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use anyhow::{Ok, Result};
use candle::{DType, IndexOp, Tensor};
use candle_transformers::models::flux;
use candle_transformers::models::mmdit::model::MMDiT;
pub struct SkipLayerGuidanceConfig {
pub scale: f64,
pub start: f64,
pub end: f64,
pub layers: Vec<usize>,
}
#[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,
slg_config: Option<SkipLayerGuidanceConfig>,
) -> 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 (step, window) in sigmas.windows(2).enumerate() {
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, &x], 0)?,
&Tensor::full(timestep as f32, (2,), x.device())?.contiguous()?,
y,
context,
None,
)?;
let mut guidance = apply_cfg(cfg_scale, &noise_pred)?;
if let Some(slg_config) = slg_config.as_ref() {
if (num_inference_steps as f64) * slg_config.start < (step as f64)
&& (step as f64) < (num_inference_steps as f64) * slg_config.end
{
let slg_noise_pred = mmdit.forward(
&x,
&Tensor::full(timestep as f32, (1,), x.device())?.contiguous()?,
&y.i(..1)?,
&context.i(..1)?,
Some(&slg_config.layers),
)?;
guidance = (guidance
+ (slg_config.scale * (noise_pred.i(..1)? - slg_noise_pred.i(..1))?)?)?;
}
}
x = (x + (guidance * (*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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