f192c86c60
Root cause: torch.load() reads 6.9GB .ckpt into Python heap + model params in CPU RAM = ~14GB peak, exceeding 16GB system RAM → OOM Killer. Fix 1 - mmap=True on all torch.load() calls (torch 2.7 supports this): With mmap, checkpoint storage is file-backed (not heap). Only the model parameters (also ~7GB) exist in physical RAM during loading. Peak RAM drops from ~14GB to ~7GB — within safe limits on 16GB machines. Files changed: pipelines.py, hunyuan3ddit.py, model.py (×2), flow_matching_sit.py Fix 2 - malloc_trim(0) after every gc.collect(): Forces glibc to return freed heap pages to OS immediately, so Python's memory pool doesn't hoard freed model memory before the next load. Fix 3 - PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True: Prevents CUDA allocator fragmentation between model switches. Fix 4 - Adaptive threshold recalculated: With mmap loading, loading a model requires ~7.5GB (model params) not 14GB. CPU offload threshold lowered from 16GB → 10.5GB, enabling fast path on machines with more headroom. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
81 lines
3.1 KiB
HTML
81 lines
3.1 KiB
HTML
<!DOCTYPE html>
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<html>
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<head>
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<!-- Import the component -->
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<script src="https://cdn.jsdelivr.net/npm/@google/model-viewer@3.1.1/dist/model-viewer.min.js" type="module"></script>
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<script>
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document.addEventListener('DOMContentLoaded', () => {
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const modelViewers = document.querySelectorAll('model-viewer');
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const isSafari = /^((?!chrome|android).)*safari/i.test(navigator.userAgent);
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modelViewers.forEach(modelViewer => {
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modelViewer.setAttribute(
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"environment-image",
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"/static/env_maps/gradient.jpg"
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);
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// if (!isSafari) {
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// modelViewer.setAttribute(
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// "environment-image",
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// "/static/env_maps/gradient.jpg"
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// );
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// } else {
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// modelViewer.addEventListener('load', (event) => {
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// const [material] = modelViewer.model.materials;
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// let color = [43, 44, 46, 255];
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// color = color.map(x => x / 255);
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// material.pbrMetallicRoughness.setMetallicFactor(0.1); // 完全金属
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// material.pbrMetallicRoughness.setRoughnessFactor(0.7); // 低粗糙度
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// material.pbrMetallicRoughness.setBaseColorFactor(color); // CornflowerBlue in RGB
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// });
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// }
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// modelViewer.addEventListener('load', (event) => {
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// const [material] = modelViewer.model.materials;
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// let color = [43, 44, 46, 255];
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// color = color.map(x => x / 255);
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// material.pbrMetallicRoughness.setMetallicFactor(0.1); // 完全金属
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// material.pbrMetallicRoughness.setRoughnessFactor(0.7); // 低粗糙度
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// material.pbrMetallicRoughness.setBaseColorFactor(color); // CornflowerBlue in RGB
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// });
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});
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});
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</script>
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<style>
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body {
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margin: 0;
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font-family: Arial, sans-serif;
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}
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.centered-container {
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display: flex;
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justify-content: center;
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align-items: center;
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border-radius: 8px;
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border-color: #e5e7eb;
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border-style: solid;
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border-width: 1px;
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}
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</style>
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</head>
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<body>
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<div class="centered-container">
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<div class="column is-mobile is-centered">
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<model-viewer id="modelviewer" style="height: 640px; width: 500px;"
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rotation-per-second="10deg"
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src="./white_mesh.glb/" disable-tap
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environment-image="neutral"
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camera-target="0m 0m 0m"
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camera-orbit="0deg 90deg 8m"
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orientation="0deg 0deg 0deg"
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shadow-intensity=".9"
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ar auto-rotate
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camera-controls>
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</model-viewer>
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</div>
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</div>
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</body>
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</html> |