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use anyhow::{Ok, Result};
use candle::{DType, Tensor};
use candle_transformers::models::flux;
use candle_transformers::models::mmdit::model::MMDiT; // for the get_noise function
#[allow(clippy::too_many_arguments)]
pub fn euler_sample(
mmdit: &MMDiT,
y: &Tensor,
context: &Tensor,
num_inference_steps: usize,
cfg_scale: f64,
time_shift: f64,
height: usize,
width: usize,
) -> Result<Tensor> {
let mut x = flux::sampling::get_noise(1, height, width, y.device())?.to_dtype(DType::F16)?;
let sigmas = (0..=num_inference_steps)
.map(|x| x as f64 / num_inference_steps as f64)
.rev()
.map(|x| time_snr_shift(time_shift, x))
.collect::<Vec<f64>>();
for window in sigmas.windows(2) {
let (s_curr, s_prev) = match window {
[a, b] => (a, b),
_ => continue,
};
let timestep = (*s_curr) * 1000.0;
let noise_pred = mmdit.forward(
&Tensor::cat(&[x.clone(), x.clone()], 0)?,
&Tensor::full(timestep as f32, (2,), x.device())?.contiguous()?,
y,
context,
)?;
x = (x + (apply_cfg(cfg_scale, &noise_pred)? * (*s_prev - *s_curr))?)?;
}
Ok(x)
}
// The "Resolution-dependent shifting of timestep schedules" recommended in the SD3 tech report paper
// https://arxiv.org/pdf/2403.03206
// Following the implementation in ComfyUI:
// https://github.com/comfyanonymous/ComfyUI/blob/3c60ecd7a83da43d694e26a77ca6b93106891251/
// comfy/model_sampling.py#L181
fn time_snr_shift(alpha: f64, t: f64) -> f64 {
alpha * t / (1.0 + (alpha - 1.0) * t)
}
fn apply_cfg(cfg_scale: f64, noise_pred: &Tensor) -> Result<Tensor> {
Ok(((cfg_scale * noise_pred.narrow(0, 0, 1)?)?
- ((cfg_scale - 1.0) * noise_pred.narrow(0, 1, 1)?)?)?)
}
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