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Add install.sh script for automated setup #53
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- Initializes CUTLASS git submodule - Validates CUDA toolkit and PyTorch environment - Builds CUDA extensions for sm_80, sm_89, sm_90, sm_120a (Blackwell) - Includes --clean option for fresh builds - Verifies installation on completion 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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- Check for Miniconda, offer to install if missing - Create conda environment with Python 3.12 - Install PyTorch nightly with CUDA 13.0 (for RTX 5090/Blackwell) - Install psutil dependency - Initialize CUTLASS git submodule - Build TurboDiffusion CUDA extensions - Install SpargeAttn for sparse attention optimization - Add GPU info verification at end Target: media-msi.covershot.app (RTX 5090) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
scripts/comfyui-turbo.sh: - Configurable paths for conda, ComfyUI, CUDA - Start/stop/status commands - Logging to /tmp/comfyui_turbo.log - Ready for cron @reboot setup Usage: ./comfyui-turbo.sh # Start ./comfyui-turbo.sh --stop # Stop ./comfyui-turbo.sh --status # Check status 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add scripts/cache_t5.py to pre-cache T5 embeddings (saves ~11GB VRAM) - Add --cached_embedding and --skip_t5 args to wan2.2_i2v_infer.py - Update install.sh with module symlinks for rcm/imaginaire/ops/SLA - Fix spas_sage_attn import name in install verification This enables 2-pass inference: cache embeddings first, then run inference without loading the 11GB T5 model. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Document memory optimization with pre-cached T5 embeddings - Add memory comparison table (30GB+ vs ~18GB peak VRAM) - Include step-by-step instructions for cache_t5.py usage - Note: cached embedding is ~4MB vs 11GB T5 model 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Auto-detect GPU compute capability - Patch SpargeAttn setup.py to add sm_120 support for RTX 5090 - Build with correct TORCH_CUDA_ARCH_LIST 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Offloads DiT models before VAE decode to free VRAM. Enables 720p 81-frame generation on 32GB GPUs. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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I think the UMT5 model is already released after the text embeddings are computed, and we did observe a decrease in memory usage during our tests. TurboDiffusion/turbodiffusion/inference/wan2.2_i2v_infer.py Lines 85 to 88 in c960373
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