// sv4d_model.go - Core SV4D architecture in Go
package sv4d

import (
    "math"
    "github.com/atlas-aerospace/neural-go/tensor"
    "github.com/atlas-aerospace/neural-go/layers"
)

type SV4DModel struct {
    UNet3D          *layers.UNet3D
    MultiViewEncoder *layers.MultiViewEncoder
    TemporalTransformer *layers.TemporalTransformer
    VAE             *layers.VideoVAE
    ProjectionHead  *layers.ProjectionHead
}

// Forward pass for multi-view video generation
func (m *SV4DModel) Forward(
    textEmbedding tensor.Tensor, // [1, 77, 768]
    cameraPoses tensor.Tensor,   // [N_views, 4, 4]
    noiseLatent tensor.Tensor,   // [N_views, T, C, H, W]
    timestep int,
) tensor.Tensor {
    // 1. Encode camera parameters to plucker coordinates
    plucker := m.encodePluckerCoordinates(cameraPoses)
    
    // 2. Cross-attention with text embedding
    textCond := m.ProjectionHead(textEmbedding)
    
    // 3. Multi-view aware denoising
    for _, block := range m.UNet3D.Blocks {
        noiseLatent = block.Forward(noiseLatent, timestep, textCond)
        
        // 4. Multi-view consistency enforcement
        noiseLatent = m.MultiViewEncoder.Forward(noiseLatent, plucker)
        
        // 5. Temporal attention across views
        noiseLatent = m.TemporalTransformer.Forward(noiseLatent)
    }
    
    // 6. Decode to video frames
    decoded := m.VAE.Decode(noiseLatent) // [N_views, T, 3, H, W]
    
    return decoded
}

// Plücker coordinate encoding for ray representation
func (m *SV4DModel) encodePluckerCoordinates(poses tensor.Tensor) tensor.Tensor {
    batchSize := poses.Shape[0]
    pluckerDim := 6
    
    result := tensor.NewZeros([]int{batchSize, pluckerDim})
    for i := 0; i < batchSize; i++ {
        // Extract rotation and translation from camera matrix [R|t]
        R := poses.Slice([]int{i, 0, 0}, []int{i, 3, 3})
        t := poses.Slice([]int{i, 0, 3}, []int{i, 3, 4})
        
        // Plücker: (d, m) where d = direction, m = moment
        d := tensor.MatMul(R, tensor.NewFromScalar(0, 0, 1)) // Forward vector
        m := tensor.Cross(t, d)
        
        result.SetSlice([]int{i}, tensor.Concat(d, m))
    }
    return result
}

// training_pipeline.go - SV4D training loop
package training

import (
    "log"
    "time"
    "github.com/atlas-aerospace/neural-go/optimizer"
)

type SV4DTrainer struct {
    Model        *SV4DModel
    Optimizer    *optimizer.AdamW
    Scheduler    *optimizer.CosineAnnealingLR
    LossFn       *MultiViewLoss
    DataLoader   *MultiViewDataLoader
}

// Multi-view consistency loss function
type MultiViewLoss struct {
    L1Weight     float64
    PerceptualWeight float64
    FlowWeight   float64
    ViewConsistencyWeight float64
}

func (l *MultiViewLoss) Compute(
    pred tensor.Tensor,   // [N_views, T, C, H, W]
    target tensor.Tensor, // [N_views, T, C, H, W]
    flows []tensor.Tensor, // Optical flow between views
) float64 {
    // 1. Pixel-wise L1 loss
    l1Loss := tensor.Mean(tensor.Abs(tensor.Sub(pred, target)))
    
    // 2. Perceptual loss using pretrained VGG
    perceptualLoss := l.perceptualLoss(pred, target)
    
    // 3. Optical flow consistency
    flowLoss := 0.0
    for i := 0; i < len(flows); i++ {
        warped := tensor.WarpImage(pred.Slice([]int{i}), flows[i])
        flowLoss += tensor.Mean(tensor.Abs(tensor.Sub(warped, target.Slice([]int{i}))))
    }
    
    // 4. Multi-view projective consistency
    viewConsistency := l.viewConsistencyLoss(pred)
    
    return l.L1Weight*l1Loss + 
           l.PerceptualWeight*perceptualLoss + 
           l.FlowWeight*flowLoss + 
           l.ViewConsistencyWeight*viewConsistency
}

func (t *SV4DTrainer) TrainEpoch() {
    startTime := time.Now()
    totalLoss := 0.0
    
    for batch := range t.DataLoader.Iterate() {
        // Forward pass
        pred := t.Model.Forward(
            batch.TextEmbeddings,
            batch.CameraPoses,
            batch.NoiseLatents,
            batch.Timesteps,
        )
        
        // Compute loss
        loss := t.LossFn.Compute(pred, batch.TargetVideos, batch.OpticalFlows)
        
        // Backward pass
        t.Optimizer.ZeroGrad()
        gradient := tensor.Gradient(loss, t.Model.Parameters())
        t.Model.Backward(gradient)
        t.Optimizer.Step()
        t.Scheduler.Step()
        
        totalLoss += loss
    }
    
    avgLoss := totalLoss / float64(t.DataLoader.NumBatches())
    log.Printf("Epoch completed - Loss: %.4f, Time: %v", avgLoss, time.Since(startTime))
}

// inference_optimizer.go - Real-time inference optimizations
package inference

import (
    "sync"
    "runtime"
    "github.com/atlas-aerospace/neural-go/tensor"
)

type SV4DInferenceEngine struct {
    Model        *SV4DModel
    Cache        *LatentCache
    Scheduler    *DDIMScheduler
    NumViews     int
    MaxBatchSize int
}

// Latent cache for incremental decoding
type LatentCache struct {
    mu      sync.RWMutex
    entries map[string]*CachedLatent
}

type CachedLatent struct {
    Latent   tensor.Tensor
    Timestep int
    Views    []int
}

// Optimized multi-view generation with caching
func (e *SV4DInferenceEngine) GenerateMultiView(
    prompt string,
    cameraPoses tensor.Tensor,
    numFrames int,
    guidanceScale float64,
) tensor.Tensor {
    // 1. Encode text
    textEmb := e.encodeText(prompt)
    
    // 2. Initialize noise
    noiseShape := []int{e.NumViews, numFrames, 4, 64, 64} // Latent dimensions
    latent := tensor.Randn(noiseShape)
    
    // 3. DDIM sampling with caching
    timesteps := e.Scheduler.GetTimesteps(50) // 50 steps for quality
    
    for _, t := range timesteps {
        // Check cache for similar views
        cacheKey := e.cacheKey(t, cameraPoses)
        if cached, ok := e.Cache.Get(cacheKey); ok {
            latent = cached.Latent
            continue
        }
        
        // Predict noise with guidance
        noisePred := e.Model.Forward(textEmb, cameraPoses, latent, t)
        
        // Classifier-free guidance
        uncondPred := e.Model.Forward(e.emptyText, cameraPoses, latent, t)
        guidedNoise := uncondPred + guidanceScale*(noisePred - uncondPred)
        
        // Update latent
        latent = e.Scheduler.Step(guidedNoise, t, latent)
        
        // Cache intermediate result
        e.Cache.Set(cacheKey, &CachedLatent{Latent: latent, Timestep: t, Views: e.NumViews})
        
        // Parallel decode for speed (Go goroutines)
        if t%10 == 0 { // Decode every 10 steps
            latent = e.parallelDecode(latent)
        }
    }
    
    // Final decode
    return e.Model.VAE.Decode(latent)
}

// Parallel decoding using goroutines
func (e *SV4DInferenceEngine) parallelDecode(latent tensor.Tensor) tensor.Tensor {
    batchSize := latent.Shape[0]
    numWorkers := runtime.NumCPU()
    chunkSize := (batchSize + numWorkers - 1) / numWorkers
    
    results := make([]tensor.Tensor, numWorkers)
    var wg sync.WaitGroup
    
    for i := 0; i < numWorkers; i++ {
        wg.Add(1)
        go func(workerIdx int) {
            defer wg.Done()
            start := workerIdx * chunkSize
            end := min(start+chunkSize, batchSize)
            
            chunk := latent.Slice([]int{start}, []int{end})
            results[workerIdx] = e.Model.VAE.Decode(chunk)
        }(i)
    }
    
    wg.Wait()
    return tensor.Concat(results...)
}

// streaming_t2v.go - StreamingT2V core implementation
package streaming

import (
    "container/list"
    "sync"
    "github.com/atlas-aerospace/neural-go/tensor"
    "github.com/atlas-aerospace/neural-go/layers"
)

type StreamingT2VModel struct {
    TextEncoder      *layers.CLIPEncoder
    FrameEncoder     *layers.VideoVAE
    Denoiser         *layers.UNet3D
    MemoryBank       *LongTermMemory
    SlidingWindow    *SlidingWindowAttention
    TemporalAlign    *TemporalAlignmentModule
    FrameInterpolator *layers.FrameInterpolation
}

// Long-term memory bank with eviction policy
type LongTermMemory struct {
    mu         sync.RWMutex
    maxSize    int
    memory     *list.List // Doubly linked list for LRU
    keyCache   map[string]*list.Element
}

type MemoryEntry struct {
    FrameIndex int
    Key        tensor.Tensor // [1, 768] - CLIP embedding
    Value      tensor.Tensor // [1, 4, 64, 64] - Latent representation
    Timestamp  int64
}

func (m *LongTermMemory) Query(query tensor.Tensor, topK int) []MemoryEntry {
    m.mu.RLock()
    defer m.mu.RUnlock()
    
    // Compute cosine similarity with all memory entries
    type scoredEntry struct {
        entry MemoryEntry
        score float64
    }
    
    var scored []scoredEntry
    for e := m.memory.Front(); e != nil; e = e.Next() {
        entry := e.Value.(MemoryEntry)
        score := tensor.CosineSimilarity(query, entry.Key)
        scored = append(scored, scoredEntry{entry, score})
    }
    
    // Sort by score descending
    sort.Slice(scored, func(i, j int) bool {
        return scored[i].score > scored[j].score
    })
    
    // Return top-K
    results := make([]MemoryEntry, min(topK, len(scored)))
    for i := 0; i < len(results); i++ {
        results[i] = scored[i].entry
    }
    return results
}

func (m *LongTermMemory) Add(entry MemoryEntry) {
    m.mu.Lock()
    defer m.mu.Unlock()
    
    // Evict oldest if full
    if m.memory.Len() >= m.maxSize {
        oldest := m.memory.Back()
        delete(m.keyCache, oldest.Value.(MemoryEntry).Key.String())
        m.memory.Remove(oldest)
    }
    
    // Add to front (most recent)
    elem := m.memory.PushFront(entry)
    m.keyCache[entry.Key.String()] = elem
}

// Sliding window attention with constant memory
type SlidingWindowAttention struct {
    WindowSize  int
    Stride      int
    Attention   *layers.MultiHeadAttention
}

func (s *SlidingWindowAttention) Forward(
    frames tensor.Tensor, // [T, C, H, W]
) tensor.Tensor {
    T := frames.Shape[0]
    output := tensor.NewZeros(frames.Shape)
    
    // Process each frame with local window
    for t := 0; t < T; t++ {
        // Determine window boundaries
        start := max(0, t-s.WindowSize/2)
        end := min(T, t+s.WindowSize/2+1)
        
        // Extract window
        window := frames.Slice([]int{start}, []int{end})
        
        // Apply attention within window
        attended := s.Attention.Forward(window)
        
        // Only keep center frame
        centerIdx := t - start
        output.SetSlice([]int{t}, attended.Slice([]int{centerIdx}))
    }
    
    return output
}

// Temporal alignment module using optical flow
type TemporalAlignmentModule struct {
    FlowEstimator *layers.RAFTModel
    WarpLayer     *layers.DifferentiableWarp
}

func (t *TemporalAlignmentModule) Align(
    currentFrame tensor.Tensor, // [1, C, H, W]
    previousFrame tensor.Tensor, // [1, C, H, W]
) tensor.Tensor {
    // Estimate optical flow
    flow := t.FlowEstimator.Forward(previousFrame, currentFrame)
    
    // Warp previous frame to current
    warpedPrev := t.WarpLayer.Forward(previousFrame, flow)
    
    // Blend with current frame for temporal consistency
    alpha := 0.7 // Blend factor
    aligned := tensor.Add(
        tensor.MulScalar(currentFrame, alpha),
        tensor.MulScalar(warpedPrev, 1-alpha),
    )
    
    return aligned
}

// streaming_generator.go - Long video generation pipeline
package streaming

import (
    "log"
    "time"
    "github.com/atlas-aerospace/neural-go/tensor"
)

type StreamingGenerator struct {
    Model        *StreamingT2VModel
    Scheduler    *DDIMScheduler
    ChunkSize    int // Frames per generation step
    OverlapFrames int // Frames to condition on from previous chunk
    MaxMemory    int // Maximum memory bank size
}

func (g *StreamingGenerator) GenerateLongVideo(
    prompt string,
    totalFrames int,
    fps int,
) tensor.Tensor {
    startTime := time.Now()
    log.Printf("Starting generation of %d frames at %d fps", totalFrames, fps)
    
    // 1. Encode text prompt
    textEmb := g.Model.TextEncoder.Encode(prompt)
    
    // 2. Initialize first chunk from noise
    firstChunk := g.generateInitialChunk(textEmb, g.ChunkSize + g.OverlapFrames)
    
    // 3. Initialize memory bank with first chunk
    g.initializeMemory(firstChunk)
    
    // 4. Generate remaining chunks
    allFrames := firstChunk
    framesGenerated := firstChunk.Shape[0]
    
    for framesGenerated < totalFrames {
        // Prepare conditioning from previous chunk
        condFrames := allFrames.Slice(
            []int{framesGenerated - g.OverlapFrames},
            []int{framesGenerated},
        )
        
        // Generate next chunk
        nextChunk := g.generateNextChunk(
            textEmb,
            condFrames,
            g.ChunkSize,
        )
        
        // Apply temporal alignment
        alignedChunk := g.alignChunks(condFrames, nextChunk)
        
        // Add to memory bank
        g.updateMemory(alignedChunk)
        
        // Append to output
        allFrames = tensor.Concat(allFrames, alignedChunk)
        framesGenerated += alignedChunk.Shape[0]
        
        log.Printf("Generated %d/%d frames (%.1f%%)",
            framesGenerated, totalFrames,
            float64(framesGenerated)/float64(totalFrames)*100)
    }
    
    elapsed := time.Since(startTime)
    log.Printf("Generation completed in %v (%.2f fps)",
        elapsed, float64(totalFrames)/elapsed.Seconds())
    
    return allFrames
}

func (g *StreamingGenerator) generateNextChunk(
    textEmb tensor.Tensor,
    condFrames tensor.Tensor,
    numFrames int,
) tensor.Tensor {
    // 1. Encode conditioning frames to latent space
    condLatents := g.Model.FrameEncoder.Encode(condFrames)
    
    // 2. Query memory bank for long-term context
    memoryContext := g.Model.MemoryBank.Query(textEmb, 5)
    
    // 3. Initialize noise for new frames
    noiseShape := []int{numFrames, 4, 64, 64}
    latent := tensor.Randn(noiseShape)
    
    // 4. Conditional denoising with memory
    timesteps := g.Scheduler.GetTimesteps(25) // Faster sampling for streaming
    
    for _, t := range timesteps {
        // Concatenate conditioning latents
        fullLatent := tensor.Concat(condLatents, latent)
        
        // Apply sliding window attention
        attended := g.Model.SlidingWindow.Forward(fullLatent)
        
        // Cross-attend with text and memory
        noisePred := g.Model.Denoiser.Forward(attended, t, textEmb, memoryContext)
        
        // Step
        latent = g.Scheduler.Step(noisePred, t, latent)
    }
    
    // Decode to pixel space
    return g.Model.FrameEncoder.Decode(latent)
}

func (g *StreamingGenerator) alignChunks(
    prevChunk tensor.Tensor,
    nextChunk tensor.Tensor,
) tensor.Tensor {
    // Align each frame of next chunk with last frame of previous chunk
    lastPrevFrame := prevChunk.Slice([]int{prevChunk.Shape[0]-1})
    
    for i := 0; i < nextChunk.Shape[0]; i++ {
        currentFrame := nextChunk.Slice([]int{i})
        aligned := g.Model.TemporalAlign.Align(currentFrame, lastPrevFrame)
        nextChunk.SetSlice([]int{i}, aligned)
    }
    
    return nextChunk
}

// memory_management.go - Advanced memory policies
package streaming

import (
    "container/heap"
    "time"
)

// Priority queue for intelligent eviction
type MemoryItem struct {
    Key       string
    Score     float64 // Importance score
    LastAccess time.Time
    Frequency int
    Index     int // For heap operations
}

type PriorityQueue []*MemoryItem

func (pq PriorityQueue) Len() int { return len(pq) }
func (pq PriorityQueue) Less(i, j int) bool {
    // Evict lowest score first
    return pq[i].Score < pq[j].Score
}

// Adaptive memory controller
type AdaptiveMemoryController struct {
    Bank        *LongTermMemory
    PQ          PriorityQueue
    TargetSize  int
    MinRetention float64
}

func (c *AdaptiveMemoryController) UpdateScores() {
    // Recalculate importance scores based on:
    // 1. Recency (temporal distance from current frame)
    // 2. Frequency of being queried
    // 3. Semantic relevance to current prompt
    
    currentTime := time.Now()
    
    for _, item := range c.PQ {
        // Recency factor (exponential decay)
        timeDiff := currentTime.Sub(item.LastAccess).Seconds()
        recencyScore := math.Exp(-timeDiff / 60.0) // 60 second half-life
        
        // Frequency factor
        freqScore := math.Log(float64(item.Frequency + 1))
        
        // Combined score
        item.Score = 0.6*recencyScore + 0.4*freqScore
    }
    
    heap.Init(&c.PQ)
    
    // Evict if over target size
    for c.PQ.Len() > c.TargetSize {
        item := heap.Pop(&c.PQ).(*MemoryItem)
        if item.Score < c.MinRetention {
            c.Bank.Delete(item.Key)
        }
    }
}

// comparative_analysis.go - Hybrid model selection
package analysis

type VideoGenerationTask struct {
    Type           TaskType
    Duration       int // seconds
    NumViews       int
    Resolution     [2]int
    RealTime       bool
}

type TaskType int
const (
    ShortMultiView TaskType = iota
    LongSingleView
    Interactive
    Cinematic
)

func SelectModel(task VideoGenerationTask) string {
    switch {
    case task.NumViews > 1 && task.Duration <= 16:
        return "Stable Video 4D"
    case task.Duration > 30 && task.NumViews == 1:
        return "StreamingT2V"
    case task.RealTime && task.Duration <= 10:
        return "StreamingT2V (optimized)"
    default:
        // Hybrid approach: SV4D for keyframes, StreamingT2V for interpolation
        return "Hybrid: SV4D + StreamingT2V"
    }
}

// Hybrid generation combining both models
func HybridGenerate(
    prompt string,
    totalDuration int,
    numViews int,
) tensor.Tensor {
    // 1. Generate keyframes with SV4D (every 30 frames)
    keyframeInterval := 30
    numKeyframes := totalDuration * 30 / keyframeInterval
    
    keyframes := make([]tensor.Tensor, numKeyframes)
    for i := 0; i < numKeyframes; i++ {
        cameraPoses := getCameraPosesForTime(i * keyframeInterval)
        keyframes[i] = SV4DGenerate(prompt, cameraPoses, 1)
    }
    
    // 2. Interpolate between keyframes with StreamingT2V
    fullVideo := tensor.NewZeros([]int{totalDuration * 30, 3, 512, 512})
    
    for i := 0; i < numKeyframes-1; i++ {
        startFrame := i * keyframeInterval
        endFrame := (i + 1) * keyframeInterval
        
        // Use StreamingT2V to generate intermediate frames
        intermediate := StreamingT2VInterpolate(
            keyframes[i],
            keyframes[i+1],
            keyframeInterval,
        )
        
        fullVideo.SetSlice(
            []int{startFrame},
            []int{endFrame},
            intermediate,
        )
    }
    
    return fullVideo
}

// deployment.go - Production serving infrastructure
package deployment

import (
    "context"
    "log"
    "net/http"
    "time"
    "github.com/gorilla/websocket"
    "github.com/atlas-aerospace/neural-go/tensor"
)

type VideoGeneratorServer struct {
    Models map[string]ModelInstance
    Queue  *PriorityQueue
    Cache  *ResultCache
}

type ModelInstance struct {
    Model      interface{} // SV4D or StreamingT2V
    GPU        string
    Load       float64
    MaxBatch   int
}

// WebSocket handler for real-time streaming
func (s *VideoGeneratorServer) HandleStreaming(w http.ResponseWriter, r *http.Request) {
    upgrader := websocket.Upgrader{
        ReadBufferSize:  1024,
        WriteBufferSize: 1024,
    }
    
    conn, err := upgrader.Upgrade(w, r, nil)
    if err != nil {
        log.Printf("WebSocket upgrade failed: %v", err)
        return
    }
    defer conn.Close()
    
    // Receive generation parameters
    var request GenerationRequest
    if err := conn.ReadJSON(&request); err != nil {
        log.Printf("Failed to read request: %v", err)
        return
    }
    
    // Select model based on request
    modelName := SelectModel(VideoGenerationTask{
        Type:     request.TaskType,
        Duration: request.Duration,
        NumViews: request.NumViews,
    })
    
    model := s.Models[modelName]
    
    // Generate video in chunks and stream
    chunkSize := 16 // frames per chunk
    totalFrames := request.Duration * 30
    
    for frameStart := 0; frameStart < totalFrames; frameStart += chunkSize {
        chunk, err := model.GenerateChunk(
            request.Prompt,
            frameStart,
            min(chunkSize, totalFrames - frameStart),
        )
        if err != nil {
            log.Printf("Generation error: %v", err)
            return
        }
        
        // Encode chunk as JPEG frames
        frames := encodeFrames(chunk)
        
        // Send over WebSocket
        if err := conn.WriteJSON(StreamingResponse{
            FrameStart: frameStart,
            Frames:     frames,
            Progress:   float64(frameStart+chunkSize) / float64(totalFrames),
        }); err != nil {
            log.Printf("Write error: %v", err)
            return
        }
        
        // Rate limiting for real-time playback
        time.Sleep(time.Second / 30 * time.Duration(chunkSize))
    }
}

// Load balancing across GPUs
func (s *VideoGeneratorServer) SelectLeastLoaded() ModelInstance {
    var best ModelInstance
    minLoad := 1.0
    
    for _, instance := range s.Models {
        load := instance.Load
        if load < minLoad {
            minLoad = load
            best = instance
        }
    }
    
    return best
}

// flash_attention.go - Optimized attention implementation
package attention

import (
    "math"
    "github.com/atlas-aerospace/neural-go/tensor"
)

// FlashAttention implementation for tiled processing
func FlashAttention(
    Q tensor.Tensor, // [B, H, T, D]
    K tensor.Tensor, // [B, H, T, D]
    V tensor.Tensor, // [B, H, T, D]
    blockSize int,   // Tile size for SRAM
) tensor.Tensor {
    B, H, T, D := Q.Shape[0], Q.Shape[1], Q.Shape[2], Q.Shape[3]
    scale := 1.0 / math.Sqrt(float64(D))
    
    // Initialize output and statistics
    O := tensor.NewZeros([]int{B, H, T, D})
    L := tensor.NewZeros([]int{B, H, T}) // Row sum for softmax
    M := tensor.NewZeros([]int{B, H, T}) // Max for numerical stability
    
    // Process in tiles
    for tileStart := 0; tileStart < T; tileStart += blockSize {
        tileEnd := min(tileStart+blockSize, T)
        
        // Load tile of K and V
        K_tile := K.Slice([]int{0, 0, tileStart}, []int{B, H, tileEnd})
        V_tile := V.Slice([]int{0, 0, tileStart}, []int{B, H, tileEnd})
        
        for rowStart := 0; rowStart < T; rowStart += blockSize {
            rowEnd := min(rowStart+blockSize, T)
            
            // Load tile of Q
            Q_tile := Q.Slice([]int{0, 0, rowStart}, []int{B, H, rowEnd})
            
            // Compute attention scores for this tile
            S := tensor.MatMul(Q_tile, tensor.Transpose(K_tile, -2, -1))
            S = tensor.MulScalar(S, scale)
            
            // Online softmax with rescaling
            m_new := tensor.ReduceMax(S, -1, true)
            M_tile := M.Slice([]int{0, 0, rowStart}, []int{B, H, rowEnd})
            M_new := tensor.Max(M_tile, m_new)
            
            P := tensor.Exp(tensor.Sub(S, M_new))
            L_new := tensor.ReduceSum(P, -1, true)
            
            // Update output
            O_tile := O.Slice([]int{0, 0, rowStart}, []int{B, H, rowEnd})
            L_tile := L.Slice([]int{0, 0, rowStart}, []int{B, H, rowEnd})
            
            // Rescale previous output
            O_tile = tensor.Mul(O_tile, tensor.Div(tensor.Exp(M_tile - M_new), L_new))
            
            // Add new contribution
            O_tile = tensor.Add(O_tile, tensor.Div(tensor.MatMul(P, V_tile), L_new))
            
            // Update statistics
            O.SetSlice([]int{0, 0, rowStart}, O_tile)
            L.SetSlice([]int{0, 0, rowStart}, L_new)
            M.SetSlice([]int{0, 0, rowStart}, M_new)
        }
    }
    
    return O
}

// future_work.go - Experimental ViDiT implementation
package vdit

import (
    "github.com/atlas-aerospace/neural-go/tensor"
    "github.com/atlas-aerospace/neural-go/layers"
)

type VideoDiffusionTransformer struct {
    PatchEmbed      *layers.PatchEmbedding
    PositionalEncoding *layers.RotaryPositionEncoding
    TransformerBlocks []*layers.TransformerBlock
    OutputProjection *layers.Linear
}

// Linear attention mechanism for O(N) complexity
type LinearAttention struct {
    FeatureDim int
    KernelFn   func(tensor.Tensor) tensor.Tensor // e.g., elu+1
}

func (l *LinearAttention) Forward(
    Q tensor.Tensor, // [B, H, T, D]
    K tensor.Tensor,
    V tensor.Tensor,
) tensor.Tensor {
    // Apply kernel to Q and K
    Q_prime := l.KernelFn(Q) // [B, H, T, D]
    K_prime := l.KernelFn(K) // [B, H, T, D]
    
    // Compute KV in O(TD²) instead of O(T²D)
    KV := tensor.MatMul(tensor.Transpose(K_prime, -2, -1), V) // [B, H, D, D]
    
    // Compute attention output
    O := tensor.MatMul(Q_prime, KV) // [B, H, T

![](/images/blog/360365799a15be0c9d312b5b7e98740c-202606101438.png)