer <token>"

Response example

{
  "id": "{job_id}",
  "name": "{job_name}",
  "status": "RUNNING",
  "progress": {
    "current_step": 320,
    "total_steps": 2000,
    "percent": 16
  },
  "result_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result",
  "cancel_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel"
}

Retrieve training job results

Use this endpoint to retrieve the latest artifacts produced by a training job (checkpoints, resolved config, samples) as hosted URLs. It can be called while the job is still RUNNING to fetch whatever is available so far. If the job ends in FAILED, you can still call this endpoint to download any artifacts that were produced before the failure (if any). Note: If the training process has already produced checkpoints/samples, the response will include whatever is available so far. The artifacts list will grow over time while the job is running.

GET /prod/v1/trainers/ai-toolkit/jobs/{job_id}/result

Request example

curl --request GET \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result" \
  --header "Authorization: Bearer <token>"

Response example

{
  "id": "{job_id}",
  "name": "{job_name}",
  "status": "STOPPED",
  "artifacts": {
    "checkpoints": [
      {
        "path": "https://example.com/output/dog_portrait_lora_00000100.safetensors"
      },
      {
        "path": "https://example.com/output/dog_portrait_lora_00000200.safetensors"
      }
    ],
    "config": {
      "path": "https://example.com/output/resolved_config.yaml"
    },
    "samples": [
      {
        "sample_index": 1,
        "type": "image",
        "step": 2000,
        "seed": 42,
        "prompt": "<YOUR_PROMPT>",
        "control_image": [],
        "path": "https://example.com/output/samples/step_2000.png"
      }
    ]
  },
  "created_at": "2026-01-31T12:00:00.143086",
  "started_at": "2026-01-31T12:03:10.143086",
  "finished_at": "2026-01-31T16:12:34.143086"
}

Cancel a job

Use this endpoint to cancel a training job that is currently queued (IN_QUEUE) or executing (RUNNING). Cancellation stops further progress, but you can still retrieve any artifacts produced so far via the result endpoint. Note: After canceling, you can still call GET .../result to retrieve any artifacts produced so far.

POST /prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel

Request example

curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel" \
  --header "Authorization: Bearer <token>"

Response example

{
  "id": "{job_id}",
  "name": "{job_name}",
  "status": "CANCELED"
}

Resume a training job

Use this endpoint to resume a training job from its latest checkpoint (if available). This is useful when a job is STOPPED (for example, preemption) but has already produced checkpoints. If a job is FAILED, inspect the error field from the status endpoint to understand and fix the underlying issue. Note:

It reuses the same job_id (does not create a new job).
When resuming, RunComfy will start from the latest checkpoint (the highest-step checkpoint). If no checkpoint exists, the job will start from step 0.

POST /prod/v1/trainers/ai-toolkit/jobs/{job_id}/resume

Request example

curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/resume" \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer <token>" \
  --data '{}'

Response example

{
  "id": "{job_id}",
  "name": "{job_name}",
  "status_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/status",
  "result_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result",
  "cancel_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel"
}

Edit a training job

Use this endpoint to edit the training configuration of a non-running job. This allows updating config_file on a job that is currently STOPPED, CANCELED, or FAILED. GPU type and count are determined when you resume the job. Note:

The config name (config.name) cannot be changed via edit. It must remain the same as the original job.
After editing, call POST .../resume to re-queue the job with the updated configuration.

POST /prod/v1/trainers/ai-toolkit/jobs/{job_id}/edit

Request body

Field	Type	Required	Description
`config_file_format`	string	✅	Must be `yaml`
`config_file`	string	✅	Full AI Toolkit YAML config file (as a JSON string). Note: `config.name` must match the original job name.

Request example

curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/edit" \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer <token>" \
  --data '{
    "config_file_format": "yaml",
    "config_file": "<YOUR_UPDATED_AI_TOOLKIT_YAML_AS_A_JSON_STRING>"
  }'

Response example

{
  "id": "{job_id}",
  "name": "{job_name}",
  "status": "STOPPED",
  "status_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/status",
  "result_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result",
  "cancel_url": "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/cancel"
}

Getting Started

API Reference

Pricing & Support

Async Queue Endpoints - Training Jobs

Endpoints

Quickstart (minimum working flow)

Preparing your config file.

Submit a training job

Request body (important fields)

Request example

Response

Monitor training job status

Training job status values

Request example

Response example

Retrieve training job results

Request example

Response example

Cancel a job

Request example

Response example

Resume a training job

Request example

Response example

Edit a training job

Request body

Request example

Response example

​Endpoints

​Quickstart (minimum working flow)

​Preparing your config file.

​Submit a training job

​Request body (important fields)

​Request example

​Response

​Monitor training job status

​Training job status values

​Request example

​Response example

​Retrieve training job results

​Request example

​Response example

​Cancel a job

​Request example

​Response example

​Resume a training job

​Request example

​Response example

​Edit a training job

​Request body

​Request example

​Response example

Endpoints

Quickstart (minimum working flow)

Preparing your config file.

Submit a training job

Request body (important fields)

Request example

Response

Monitor training job status

Training job status values

Request example

Response example

Retrieve training job results

Request example

Response example

Cancel a job

Request example

Response example

Resume a training job

Request example

Response example

Edit a training job

Request body

Request example

Response example