Search across models, guides, examples and the API.
↑↓ Navigateenter Openesc Close
Dashboard
live
MODEL IDrunware:controlnet-preprocess@seg
Try in Playground

ControlNet Preprocess Seg

Available with Zero Data Retention

ControlNet Preprocess Seg converts an input image into a semantic segmentation map for ControlNet conditioning. It is best for preserving broad scene organization such as sky, ground, buildings, clothing, vegetation, and other region-level structure while allowing major stylistic changes.

ControlNet Preprocess Seg

semantic-segmentation

Oyster Hatchery Dusk Relight Guide$0.0013~5s
Oyster Hatchery Dusk Relight Guide
Try in Playground
import { createClient } from '@runware/sdk'

const client = await createClient({ apiKey: process.env.RUNWARE_API_KEY })
await client.connect()

const [result] = await client.run({
  model: 'runware:controlnet-preprocess@seg',
  inputs: {
    image: 'https://assets.runware.ai/assets/inputs/b14013c1-fdfa-4597-9538-502b7bc8049b.jpg'
  }
})
import asyncio
import os

from runware import Runware


async def main():
    async with Runware(api_key=os.environ["RUNWARE_API_KEY"]) as client:
        results = await client.run({
            "model": "runware:controlnet-preprocess@seg",
            "inputs": {
                "image": "https://assets.runware.ai/assets/inputs/b14013c1-fdfa-4597-9538-502b7bc8049b.jpg"
            }
        })


asyncio.run(main())
curl https://api.runware.ai/v1 \
  -H "Authorization: Bearer $RUNWARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '[
    {
      "taskType": "controlNetPreprocess",
      "taskUUID": "fe99cb72-5ae5-4a57-b6d4-e870116fb5e4",
      "model": "runware:controlnet-preprocess@seg",
      "inputs": {
        "image": "https://assets.runware.ai/assets/inputs/b14013c1-fdfa-4597-9538-502b7bc8049b.jpg"
      }
    }
  ]'
runware run runware:controlnet-preprocess@seg \
  inputs.image=https://assets.runware.ai/assets/inputs/b14013c1-fdfa-4597-9538-502b7bc8049b.jpg
{
  "taskType": "controlNetPreprocess",
  "taskUUID": "fe99cb72-5ae5-4a57-b6d4-e870116fb5e4",
  "model": "runware:controlnet-preprocess@seg",
  "inputs": {
    "image": "https://assets.runware.ai/assets/inputs/b14013c1-fdfa-4597-9538-502b7bc8049b.jpg"
  }
}
Response
{
  "taskType": "imageControlNetPreProcess",
  "taskUUID": "fe99cb72-5ae5-4a57-b6d4-e870116fb5e4",
  "guideImageURL": "https://im.runware.ai/image/os/a03d21/ws/3/ii/2411dfa4-b824-46c9-8eaf-e32434a4b46d.jpg",
  "cost": 0.0013,
  "guideImageUUID": "2411dfa4-b824-46c9-8eaf-e32434a4b46d",
  "inputImageUUID": "f036855a-4174-4dda-bc08-d9fec3cedc59"
}

semantic-segmentation

Cargo Trike Colorway Segmentation Guide$0.0013~6s
Cargo Trike Colorway Segmentation Guide
Try in Playground
import { createClient } from '@runware/sdk'

const client = await createClient({ apiKey: process.env.RUNWARE_API_KEY })
await client.connect()

const [result] = await client.run({
  model: 'runware:controlnet-preprocess@seg',
  inputs: {
    image: 'https://assets.runware.ai/assets/inputs/24cd483a-3b39-4d62-98cd-59187bd6d888.jpg'
  }
})
import asyncio
import os

from runware import Runware


async def main():
    async with Runware(api_key=os.environ["RUNWARE_API_KEY"]) as client:
        results = await client.run({
            "model": "runware:controlnet-preprocess@seg",
            "inputs": {
                "image": "https://assets.runware.ai/assets/inputs/24cd483a-3b39-4d62-98cd-59187bd6d888.jpg"
            }
        })


asyncio.run(main())
curl https://api.runware.ai/v1 \
  -H "Authorization: Bearer $RUNWARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '[
    {
      "taskType": "controlNetPreprocess",
      "taskUUID": "db981afb-2424-43ad-b41b-87cd6967ae17",
      "model": "runware:controlnet-preprocess@seg",
      "inputs": {
        "image": "https://assets.runware.ai/assets/inputs/24cd483a-3b39-4d62-98cd-59187bd6d888.jpg"
      }
    }
  ]'
runware run runware:controlnet-preprocess@seg \
  inputs.image=https://assets.runware.ai/assets/inputs/24cd483a-3b39-4d62-98cd-59187bd6d888.jpg
{
  "taskType": "controlNetPreprocess",
  "taskUUID": "db981afb-2424-43ad-b41b-87cd6967ae17",
  "model": "runware:controlnet-preprocess@seg",
  "inputs": {
    "image": "https://assets.runware.ai/assets/inputs/24cd483a-3b39-4d62-98cd-59187bd6d888.jpg"
  }
}
Response
{
  "taskType": "imageControlNetPreProcess",
  "taskUUID": "db981afb-2424-43ad-b41b-87cd6967ae17",
  "guideImageURL": "https://im.runware.ai/image/os/a04d20/ws/3/ii/cf05ddf3-7a6a-4d09-ab4f-692fc15e0033.jpg",
  "cost": 0.0013,
  "guideImageUUID": "cf05ddf3-7a6a-4d09-ab4f-692fc15e0033",
  "inputImageUUID": "8558d307-e052-4c4f-a63b-b0a2bcf3fd45"
}

semantic-segmentation

Drysuit Colorway Segmentation Map$0.0013~5s
Drysuit Colorway Segmentation Map
Try in Playground
import { createClient } from '@runware/sdk'

const client = await createClient({ apiKey: process.env.RUNWARE_API_KEY })
await client.connect()

const [result] = await client.run({
  model: 'runware:controlnet-preprocess@seg',
  inputs: {
    image: 'https://assets.runware.ai/assets/inputs/b3fddc4f-3956-4973-b86b-748bc45c41dd.jpg'
  }
})
import asyncio
import os

from runware import Runware


async def main():
    async with Runware(api_key=os.environ["RUNWARE_API_KEY"]) as client:
        results = await client.run({
            "model": "runware:controlnet-preprocess@seg",
            "inputs": {
                "image": "https://assets.runware.ai/assets/inputs/b3fddc4f-3956-4973-b86b-748bc45c41dd.jpg"
            }
        })


asyncio.run(main())
curl https://api.runware.ai/v1 \
  -H "Authorization: Bearer $RUNWARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '[
    {
      "taskType": "controlNetPreprocess",
      "taskUUID": "135d797b-10e2-44c8-824c-7749a9643147",
      "model": "runware:controlnet-preprocess@seg",
      "inputs": {
        "image": "https://assets.runware.ai/assets/inputs/b3fddc4f-3956-4973-b86b-748bc45c41dd.jpg"
      }
    }
  ]'
runware run runware:controlnet-preprocess@seg \
  inputs.image=https://assets.runware.ai/assets/inputs/b3fddc4f-3956-4973-b86b-748bc45c41dd.jpg
{
  "taskType": "controlNetPreprocess",
  "taskUUID": "135d797b-10e2-44c8-824c-7749a9643147",
  "model": "runware:controlnet-preprocess@seg",
  "inputs": {
    "image": "https://assets.runware.ai/assets/inputs/b3fddc4f-3956-4973-b86b-748bc45c41dd.jpg"
  }
}
Response
{
  "taskType": "imageControlNetPreProcess",
  "taskUUID": "135d797b-10e2-44c8-824c-7749a9643147",
  "guideImageURL": "https://im.runware.ai/image/os/a07dlim3/ws/3/ii/0bc1abba-efcc-4668-887e-eaf2929cb820.jpg",
  "cost": 0.0013,
  "guideImageUUID": "0bc1abba-efcc-4668-887e-eaf2929cb820",
  "inputImageUUID": "7e4f5c06-d2a2-457f-8e62-7bc578af4232"
}

semantic-segmentation

Glassblower Headshot Retouch Segmentation$0.0013~7s
Glassblower Headshot Retouch Segmentation
Try in Playground
import { createClient } from '@runware/sdk'

const client = await createClient({ apiKey: process.env.RUNWARE_API_KEY })
await client.connect()

const [result] = await client.run({
  model: 'runware:controlnet-preprocess@seg',
  inputs: {
    image: 'https://assets.runware.ai/assets/inputs/a61e644a-67b2-4908-ba42-7b34272072d6.jpg'
  }
})
import asyncio
import os

from runware import Runware


async def main():
    async with Runware(api_key=os.environ["RUNWARE_API_KEY"]) as client:
        results = await client.run({
            "model": "runware:controlnet-preprocess@seg",
            "inputs": {
                "image": "https://assets.runware.ai/assets/inputs/a61e644a-67b2-4908-ba42-7b34272072d6.jpg"
            }
        })


asyncio.run(main())
curl https://api.runware.ai/v1 \
  -H "Authorization: Bearer $RUNWARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '[
    {
      "taskType": "controlNetPreprocess",
      "taskUUID": "eda9dc4b-22cc-487a-8836-87765398a743",
      "model": "runware:controlnet-preprocess@seg",
      "inputs": {
        "image": "https://assets.runware.ai/assets/inputs/a61e644a-67b2-4908-ba42-7b34272072d6.jpg"
      }
    }
  ]'
runware run runware:controlnet-preprocess@seg \
  inputs.image=https://assets.runware.ai/assets/inputs/a61e644a-67b2-4908-ba42-7b34272072d6.jpg
{
  "taskType": "controlNetPreprocess",
  "taskUUID": "eda9dc4b-22cc-487a-8836-87765398a743",
  "model": "runware:controlnet-preprocess@seg",
  "inputs": {
    "image": "https://assets.runware.ai/assets/inputs/a61e644a-67b2-4908-ba42-7b34272072d6.jpg"
  }
}
Response
{
  "taskType": "imageControlNetPreProcess",
  "taskUUID": "eda9dc4b-22cc-487a-8836-87765398a743",
  "guideImageURL": "https://im.runware.ai/image/os/a06dlim3/ws/3/ii/34943a41-6689-489c-b8ad-977519d58d4b.jpg",
  "cost": 0.0013,
  "guideImageUUID": "34943a41-6689-489c-b8ad-977519d58d4b",
  "inputImageUUID": "7d235a58-c451-413a-8e04-e87a05c93120"
}
Ask AI
Send feedback