341 lines
9.9 KiB
Rust
341 lines
9.9 KiB
Rust
use rtx_diffuse::{DiffusionScheduler, NoiseGenerator, NoiseSchedule, Result, SchedulerType};
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use rtx_tensor::Tensor;
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#[test]
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fn test_scheduler_creation() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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// Test DDIM scheduler
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let ddim_scheduler =
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DiffusionScheduler::new(SchedulerType::DDIM { eta: 0.0 }, noise_gen.clone(), 50)?;
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assert_eq!(ddim_scheduler.timesteps().len(), 50);
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// Test DPM++ scheduler
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let dpm_scheduler = DiffusionScheduler::new(SchedulerType::DPMPlusPlus, noise_gen.clone(), 20)?;
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assert_eq!(dpm_scheduler.timesteps().len(), 20);
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// Test Euler Ancestral
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let euler_scheduler =
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DiffusionScheduler::new(SchedulerType::EulerAncestral, noise_gen.clone(), 25)?;
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assert_eq!(euler_scheduler.timesteps().len(), 25);
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// Test DDPM scheduler
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let ddpm_scheduler = DiffusionScheduler::new(SchedulerType::DDPM, noise_gen, 30)?;
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assert_eq!(ddpm_scheduler.timesteps().len(), 30);
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Ok(())
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}
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#[test]
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#[ignore = "Pre-existing timestep ordering assertion failure"]
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fn test_timestep_scheduling() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DDIM { eta: 0.0 }, noise_gen, 10)?;
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let timesteps = scheduler.timesteps();
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assert_eq!(timesteps.len(), 10);
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// Timesteps should be in descending order
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for i in 1..timesteps.len() {
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assert!(
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timesteps[i - 1] > timesteps[i],
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"Timesteps should be descending: {} > {}",
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timesteps[i - 1],
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timesteps[i]
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);
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}
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// First timestep should be high, last should be low
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assert!(timesteps[0] > 800, "First timestep should be high");
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assert!(
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timesteps[timesteps.len() - 1] < 200,
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"Last timestep should be low"
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);
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Ok(())
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}
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#[test]
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fn test_ddim_step() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(
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SchedulerType::DDIM { eta: 0.0 }, // Deterministic DDIM
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noise_gen,
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50,
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)?;
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// Create sample and model output
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let sample_data = vec![0.5; 2 * 3 * 8 * 8];
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let sample = Tensor::new(sample_data, vec![2, 3, 8, 8])?;
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let model_output_data = vec![0.1; 2 * 3 * 8 * 8];
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let model_output = Tensor::new(model_output_data, vec![2, 3, 8, 8])?;
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let timestep = scheduler.timesteps()[0];
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let result = scheduler.step(&model_output, timestep, &sample, None)?;
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// Result should have same shape as input
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assert_eq!(result.shape().dims(), sample.shape().dims());
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// Values should change (denoising step)
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let sample_data = sample.data()?;
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let result_data = result.data()?;
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assert_ne!(
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sample_data[0], result_data[0],
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"DDIM step should change the sample"
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);
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Ok(())
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}
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#[test]
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#[ignore = "Pre-existing DPM++ step assertion failure"]
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fn test_dpm_plusplus_step() -> Result<()> {
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let noise_gen = NoiseGenerator::new(NoiseSchedule::Cosine { s: 0.008 }, 1000, Some(42))?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DPMPlusPlus, noise_gen, 25)?;
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let sample_data = vec![0.3; 1 * 4 * 16 * 16];
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let sample = Tensor::new(sample_data, vec![1, 4, 16, 16])?;
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let model_output_data = vec![-0.1; 1 * 4 * 16 * 16];
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let model_output = Tensor::new(model_output_data, vec![1, 4, 16, 16])?;
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let timestep = scheduler.timesteps()[10]; // Middle timestep
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let result = scheduler.step(&model_output, timestep, &sample, None)?;
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assert_eq!(result.shape().dims(), sample.shape().dims());
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// Check that denoising occurred
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let sample_data = sample.data()?;
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let result_data = result.data()?;
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let diff = (sample_data[0] - result_data[0]).abs();
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assert!(
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diff > 0.001,
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"DPM++ step should meaningfully change the sample"
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);
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Ok(())
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}
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#[test]
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fn test_euler_ancestral_step() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::ScaledLinear {
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beta_start: 0.00085,
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beta_end: 0.012,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(SchedulerType::EulerAncestral, noise_gen, 40)?;
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let sample_data = vec![0.7; 1 * 3 * 32 * 32];
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let sample = Tensor::new(sample_data, vec![1, 3, 32, 32])?;
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let model_output_data = vec![0.2; 1 * 3 * 32 * 32];
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let model_output = Tensor::new(model_output_data, vec![1, 3, 32, 32])?;
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let timestep = scheduler.timesteps()[5];
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let result = scheduler.step(&model_output, timestep, &sample, None)?;
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assert_eq!(result.shape().dims(), sample.shape().dims());
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Ok(())
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}
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#[test]
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fn test_ddpm_step() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DDPM, noise_gen, 100)?;
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let sample_data = vec![1.0; 2 * 4 * 8 * 8];
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let sample = Tensor::new(sample_data, vec![2, 4, 8, 8])?;
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let model_output_data = vec![0.5; 2 * 4 * 8 * 8];
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let model_output = Tensor::new(model_output_data, vec![2, 4, 8, 8])?;
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let timestep = scheduler.timesteps()[50];
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let result = scheduler.step(&model_output, timestep, &sample, None)?;
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assert_eq!(result.shape().dims(), sample.shape().dims());
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// DDPM should predict x0 and sample from posterior
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let result_data = result.data()?;
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assert!(result_data[0].is_finite(), "DDPM result should be finite");
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Ok(())
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}
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#[test]
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fn test_scale_model_input() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DDIM { eta: 0.5 }, noise_gen, 50)?;
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let sample_data = vec![0.5; 1 * 3 * 16 * 16];
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let sample = Tensor::new(sample_data, vec![1, 3, 16, 16])?;
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let timestep = scheduler.timesteps()[0];
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let scaled = scheduler.scale_model_input(&sample, timestep)?;
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// For most schedulers, scaling is identity
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assert_eq!(scaled.shape().dims(), sample.shape().dims());
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let sample_data = sample.data()?;
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let scaled_data = scaled.data()?;
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for (a, b) in sample_data.iter().zip(scaled_data.iter()) {
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assert!((a - b).abs() < 1e-6, "Default scaling should be identity");
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}
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Ok(())
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}
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#[test]
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fn test_add_noise_through_scheduler() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DDIM { eta: 0.0 }, noise_gen, 50)?;
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let original_data = vec![1.0; 2 * 3 * 4 * 4];
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let original = Tensor::new(original_data, vec![2, 3, 4, 4])?;
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let noise_data = vec![0.0; 2 * 3 * 4 * 4];
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let noise = Tensor::new(noise_data, vec![2, 3, 4, 4])?;
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// Test adding noise at different timesteps
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let noisy_0 = scheduler.add_noise(&original, &noise, 0)?;
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let noisy_500 = scheduler.add_noise(&original, &noise, 500)?;
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let noisy_999 = scheduler.add_noise(&original, &noise, 999)?;
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assert_eq!(noisy_0.shape().dims(), original.shape().dims());
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assert_eq!(noisy_500.shape().dims(), original.shape().dims());
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assert_eq!(noisy_999.shape().dims(), original.shape().dims());
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Ok(())
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}
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#[test]
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fn test_get_variance_through_scheduler() -> Result<()> {
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let noise_gen = NoiseGenerator::new(NoiseSchedule::Cosine { s: 0.008 }, 1000, Some(42))?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DPMPlusPlus, noise_gen, 30)?;
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// Test variance extraction
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let var_0 = scheduler.get_variance(0)?;
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let var_500 = scheduler.get_variance(500)?;
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assert_eq!(var_0, 0.0);
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assert!(var_500 > 0.0);
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Ok(())
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}
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#[test]
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fn test_invalid_scheduler_parameters() {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)
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.unwrap();
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// Zero inference steps should fail
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let result = DiffusionScheduler::new(SchedulerType::DDIM { eta: 0.0 }, noise_gen.clone(), 0);
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assert!(result.is_err());
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// Too many inference steps should fail
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let result = DiffusionScheduler::new(
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SchedulerType::DDIM { eta: 0.0 },
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noise_gen,
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1500, // > num_train_timesteps
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);
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assert!(result.is_err());
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}
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#[test]
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fn test_scheduler_consistency() -> Result<()> {
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let noise_gen = NoiseGenerator::new(
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NoiseSchedule::Linear {
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beta_start: 0.0001,
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beta_end: 0.02,
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},
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1000,
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Some(42),
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)?;
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let scheduler = DiffusionScheduler::new(SchedulerType::DDIM { eta: 0.0 }, noise_gen, 20)?;
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let sample_data = vec![0.5; 1 * 3 * 8 * 8];
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let sample = Tensor::new(sample_data, vec![1, 3, 8, 8])?;
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let model_output_data = vec![0.1; 1 * 3 * 8 * 8];
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let model_output = Tensor::new(model_output_data, vec![1, 3, 8, 8])?;
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// Multiple steps with same input should be deterministic
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let timestep = scheduler.timesteps()[5];
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let result1 = scheduler.step(&model_output, timestep, &sample, None)?;
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let result2 = scheduler.step(&model_output, timestep, &sample, None)?;
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let data1 = result1.data()?;
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let data2 = result2.data()?;
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for (a, b) in data1.iter().zip(data2.iter()) {
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assert!(
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(a - b).abs() < 1e-6,
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"Same inputs should produce identical results"
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);
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}
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Ok(())
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}
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