#[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_ltx-video-v095"
))]
#[cfg_attr(
docsrs,
doc(cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_ltx-video-v095"
)))
)]
pub mod extend;
#[cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_ltx-video-v095"
))]
#[cfg_attr(
docsrs,
doc(cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_ltx-video-v095"
)))
)]
pub mod image_to_video;
#[cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_ltx-video-v095"
))]
#[cfg_attr(
docsrs,
doc(cfg(any(
feature = "endpoints",
feature = "endpoints_fal-ai",
feature = "endpoints_fal-ai_ltx-video-v095"
)))
)]
pub mod multiconditioning;
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ExtendVideoInput {
/// Aspect ratio of the generated video (16:9 or 9:16).
#[serde(skip_serializing_if = "Option::is_none")]
pub aspect_ratio: Option<String>,
/// Whether to expand the prompt using the model's own capabilities.
#[serde(skip_serializing_if = "Option::is_none")]
pub expand_prompt: Option<bool>,
/// Negative prompt for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub negative_prompt: Option<String>,
/// Number of inference steps
#[serde(skip_serializing_if = "Option::is_none")]
pub num_inference_steps: Option<i64>,
/// Text prompt to guide generation
/// "Woman walking on a street in Tokyo"
pub prompt: String,
/// Resolution of the generated video (480p or 720p).
#[serde(skip_serializing_if = "Option::is_none")]
pub resolution: Option<String>,
/// Random seed for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
/// Video to be extended.
/// {"start_frame_num":24,"video_url":"https://storage.googleapis.com/falserverless/web-examples/wan/t2v.mp4"}
pub video: VideoConditioningInput,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ExtendVideoOutput {
/// The seed used for generation.
pub seed: i64,
/// The generated video file.
/// {"url":"https://storage.googleapis.com/falserverless/example_outputs/ltx-v095_extend.mp4"}
pub video: File,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct File {
/// The mime type of the file.
/// "image/png"
#[serde(skip_serializing_if = "Option::is_none")]
pub content_type: Option<String>,
/// File data
#[serde(skip_serializing_if = "Option::is_none")]
pub file_data: Option<String>,
/// The name of the file. It will be auto-generated if not provided.
/// "z9RV14K95DvU.png"
#[serde(skip_serializing_if = "Option::is_none")]
pub file_name: Option<String>,
/// The size of the file in bytes.
/// 4404019
#[serde(skip_serializing_if = "Option::is_none")]
pub file_size: Option<i64>,
/// The URL where the file can be downloaded from.
pub url: String,
}
#[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 ImageConditioningInput {
/// URL of image to use as conditioning
pub image_url: String,
/// Frame number of the image from which the conditioning starts. Must be a multiple of 8.
pub start_frame_num: i64,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ImageToVideoInput {
/// Aspect ratio of the generated video (16:9 or 9:16).
#[serde(skip_serializing_if = "Option::is_none")]
pub aspect_ratio: Option<String>,
/// Whether to expand the prompt using the model's own capabilities.
#[serde(skip_serializing_if = "Option::is_none")]
pub expand_prompt: Option<bool>,
/// Image URL for Image-to-Video task
/// "https://h2.inkwai.com/bs2/upload-ylab-stunt/se/ai_portal_queue_mmu_image_upscale_aiweb/3214b798-e1b4-4b00-b7af-72b5b0417420_raw_image_0.jpg"
pub image_url: String,
/// Negative prompt for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub negative_prompt: Option<String>,
/// Number of inference steps
#[serde(skip_serializing_if = "Option::is_none")]
pub num_inference_steps: Option<i64>,
/// Text prompt to guide generation
/// "The astronaut gets up and walks away"
pub prompt: String,
/// Resolution of the generated video (480p or 720p).
#[serde(skip_serializing_if = "Option::is_none")]
pub resolution: Option<String>,
/// Random seed for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct ImageToVideoOutput {
/// The seed used for generation.
pub seed: i64,
/// The generated video file.
/// {"url":"https://storage.googleapis.com/falserverless/example_outputs/ltx_i2v_output.mp4"}
pub video: File,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct MultiConditioningVideoInput {
/// Aspect ratio of the generated video (16:9 or 9:16).
#[serde(skip_serializing_if = "Option::is_none")]
pub aspect_ratio: Option<String>,
/// Whether to expand the prompt using the model's own capabilities.
#[serde(skip_serializing_if = "Option::is_none")]
pub expand_prompt: Option<bool>,
/// URL of images to use as conditioning
/// [{"image_url":"https://storage.googleapis.com/falserverless/model_tests/ltx/NswO1P8sCLzrh1WefqQFK_9a6bdbfa54b944c9a770338159a113fd.jpg","start_frame_num":0},{"image_url":"https://storage.googleapis.com/falserverless/model_tests/ltx/YAPOGvmS2tM_Krdp7q6-d_267c97e017c34f679844a4477dfcec38.jpg","start_frame_num":120}]
#[serde(skip_serializing_if = "Option::is_none")]
pub images: Option<Vec<Option<ImageConditioningInput>>>,
/// Negative prompt for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub negative_prompt: Option<String>,
/// Number of inference steps
#[serde(skip_serializing_if = "Option::is_none")]
pub num_inference_steps: Option<i64>,
/// Text prompt to guide generation
/// "\n A vibrant, abstract composition featuring a person with outstretched arms, rendered in a kaleidoscope of colors against a deep, dark background. The figure is composed of intricate, swirling patterns reminiscent of a mosaic, with hues of orange, yellow, blue, and green that evoke the style of artists such as Wassily Kandinsky or Bridget Riley. \n\nThe camera zooms into the face striking portrait of a man, reimagined through the lens of old-school video-game graphics. The subject's face is rendered in a kaleidoscope of colors, with bold blues and reds set against a vibrant yellow backdrop. His dark hair is pulled back, framing his profile in a dramatic pose\n "
pub prompt: String,
/// Resolution of the generated video (480p or 720p).
#[serde(skip_serializing_if = "Option::is_none")]
pub resolution: Option<String>,
/// Random seed for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
/// Videos to use as conditioning
#[serde(skip_serializing_if = "Option::is_none")]
pub videos: Option<Vec<Option<VideoConditioningInput>>>,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct MulticonditioningVideoOutput {
/// The seed used for generation.
pub seed: i64,
/// The generated video file.
/// {"url":"https://storage.googleapis.com/falserverless/gallery/ltx-multicondition.mp4"}
pub video: File,
}
#[derive(Debug, Serialize, Deserialize, Default)]
pub struct TextToVideoInput {
/// Aspect ratio of the generated video (16:9 or 9:16).
#[serde(skip_serializing_if = "Option::is_none")]
pub aspect_ratio: Option<String>,
/// Whether to expand the prompt using the model's own capabilities.
#[serde(skip_serializing_if = "Option::is_none")]
pub expand_prompt: Option<bool>,
/// Negative prompt for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub negative_prompt: Option<String>,
/// Number of inference steps
#[serde(skip_serializing_if = "Option::is_none")]
pub num_inference_steps: Option<i64>,
/// Text prompt to guide generation
/// "A cute cat walking on a sidewalk"
pub prompt: String,
/// Resolution of the generated video (480p or 720p).
#[serde(skip_serializing_if = "Option::is_none")]
pub resolution: Option<String>,
/// Random seed for generation
#[serde(skip_serializing_if = "Option::is_none")]
pub seed: Option<i64>,
}
#[derive(Debug, Serialize, Deserialize)]
pub struct TextToVideoOutput {
/// The seed used for generation.
pub seed: i64,
/// The generated video file.
/// {"url":"https://storage.googleapis.com/falserverless/example_outputs/ltx-t2v_output.mp4"}
pub video: File,
}
#[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, Default)]
pub struct VideoConditioningInput {
/// Frame number of the video from which the conditioning starts. Must be a multiple of 8.
pub start_frame_num: i64,
/// URL of video to be extended
pub video_url: String,
}
/// LTX Video-0.9.5
///
/// Category: text-to-video
/// Machine Type: H100
///
///
/// Generate a video from a prompt.
pub fn ltx_video_v095(params: TextToVideoInput) -> FalRequest<TextToVideoInput, TextToVideoOutput> {
FalRequest::new("fal-ai/ltx-video-v095", params)
}