Stable Diffusion with LoRAs | Image to Image

fal-ai/lora/image-to-image

Run Any Stable Diffusion model with customizable LoRA weights.

Inference

Commercial use

Schema

LLMs

Playground API

About

Image 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

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

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/lora/image-to-image", {
  input: {
    model_name: "stabilityai/stable-diffusion-xl-base-1.0",
    prompt: "Photo of a european medieval 40 year old queen, silver hair, highly detailed face, detailed eyes, head shot, intricate crown, age spots, wrinkles"
  },
  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"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

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/lora/image-to-image", {
  input: {
    model_name: "stabilityai/stable-diffusion-xl-base-1.0",
    prompt: "Photo of a european medieval 40 year old queen, silver hair, highly detailed face, detailed eyes, head shot, intricate crown, age spots, wrinkles"
  },
  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/lora/image-to-image", {
  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/lora/image-to-image", {
  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);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

model_name string* required

URL or HuggingFace ID of the base model to generate the image.

unet_name string

URL or HuggingFace ID of the custom U-Net model to use for the image generation.

variant string

The variant of the model to use for huggingface models, e.g. 'fp16'.

prompt string* required

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

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: ""

prompt_weighting boolean

If set to true, the prompt weighting syntax will be used. Additionally, this will lift the 77 token limit by averaging embeddings.

image_url string

URL of image to use for image to image/inpainting.

noise_strength float

The amount of noise to add to noise image for image. Only used if the image_url is provided. 1.0 is complete noise and 0 is no noise. Default value: 0.5

loras list<LoraWeight>

The LoRAs to use for the image generation. You can use any number of LoRAs and they will be merged together to generate the final image.

embeddings list<Embedding>

The embeddings to use for the image generation. Only a single embedding is supported at the moment. The embeddings will be used to map the tokens in the prompt to the embedding weights.

controlnets list<ControlNet>

The control nets to use for the image generation. You can use any number of control nets and they will be applied to the image at the specified timesteps.

controlnet_guess_mode boolean

If set to true, the controlnet will be applied to only the conditional predictions.

ip_adapter list<IPAdapter>

The IP adapter to use for the image generation.

image_encoder_path string

The path to the image encoder model to use for the image generation.

image_encoder_subfolder string

The subfolder of the image encoder model to use for the image generation.

image_encoder_weight_name string

The weight name of the image encoder model to use for the image generation. Default value: "pytorch_model.bin"

ic_light_model_url string

The URL of the IC Light model to use for the image generation.

ic_light_model_background_image_url string

The URL of the IC Light model background image to use for the image generation. Make sure to use a background compatible with the model.

ic_light_image_url string

The URL of the IC Light model image to use for the image generation.

seed integer

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

num_inference_steps integer

Increasing the amount of steps tells Stable Diffusion that it should take more steps to generate your final result which can increase the amount of detail in your image. Default value: 30

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

clip_skip integer

Skips part of the image generation process, leading to slightly different results. This means the image renders faster, too.

scheduler SchedulerEnum

Scheduler / sampler to use for the image denoising process.

Possible enum values: DPM++ 2M, DPM++ 2M Karras, DPM++ 2M SDE, DPM++ 2M SDE Karras, Euler, Euler A, Euler (trailing timesteps), LCM, LCM (trailing timesteps), DDIM, TCD

timesteps TimestepsInput

Optionally override the timesteps to use for the denoising process. Only works with schedulers which support the timesteps argument in their set_timesteps method. Defaults to not overriding, in which case the scheduler automatically sets the timesteps based on the num_inference_steps parameter. If set to a custom timestep schedule, the num_inference_steps parameter will be ignored. Cannot be set if sigmas is set.

sigmas SigmasInput

Optionally override the sigmas to use for the denoising process. Only works with schedulers which support the sigmas argument in their set_sigmas method. Defaults to not overriding, in which case the scheduler automatically sets the sigmas based on the num_inference_steps parameter. If set to a custom sigma schedule, the num_inference_steps parameter will be ignored. Cannot be set if timesteps is set.

prediction_type PredictionTypeEnum

The type of prediction to use for the image generation. The epsilon is the default. Default value: "epsilon"

Possible enum values: v_prediction, epsilon

rescale_betas_snr_zero boolean

Whether to set the rescale_betas_snr_zero option or not for the sampler

image_format ImageFormatEnum

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

Possible enum values: jpeg, png

num_images integer

Number of images to generate in one request. Note that the higher the batch size, the longer it will take to generate the images. Default value: 1

enable_safety_checker boolean

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

tile_width integer

The size of the tiles to be used for the image generation. Default value: 4096

tile_height integer

The size of the tiles to be used for the image generation. Default value: 4096

tile_stride_width integer

The stride of the tiles to be used for the image generation. Default value: 2048

tile_stride_height integer

The stride of the tiles to be used for the image generation. Default value: 2048

eta float

The eta value to be used for the image generation.

debug_latents boolean

If set to true, the latents will be saved for debugging.

debug_per_pass_latents boolean

If set to true, the latents will be saved for debugging per pass.

{
  "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
  "prompt": "Photo of a european medieval 40 year old queen, silver hair, highly detailed face, detailed eyes, head shot, intricate crown, age spots, wrinkles",
  "negative_prompt": "cartoon, painting, illustration, worst quality, low quality, normal quality",
  "prompt_weighting": true,
  "noise_strength": 0.5,
  "loras": [],
  "embeddings": [],
  "controlnets": [],
  "ip_adapter": [],
  "image_encoder_weight_name": "pytorch_model.bin",
  "num_inference_steps": 30,
  "guidance_scale": 7.5,
  "timesteps": {
    "method": "default",
    "array": []
  },
  "sigmas": {
    "method": "default",
    "array": []
  },
  "prediction_type": "epsilon",
  "image_format": "jpeg",
  "num_images": 1,
  "tile_width": 4096,
  "tile_height": 4096,
  "tile_stride_width": 2048,
  "tile_stride_height": 2048
}

Output#

images list<Image>* required

The generated image files info.

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.

debug_latents File

The latents saved for debugging.

debug_per_pass_latents File

The latents saved for debugging per pass.

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

Other types#

LoraWeight#

path string* required

URL or the path to the LoRA weights.

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

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

file_data string

File data

Image#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

file_data string

File data

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

Embedding#

path string* required

URL or the path to the embedding weights.

tokens list<string>

The tokens to map the embedding weights to. Use these tokens in your prompts.

IPAdapter#

ip_adapter_image_url string | list<string>* required

URL of the image to be used as the IP adapter.

ip_adapter_mask_url string

The mask to use for the IP adapter. When using a mask, the ip-adapter image size and the mask size must be the same

path string* required

URL or the path to the IP adapter weights.

model_subfolder string

Subfolder in the model directory where the IP adapter weights are stored.

weight_name string

Name of the weight file.

insight_face_model_path string

URL or the path to the InsightFace model weights.

scale float

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

scale_json Scale Json

The scale of the IP adapter weight. This is used to scale the IP adapter weight before merging it with the base model.

unconditional_noising_factor float

The factor to apply to the unconditional noising of the IP adapter.

image_projection_shortcut boolean

The value to set the image projection shortcut to. For FaceID plus V1 models, this should be set to False. For FaceID plus V2 models, this should be set to True. Default is True. Default value: true

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

ControlNet#

path string* required

URL or the path to the control net weights.

config_url string

optional URL to the controlnet config.json file.

variant string

The optional variant if a Hugging Face repo key is used.

image_url string* required

URL of the image to be used as the control net.

mask_url string

The mask to use for the controlnet. When using a mask, the control image size and the mask size must be the same and divisible by 32.

conditioning_scale float

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

start_percentage float

The percentage of the image to start applying the controlnet in terms of the total timesteps.

end_percentage float

The percentage of the image to end applying the controlnet in terms of the total timesteps. Default value: 1

ip_adapter_index integer

The index of the IP adapter to be applied to the controlnet. This is only needed for InstantID ControlNets.

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

LoraWeight#

File#

Image#

Embedding#

IPAdapter#

ImageSize#

ControlNet#

Related Models

Stable Diffusion with LoRAs Image to Image

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

LoraWeight#

File#

Image#

Embedding#

IPAdapter#

ImageSize#

ControlNet#

Related Models