Bria Fibo Edit API

Endpoint: POST https://fal.run/bria/fibo-edit/edit Endpoint ID: bria/fibo-edit/edit

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(
    "bria/fibo-edit/edit",
    arguments={},
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/edit", {
  input: {},
  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/bria/fibo-edit/edit \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{}'

Examples

Input Schema

string

Reference image (file or URL).

string

Mask image (file or URL). Optional

string

Instruction for image editing.

StructuredInstruction

The structured prompt to generate an image from.

StructuredInstruction

The original vgl used to generate the image.

StructuredInstruction

The new vgl describing image after edit.

integer

default:"5555"

Random seed for reproducibility. Default value: 5555

integer

default:"30"

Number of inference steps. Default value: 30Range: 20 to 50

string

default:""

Negative prompt for image generation. Default value: ""

float | integer

default:"5"

Guidance scale for text. Default value: 5

boolean

default:"false"

If true, returns the image directly in the response (increases latency).

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://v3b.fal.media/files/b/0a8b07e8/GYKVk2EVivg_MC3jRRZi3_png%20-%202026-01-13T094835.850%20(3).png",
  "instruction": "change lighting to starlight nighttime",
  "seed": 5555,
  "steps_num": 30,
  "negative_prompt": "",
  "guidance_scale": 5,
  "sync_mode": false
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": [
    {
      "url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/ewT7wv-jMgkqs7z7xQNNL_e8707c299d034feab7a64d903118098f.png"
    }
  ]
}

Endpoint: POST https://fal.run/bria/fibo-edit/restore Endpoint ID: bria/fibo-edit/restore

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(
    "bria/fibo-edit/restore",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T134151.337.png"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/restore", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T134151.337.png"
    },
  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/bria/fibo-edit/restore \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T134151.337.png"
}'

Input Schema

string

required

The source image.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T134151.337.png"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/relight Endpoint ID: bria/fibo-edit/relight

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(
    "bria/fibo-edit/relight",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/bria_result+-+2026-01-13T095546.173.png",
        "light_direction": "front",
        "light_type": "soft overcast daylight lighting"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/relight", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/bria_result+-+2026-01-13T095546.173.png",
      light_direction: "front",
      light_type: "soft overcast daylight lighting"
    },
  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/bria/fibo-edit/relight \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/bria_result+-+2026-01-13T095546.173.png",
  "light_direction": "front",
  "light_type": "soft overcast daylight lighting"
}'

Input Schema

string

required

The source image.

Enum

required

Where the light comes from.Possible values: front, side, bottom, top-down

LightTypeEnum

required

The quality/style/time of day.Possible values: midday, blue hour light, low-angle sunlight, sunrise light, spotlight on subject, overcast light, soft overcast daylight lighting, cloud-filtered lighting, fog-diffused lighting, moonlight lighting, starlight nighttime, soft bokeh lighting, harsh studio lighting

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/bria_result+-+2026-01-13T095546.173.png",
  "light_direction": "front",
  "light_type": "soft overcast daylight lighting"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/sketch_to_colored_image Endpoint ID: bria/fibo-edit/sketch_to_colored_image

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(
    "bria/fibo-edit/sketch_to_colored_image",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_b_w_sketch_of_a_cat.png"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/sketch_to_colored_image", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_b_w_sketch_of_a_cat.png"
    },
  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/bria/fibo-edit/sketch_to_colored_image \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_b_w_sketch_of_a_cat.png"
}'

Input Schema

string

required

The source image.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_b_w_sketch_of_a_cat.png"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/erase_by_text Endpoint ID: bria/fibo-edit/erase_by_text

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(
    "bria/fibo-edit/erase_by_text",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
        "object_name": "Table"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/erase_by_text", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
      object_name: "Table"
    },
  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/bria/fibo-edit/erase_by_text \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
  "object_name": "Table"
}'

Input Schema

string

required

The source image.

string

required

The name of the object to remove.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
  "object_name": "Table"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/colorize Endpoint ID: bria/fibo-edit/colorize

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(
    "bria/fibo-edit/colorize",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T083840.113.png",
        "color": "contemporary color"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/colorize", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T083840.113.png",
      color: "contemporary color"
    },
  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/bria/fibo-edit/colorize \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T083840.113.png",
  "color": "contemporary color"
}'

Input Schema

string

required

The source image.

ColorEnum

required

Select the color palette or aesthetic for the output imagePossible values: contemporary color, vivid color, black and white colors, sepia vintage

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/png+-+2026-01-13T083840.113.png",
  "color": "contemporary color"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/restyle Endpoint ID: bria/fibo-edit/restyle

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(
    "bria/fibo-edit/restyle",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/high_camera_angle_warm_filter.png",
        "style": "3D Render"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/restyle", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/high_camera_angle_warm_filter.png",
      style: "3D Render"
    },
  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/bria/fibo-edit/restyle \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/high_camera_angle_warm_filter.png",
  "style": "3D Render"
}'

Input Schema

string

required

The source image.

StyleEnum

required

Select the desired artistic style for the output image.Possible values: 3D Render, Cubism, Oil Painting, Anime, Cartoon, Coloring Book, Retro Ad, Pop Art Halftone, Vector Art, Story Board, Art Nouveau, Cross Etching, Wood Cut

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/high_camera_angle_warm_filter.png",
  "style": "3D Render"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/rewrite_text Endpoint ID: bria/fibo-edit/rewrite_text

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(
    "bria/fibo-edit/rewrite_text",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_an_image_of_cake__with_text_on_it_saying___Hi_there__.png",
        "new_text": "FIBO Edit!"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/rewrite_text", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_an_image_of_cake__with_text_on_it_saying___Hi_there__.png",
      new_text: "FIBO Edit!"
    },
  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/bria/fibo-edit/rewrite_text \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_an_image_of_cake__with_text_on_it_saying___Hi_there__.png",
  "new_text": "FIBO Edit!"
}'

Input Schema

string

required

The source image.

string

required

The new text string to appear in the image.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_an_image_of_cake__with_text_on_it_saying___Hi_there__.png",
  "new_text": "FIBO Edit!"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/add_object_by_text Endpoint ID: bria/fibo-edit/add_object_by_text

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(
    "bria/fibo-edit/add_object_by_text",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
        "instruction": "Place a red vase with flowers on the table."
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/add_object_by_text", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
      instruction: "Place a red vase with flowers on the table."
    },
  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/bria/fibo-edit/add_object_by_text \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
  "instruction": "Place a red vase with flowers on the table."
}'

Input Schema

string

required

The source image.

string

required

The full natural language command describing what to add and where.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/an_empty_table_in_living_room.png",
  "instruction": "Place a red vase with flowers on the table."
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/blend Endpoint ID: bria/fibo-edit/blend

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(
    "bria/fibo-edit/blend",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
        "instruction": "Place the art on the shirt, keep the art exactly the same"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/blend", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
      instruction: "Place the art on the shirt, keep the art exactly the same"
    },
  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/bria/fibo-edit/blend \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
  "instruction": "Place the art on the shirt, keep the art exactly the same"
}'

Input Schema

string

required

The source image.

string

required

Instruct what elements you would like to blend in your image.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
  "instruction": "Place the art on the shirt, keep the art exactly the same"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/replace_object_by_text Endpoint ID: bria/fibo-edit/replace_object_by_text

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(
    "bria/fibo-edit/replace_object_by_text",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/a_bowl_of_fruits__should_have_a_red_apple.png",
        "instruction": "Replace the red apple with a green pear"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/replace_object_by_text", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/a_bowl_of_fruits__should_have_a_red_apple.png",
      instruction: "Replace the red apple with a green pear"
    },
  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/bria/fibo-edit/replace_object_by_text \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/a_bowl_of_fruits__should_have_a_red_apple.png",
  "instruction": "Replace the red apple with a green pear"
}'

Input Schema

string

required

The source image.

string

required

The full natural language command describing what to replace.

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/a_bowl_of_fruits__should_have_a_red_apple.png",
  "instruction": "Replace the red apple with a green pear"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Endpoint: POST https://fal.run/bria/fibo-edit/reseason Endpoint ID: bria/fibo-edit/reseason

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(
    "bria/fibo-edit/reseason",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_realistic_image_of_a_green_field_in_the_spring__also_add_trees.png",
        "season": "winter"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("bria/fibo-edit/reseason", {
  input: {
      image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_realistic_image_of_a_green_field_in_the_spring__also_add_trees.png",
      season: "winter"
    },
  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/bria/fibo-edit/reseason \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_realistic_image_of_a_green_field_in_the_spring__also_add_trees.png",
  "season": "winter"
}'

Input Schema

string

required

The source image.

SeasonEnum

required

The desired season.Possible values: spring, summer, autumn, winter

Output Schema

Image

required

Generated image.

list<Image>

default:""

Generated images.

Structured Instruction

required

Current instruction.

Input Example

{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/create_a_realistic_image_of_a_green_field_in_the_spring__also_add_trees.png",
  "season": "winter"
}

Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}

Fibo Edit [Restore] — Image Generation

Limitations

steps_num range: 20 to 50
light_direction restricted to: front, side, bottom, top-down
color restricted to: contemporary color, vivid color, black and white colors, sepia vintage
season restricted to: spring, summer, autumn, winter

Try it in the Playground

​Quick Start

​Examples

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Quick Start

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​Input Example

​Output Example

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​Quick Start

​Input Schema

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​Input Example

​Output Example

​Related

​Limitations

Quick Start

Examples

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Related

Limitations