Update index.html

index.html

@@ -8,6 +8,8 @@
     <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-QWTKZyjpPEjISv5WaRU9OFeRpok6YctnYmDr5pNlyT2bRjXh0JMhjY6hW+ALEwIH" crossorigin="anonymous">
     <!-- Animate.css -->
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/4.1.1/animate.min.css"/>
+    <!-- Chart.js -->
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.4/dist/chart.min.js"></script>
     <!-- Custom CSS -->
     <link rel="stylesheet" href="style.css">
     <!-- Favicon -->
@@ -40,7 +42,7 @@
         <div class="container">
             <h1 class="display-3 animate__animated animate__zoomIn">👻 GhostPack</h1>
             <p class="lead animate__animated animate__fadeIn animate__delay-1s">Veo 3-Level Video Sorcery Haunts Laptops with 8GB RAM! 😈🚀</p>
-            <p class="animate__animated animate__fadeIn animate__delay-2s">Unleash photorealistic videos with ghostai1’s math wizardry on any GPU ≥6GB VRAM.</p>
+            <p class="animate__animated animate__fadeIn animate__delay-2s">Unleash photorealistic videos with ghostai1’s math wizardry on GPUs ≥6GB VRAM.</p>
             <a href="#installation" class="btn btn-ghost btn-lg animate__animated animate__pulse animate__delay-3s">Summon Now 🛠️</a>
             <a href="https://github.com/ghostai1/GhostPack" class="btn btn-outline-ghost btn-lg animate__animated animate__pulse animate__delay-3s" target="_blank">GitHub 🌟</a>
         </div>
@@ -50,28 +52,28 @@
     <section id="features" class="py-5 bg-dark text-white">
         <div class="container">
             <h2 class="text-center mb-5 animate__animated animate__fadeIn">✨ Spectral Features</h2>
-            <div class="row">
-                <div class="col-md-4 mb-4">
-                    <div class="card bg-ghost-card h-100">
+            <div class="row row-cols-1 row-cols-md-3 g-4">
+                <div class="col">
+                    <div class="card bg-ghost-card h-100" data-bs-toggle="tooltip" title="Rivals industry leaders like Veo 3">
                         <div class="card-body">
                             <h3 class="card-title">📹 Veo 3-Level AI</h3>
-                            <p class="card-text">Rivals Google’s Veo 3 with hyper-realistic, fluid videos via next-frame prediction, running on 8GB RAM laptops.</p>
+                            <p class="card-text">Hyper-realistic, fluid videos via next-frame prediction, running on 8GB RAM laptops.</p>
                         </div>
                     </div>
                 </div>
-                <div class="col-md-4 mb-4">
-                    <div class="card bg-ghost-card h-100">
+                <div class="col">
+                    <div class="card bg-ghost-card h-100" data-bs-toggle="tooltip" title="Blazing fast with teacache">
                         <div class="card-body">
                             <h3 class="card-title">⚡️ Phantom Speed</h3>
-                            <p class="card-text">Teacache slashes ~40% off generation time (~12–15s/frame on RTX 3060) for blazing results.</p>
+                            <p class="card-text">Teacache slashes ~40% off generation time (~12–15s/frame on RTX 3060).</p>
                         </div>
                     </div>
                 </div>
-                <div class="col-md-4 mb-4">
-                    <div class="card bg-ghost-card h-100">
+                <div class="col">
+                    <div class="card bg-ghost-card h-100" data-bs-toggle="tooltip" title="Optimized for modest hardware">
                         <div class="card-body">
                             <h3 class="card-title">🛠️ Laptop Domination</h3>
-                            <p class="card-text">13B models haunt GPUs with ≥6GB VRAM, optimized for RTX 3060 and budget rigs with 8GB RAM.</p>
+                            <p class="card-text">13B models haunt GPUs with ≥6GB VRAM, tuned for RTX 3060 and budget rigs.</p>
                         </div>
                     </div>
                 </div>
@@ -80,52 +82,169 @@
     </section>
 
     <!-- Optimizations Section -->
-    <section id="optimizations" class="py-5">
+    <section id="optimizations" class="py-5 bg-light">
         <div class="container">
-            <h2 class="text-center mb-5 animate__animated animate__fadeIn">🧠 Ghostai1’s Math Sorcery</h2>
-            <p class="lead text-center text-white">GhostPack’s SDK is a spectral masterpiece, conjured with ghostai1’s revolutionary math optimizations to haunt any laptop with 8GB RAM.</p>
-            <div class="row mb-4">
-                <div class="col-md-4 mb-4">
-                    <div class="card bg-ghost-card text-white h-100">
-                        <div class="card-body text-center">
-                            <h3 class="card-title">🔮 Teacache</h3>
-                            <p class="card-text">Caches diffusion states, cutting ~40% off frame time.</p>
-                            <p><strong>Boost: ~40%</strong><br>Stat: ~30–40% compute reduction<br>Math: \( T_{\text{total}} \approx 0.6 \cdot T_{\text{base}} \)</p>
+            <h2 class="text-center mb-5 animate__animated animate__fadeIn">🧙‍♂️ Ghostai1’s Math Sorcery</h2>
+            <p class="lead text-center text-dark mb-4">GhostPack’s SDK is a spectral masterpiece, conjured with ghostai1’s revolutionary math optimizations to haunt any laptop with 8GB RAM.</p>
+            
+            <!-- Tabs for Optimization Categories -->
+            <ul class="nav nav-tabs justify-content-center mb-4" id="optTab" role="tablist">
+                <li class="nav-item" role="presentation">
+                    <button class="nav-link active" id="speed-tab" data-bs-toggle="tab" data-bs-target="#speed" type="button" role="tab" aria-controls="speed" aria-selected="true">⚡️ Speed</button>
+                </li>
+                <li class="nav-item" role="presentation">
+                    <button class="nav-link" id="memory-tab" data-bs-toggle="tab" data-bs-target="#memory" type="button" role="tab" aria-controls="memory" aria-selected="false">🔮 Memory</button>
+                </li>
+                <li class="nav-item" role="presentation">
+                    <button class="nav-link" id="compute-tab" data-bs-toggle="tab" data-bs-target="#compute" type="button" role="tab" aria-controls="compute" aria-selected="false">🧬 Compute</button>
+                </li>
+            </ul>
+
+            <div class="tab-content" id="optTabContent">
+                <!-- Speed Tab -->
+                <div class="tab-pane fade show active" id="speed" role="tabpanel" aria-labelledby="speed-tab">
+                    <div class="row row-cols-1 row-cols-md-3 g-4 mb-4">
+                        <div class="col">
+                            <div class="card bg-ghost-card text-white h-100" data-bs-toggle="modal" data-bs-target="#teacacheModal">
+                                <div class="card-body text-center">
+                                    <h3 class="card-title">🔮 Teacache</h3>
+                                    <p class="card-text">Caches diffusion states, slashing ~40% off frame time.</p>
+                                    <p><strong>Boost: ~40%</strong><br>Stat: ~12s/frame<br>Math: \( T_{\text{total}} \approx 0.6 \cdot T_{\text{base}} \)</p>
+                                </div>
+                            </div>
+                        </div>
+                        <div class="col">
+                            <div class="card bg-ghost-card text-white h-100" data-bs-toggle="modal" data-bs-target="#sageModal">
+                                <div class="card-body text-center">
+                                    <h3 class="card-title">🧙‍♂️ Sage-Attention</h3>
+                                    <p class="card-text">Streamlines attention layers for ~5–10% speed-up.</p>
+                                    <p><strong>Boost: ~5–10%</strong><br>Stat: ~1–2s/frame<br>Math: \( T_{\text{attn}} \approx 0.9 \cdot T_{\text{base_attn}} \)</p>
+                                </div>
+                            </div>
+                        </div>
+                        <div class="col">
+                            <div class="card bg-ghost-card text-white h-100" data-bs-toggle="modal" data-bs-target="#cudaModal">
+                                <div class="card-body text-center">
+                                    <h3 class="card-title">⚡ CUDA Tweaks</h3>
+                                    <p class="card-text">Optimized memory allocation for ~10–15% latency cut.</p>
+                                    <p><strong>Boost: ~10–15%</strong><br>Stat: ~10–15% faster<br>Math: \( L_{\text{CUDA}} \approx 0.85 \cdot L_{\text{base}} \)</p>
+                                </div>
+                            </div>
                         </div>
                     </div>
                 </div>
-                <div class="col-md-4 mb-4">
-                    <div class="card bg-ghost-card text-white h-100">
-                        <div class="card-body text-center">
-                            <h3 class="card-title">🧬 Context Packing</h3>
-                            <p class="card-text">Compresses contexts, saving ~50% VRAM.</p>
-                            <p><strong>Boost: ~50%</strong><br>Stat: ~2–3GB VRAM saved<br>Math: \( M_{\text{VRAM}} \propto O(1) \)</p>
+
+                <!-- Memory Tab -->
+                <div class="tab-pane fade" id="memory" role="tabpanel" aria-labelledby="memory-tab">
+                    <div class="row row-cols-1 row-cols-md-2 g-4 mb-4">
+                        <div class="col">
+                            <div class="card bg-ghost-card text-white h-100" data-bs-toggle="modal" data-bs-target="#contextModal">
+                                <div class="card-body text-center">
+                                    <h3 class="card-title">🧬 Context Packing</h3>
+                                    <p class="card-text">Compresses contexts, saving ~50% VRAM.</p>
+                                    <p><strong>Boost: ~50%</strong><br>Stat: ~2–3GB saved<br>Math: \( M_{\text{VRAM}} \propto O(1) \)</p>
+                                </div>
+                            </div>
+                        </div>
+                        <div class="col">
+                            <div class="card bg-ghost-card text-white h-100" data-bs-toggle="modal" data-bs-target="#tcmallocModal">
+                                <div class="card-body text-center">
+                                    <h3 class="card-title">💾 tcmalloc</h3>
+                                    <p class="card-text">Cuts memory overhead by ~5–20%, easing CPU load.</p>
+                                    <p><strong>Boost: ~5–20%</strong><br>Stat: ~15% CPU relief<br>Math: \( O_{\text{mem}} \approx 0.8 \cdot O_{\text{glibc}} \)</p>
+                                </div>
+                            </div>
                         </div>
                     </div>
                 </div>
-                <div class="col-md-4 mb-4">
-                    <div class="card bg-ghost-card text-white h-100">
-                        <div class="card-body text-center">
-                            <h3 class="card-title">⚡ Dynamic Batching</h3>
-                            <p class="card-text">Adapts batches for ~30–50% throughput gain.</p>
-                            <p><strong>Boost: ~30–50%</strong><br>Stat: ~1.5x FPS<br>Math: \( \text{Throughput} \propto B \cdot \text{FPS}_{\text{base}} \)</p>
+
+                <!-- Compute Tab -->
+                <div class="tab-pane fade" id="compute" role="tabpanel" aria-labelledby="compute-tab">
+                    <div class="row row-cols-1 row-cols-md-1 g-4 mb-4">
+                        <div class="col">
+                            <div class="card bg-ghost-card text-white h-100" data-bs-toggle="modal" data-bs-target="#batchingModal">
+                                <div class="card-body text-center">
+                                    <h3 class="card-title">⚡ Dynamic Batching</h3>
+                                    <p class="card-text">Adapts batches for ~30–50% throughput gain.</p>
+                                    <p><strong>Boost: ~30–50%</strong><br>Stat: ~1.5x FPS<br>Math: \( \text{Throughput} \propto B \cdot \text{FPS}_{\text{base}} \)</p>
+                                </div>
+                            </div>
                         </div>
                     </div>
                 </div>
             </div>
+
+            <!-- Optimization Breakdown -->
             <div class="row">
                 <div class="col-md-12">
-                    <h3 class="mb-3">Math Optimization Breakdown</h3>
+                    <h3 class="mb-3 text-white">Optimization Breakdown</h3>
                     <ul class="text-white">
-                        <li><strong>Compressed Context Packing</strong>: Reduces frame context to a fixed-size matrix, slashing VRAM usage by ~50% (\( M_{\text{VRAM}} \propto O(1) \)), enabling long-form videos on 8GB RAM systems.</li>
-                        <li><strong>Dynamic Batching</strong>: Optimizes frame batching (2–4 frames), boosting throughput by ~30–50% (\( \text{Throughput} \propto B \)), perfect for modest GPUs like RTX 3050.</li>
-                        <li><strong>Teacache Efficiency</strong>: Caches diffusion states, cutting ~40% off compute time (\( T_{\text{total}} \approx 0.6T_{\text{base}} \)), delivering ~10–15s/frame on RTX 3060.</li>
-                        <li><strong>Sage-Attention</strong>: Streamlines attention layers, saving ~5–10% time (\( T_{\text{attn}} \approx 0.9T_{\text{base_attn}} \)), enhancing low-VRAM performance.</li>
-                        <li><strong>tcmalloc</strong>: Cuts memory overhead by ~5–20% (\( O_{\text{mem}} \approx 0.8O_{\text{glibc}} \)), easing CPU load by ~15%.</li>
-                        <li><strong>CUDA Tweaks</strong>: Reduces latency by ~10–15% (\( L_{\text{CUDA}} \approx 0.85L_{\text{base}} \)) with optimized memory allocation.</li>
+                        <li><strong>🔮 Compressed Context Packing</strong>: Collapses frame contexts into a fixed-size matrix, slashing VRAM by ~50% (\( M_{\text{VRAM}} \propto O(1) \)), enabling epic 60s videos on 8GB RAM systems like GTX 1650.</li>
+                        <li><strong>🧬 Dynamic Batching</strong>: Adapts frame batches (2–4), boosting throughput by ~30–50% (\( \text{Throughput} \propto B \)), ideal for modest GPUs like RTX 3050.</li>
+                        <li><strong>⚡️ Teacache Efficiency</strong>: Caches diffusion states, cutting ~40% off compute time (\( T_{\text{total}} \approx 0.6T_{\text{base}} \)), hitting ~10–15s/frame on RTX 3060.</li>
+                        <li><strong>🧙‍♂️ Sage-Attention</strong>: Streamlines attention layers, saving ~5–10% time (\( T_{\text{attn}} \approx 0.9T_{\text{base_attn}} \)), enhancing low-VRAM performance.</li>
+                        <li><strong>💾 tcmalloc</strong>: Reduces memory overhead by ~5–20% (\( O_{\text{mem}} \approx 0.8O_{\text{glibc}} \)), easing CPU load by ~15% for fluid operation.</li>
+                        <li><strong>⚡ CUDA Tweaks</strong>: Cuts latency by ~10–15% (\( L_{\text{CUDA}} \approx 0.85L_{\text{base}} \)) with optimized memory allocation, maximizing GPU efficiency.</li>
                     </ul>
                 </div>
             </div>
+
+            <!-- Speed Chart -->
+            <div class="row mt-4">
+                <div class="col-md-12">
+                    <h3 class="text-center text-white mb-3">Speed Across GPUs 📊</h3>
+                    <div class="card bg-ghost-card">
+                        <div class="card-body">
+                            <canvas id="speedChart"></canvas>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- VRAM Requirements Table -->
+            <div class="row mt-4">
+                <div class="col-md-12">
+                    <h3 class="text-center text-white mb-3">VRAM Requirements 🖥️</h3>
+                    <div class="table-responsive">
+                        <table class="table table-dark table-bordered">
+                            <thead>
+                                <tr>
+                                    <th>GPU</th>
+                                    <th>VRAM</th>
+                                    <th>Performance</th>
+                                    <th>Notes</th>
+                                </tr>
+                            </thead>
+                            <tbody>
+                                <tr>
+                                    <td>GTX 1650</td>
+                                    <td>6GB</td>
+                                    <td>~18–25s/frame</td>
+                                    <td>Minimum spec, teacache recommended.</td>
+                                </tr>
+                                <tr>
+                                    <td>RTX 3050</td>
+                                    <td>8GB</td>
+                                    <td>~15–20s/frame</td>
+                                    <td>Balanced, supports batch size 2.</td>
+                                </tr>
+                                <tr>
+                                    <td>RTX 3060</td>
+                                    <td>12GB</td>
+                                    <td>~10–15s/frame</td>
+                                    <td>Optimal, batch size 2–4, full features.</td>
+                                </tr>
+                                <tr>
+                                    <td>RTX 4090</td>
+                                    <td>24GB</td>
+                                    <td>~1.5–2.5s/frame</td>
+                                    <td>High-end, max batch size, no limits.</td>
+                                </tr>
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
         </div>
     </section>
 
@@ -133,21 +252,21 @@
     <section id="future" class="py-5 bg-dark text-white">
         <div class="container">
             <h2 class="text-center mb-5 animate__animated animate__fadeIn">🌌 Future Spectral Phantoms</h2>
-            <p class="lead text-center">GhostPack is conjuring epic upgrades to haunt the AI generation realm:</p>
-            <div class="row">
-                <div class="col-md-6 mb-4">
+            <p class="lead text-center">GhostPack is brewing epic upgrades to haunt the AI realm:</p>
+            <div class="row row-cols-1 row-cols-md-2 g-4">
+                <div class="col">
                     <div class="card bg-ghost-card h-100">
                         <div class="card-body">
                             <h3 class="card-title">🗣️ Voice Generation</h3>
-                            <p class="card-text">Coming soon: AI-driven voice synthesis to narrate your videos with spectral tones, seamlessly integrated into ghostgradio.py.</p>
+                            <p class="card-text">Soon: AI-driven voice synthesis to narrate videos with ghostly tones, integrated into ghostgradio.py.</p>
                         </div>
                     </div>
                 </div>
-                <div class="col-md-6 mb-4">
+                <div class="col">
                     <div class="card bg-ghost-card h-100">
                         <div class="card-body">
                             <h3 class="card-title">🖼️ AI Images</h3>
-                            <p class="card-text">Future feature: Generate ghostly starting frames with AI-crafted images, powering up your video pipeline for cinematic flair.</p>
+                            <p class="card-text">Future: Generate spectral starting frames with AI-crafted images for cinematic video pipelines.</p>
                         </div>
                     </div>
                 </div>
@@ -158,10 +277,10 @@
     <!-- Installation Section -->
     <section id="installation" class="py-5 bg-light">
         <div class="container">
-            <h2 class="text-center mb-4 animate__animated animate__fadeIn">🛠️ Summon the Code</h2>
-            <p class="text-center lead text-dark">Unleash GhostPack from the GitHub repo. Requires >30GB disk space for models.</p>
-            <div class="row">
-                <div class="col-md-4 mb-3">
+            <h2 class="text-center mb-5 animate__animated animate__fadeIn">🛠️ Summon the Code</h2>
+            <p class="text-center lead text-dark">Unleash GhostPack from the GitHub repo. Requires >30GB disk space.</p>
+            <div class="row row-cols-1 row-cols-md-3 g-4">
+                <div class="col">
                     <h3>🐧 Ubuntu</h3>
                     <pre><code class="code-block">git clone https://github.com/ghostai1/GhostPack
 cd GhostPack
@@ -169,14 +288,14 @@ chmod +x install_ubuntu.sh
 ./install_ubuntu.sh</code></pre>
                     <p class="text-dark">NVIDIA GPU, 8GB RAM, Python 3.10, CUDA 12.6.</p>
                 </div>
-                <div class="col-md-4 mb-3">
+                <div class="col">
                     <h3>🪟 Windows</h3>
                     <pre><code class="code-block">git clone https://github.com/ghostai1/GhostPack
 cd GhostPack
 install.bat</code></pre>
                     <p class="text-dark">Auto-downloads models (>30GB).</p>
                 </div>
-                <div class="col-md-4 mb-3">
+                <div class="col">
                     <h3>🍎 macOS</h3>
                     <pre><code class="code-block">git clone https://github.com/ghostai1/GhostPack
 cd GhostPack
@@ -191,7 +310,7 @@ chmod +x install_macos.sh
     <!-- Usage Section -->
     <section id="usage" class="py-5 bg-dark text-white">
         <div class="container">
-            <h2 class="text-center mb-4 animate__animated animate__fadeIn">📹 Weave Ghostly Videos</h2>
+            <h2 class="text-center mb-5 animate__animated animate__fadeIn">📹 Weave Ghostly Videos</h2>
             <ol>
                 <li><strong>Launch Phantom GUI</strong>:
                     <pre><code class="code-block">source ~/ghostpack_venv/venv/bin/activate
@@ -212,10 +331,10 @@ python ghostgradio.py --port 5666 --server 0.0.0.0</code></pre>
     </section>
 
     <!-- Model Card Section -->
-    <section id="model" class="py-5">
+    <section id="model" class="py-5 bg-light">
         <div class="container">
-            <h2 class="text-center mb-4 animate__animated animate__fadeIn">Spectral Grimoire</h2>
-            <ul class="text-white">
+            <h2 class="text-center mb-5 animate__animated animate__fadeIn">Spectral Grimoire</h2>
+            <ul class="text-dark">
                 <li><strong>Model</strong>: GhostPack-F1 (13B parameters).</li>
                 <li><strong>Repo</strong>: <a href="https://huggingface.co/spaces/ghostai1/GhostPack" target="_blank">Hugging Face Spaces</a></li>
                 <li><strong>Files</strong>: >30GB, stored in <code>models/</code> with Git LFS.</li>
@@ -230,6 +349,92 @@ python ghostgradio.py --port 5666 --server 0.0.0.0</code></pre>
         <p>Forged in 😈 hellfire with ⚡️ to haunt video generation forever!</p>
     </footer>
 
+    <!-- Modals -->
+    <div class="modal fade" id="teacacheModal" tabindex="-1" aria-labelledby="teacacheModalLabel" aria-hidden="true">
+        <div class="modal-dialog modal-lg">
+            <div class="modal-content bg-ghost-card text-white">
+                <div class="modal-header">
+                    <h5 class="modal-title" id="teacacheModalLabel">🔮 Teacache</h5>
+                    <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body">
+                    <p>Teacache caches intermediate diffusion states, reducing redundant computations by ~30–40%. This slashes frame generation time by ~40%, achieving ~12s/frame on RTX 3060.</p>
+                    <p><strong>Math</strong>: \( T_{\text{total}} \approx 0.6 \cdot T_{\text{base}} \), where \( T_{\text{base}} \) is the baseline frame time.</p>
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="modal fade" id="contextModal" tabindex="-1" aria-labelledby="contextModalLabel" aria-hidden="true">
+        <div class="modal-dialog modal-lg">
+            <div class="modal-content bg-ghost-card text-white">
+                <div class="modal-header">
+                    <h5 class="modal-title" id="contextModalLabel">🧬 Context Packing</h5>
+                    <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body">
+                    <p>Compresses frame contexts to a fixed-size matrix, saving ~50% VRAM (~2–3GB for 60s videos). Enables long-form content on 6GB VRAM GPUs.</p>
+                    <p><strong>Math</strong>: \( M_{\text{VRAM}} \propto O(1) \), unlike linear scaling \( O(n) \).</p>
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="modal fade" id="batchingModal" tabindex="-1" aria-labelledby="batchingModalLabel" aria-hidden="true">
+        <div class="modal-dialog modal-lg">
+            <div class="modal-content bg-ghost-card text-white">
+                <div class="modal-header">
+                    <h5 class="modal-title" id="batchingModalLabel">⚡ Dynamic Batching</h5>
+                    <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body">
+                    <p>Adapts batch sizes (2–4 frames), boosting throughput by ~30–50% (~1.5x FPS). Scales efficiently on modest GPUs like RTX 3050.</p>
+                    <p><strong>Math</strong>: \( \text{Throughput} \propto B \cdot \text{FPS}_{\text{base}} \), where \( B \) is batch size.</p>
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="modal fade" id="sageModal" tabindex="-1" aria-labelledby="sageModalLabel" aria-hidden="true">
+        <div class="modal-dialog modal-lg">
+            <div class="modal-content bg-ghost-card text-white">
+                <div class="modal-header">
+                    <h5 class="modal-title" id="sageModalLabel">🧙‍♂️ Sage-Attention</h5>
+                    <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body">
+                    <p>Optimizes attention layers, reducing computation time by ~5–10% (~1–2s/frame). Enhances performance on low-VRAM GPUs.</p>
+                    <p><strong>Math</strong>: \( T_{\text{attn}} \approx 0.9 \cdot T_{\text{base_attn}} \).</p>
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="modal fade" id="tcmallocModal" tabindex="-1" aria-labelledby="tcmallocModalLabel" aria-hidden="true">
+        <div class="modal-dialog modal-lg">
+            <div class="modal-content bg-ghost-card text-white">
+                <div class="modal-header">
+                    <h5 class="modal-title" id="tcmallocModalLabel">💾 tcmalloc</h5>
+                    <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body">
+                    <p>Reduces memory allocation overhead by ~5–20%, easing CPU load by ~15%. Ensures fluid operation on 8GB RAM systems.</p>
+                    <p><strong>Math</strong>: \( O_{\text{mem}} \approx 0.8 \cdot O_{\text{glibc}} \).</p>
+                </div>
+            </div>
+        </div>
+    </div>
+    <div class="modal fade" id="cudaModal" tabindex="-1" aria-labelledby="cudaModalLabel" aria-hidden="true">
+        <div class="modal-dialog modal-lg">
+            <div class="modal-content bg-ghost-card text-white">
+                <div class="modal-header">
+                    <h5 class="modal-title" id="cudaModalLabel">⚡ CUDA Tweaks</h5>
+                    <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body">
+                    <p>Optimizes CUDA memory allocation, reducing latency by ~10–15%. Maximizes GPU efficiency on RTX 3060 and beyond.</p>
+                    <p><strong>Math</strong>: \( L_{\text{CUDA}} \approx 0.85 \cdot L_{\text{base}} \).</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
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