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SD 1.5 Depth ControlNet Image to Image

SD 1.5 Depth ControlNet
fal-ai/sd15-depth-controlnet
Inference
Commercial use

About

Text To Image

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

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

const result = await fal.subscribe("fal-ai/sd15-depth-controlnet", {
  input: {
    prompt: "Ice fortress, aurora skies, polar wildlife, twilight",
    control_image_url: "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg"
  },
  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);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

The client API provides a convenient way to submit requests to the model.

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

const { request_id } = await fal.queue.submit("fal-ai/sd15-depth-controlnet", {
  input: {
    prompt: "Ice fortress, aurora skies, polar wildlife, twilight",
    control_image_url: "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

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

const status = await fal.queue.status("fal-ai/sd15-depth-controlnet", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

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

const result = await fal.queue.result("fal-ai/sd15-depth-controlnet", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

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

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

The prompt to use for generating the image. Be as descriptive as possible for best results.

control_image_url string* required

The URL of the control image.

controlnet_conditioning_scale float

The scale of the controlnet conditioning. Default value: 0.5

negative_prompt string

The negative prompt to use. Use it to address details that you don't want in the image. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). Default value: ""

image_size ImageSize | Enum

The size of the generated image. Leave it none to automatically infer from the control image.

Possible enum values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9

Note: For custom image sizes, you can pass the width and height as an object:

"image_size": {
  "width": 1280,
  "height": 720
}
num_inference_steps integer

The number of inference steps to perform. Default value: 35

seed integer

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

enable_deep_cache boolean

If set to true, DeepCache will be enabled. TBD

guidance_scale float

The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt when looking for a related image to show you. Default value: 7.5

sync_mode boolean

If set to true, the function will wait for the image to be generated and uploaded before returning the response. This will increase the latency of the function but it allows you to get the image directly in the response without going through the CDN.

num_images integer

The number of images to generate. Default value: 1

loras list<LoraWeight>

The list of LoRA weights to use. Default value: ``

enable_safety_checker boolean

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

expand_prompt boolean

If set to true, the prompt will be expanded with additional prompts.

{
  "prompt": "Ice fortress, aurora skies, polar wildlife, twilight",
  "control_image_url": "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
  "controlnet_conditioning_scale": 0.5,
  "negative_prompt": "cartoon, illustration, animation. face. male, female",
  "image_size": null,
  "num_inference_steps": 35,
  "guidance_scale": 7.5,
  "num_images": 1,
  "loras": []
}

Output#

images list<Image>* required

The generated image files info.

timings Timings* required
seed 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.

has_nsfw_concepts list<boolean>* required

Whether the generated images contain NSFW concepts.

prompt string* required

The prompt used for generating the image.

{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}

Other types#

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

LoraWeight#

path string* required

URL or the path to the LoRA weights. Or HF model name.

scale float

The scale of the LoRA weight. This is used to scale the LoRA weight before merging it with the base model. Default value: 1

Image#

url string* required
width integer* required
height integer* required
content_type string

Default value: "image/jpeg"