Add the ddim scheduler. (#330)

candle-examples/examples/stable-diffusion/schedulers.rs

@@ -0,0 +1,45 @@
+#![allow(dead_code)]
+//! # Diffusion pipelines and models
+//!
+//! Noise schedulers can be used to set the trade-off between
+//! inference speed and quality.
+
+use candle::{Result, Tensor};
+
+/// This represents how beta ranges from its minimum value to the maximum
+/// during training.
+#[derive(Debug, Clone, Copy)]
+pub enum BetaSchedule {
+    /// Linear interpolation.
+    Linear,
+    /// Linear interpolation of the square root of beta.
+    ScaledLinear,
+    /// Glide cosine schedule
+    SquaredcosCapV2,
+}
+
+#[derive(Debug, Clone, Copy)]
+pub enum PredictionType {
+    Epsilon,
+    VPrediction,
+    Sample,
+}
+
+/// Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+/// `(1-beta)` over time from `t = [0,1]`.
+///
+/// Contains a function `alpha_bar` that takes an argument `t` and transforms it to the cumulative product of `(1-beta)`
+/// up to that part of the diffusion process.
+pub(crate) fn betas_for_alpha_bar(num_diffusion_timesteps: usize, max_beta: f64) -> Result<Tensor> {
+    let alpha_bar = |time_step: usize| {
+        f64::cos((time_step as f64 + 0.008) / 1.008 * std::f64::consts::FRAC_PI_2).powi(2)
+    };
+    let mut betas = Vec::with_capacity(num_diffusion_timesteps);
+    for i in 0..num_diffusion_timesteps {
+        let t1 = i / num_diffusion_timesteps;
+        let t2 = (i + 1) / num_diffusion_timesteps;
+        betas.push((1.0 - alpha_bar(t2) / alpha_bar(t1)).min(max_beta));
+    }
+    let betas_len = betas.len();
+    Tensor::from_vec(betas, betas_len, &candle::Device::Cpu)
+}