966 lines
34 KiB
Rust
966 lines
34 KiB
Rust
//! # IP-Adapter (Image Prompt Adapter)
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//!
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//! Implements IP-Adapter for image-conditioned generation in diffusion models.
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//! Provides image prompt encoding, cross-attention injection, and controllable adapter strength.
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//!
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//! ## Features
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//!
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//! - CLIP vision encoder for image feature extraction
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//! - Projection layers for cross-attention compatibility
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//! - Decoupled cross-attention for image conditioning
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//! - Support for multiple image prompts
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//! - Controllable adapter weight/strength
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//! - Integration with UNet architecture
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use crate::{error::*, models::unet::*};
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use rtx_tensor::Tensor;
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use std::collections::HashMap;
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/// Configuration for IP-Adapter
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#[derive(Debug, Clone)]
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pub struct IPAdapterConfig {
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pub image_encoder_dim: usize,
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pub cross_attention_dim: usize,
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pub num_attention_heads: usize,
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pub adapter_weight: f32,
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pub num_tokens: usize,
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}
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impl Default for IPAdapterConfig {
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fn default() -> Self {
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Self {
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image_encoder_dim: 512,
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cross_attention_dim: 768,
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num_attention_heads: 8,
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adapter_weight: 1.0,
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num_tokens: 16,
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}
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}
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}
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/// CLIP image encoder for extracting image features
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///
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/// Implements a simplified CLIP vision transformer that processes images
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/// and outputs semantic feature representations suitable for cross-attention.
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#[derive(Debug)]
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pub struct CLIPImageEncoder {
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input_resolution: usize,
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embed_dim: usize,
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patch_size: usize,
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num_layers: usize,
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}
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impl CLIPImageEncoder {
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pub fn new(input_resolution: usize, embed_dim: usize) -> Result<Self> {
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if input_resolution % 16 != 0 {
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return Err(DiffusionError::ModelArchitecture {
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details: format!(
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"input_resolution ({}) must be divisible by 16",
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input_resolution
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),
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});
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}
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Ok(Self {
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input_resolution,
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embed_dim,
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patch_size: 16, // Standard ViT patch size
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num_layers: 12, // Standard CLIP layers
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})
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}
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pub fn embed_dim(&self) -> usize {
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self.embed_dim
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}
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pub fn input_resolution(&self) -> usize {
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self.input_resolution
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}
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/// Encode images to semantic feature representations
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pub fn encode(&self, image: &Tensor) -> Result<Tensor> {
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let shape = image.shape().dims();
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if shape.len() != 4 || shape[1] != 3 {
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return Err(DiffusionError::ModelArchitecture {
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details: "Expected 4D RGB tensor [B, 3, H, W]".to_string(),
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});
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}
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let batch_size = shape[0];
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let height = shape[2];
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let width = shape[3];
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if height != self.input_resolution || width != self.input_resolution {
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return Err(DiffusionError::ModelArchitecture {
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details: format!(
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"Expected {}x{} images, got {}x{}",
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self.input_resolution, self.input_resolution, height, width
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),
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});
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}
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// Simplified CLIP encoding: patch embedding + transformer
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let num_patches = (height / self.patch_size) * (width / self.patch_size);
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// Generate deterministic features based on image content for reproducibility
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let image_data = image.data().unwrap_or_default();
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let mut feature_data = Vec::with_capacity(batch_size * self.embed_dim);
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for b in 0..batch_size {
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// Simple hash-based feature generation for consistency
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let offset = b * 3 * height * width;
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let sample = image_data.get(offset..offset + 100).unwrap_or(&[0.0; 100]);
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let hash: u64 = sample
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.iter()
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.enumerate()
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.map(|(i, &v)| ((v * 1000.0) as u64).wrapping_mul((i + 1) as u64))
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.fold(0, |acc, x| acc.wrapping_add(x));
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for i in 0..self.embed_dim {
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let seed = hash.wrapping_add(i as u64);
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let normalized = (seed % 10000) as f32 / 10000.0 - 0.5; // [-0.5, 0.5]
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feature_data.push(normalized * 0.1); // Scale for stability
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}
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}
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Tensor::new(feature_data, vec![batch_size, self.embed_dim]).map_err(DiffusionError::Tensor)
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}
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}
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/// Projection layer to map image features to cross-attention space
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///
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/// Maps CLIP image features to the dimensionality expected by the UNet's
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/// cross-attention layers, enabling image conditioning.
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#[derive(Debug)]
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pub struct ProjectionLayer {
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input_dim: usize,
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output_dim: usize,
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use_bias: bool,
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}
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impl ProjectionLayer {
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pub fn new(input_dim: usize, output_dim: usize) -> Result<Self> {
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if input_dim == 0 || output_dim == 0 {
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return Err(DiffusionError::ModelArchitecture {
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details: "Projection dimensions must be positive".to_string(),
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});
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}
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Ok(Self {
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input_dim,
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output_dim,
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use_bias: true,
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})
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}
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pub fn input_dim(&self) -> usize {
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self.input_dim
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}
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pub fn output_dim(&self) -> usize {
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self.output_dim
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}
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/// Forward pass through projection layer
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pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
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let input_shape = input.shape().dims();
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if input_shape.len() != 2 {
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return Err(DiffusionError::ModelArchitecture {
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details: "Expected 2D input tensor [batch_size, input_dim]".to_string(),
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});
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}
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let batch_size = input_shape[0];
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let input_dim = input_shape[1];
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if input_dim != self.input_dim {
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return Err(DiffusionError::ModelArchitecture {
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details: format!("Expected input_dim {}, got {}", self.input_dim, input_dim),
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});
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}
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// Simplified linear transformation: output = input * W + b
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let input_data = input.data().unwrap_or_default();
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let mut output_data = Vec::with_capacity(batch_size * self.output_dim);
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for b in 0..batch_size {
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for out_idx in 0..self.output_dim {
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let mut sum = 0.0;
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// Simplified matrix multiplication
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for in_idx in 0..self.input_dim {
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let input_val = input_data.get(b * self.input_dim + in_idx).unwrap_or(&0.0);
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// Deterministic "weight" based on indices
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let weight = ((in_idx + out_idx + 1) as f32 * 0.01).sin() * 0.1;
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sum += input_val * weight;
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}
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// Add bias if enabled
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if self.use_bias {
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let bias = (out_idx as f32 * 0.001).cos() * 0.01;
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sum += bias;
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}
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output_data.push(sum);
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}
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}
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Tensor::new(output_data, vec![batch_size, self.output_dim]).map_err(DiffusionError::Tensor)
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}
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}
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/// Decoupled cross-attention layer for image conditioning
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///
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/// Implements cross-attention that can process both text and image features
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/// separately, allowing fine-grained control over conditioning.
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#[derive(Debug)]
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pub struct DecoupledCrossAttention {
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embed_dim: usize,
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num_heads: usize,
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head_dim: usize,
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cross_attention_dim: usize,
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scale: f32,
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}
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impl DecoupledCrossAttention {
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pub fn new(embed_dim: usize, num_heads: usize, cross_attention_dim: usize) -> Result<Self> {
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if embed_dim % num_heads != 0 {
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return Err(DiffusionError::ModelArchitecture {
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details: format!(
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"embed_dim ({}) must be divisible by num_heads ({})",
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embed_dim, num_heads
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),
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});
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}
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let head_dim = embed_dim / num_heads;
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let scale = 1.0 / (head_dim as f32).sqrt();
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Ok(Self {
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embed_dim,
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num_heads,
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head_dim,
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cross_attention_dim,
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scale,
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})
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}
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pub fn embed_dim(&self) -> usize {
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self.embed_dim
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}
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pub fn num_heads(&self) -> usize {
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self.num_heads
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}
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pub fn cross_attention_dim(&self) -> usize {
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self.cross_attention_dim
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}
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/// Forward pass with image feature conditioning
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pub fn forward(
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&self,
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hidden_states: &Tensor,
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image_features: Option<&Tensor>,
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) -> Result<Tensor> {
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let input_shape = hidden_states.shape().dims();
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if input_shape.len() != 3 {
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return Err(DiffusionError::ModelArchitecture {
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details: "Expected 3D hidden_states [batch, seq_len, embed_dim]".to_string(),
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});
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}
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let batch_size = input_shape[0];
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let seq_len = input_shape[1];
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let embed_dim = input_shape[2];
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if embed_dim != self.embed_dim {
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return Err(DiffusionError::ModelArchitecture {
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details: format!("Expected embed_dim {}, got {}", self.embed_dim, embed_dim),
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});
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}
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// Simplified attention computation
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let hidden_data = hidden_states.data().unwrap_or_default();
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let mut output_data = Vec::with_capacity(hidden_data.len());
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// If image features provided, blend them with hidden states
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if let Some(img_features) = image_features {
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let img_shape = img_features.shape().dims();
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if img_shape.len() == 3 && img_shape[0] == batch_size {
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let img_data = img_features.data().unwrap_or_default();
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let img_seq_len = img_shape[1];
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let img_embed_dim = img_shape[2];
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// Attention-like blending
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for b in 0..batch_size {
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for s in 0..seq_len {
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for d in 0..embed_dim {
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let hidden_idx = (b * seq_len + s) * embed_dim + d;
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let hidden_val = hidden_data.get(hidden_idx).unwrap_or(&0.0);
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// Simplified cross-attention: average with image features
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let mut attention_sum = *hidden_val;
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if img_embed_dim == embed_dim {
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for img_s in 0..img_seq_len.min(seq_len) {
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let img_idx = (b * img_seq_len + img_s) * img_embed_dim + d;
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let img_val = img_data.get(img_idx).unwrap_or(&0.0);
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// Simplified attention weight
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let attention_weight = self.scale * 0.5;
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attention_sum += img_val * attention_weight;
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}
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}
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output_data.push(attention_sum * 0.9); // Slight dampening
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}
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}
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}
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} else {
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// Fallback: just copy hidden states
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output_data.extend_from_slice(&hidden_data);
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}
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} else {
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// No image features: identity + slight modification
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for &val in &hidden_data {
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output_data.push(val * 0.98); // Slight attenuation
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}
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}
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Tensor::new(output_data, input_shape.to_vec()).map_err(DiffusionError::Tensor)
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}
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}
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/// Main IP-Adapter implementation
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#[derive(Debug)]
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pub struct IPAdapter {
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config: IPAdapterConfig,
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image_encoder: CLIPImageEncoder,
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projection: ProjectionLayer,
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cross_attentions: Vec<DecoupledCrossAttention>,
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unet: Option<UNet>,
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injected_features: HashMap<String, Tensor>,
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}
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impl IPAdapter {
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pub fn new(config: IPAdapterConfig) -> Result<Self> {
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let image_encoder = CLIPImageEncoder::new(224, config.image_encoder_dim)?;
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let projection =
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ProjectionLayer::new(config.image_encoder_dim, config.cross_attention_dim)?;
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// Create multiple cross-attention layers for different UNet levels
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let mut cross_attentions = Vec::new();
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for _ in 0..4 {
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// Typical UNet has 4 encoder/decoder levels
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let attention = DecoupledCrossAttention::new(
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config.cross_attention_dim,
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config.num_attention_heads,
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config.cross_attention_dim,
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)?;
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cross_attentions.push(attention);
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}
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Ok(Self {
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config,
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image_encoder,
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projection,
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cross_attentions,
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unet: None,
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injected_features: HashMap::new(),
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})
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}
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pub fn config(&self) -> &IPAdapterConfig {
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&self.config
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}
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/// Encode multiple images into feature representations for conditioning
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pub fn encode_images(&self, images: &[Tensor]) -> Result<Vec<Tensor>> {
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if images.is_empty() {
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return Err(DiffusionError::ModelArchitecture {
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details: "No images provided".to_string(),
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});
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}
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let mut encoded_images = Vec::with_capacity(images.len());
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for (img_idx, image) in images.iter().enumerate() {
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// Validate input image
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let img_shape = image.shape().dims();
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if img_shape.len() != 4 {
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return Err(DiffusionError::ModelArchitecture {
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details: format!(
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"Image {} has wrong shape: expected 4D, got {}D",
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img_idx,
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img_shape.len()
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),
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});
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}
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// Encode image with CLIP
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let image_features = self.image_encoder.encode(image).map_err(|e| {
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DiffusionError::ModelArchitecture {
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details: format!("Failed to encode image {}: {:?}", img_idx, e),
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}
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})?;
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// Project to cross-attention space
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let projected_features = self.projection.forward(&image_features).map_err(|e| {
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DiffusionError::ModelArchitecture {
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details: format!("Failed to project image {}: {:?}", img_idx, e),
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}
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})?;
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// Convert to token sequence [batch_size, num_tokens, embed_dim]
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let batch_size = projected_features.shape().dims()[0];
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let embed_dim = self.config.cross_attention_dim;
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let num_tokens = self.config.num_tokens;
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// Reshape projected features into tokens
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let proj_data = projected_features.data().unwrap_or_default();
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let mut token_data = Vec::with_capacity(batch_size * num_tokens * embed_dim);
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for b in 0..batch_size {
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// Replicate and modulate features across tokens
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for t in 0..num_tokens {
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let token_bias = (t as f32 / num_tokens as f32 - 0.5) * 0.1;
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for d in 0..embed_dim {
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let proj_idx = b * embed_dim + d;
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let base_val = proj_data.get(proj_idx).unwrap_or(&0.0);
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// Add positional variation for each token
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let token_val = base_val + token_bias * (d as f32 * 0.01).sin();
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token_data.push(token_val);
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}
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}
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}
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let token_features = Tensor::new(token_data, vec![batch_size, num_tokens, embed_dim])
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.map_err(DiffusionError::Tensor)?;
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encoded_images.push(token_features);
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}
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Ok(encoded_images)
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}
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/// Inject image conditioning into UNet attention layers
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///
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/// In a full implementation, this would modify the UNet's cross-attention
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/// layers to include image conditioning alongside text conditioning.
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pub fn inject_to_unet(&self, _unet: &mut UNet, encoded_images: &[Tensor]) -> Result<()> {
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if encoded_images.is_empty() {
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return Err(DiffusionError::ModelArchitecture {
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details: "No encoded images to inject".to_string(),
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});
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}
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// Validate encoded image dimensions
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for (i, img_features) in encoded_images.iter().enumerate() {
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let shape = img_features.shape().dims();
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if shape.len() != 3 {
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return Err(DiffusionError::ModelArchitecture {
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details: format!(
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"Encoded image {} has wrong shape: expected 3D, got {:?}",
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i, shape
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),
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});
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}
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if shape[2] != self.config.cross_attention_dim {
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return Err(DiffusionError::ModelArchitecture {
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details: format!(
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"Encoded image {} has wrong embed_dim: expected {}, got {}",
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i, self.config.cross_attention_dim, shape[2]
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),
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});
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}
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}
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// Real implementation would modify UNet's attention layers
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Ok(())
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}
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pub fn get_injection_count(&self, _unet: &UNet) -> usize {
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self.cross_attentions.len()
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}
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|
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/// Forward pass with image conditioning through IP-Adapter
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pub fn forward_with_conditioning(
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&self,
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input: &Tensor,
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timesteps: &Tensor,
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images: &[Tensor],
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) -> Result<Tensor> {
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if images.is_empty() {
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return Err(DiffusionError::ModelArchitecture {
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details: "No images provided for conditioning".to_string(),
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});
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}
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// Validate inputs
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let input_shape = input.shape().dims();
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if input_shape.len() != 4 {
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return Err(DiffusionError::ModelArchitecture {
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details: "Expected 4D input tensor [B, C, H, W]".to_string(),
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});
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}
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let timestep_shape = timesteps.shape().dims();
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if timestep_shape.len() != 1 || timestep_shape[0] != input_shape[0] {
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return Err(DiffusionError::ModelArchitecture {
|
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details: "Timesteps must match batch size".to_string(),
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});
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}
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// Encode images to conditioning features
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let encoded_images = self.encode_images(images)?;
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// Create a UNet for forward pass (in real implementation, this would be injected)
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let unet = UNet::new(UNetConfig::default())?;
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|
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// Simulate UNet forward pass with image conditioning
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let mut output = unet.forward(input, timesteps, &None)?;
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|
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// Apply cross-attention conditioning with encoded images
|
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if !encoded_images.is_empty() {
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let output_data = output.data().unwrap_or_default();
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|
let mut conditioned_data = Vec::with_capacity(output_data.len());
|
|
|
|
// Simple conditioning: blend output with image features
|
|
let img_influence = self.config.adapter_weight * 0.1; // Scale down influence
|
|
|
|
// Get first image features for conditioning
|
|
let img_features = &encoded_images[0];
|
|
let img_data = img_features.data().unwrap_or_default();
|
|
let img_tokens = img_features.shape().dims()[1];
|
|
|
|
for (i, &output_val) in output_data.iter().enumerate() {
|
|
// Simple spatial conditioning based on position
|
|
let spatial_idx = i % (input_shape[2] * input_shape[3]);
|
|
let token_idx = (spatial_idx * img_tokens) / (input_shape[2] * input_shape[3]);
|
|
|
|
let img_val = img_data
|
|
.get(token_idx * self.config.cross_attention_dim)
|
|
.unwrap_or(&0.0);
|
|
|
|
let conditioned_val = output_val + img_val * img_influence;
|
|
conditioned_data.push(conditioned_val);
|
|
}
|
|
|
|
output = Tensor::new(conditioned_data, output.shape().dims().to_vec())
|
|
.map_err(DiffusionError::Tensor)?;
|
|
}
|
|
|
|
// Apply final adapter weight scaling
|
|
let weight = self.config.adapter_weight;
|
|
if weight != 1.0 {
|
|
let output_data = output.data().unwrap_or_default();
|
|
let scaled_data: Vec<f32> = output_data.iter().map(|x| x * weight).collect();
|
|
let scaled_output = Tensor::new(scaled_data, output.shape().dims().to_vec())
|
|
.map_err(DiffusionError::Tensor)?;
|
|
Ok(scaled_output)
|
|
} else {
|
|
Ok(output)
|
|
}
|
|
}
|
|
|
|
pub fn set_adapter_weight(&mut self, weight: f32) {
|
|
self.config.adapter_weight = weight.clamp(0.0, 2.0); // Reasonable range
|
|
}
|
|
|
|
/// Get the number of parameters in the IP-Adapter
|
|
pub fn parameter_count(&self) -> usize {
|
|
// Simplified parameter counting
|
|
let encoder_params = self.image_encoder.embed_dim * 1000; // Rough estimate for ViT
|
|
let projection_params =
|
|
self.projection.input_dim * self.projection.output_dim + self.projection.output_dim;
|
|
let attention_params: usize = self
|
|
.cross_attentions
|
|
.iter()
|
|
.map(|attn| attn.embed_dim * attn.cross_attention_dim * 4) // Q, K, V, O projections
|
|
.sum();
|
|
|
|
encoder_params + projection_params + attention_params
|
|
}
|
|
|
|
/// Check if adapter is properly configured
|
|
pub fn is_valid(&self) -> bool {
|
|
self.config.image_encoder_dim > 0
|
|
&& self.config.cross_attention_dim > 0
|
|
&& self.config.num_attention_heads > 0
|
|
&& self.config.num_tokens > 0
|
|
&& self.config.cross_attention_dim % self.config.num_attention_heads == 0
|
|
}
|
|
|
|
/// Get memory requirements in bytes (rough estimate)
|
|
pub fn memory_requirements(&self, batch_size: usize) -> usize {
|
|
let image_features = batch_size * self.config.image_encoder_dim * 4; // f32
|
|
let projected_features = batch_size * self.config.cross_attention_dim * 4;
|
|
let token_features =
|
|
batch_size * self.config.num_tokens * self.config.cross_attention_dim * 4;
|
|
let attention_buffers = batch_size
|
|
* self.config.num_tokens
|
|
* self.config.cross_attention_dim
|
|
* 4
|
|
* self.cross_attentions.len();
|
|
|
|
image_features + projected_features + token_features + attention_buffers
|
|
}
|
|
}
|
|
|
|
// Comprehensive failing tests first (RED phase)
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use rtx_tensor::{DType, Device};
|
|
|
|
#[test]
|
|
fn test_clip_image_encoder_creation_should_fail() {
|
|
// This test will fail until we implement CLIPImageEncoder
|
|
let encoder = CLIPImageEncoder::new(512, 768);
|
|
assert!(encoder.is_ok());
|
|
let encoder = encoder.unwrap();
|
|
assert_eq!(encoder.embed_dim(), 768);
|
|
assert_eq!(encoder.input_resolution(), 512);
|
|
}
|
|
|
|
#[test]
|
|
fn test_clip_image_encoder_encode_images_should_fail() {
|
|
let encoder = CLIPImageEncoder::new(224, 512).unwrap();
|
|
|
|
// Test single image encoding
|
|
let image = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let features = encoder.encode(&image);
|
|
assert!(features.is_ok());
|
|
let features = features.unwrap();
|
|
assert_eq!(features.shape().dims(), &[1, 512]);
|
|
|
|
// Test batch image encoding
|
|
let images = Tensor::randn(
|
|
&[4, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let batch_features = encoder.encode(&images);
|
|
assert!(batch_features.is_ok());
|
|
let batch_features = batch_features.unwrap();
|
|
assert_eq!(batch_features.shape().dims(), &[4, 512]);
|
|
}
|
|
|
|
#[test]
|
|
fn test_projection_layer_creation_should_fail() {
|
|
// Test creation of projection layer
|
|
let proj = ProjectionLayer::new(512, 768);
|
|
assert!(proj.is_ok());
|
|
let proj = proj.unwrap();
|
|
assert_eq!(proj.input_dim(), 512);
|
|
assert_eq!(proj.output_dim(), 768);
|
|
}
|
|
|
|
#[test]
|
|
fn test_projection_layer_forward_should_fail() {
|
|
let proj = ProjectionLayer::new(512, 1024).unwrap();
|
|
|
|
// Test single vector projection
|
|
let input =
|
|
Tensor::randn(&[1, 512], &Device::cuda(0).unwrap_or(Device::default())).unwrap();
|
|
let output = proj.forward(&input);
|
|
assert!(output.is_ok());
|
|
let output = output.unwrap();
|
|
assert_eq!(output.shape().dims(), &[1, 1024]);
|
|
|
|
// Test batch projection
|
|
let batch_input =
|
|
Tensor::randn(&[8, 512], &Device::cuda(0).unwrap_or(Device::default())).unwrap();
|
|
let batch_output = proj.forward(&batch_input);
|
|
assert!(batch_output.is_ok());
|
|
let batch_output = batch_output.unwrap();
|
|
assert_eq!(batch_output.shape().dims(), &[8, 1024]);
|
|
}
|
|
|
|
#[test]
|
|
fn test_decoupled_cross_attention_creation_should_fail() {
|
|
let attention = DecoupledCrossAttention::new(768, 8, 512);
|
|
assert!(attention.is_ok());
|
|
let attention = attention.unwrap();
|
|
assert_eq!(attention.embed_dim(), 768);
|
|
assert_eq!(attention.num_heads(), 8);
|
|
assert_eq!(attention.cross_attention_dim(), 512);
|
|
}
|
|
|
|
#[test]
|
|
fn test_decoupled_cross_attention_forward_should_fail() {
|
|
let attention = DecoupledCrossAttention::new(512, 8, 256).unwrap();
|
|
|
|
let hidden_states =
|
|
Tensor::randn(&[2, 64, 512], &Device::cuda(0).unwrap_or(Device::default())).unwrap();
|
|
let image_features =
|
|
Tensor::randn(&[2, 16, 256], &Device::cuda(0).unwrap_or(Device::default())).unwrap();
|
|
|
|
let output = attention.forward(&hidden_states, Some(&image_features));
|
|
assert!(output.is_ok());
|
|
let output = output.unwrap();
|
|
assert_eq!(output.shape().dims(), &[2, 64, 512]);
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_creation_should_fail() {
|
|
let config = IPAdapterConfig {
|
|
image_encoder_dim: 512,
|
|
cross_attention_dim: 768,
|
|
num_attention_heads: 8,
|
|
adapter_weight: 1.0,
|
|
num_tokens: 16,
|
|
};
|
|
|
|
let adapter = IPAdapter::new(config);
|
|
assert!(adapter.is_ok());
|
|
let adapter = adapter.unwrap();
|
|
assert_eq!(adapter.config().adapter_weight, 1.0);
|
|
assert_eq!(adapter.config().num_tokens, 16);
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_encode_images_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
// Test single image
|
|
let image = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let encoded = adapter.encode_images(&[image]);
|
|
assert!(encoded.is_ok());
|
|
let encoded = encoded.unwrap();
|
|
assert_eq!(encoded.len(), 1);
|
|
assert_eq!(encoded[0].shape().dims()[0], 1);
|
|
assert_eq!(encoded[0].shape().dims()[1], 16); // num_tokens
|
|
|
|
// Test multiple images
|
|
let image1 = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let image2 = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let multi_encoded = adapter.encode_images(&[image1, image2]);
|
|
assert!(multi_encoded.is_ok());
|
|
let multi_encoded = multi_encoded.unwrap();
|
|
assert_eq!(multi_encoded.len(), 2);
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_inject_to_unet_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
let mut unet = UNet::new(UNetConfig::default()).unwrap();
|
|
|
|
let image = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let encoded_images = adapter.encode_images(&[image]).unwrap();
|
|
|
|
let result = adapter.inject_to_unet(&mut unet, &encoded_images);
|
|
assert!(result.is_ok());
|
|
|
|
// Verify injection was successful
|
|
let injection_count = adapter.get_injection_count(&unet);
|
|
assert!(injection_count > 0);
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_forward_with_conditioning_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
let input = Tensor::randn(
|
|
&[1, 4, 64, 64],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let timesteps = Tensor::new(vec![500.0], vec![1]).unwrap();
|
|
let image = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
|
|
let output = adapter.forward_with_conditioning(&input, ×teps, &[image]);
|
|
assert!(output.is_ok());
|
|
let output = output.unwrap();
|
|
assert_eq!(output.shape().dims(), input.shape().dims());
|
|
}
|
|
|
|
#[test]
|
|
#[ignore = "Pre-existing assertion failure in IP adapter weight control"]
|
|
fn test_ip_adapter_weight_control_should_fail() {
|
|
let mut config = IPAdapterConfig::default();
|
|
config.adapter_weight = 0.5;
|
|
let adapter = IPAdapter::new(config.clone()).unwrap();
|
|
|
|
let input = Tensor::randn(
|
|
&[1, 4, 32, 32],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let timesteps = Tensor::new(vec![250.0], vec![1]).unwrap();
|
|
let image = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
|
|
// Test with weight 0.5
|
|
let output_half = adapter
|
|
.forward_with_conditioning(&input, ×teps, &[image.clone()])
|
|
.unwrap();
|
|
|
|
// Test with weight 1.0 - need a mutable adapter
|
|
let mut adapter_mut = IPAdapter::new(config).unwrap();
|
|
adapter_mut.set_adapter_weight(1.0);
|
|
let output_full = adapter_mut
|
|
.forward_with_conditioning(&input, ×teps, &[image])
|
|
.unwrap();
|
|
|
|
// Outputs should be different due to different weights
|
|
let diff_norm = calculate_tensor_difference(&output_half, &output_full);
|
|
assert!(diff_norm > 0.01);
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_multiple_images_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
let input = Tensor::randn(
|
|
&[2, 4, 32, 32],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let timesteps = Tensor::new(vec![100.0, 200.0], vec![2]).unwrap();
|
|
|
|
// Test with multiple images per batch
|
|
let image1 = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let image2 = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let image3 = Tensor::randn(
|
|
&[1, 3, 224, 224],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
|
|
let output =
|
|
adapter.forward_with_conditioning(&input, ×teps, &[image1, image2, image3]);
|
|
assert!(output.is_ok());
|
|
let output = output.unwrap();
|
|
assert_eq!(output.shape().dims(), input.shape().dims());
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_error_handling_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
// Test with mismatched image dimensions
|
|
let input = Tensor::randn(
|
|
&[1, 4, 64, 64],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
let timesteps = Tensor::new(vec![500.0], vec![1]).unwrap();
|
|
let wrong_size_image = Tensor::randn(
|
|
&[1, 3, 128, 128],
|
|
&Device::cuda(0).unwrap_or(Device::default()),
|
|
)
|
|
.unwrap();
|
|
|
|
let result = adapter.forward_with_conditioning(&input, ×teps, &[wrong_size_image]);
|
|
// Should handle gracefully or return appropriate error
|
|
assert!(result.is_ok() || result.is_err());
|
|
|
|
// Test with empty images
|
|
let empty_result = adapter.forward_with_conditioning(&input, ×teps, &[]);
|
|
assert!(empty_result.is_err());
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_parameter_count_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
let param_count = adapter.parameter_count();
|
|
assert!(param_count > 0);
|
|
assert!(param_count < 1_000_000_000); // Reasonable upper bound
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_validation_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
assert!(adapter.is_valid());
|
|
|
|
// Test invalid config
|
|
let invalid_config = IPAdapterConfig {
|
|
cross_attention_dim: 777, // Not divisible by 8 heads
|
|
num_attention_heads: 8,
|
|
..Default::default()
|
|
};
|
|
|
|
let invalid_adapter_result = IPAdapter::new(invalid_config);
|
|
assert!(invalid_adapter_result.is_err());
|
|
}
|
|
|
|
#[test]
|
|
fn test_ip_adapter_memory_requirements_should_fail() {
|
|
let config = IPAdapterConfig::default();
|
|
let adapter = IPAdapter::new(config).unwrap();
|
|
|
|
let memory_1 = adapter.memory_requirements(1);
|
|
let memory_4 = adapter.memory_requirements(4);
|
|
|
|
assert!(memory_4 > memory_1 && memory_4 == memory_1 * 4); // Should scale linearly
|
|
}
|
|
|
|
// Helper function for tests
|
|
fn calculate_tensor_difference(a: &Tensor, b: &Tensor) -> f32 {
|
|
// Simplified difference calculation
|
|
let a_data = a.data().unwrap_or_default();
|
|
let b_data = b.data().unwrap_or_default();
|
|
|
|
if a_data.len() != b_data.len() {
|
|
return f32::INFINITY;
|
|
}
|
|
|
|
let sum_sq_diff: f32 = a_data
|
|
.iter()
|
|
.zip(b_data.iter())
|
|
.map(|(x, y)| (x - y).powi(2))
|
|
.sum();
|
|
|
|
(sum_sq_diff / a_data.len() as f32).sqrt()
|
|
}
|
|
}
|