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Two files that let ComfyUI run **MiniMax H3 video generation in 4 steps** using FastVideo's Video Sparse Attention. On a 24 GB RTX 3090 Ti, this drops a 5-second, 0.8 MP clip from ~8 minutes (base, 20 steps) to under 1 minute (draft) or ~2.5 minutes (best quality). Requires the companion custom node — you can't load these with a normal LoRA loader alone. > **Most people should probably use one of the established H3 turbo LoRAs** (e.g. Comfy-Org's official 4-step turbo, lightx2v's 8-step turbo) — they're simpler to set up and don't need a custom node. This project exists for people who want to experiment with FastVideo's sparse-attention approach and see if the speed/quality tradeoff works better for their use case, or for people on older cards (RTX 3090 / 4090) who can't run FastVideo's latest official releases that target Hopper/Blackwell GPUs. Compare and pick what you like. > ⚠️ **Experimental.** Tested on one machine (RTX 3090 Ti). Requires the companion custom node — this is not a drop-in for standard LoRA loading. See the node repo before downloading.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Visual QA
Input Text · Image→ Output Text
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r17-v1

barreiros15

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AWQ **W4A16** quantization of `Qwen/Qwen3.8-27B`, produced with llm-compressor in `compressed-tensors` `pack-quantized` format — the best-optimised Marlin path on Ampere (SM 8.6). Qwen3.8-27B is a **hybrid** model. Only **16 of its 64** decoder layers use full attention; the other **48 use Gated DeltaNet** (`linear_attn.*`). Those DeltaNet projections are extremely sensitive to quantization, so they are left at full BF16 precision here, along with the vision tower and the multi-token-prediction head. That means only **256 Linear modules** are quantized — 16 layers × 4 attention projections plus 64 layers × 3 MLP projections — rather than the ~448 you would get by assuming a uniform architecture. Keeping 48 layers of DeltaNet in BF16 costs roughly 7 GB versus builds that quantize everything, and that is the entire difference in file size.

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Input Text · Image→ Output Text
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nb-whisper-coreml

Barrymanalow

**This is a format conversion, not a new model.** No retraining, no fine-tuning, no distillation. Weights and behaviour are NbAiLab's; the changes are the PyTorch → CoreML conversion and the folder layout WhisperKit expects. All credit for the model belongs to NbAiLab. Float16 activations. The layout mirrors `argmaxinc/whisperkit-coreml`, which is what `WhisperKitConfig(model:modelRepo:)` expects to find. With papegøye, a bilingual Norwegian/English macOS dictation daemon — it downloads this repo automatically:

Speech recognition
Input Audio→ Output Text
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