TurboDiffusion: ×ばつ Acceleration for Video Diffusion Models
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Updated
Aug 27, 2026 - Python
TurboDiffusion: ×ばつ Acceleration for Video Diffusion Models
[ICML2025] SpargeAttention: A training-free sparse attention that accelerates any model inference.
Cross-platform installer for Triton and SageAttention on ComfyUI. Simplifies GPU-accelerated inference setup for Windows users with automated dependency management and RTX 5090 support.
NVIDIA Sol-Attn for ComfyUI / Triton kernel on SM89 - SM121, with zero-copy MiniMax H3 nodes: memory-efficient attention, scheduled tau with graph preview, and feed-forward chunking. Measured ×ばつ vs SageAttention and −37% MLP peak VRAM on H3
🪟 为 Windows AI 开发者提供预编译 wheel 文件的集中仓库 | 自动抓取并整理 PyTorch、Flash Attention、xformers、SageAttention 等常用库的最新版本 | 免编译,开箱即用 | 特别适合 ComfyUI 和 Stable Diffusion 用户
Bleeding-edge ComfyUI for NVIDIA DGX Spark (GB10/Blackwell/sm_121a). CUDA 13 + SageAttention v3 (sm_121a) + NVFP4 + 14 custom-node packs + Flux 2 Dev / LTX 2.3 22B / ACE-Step v1.5 XL Turbo pre-bundled with abliterated text-encoder paths.
Automatically benchmark and optimize attention in diffusion models. 1.5-2x speedup on RTX 4090.
Selective fused FP8 checkpoint + ComfyUI runtime for LingBot-Video Dense 1.3B. Measured 1.27x in a local one-step RTX 5080 sampler smoke test; T2V, TI2V, experimental FLF.
An all-in-one docker image that runs the latest ComfyUI with SageAttention.
Automated Windows installer for ComfyUI with NVIDIA GPU optimizations
Modular ComfyUI optimizations for MiniMax H3: fused NVFP4 MLP, low-memory Sage2, CAB low-step sampling, long-sequence VRAM safeguards, benchmarks and workflows.
MiniMax H3 SageAttention & Spectrum workflow pack for ComfyUI, providing ready-to-run API JSON workflows for text/image/reference-to-video rendering with clean output naming and low-diff, reproducible setup.
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