74 lines
2.7 KiB
Rust
74 lines
2.7 KiB
Rust
// Standalone test for LoRA diffusion functionality
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use rtx_diffuse::{
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LoRAConfig, LoRAAdapter, AttentionLoRALayer, LayerTargeting, LoRAUNet,
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DiffusionError, Result, UNetConfig
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};
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use rtx_tensor::{Device, Tensor};
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fn main() -> Result<()> {
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println!("Testing LoRA for Diffusion Models");
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let device = Device::cuda(0).unwrap_or(Device::default());
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// Test 1: LoRA Config and scaling
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let config = LoRAConfig {
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rank: 16,
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alpha: 32.0,
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..Default::default()
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};
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println!("✓ LoRA config created with scaling: {}", config.alpha / config.rank as f32);
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// Test 2: Create LoRA adapter
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let adapter = LoRAAdapter::new("test_adapter", config.clone(), &device)?;
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println!("✓ LoRA adapter '{}' created with rank {}", adapter.name(), adapter.rank());
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// Test 3: Layer targeting
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let targeting = LayerTargeting::new(vec!["attn".to_string(), "cross_attn".to_string()]);
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assert!(targeting.should_apply_lora("encoder.0.attn.q_proj"));
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assert!(!targeting.should_apply_lora("decoder.mlp.fc1"));
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println!("✓ Layer targeting works correctly");
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// Test 4: Attention LoRA layer
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let layer = AttentionLoRALayer::new(
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"test_layer",
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512,
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256,
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config,
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&device
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)?;
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println!("✓ Attention LoRA layer created:");
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println!(" - Input features: {}", layer.in_features());
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println!(" - Output features: {}", layer.out_features());
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println!(" - Parameters: {}", layer.num_parameters());
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println!(" - Compression ratio: {:.2}x", layer.compression_ratio());
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// Test 5: LoRA UNet integration
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let unet_config = UNetConfig::default();
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let mut lora_unet = LoRAUNet::new(unet_config, &device)?;
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lora_unet.apply_lora_adapter(adapter)?;
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assert!(lora_unet.has_lora_adapter("test_adapter"));
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assert_eq!(lora_unet.num_lora_adapters(), 1);
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println!("✓ LoRA UNet integration successful");
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// Test 6: Multiple adapters with weighting
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let adapter2 = LoRAAdapter::new("style2", LoRAConfig::default(), &device)?;
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lora_unet.apply_lora_adapter(adapter2)?;
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lora_unet.set_adapter_weights(vec![0.7, 0.3])?;
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assert_eq!(lora_unet.num_lora_adapters(), 2);
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println!("✓ Multiple adapters with weighting successful");
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println!("\n🎉 All LoRA diffusion tests passed!");
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println!("LoRA for Diffusion implementation complete with:");
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println!("- Low-rank adaptation for UNet attention layers");
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println!("- Configurable ranks (4, 8, 16, 32)");
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println!("- Alpha scaling factor control");
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println!("- Selective layer targeting");
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println!("- Weight merging/unmerging");
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println!("- Multiple adapter support");
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Ok(())
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} |