Z Image Turbo Tiling API

Tiling
Lora

Endpoint: POST https://fal.run/fal-ai/z-image/turbo/tiling Endpoint ID: fal-ai/z-image/turbo/tiling

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/z-image/turbo/tiling",
    arguments={
        "prompt": "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit."
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("fal-ai/z-image/turbo/tiling", {
  input: {
      prompt: "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit."
    },
  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/z-image/turbo/tiling \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit."
}'

Input Schema

string

required

The prompt to generate an image from.

ImageSize | Enum

default:"square_hd"

The size of the generated image. Use ‘auto’ to match the input image size (or 1024x1024 if no image). Default value: square_hdPossible values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9, auto

integer

default:"8"

The number of inference steps to perform. Default value: 8Range: 1 to 8

integer

The same seed and the same prompt given to the same version of the model will output the same image every time.

boolean

default:"false"

If True, the media will be returned as a data URI and the output data won’t be available in the request history.

integer

default:"1"

The number of images to generate. Default value: 1Range: 1 to 4

boolean

default:"true"

If set to true, the safety checker will be enabled. Default value: true

OutputFormatEnum

default:"png"

The format of the generated image. Default value: "png"Possible values: jpeg, png, webp

AccelerationEnum

default:"regular"

The acceleration level to use. Default value: "regular"Possible values: none, regular, high

boolean

default:"false"

Whether to enable prompt expansion. Note: this will increase the price by 0.0025 credits per request.

string

URL of an image for image-to-image or inpainting. When provided without mask_image_url, performs image-to-image; with mask_image_url, performs inpainting.

string

URL of a mask image for inpainting. White regions are regenerated, black regions are preserved. Requires image_url.

float

default:"0.6"

How much to transform the input image. Only used when image_url is provided. Default value: 0.6

integer

default:"128"

Tile size in latent space (64 = 512px, 128 = 1024px, 256 = 2048px). Default value: 128Range: 32 to 256

integer

default:"64"

Tile stride in latent space. (32 = 256px, 64 = 512px, 128 = 1024px). Default value: 64Range: 16 to 128

TilingModeEnum

default:"both"

Tiling direction: ‘both’ (omnidirectional), ‘horizontal’ (left-right only), ‘vertical’ (top-bottom only). Default value: "both"Possible values: both, horizontal, vertical

Output Schema

list<ImageFile>

required

The generated image files info.

Timings

required

The timings of the generation process.

integer

required

Seed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.

list<boolean>

required

Whether the generated images contain NSFW concepts.

string

required

The prompt used for generating the image.

Input Example

{
  "prompt": "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit.",
  "image_size": "square_hd",
  "num_inference_steps": 8,
  "sync_mode": false,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png",
  "acceleration": "regular",
  "enable_prompt_expansion": false,
  "strength": 0.6,
  "tile_size": 128,
  "tile_stride": 64,
  "tiling_mode": "both"
}

Output Example

{
  "images": [
    {
      "content_type": "image/png",
      "height": 2048,
      "url": "https://v3b.fal.media/files/b/0a9276c3/6KxxMb0bPmfNLH3DqcuSf_qRUe8cIL.png",
      "width": 2048
    }
  ],
  "prompt": ""
}

Endpoint: POST https://fal.run/fal-ai/z-image/turbo/tiling/lora Endpoint ID: fal-ai/z-image/turbo/tiling/lora

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/z-image/turbo/tiling/lora",
    arguments={
        "prompt": "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit."
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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

const result = await fal.subscribe("fal-ai/z-image/turbo/tiling/lora", {
  input: {
      prompt: "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit."
    },
  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/z-image/turbo/tiling/lora \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit."
}'

Input Schema

string

required

The prompt to generate an image from.

ImageSize | Enum

default:"square_hd"

integer

default:"8"

The number of inference steps to perform. Default value: 8Range: 1 to 8

integer

The same seed and the same prompt given to the same version of the model will output the same image every time.

boolean

default:"false"

If True, the media will be returned as a data URI and the output data won’t be available in the request history.

integer

default:"1"

The number of images to generate. Default value: 1Range: 1 to 4

boolean

default:"true"

If set to true, the safety checker will be enabled. Default value: true

OutputFormatEnum

default:"png"

The format of the generated image. Default value: "png"Possible values: jpeg, png, webp

AccelerationEnum

default:"regular"

The acceleration level to use. Default value: "regular"Possible values: none, regular, high

boolean

default:"false"

Whether to enable prompt expansion. Note: this will increase the price by 0.0025 credits per request.

string

URL of an image for image-to-image or inpainting. When provided without mask_image_url, performs image-to-image; with mask_image_url, performs inpainting.

string

URL of a mask image for inpainting. White regions are regenerated, black regions are preserved. Requires image_url.

float

default:"0.6"

How much to transform the input image. Only used when image_url is provided. Default value: 0.6

integer

default:"128"

Tile size in latent space (64 = 512px, 128 = 1024px, 256 = 2048px). Default value: 128Range: 32 to 256

integer

default:"64"

Tile stride in latent space. (32 = 256px, 64 = 512px, 128 = 1024px). Default value: 64Range: 16 to 128

TilingModeEnum

default:"both"

Tiling direction: ‘both’ (omnidirectional), ‘horizontal’ (left-right only), ‘vertical’ (top-bottom only). Default value: "both"Possible values: both, horizontal, vertical

list<LoRAInput>

default:""

List of LoRA weights to apply (maximum 3).

Output Schema

list<ImageFile>

required

The generated image files info.

Timings

required

The timings of the generation process.

integer

required

Seed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.

list<boolean>

required

Whether the generated images contain NSFW concepts.

string

required

The prompt used for generating the image.

Input Example

{
  "prompt": "A hyper-realistic, high-resolution 4k texture of an ancient weathered brick wall heavily overgrown with lush green moss and soft lichens. The bricks are aged, featuring deep earthy tones, natural cracks, and gritty textures. Vibrant emerald moss fills the mortar lines and spills over the rough surfaces of the stones. Uniform, flat cinematic lighting ensures no harsh shadows, highlighting the intricate organic details and damp stone surfaces. The composition is a perfectly balanced overhead view, showcasing a rich tapestry of botanical growth and masonry craftsmanship with professional clarity and hyper-detailed grit.",
  "image_size": "square_hd",
  "num_inference_steps": 8,
  "sync_mode": false,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png",
  "acceleration": "regular",
  "enable_prompt_expansion": false,
  "strength": 0.6,
  "tile_size": 128,
  "tile_stride": 64,
  "tiling_mode": "both",
  "loras": []
}

Output Example

{
  "images": [
    {
      "content_type": "image/png",
      "height": 2048,
      "url": "https://v3b.fal.media/files/b/0a9276c3/6KxxMb0bPmfNLH3DqcuSf_qRUe8cIL.png",
      "width": 2048
    }
  ],
  "prompt": ""
}

Z-Image Turbo — Image Generation
Z Image Base — Image Generation
Z Image Base (LoRA) — Image Generation
Z-Image Turbo Seamless Tiling — Image Generation

Limitations

image_size restricted to: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9, auto
num_inference_steps range: 1 to 8
num_images range: 1 to 4
output_format restricted to: jpeg, png, webp
acceleration restricted to: none, regular, high
tile_size range: 32 to 256
tile_stride range: 16 to 128
tiling_mode restricted to: both, horizontal, vertical
Content moderation via safety checker

Video Generation

Image Generation

Audio

Vision

3D

Z Image Turbo Tiling API

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

Limitations

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

​Related

​Limitations

Quick Start

Input Schema

Output Schema

Input Example

Output Example

Quick Start

Input Schema

Output Schema

Input Example

Output Example

Related

Limitations