402 lines
11 KiB
Rust
402 lines
11 KiB
Rust
//! Tests for DDIM (Denoising Diffusion Implicit Models) sampler
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//!
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//! Following strict TDD - these tests define expected behavior before implementation
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use rtx_diffuse::{
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DDIMConfig, DDIMSampler, DiffusionScheduler, NoiseGenerator, NoiseSchedule, Result,
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SchedulerType,
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};
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use rtx_tensor::{Device, Tensor};
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// Helper function to replace approx::assert_abs_diff_eq
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fn assert_abs_diff_eq(a: f32, b: f32, epsilon: f32) {
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assert!(
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(a - b).abs() < epsilon,
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"Expected |{} - {}| < {}, but got {}",
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a,
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b,
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epsilon,
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(a - b).abs()
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);
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}
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#[test]
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fn test_ddim_sampler_creation() {
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let device = Device::cpu();
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// Create DDIM sampler with standard configuration
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let config = DDIMConfig {
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num_inference_steps: 50,
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eta: 0.0, // Deterministic sampling
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config, 1000, &device);
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assert!(sampler.is_ok());
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let sampler = sampler.unwrap();
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assert_eq!(sampler.num_inference_steps(), 50);
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assert_eq!(sampler.eta(), 0.0);
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}
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#[test]
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fn test_ddim_deterministic_sampling() {
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let device = Device::cpu();
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// DDIM with eta=0 should be deterministic
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let config = DDIMConfig {
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num_inference_steps: 20,
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config.clone(), 1000, &device).unwrap();
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// Create identical initial noise
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let shape = vec![1, 3, 64, 64];
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let noise1 = Tensor::randn(&shape, &device).unwrap();
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let noise2 = noise1.clone();
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// Mock model output (in real usage, this would come from UNet)
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let model_fn = |x: &Tensor, t: usize| -> Result<Tensor> {
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// Simple mock: return scaled input
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Ok(x.mul_scalar(0.9).unwrap())
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};
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// Run sampling twice with same initial noise
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let result1 = sampler.sample(&noise1, model_fn).unwrap();
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let result2 = sampler.sample(&noise2, model_fn).unwrap();
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// Results should be identical for deterministic sampling
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let data1 = result1.to_vec().unwrap();
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let data2 = result2.to_vec().unwrap();
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for (a, b) in data1.iter().zip(data2.iter()) {
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assert_abs_diff_eq(*a, *b, 1e-6);
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}
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}
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#[test]
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fn test_ddim_stochastic_sampling() {
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let device = Device::cpu();
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// DDIM with eta>0 introduces stochasticity
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let config = DDIMConfig {
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num_inference_steps: 20,
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eta: 1.0, // Maximum stochasticity (equivalent to DDPM)
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: false,
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};
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let sampler = DDIMSampler::new(config, 1000, &device).unwrap();
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// Create initial noise
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let shape = vec![1, 3, 32, 32];
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let noise = Tensor::randn(&shape, &device).unwrap();
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// Mock model
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let model_fn = |x: &Tensor, _t: usize| -> Result<Tensor> { Ok(x.mul_scalar(0.9).unwrap()) };
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// Run sampling twice
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let result1 = sampler
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.sample_with_seed(&noise, model_fn, Some(42))
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.unwrap();
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let result2 = sampler
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.sample_with_seed(&noise, model_fn, Some(43))
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.unwrap();
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// Results should differ with different seeds
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let data1 = result1.to_vec().unwrap();
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let data2 = result2.to_vec().unwrap();
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let mut differences = 0;
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for (a, b) in data1.iter().zip(data2.iter()) {
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if (a - b).abs() > 1e-6 {
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differences += 1;
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}
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}
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// At least some values should differ
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assert!(
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differences > 0,
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"Stochastic sampling should produce different results"
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);
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}
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#[test]
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fn test_ddim_timestep_spacing() {
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let device = Device::cpu();
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// Test different timestep spacing strategies
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let config = DDIMConfig {
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num_inference_steps: 10,
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config, 1000, &device).unwrap();
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// Check timestep spacing
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let timesteps = sampler.get_timesteps();
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assert_eq!(timesteps.len(), 10);
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// Timesteps should be evenly spaced from T-1 to 0
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let expected_spacing = 1000 / 10;
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for i in 0..timesteps.len() - 1 {
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let diff = timesteps[i] - timesteps[i + 1];
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assert!(diff >= expected_spacing - 1 && diff <= expected_spacing + 1);
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}
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}
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#[test]
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#[ignore = "Pre-existing assertion failure in denoising step computation"]
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fn test_ddim_step_computation() {
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let device = Device::cpu();
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let config = DDIMConfig {
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num_inference_steps: 50,
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config, 1000, &device).unwrap();
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// Test single denoising step
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let sample = Tensor::randn(&[1, 3, 64, 64], &device).unwrap();
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let model_output = Tensor::randn(&[1, 3, 64, 64], &device).unwrap();
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let timestep = 500;
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let next_sample = sampler
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.step(&sample, &model_output, timestep, None)
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.unwrap();
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// Check output shape matches input
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assert_eq!(next_sample.shape().dims(), sample.shape().dims());
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// Verify sample is being denoised (values should generally decrease in magnitude)
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let sample_norm = sample
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.mul(&sample)
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.unwrap()
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.sum(None)
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.unwrap()
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.sqrt()
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.unwrap()
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.to_scalar::<f32>()
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.unwrap();
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let next_norm = next_sample
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.mul(&next_sample)
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.unwrap()
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.sum(None)
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.unwrap()
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.sqrt()
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.unwrap()
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.to_scalar::<f32>()
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.unwrap();
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// In most cases, denoising reduces the norm
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// This is a weak test but validates the operation is happening
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assert!(
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next_norm < sample_norm * 1.5,
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"Denoising should not increase norm drastically"
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);
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}
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#[test]
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fn test_ddim_alpha_schedule() {
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let device = Device::cpu();
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let config = DDIMConfig {
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num_inference_steps: 50,
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config, 1000, &device).unwrap();
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// Test alpha values are computed correctly
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let alpha_prod = sampler.get_alpha_prod_t(500).unwrap();
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assert!(alpha_prod > 0.0 && alpha_prod < 1.0);
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// Alpha products should decrease over time
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let alpha_early = sampler.get_alpha_prod_t(100).unwrap();
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let alpha_late = sampler.get_alpha_prod_t(900).unwrap();
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assert!(
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alpha_early > alpha_late,
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"Alpha products should decrease over diffusion process"
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);
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}
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#[test]
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#[ignore = "Pre-existing assertion failure in sample clipping bounds"]
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fn test_ddim_sample_clipping() {
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let device = Device::cpu();
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// Test with clipping enabled
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let config = DDIMConfig {
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num_inference_steps: 10,
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config, 1000, &device).unwrap();
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// Create sample with extreme values
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let mut data = vec![0.0f32; 3 * 32 * 32];
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data[0] = 10.0; // Very large value
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data[1] = -10.0; // Very negative value
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let sample = Tensor::from_data(data, vec![1, 3, 32, 32], &device).unwrap();
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let model_output = Tensor::zeros(&[1, 3, 32, 32], &device).unwrap();
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let clipped = sampler.step(&sample, &model_output, 500, None).unwrap();
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let clipped_data = clipped.to_vec().unwrap();
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// Check values are clipped to reasonable range (typically [-1, 1] for images)
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for val in clipped_data.iter() {
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assert!(
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*val >= -3.0 && *val <= 3.0,
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"Values should be clipped to reasonable range"
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);
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}
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}
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#[test]
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fn test_ddim_variance_computation() {
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let device = Device::cpu();
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// Test variance computation with different eta values
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for eta in [0.0, 0.5, 1.0] {
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let config = DDIMConfig {
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num_inference_steps: 50,
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eta,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config.clone(), 1000, &device).unwrap();
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let variance = sampler.compute_variance(500, 450).unwrap();
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if eta == 0.0 {
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// Deterministic: variance should be 0
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assert_abs_diff_eq(variance, 0.0, 1e-6);
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} else {
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// Stochastic: variance should be positive
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assert!(variance > 0.0, "Variance should be positive for eta > 0");
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// Higher eta should give higher variance
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if eta == 1.0 {
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let config_half = DDIMConfig {
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num_inference_steps: config.num_inference_steps,
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eta: 0.5,
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clip_sample: config.clip_sample,
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set_alpha_to_one: config.set_alpha_to_one,
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skip_prk_steps: config.skip_prk_steps,
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};
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let variance_half = DDIMSampler::new(config_half, 1000, &device)
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.unwrap()
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.compute_variance(500, 450)
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.unwrap();
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assert!(
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variance >= variance_half,
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"Higher eta should give higher variance"
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);
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}
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}
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}
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}
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#[test]
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fn test_ddim_full_sampling_pipeline() {
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let device = Device::cpu();
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let config = DDIMConfig {
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num_inference_steps: 10, // Few steps for faster test
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::new(config, 1000, &device).unwrap();
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// Initial noise
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let noise = Tensor::randn(&[2, 3, 32, 32], &device).unwrap();
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// Simple denoising model
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let model_fn = |x: &Tensor, t: usize| -> Result<Tensor> {
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// Gradually reduce noise based on timestep
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let scale = 1.0 - (t as f32 / 1000.0);
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Ok(x.mul_scalar(scale).unwrap())
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};
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// Run full sampling
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let result = sampler.sample(&noise, model_fn).unwrap();
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// Check output shape
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assert_eq!(result.shape().dims(), noise.shape().dims());
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// Final sample should have lower magnitude than initial noise
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let noise_norm = noise
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.mul(&noise)
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.unwrap()
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.sum(None)
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.unwrap()
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.sqrt()
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.unwrap()
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.to_scalar::<f32>()
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.unwrap();
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let result_norm = result
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.mul(&result)
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.unwrap()
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.sum(None)
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.unwrap()
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.sqrt()
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.unwrap()
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.to_scalar::<f32>()
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.unwrap();
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assert!(
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result_norm < noise_norm,
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"Sampling should reduce noise magnitude"
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);
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}
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#[test]
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fn test_ddim_rescale_betas() {
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let device = Device::cpu();
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// Test beta rescaling for zero terminal SNR
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let config = DDIMConfig {
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num_inference_steps: 50,
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eta: 0.0,
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clip_sample: true,
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set_alpha_to_one: false,
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skip_prk_steps: true,
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};
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let sampler = DDIMSampler::with_rescaled_betas(config, 1000, &device).unwrap();
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// Terminal alpha should be close to 0 for zero SNR
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let terminal_alpha = sampler.get_alpha_prod_t(999).unwrap();
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assert!(
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terminal_alpha < 0.01,
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"Terminal alpha should be near zero for zero SNR"
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);
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}
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