Realistic Vision

fal-ai/realistic-vision
Inference
Commercial use

1. Calling the API#

Install the client#

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

npm install --save @fal-ai/serverless-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 * as fal from "@fal-ai/serverless-client";

const result = await fal.subscribe("fal-ai/realistic-vision", {
  input: {
    prompt: "A hyperdetailed photograph of a Cat dressed as a mafia boss holding a fish walking down a Japanese fish market with an angry face, 8k resolution, best quality, beautiful photograph, dynamic lighting,"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

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 * as fal from "@fal-ai/serverless-client";

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

3. 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 * as fal from "@fal-ai/serverless-client";

// Upload a file (you can get a file reference from an input element or a drag-and-drop event)
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

// Use the URL in your request
const result = await fal.subscribe("fal-ai/realistic-vision", { image_url: url });

Read more about file handling in our file upload guide.

4. Schema#

Input#

model_namestring

The Realistic Vision model to use.

prompt*string

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

negative_promptstring

The negative prompt to use. Use it to address details that you don't want in the image. Default value: "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"

image_sizeImageSize | Enum

Default value: [object Object]

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

num_inference_stepsinteger

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

guidance_scalefloat

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: 5

loraslist<LoraWeight>

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

embeddingslist<Embedding>

The list of embeddings to use. Default value: ``

expand_promptboolean

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

num_imagesinteger

The number of images to generate. Default value: 1

seedinteger

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

enable_safety_checkerboolean

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

sync_modeboolean

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.

formatFormatEnum

The format of the generated image. Default value: "jpeg"

Possible values: "jpeg", "png"

safety_checker_versionSafetyCheckerVersionEnum

The version of the safety checker to use. v1 is the default CompVis safety checker. v2 uses a custom ViT model. Default value: "v1"

Possible values: "v1", "v2"

{
  "model_name": "SG161222/Realistic_Vision_V6.0_B1_noVAE",
  "prompt": "A hyperdetailed photograph of a Cat dressed as a mafia boss holding a fish walking down a Japanese fish market with an angry face, 8k resolution, best quality, beautiful photograph, dynamic lighting,",
  "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
  "image_size": {
    "height": 1024,
    "width": 1024
  },
  "num_inference_steps": 35,
  "guidance_scale": 5,
  "loras": [],
  "embeddings": [],
  "num_images": 1,
  "enable_safety_checker": true,
  "format": "jpeg",
  "safety_checker_version": "v1"
}

Output#

images*list<Image>

The generated image files info.

timings*Timings
seed*integer

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>

Whether the generated images contain NSFW concepts.

prompt*string

The prompt used for generating the image.

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