mirror of
https://github.com/huggingface/candle.git
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37e0ab8c64
* Stable diffusion 3.5 support. * Clippy fixes. * CFG fix. * Remove some unnecessary clones. * Avoid duplicating some of the code.
56 lines
1.8 KiB
Rust
56 lines
1.8 KiB
Rust
use anyhow::{Ok, Result};
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use candle::{DType, Tensor};
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use candle_transformers::models::flux;
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use candle_transformers::models::mmdit::model::MMDiT; // for the get_noise function
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#[allow(clippy::too_many_arguments)]
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pub fn euler_sample(
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mmdit: &MMDiT,
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y: &Tensor,
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context: &Tensor,
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num_inference_steps: usize,
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cfg_scale: f64,
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time_shift: f64,
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height: usize,
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width: usize,
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) -> Result<Tensor> {
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let mut x = flux::sampling::get_noise(1, height, width, y.device())?.to_dtype(DType::F16)?;
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let sigmas = (0..=num_inference_steps)
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.map(|x| x as f64 / num_inference_steps as f64)
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.rev()
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.map(|x| time_snr_shift(time_shift, x))
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.collect::<Vec<f64>>();
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for window in sigmas.windows(2) {
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let (s_curr, s_prev) = match window {
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[a, b] => (a, b),
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_ => continue,
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};
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let timestep = (*s_curr) * 1000.0;
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let noise_pred = mmdit.forward(
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&Tensor::cat(&[&x, &x], 0)?,
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&Tensor::full(timestep as f32, (2,), x.device())?.contiguous()?,
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y,
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context,
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)?;
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x = (x + (apply_cfg(cfg_scale, &noise_pred)? * (*s_prev - *s_curr))?)?;
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}
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Ok(x)
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}
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// The "Resolution-dependent shifting of timestep schedules" recommended in the SD3 tech report paper
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// https://arxiv.org/pdf/2403.03206
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// Following the implementation in ComfyUI:
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// https://github.com/comfyanonymous/ComfyUI/blob/3c60ecd7a83da43d694e26a77ca6b93106891251/
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// comfy/model_sampling.py#L181
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fn time_snr_shift(alpha: f64, t: f64) -> f64 {
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alpha * t / (1.0 + (alpha - 1.0) * t)
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}
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fn apply_cfg(cfg_scale: f64, noise_pred: &Tensor) -> Result<Tensor> {
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Ok(((cfg_scale * noise_pred.narrow(0, 0, 1)?)?
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- ((cfg_scale - 1.0) * noise_pred.narrow(0, 1, 1)?)?)?)
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}
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