Florence 2 Large API

Endpoint: POST https://fal.run/fal-ai/florence-2-large/more-detailed-caption Endpoint ID: fal-ai/florence-2-large/more-detailed-caption

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/florence-2-large/more-detailed-caption",
    arguments={
        "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/florence-2-large/more-detailed-caption", {
  input: {
      image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/florence-2-large/more-detailed-caption \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}'

Input Schema

string

required

The URL of the image to be processed.

Output Schema

string

required

Results from the model

Input Example

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}

Output Example

{
  "results": ""
}

Endpoint: POST https://fal.run/fal-ai/florence-2-large/detailed-caption Endpoint ID: fal-ai/florence-2-large/detailed-caption

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/florence-2-large/detailed-caption",
    arguments={
        "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/florence-2-large/detailed-caption", {
  input: {
      image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/florence-2-large/detailed-caption \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}'

Input Schema

string

required

The URL of the image to be processed.

Output Schema

string

required

Results from the model

Input Example

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}

Output Example

{
  "results": ""
}

Endpoint: POST https://fal.run/fal-ai/florence-2-large/caption Endpoint ID: fal-ai/florence-2-large/caption

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/florence-2-large/caption",
    arguments={
        "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/florence-2-large/caption", {
  input: {
      image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/florence-2-large/caption \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}'

Input Schema

string

required

The URL of the image to be processed.

Output Schema

string

required

Results from the model

Input Example

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}

Output Example

{
  "results": ""
}

Endpoint: POST https://fal.run/fal-ai/florence-2-large/ocr Endpoint ID: fal-ai/florence-2-large/ocr

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/florence-2-large/ocr",
    arguments={
        "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/florence-2-large/ocr", {
  input: {
      image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/florence-2-large/ocr \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}'

Input Schema

string

required

The URL of the image to be processed.

Output Schema

string

required

Results from the model

Input Example

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
}

Output Example

{
  "results": ""
}

Endpoint: POST https://fal.run/fal-ai/florence-2-large/region-to-description Endpoint ID: fal-ai/florence-2-large/region-to-description

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/florence-2-large/region-to-description",
    arguments={
        "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
        "region": {
            "x1": 100,
            "x2": 200,
            "y1": 100,
            "y2": 200
        }
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/florence-2-large/region-to-description", {
  input: {
      image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
      region: {
        x1: 100,
        x2: 200,
        y1: 100,
        y2: 200
      }
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/florence-2-large/region-to-description \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
  "region": {
    "x1": 100,
    "x2": 200,
    "y1": 100,
    "y2": 200
  }
}'

Input Schema

string

required

The URL of the image to be processed.

Region

required

The user input coordinates

Output Schema

string

required

Results from the model

Input Example

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
  "region": {
    "x1": 100,
    "x2": 200,
    "y1": 100,
    "y2": 200
  }
}

Output Example

{
  "results": ""
}

Endpoint: POST https://fal.run/fal-ai/florence-2-large/region-to-category Endpoint ID: fal-ai/florence-2-large/region-to-category

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/florence-2-large/region-to-category",
    arguments={
        "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
        "region": {
            "x1": 100,
            "x2": 200,
            "y1": 100,
            "y2": 200
        }
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/florence-2-large/region-to-category", {
  input: {
      image_url: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
      region: {
        x1: 100,
        x2: 200,
        y1: 100,
        y2: 200
      }
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/florence-2-large/region-to-category \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
  "region": {
    "x1": 100,
    "x2": 200,
    "y1": 100,
    "y2": 200
  }
}'

Input Schema

string

required

The URL of the image to be processed.

Region

required

The user input coordinates

Output Schema

string

required

Results from the model

Input Example

{
  "image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
  "region": {
    "x1": 100,
    "x2": 200,
    "y1": 100,
    "y2": 200
  }
}

Output Example

{
  "results": ""
}

Florence-2 Large — Vision
Florence-2 Large — Image Generation

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

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