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.
fal-ai/lora/image-to-image
Image To Image
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/client
@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.
Set FAL_KEY
as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"
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);
The API uses an API Key for authentication. It is recommended you set the FAL_KEY
environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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",
});
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,
});
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);
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.
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.
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.
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);
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.
model_name
string
* requiredURL 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
* requiredThe 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
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. Default value: ``
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. Default value: ``
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. Default value: ``
controlnet_guess_mode
boolean
If set to true, the controlnet will be applied to only the conditional predictions.
The IP adapter to use for the image generation. Default value: ``
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
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. Default value: [object Object]
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. Default value: [object Object]
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": []
},
"image_format": "jpeg",
"num_images": 1,
"tile_width": 4096,
"tile_height": 4096,
"tile_stride_width": 2048,
"tile_stride_height": 2048
}
The generated image files info.
seed
integer
* requiredSeed 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.
Whether the generated images contain NSFW concepts.
The latents saved for debugging.
The latents saved for debugging per pass.
{
"images": [
{
"url": "",
"content_type": "image/png",
"file_name": "z9RV14K95DvU.png",
"file_size": 4404019,
"width": 1024,
"height": 1024
}
]
}
path
string
* requiredURL 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
url
string
* requiredThe 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
url
string
* requiredThe 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.
path
string
* requiredURL or the path to the embedding weights.
The tokens to map the embedding weights to. Use these tokens in your prompts. Default value: <s0>,<s1>
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
* requiredURL 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
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
width
integer
The width of the generated image. Default value: 512
height
integer
The height of the generated image. Default value: 512
path
string
* requiredURL 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
* requiredURL 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.