Struct ImageToImageControlNetInput

pub struct ImageToImageControlNetInput {

    pub control_image_url: String,
    pub controlnet_conditioning_scale: Option<f64>,
    pub enable_safety_checker: Option<bool>,
    pub expand_prompt: Option<bool>,
    pub guidance_scale: Option<f64>,
    pub image_size: Option<ImageSizeProperty>,
    pub image_url: String,
    pub loras: Option<Vec<Option<LoraWeight>>>,
    pub negative_prompt: Option<String>,
    pub num_images: Option<i64>,
    pub num_inference_steps: Option<i64>,
    pub prompt: String,
    pub seed: Option<i64>,
    pub strength: Option<f64>,
    pub sync_mode: Option<bool>,
}

Fields

control_image_url: String

The URL of the control image./// The URL of the control image./// “https://avatars.githubusercontent.com/u/74778219”

controlnet_conditioning_scale: Option<f64>

The scale of the controlnet conditioning.

enable_safety_checker: Option<bool>

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

expand_prompt: Option<bool>

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

guidance_scale: Option<f64>

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.

image_size: Option<ImageSizeProperty>

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

image_url: String

The URL of the image to use as a starting point for the generation./// The URL of the image to use as a starting point for the generation./// “https://fal-cdn.batuhan-941.workers.dev/files/tiger/IExuP-WICqaIesLZAZPur.jpeg”

loras: Option<Vec<Option<LoraWeight>>>

The list of LoRA weights to use.

negative_prompt: Option<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)./// 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)./// “cartoon, illustration, animation. face. male, female”

num_images: Option<i64>

The number of images to generate.

num_inference_steps: Option<i64>

The number of inference steps to perform.

prompt: String

The prompt to use for generating the image. Be as descriptive as possible for best results./// The prompt to use for generating the image. Be as descriptive as possible for best results./// “Ice fortress, aurora skies, polar wildlife, twilight”

seed: Option<i64>

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

strength: Option<f64>

determines how much the generated image resembles the initial image

sync_mode: Option<bool>

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.

Trait Implementations

impl Debug for ImageToImageControlNetInput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ImageToImageControlNetInput

fn default() -> ImageToImageControlNetInput

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for ImageToImageControlNetInput

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for ImageToImageControlNetInput

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.