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#[allow(unused_imports)]
use crate::prelude::*;
#[allow(unused_imports)]
use serde::{Deserialize, Serialize};
#[allow(unused_imports)]
use std::collections::HashMap;
#[cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_flux-control-lora-depth"
))]
#[cfg_attr(
docsrs,
doc(cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_flux-control-lora-depth"
)))
)]
pub mod image_to_image;
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct HTTPValidationError {
#[serde(skip_serializing_if = "Option::is_none")]
pub detail: Option<Vec<Option<ValidationError>>>,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct Image {
#[serde(skip_serializing_if = "Option::is_none")]
pub content_type: Option<String>,
pub height: i64,
pub url: String,
pub width: i64,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ImageSize {
/// The height of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub height: Option<i64>,
/// The width of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub width: Option<i64>,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ImageToImageInput {
/// The image to use for control lora. This is used to control the style of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub control_lora_image_url: Option<String>,
/// The strength of the control lora.
#[serde(skip_serializing_if = "Option::is_none")]
pub control_lora_strength: Option<f64>,
/// If set to true, the safety checker will be enabled.
#[serde(skip_serializing_if = "Option::is_none")]
pub enable_safety_checker: Option<bool>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub guidance_scale: Option<f64>,
/// The size of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub image_size: Option<ImageSizeProperty>,
/// URL of image to use for inpainting. or img2img
/// "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
pub image_url: String,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub loras: Option<Vec<Option<LoraWeight>>>,
/// The number of images to generate.
#[serde(skip_serializing_if = "Option::is_none")]
pub num_images: Option<i64>,
/// The number of inference steps to perform.
#[serde(skip_serializing_if = "Option::is_none")]
pub num_inference_steps: Option<i64>,
/// The format of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub output_format: Option<String>,
/// The prompt to generate an image from.
/// "A photo of a lion sitting on a stone bench"
pub prompt: String,
/// The same seed and the same prompt given to the same version of the model
/// will output the same image every time.
#[serde(skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
/// The strength to use for inpainting/image-to-image. Only used if the image_url is provided. 1.0 is completely remakes the image while 0.0 preserves the original.
#[serde(skip_serializing_if = "Option::is_none")]
pub strength: Option<f64>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub sync_mode: Option<bool>,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct LoraWeight {
/// URL or the path to the LoRA weights.
pub path: String,
/// The scale of the LoRA weight. This is used to scale the LoRA weight
/// before merging it with the base model.
#[serde(skip_serializing_if = "Option::is_none")]
pub scale: Option<f64>,
}
#[derive(Debug, Serialize, Deserialize)]
pub struct Output {
/// Whether the generated images contain NSFW concepts.
pub has_nsfw_concepts: Vec<bool>,
/// The generated image files info.
pub images: Vec<Image>,
/// The prompt used for generating the image.
pub prompt: String,
/// 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.
pub seed: i64,
pub timings: Timings,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct TextToImageInput {
/// The image to use for control lora. This is used to control the style of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub control_lora_image_url: Option<String>,
/// The strength of the control lora.
#[serde(skip_serializing_if = "Option::is_none")]
pub control_lora_strength: Option<f64>,
/// If set to true, the safety checker will be enabled.
#[serde(skip_serializing_if = "Option::is_none")]
pub enable_safety_checker: Option<bool>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub guidance_scale: Option<f64>,
/// The size of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub image_size: Option<ImageSizeProperty>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub loras: Option<Vec<Option<LoraWeight>>>,
/// The number of images to generate.
#[serde(skip_serializing_if = "Option::is_none")]
pub num_images: Option<i64>,
/// The number of inference steps to perform.
#[serde(skip_serializing_if = "Option::is_none")]
pub num_inference_steps: Option<i64>,
/// The format of the generated image.
#[serde(skip_serializing_if = "Option::is_none")]
pub output_format: Option<String>,
/// The prompt to generate an image from.
/// "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture."
pub prompt: String,
/// The same seed and the same prompt given to the same version of the model
/// will output the same image every time.
#[serde(skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub sync_mode: Option<bool>,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ValidationError {
pub loc: Vec<serde_json::Value>,
pub msg: String,
#[serde(rename = "type")]
pub ty: String,
}
#[derive(Debug, Serialize, Deserialize, smart_default::SmartDefault)]
#[allow(non_camel_case_types)]
pub enum ImageSizeProperty {
#[default]
ImageSize(ImageSize),
#[serde(rename = "square_hd")]
SquareHd,
#[serde(rename = "square")]
Square,
#[serde(rename = "portrait_4_3")]
Portrait43,
#[serde(rename = "portrait_16_9")]
Portrait169,
#[serde(rename = "landscape_4_3")]
Landscape43,
#[serde(rename = "landscape_16_9")]
Landscape169,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct Timings {
#[serde(skip_serializing_if = "Option::is_none")]
#[serde(rename = "type")]
pub ty: Option<serde_json::Value>,
}
/// FLUX.1 [dev] Control LoRA Depth
///
/// Category: text-to-image
/// Machine Type: H100
/// License Type: commercial
///
/// FLUX.1 [dev], next generation text-to-image model.
pub fn flux_control_lora_depth(params: TextToImageInput) -> FalRequest<TextToImageInput, Output> {
FalRequest::new("fal-ai/flux-control-lora-depth", params)
}