From 4a07e3cc93e59f5eeee7a7a58514a5bdfca15cf0 Mon Sep 17 00:00:00 2001 From: Aryan Kumar <156166681+aryan5v@users.noreply.github.com> Date: Tue, 10 Nov 2026 21:58:54 +0000 Subject: [PATCH] [docs]: combine the README link fix and support-matrix update Repoint the README and news links at haoailab.com/FastVideo, and list the registered model IDs missing from the support matrix. --- README.md | 32 ++++++++++++++--------------- docs-site/scripts/transfer-docs.mjs | 4 ++-- docs/inference/support_matrix.md | 15 ++++++++------ 3 files changed, 27 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 7b0b09c7e..d4b588937 100644 --- a/README.md +++ b/README.md @@ -3,24 +3,24 @@

- | Documentation | Cookbook | Quick Start | Weekly Dev Meeting | 🟣💬 Slack | 🟣💬 WeChat | + | Documentation | Cookbook | Quick Start | Weekly Dev Meeting | 🟣💬 Slack | 🟣💬 WeChat |

**FastVideo is a unified post-training and real-time inference framework for accelerated video generation.** ## Recent news - `2026/11/10`: Day-0 support for [PAN 2](https://huggingface.co/IFM/PAN2), a world model from IFM. See the [cookbook](https://haoailab.com/FastVideo/cookbook/pan/pan2/). -- `2026/10/21`: Release [FastH3 Omni](https://huggingface.co/FastVideo/FastVideo-FastH3-Omni-8-Step-V1) (Ref2VA): reference-conditioned video-and-audio generation from a prompt plus ordered image, video and audio references, in eight fused forwards. It runs on consumer Blackwell (RTX 5090, RTX PRO 6000, DGX Spark — [NVFP4 QAD](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD)), on RTX 4090 (FP8) and on Apple Silicon (MLX). See the [cookbook](https://haoailab.com/FastVideo/cookbook/minimax-h3/) and the [inference guide](https://hao-ai-lab.github.io/FastVideo/inference/fasth3_omniref/). +- `2026/10/21`: Release [FastH3 Omni](https://huggingface.co/FastVideo/FastVideo-FastH3-Omni-8-Step-V1) (Ref2VA): reference-conditioned video-and-audio generation from a prompt plus ordered image, video and audio references, in eight fused forwards. It runs on consumer Blackwell (RTX 5090, RTX PRO 6000, DGX Spark — [NVFP4 QAD](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD)), on RTX 4090 (FP8) and on Apple Silicon (MLX). See the [cookbook](https://haoailab.com/FastVideo/cookbook/minimax-h3/) and the [inference guide](https://haoailab.com/FastVideo/inference/fasth3_omniref/). - `2026/10/06`: FastVideo now supports [Kandinsky 6](https://x.com/kandinskylab_ai/status/2107374635218055345) from Kandinsky Lab: text- and image-to-video with synchronized audio (base and 10-step distilled pi-Flow checkpoints) plus video super-resolution up to 4x. See the [Kandinsky 6 recipes](https://haoailab.com/FastVideo/cookbook/kandinsky6/). - `2026/10/06`: FastH3 V2 now runs on a single consumer machine: NVIDIA RTX 5090, RTX 4090 and RTX PRO 6000 GPUs, DGX Spark and Apple Silicon. We also release [FastH3 Trim](https://huggingface.co/FastVideo/FastVideo-FastH3-Trim-8-Step-NVFP4), an experimental pruned model that is 4.2× smaller than base H3 and runs in as little as 8 GB of GPU memory. Get the [models](https://huggingface.co/collections/FastVideo/fastvideo-fasth3) and read the [Blog](https://haoailab.com/blogs/fasth3-rtx/). - `2026/09/15`: Release [FastH3 8-Step V2](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2), an eight-forward data-free DMD2 checkpoint distilled from MiniMax-H3 with 80% Video Sparse Attention. Run it with `examples/inference/basic/basic_fasth3_8step.py` or the [FastH3 8-Step V2 recipe](https://haoailab.com/FastVideo/cookbook/minimax-h3/). - `2026/09/01`: FastH3 now runs locally on Apple Silicon through MLX and on NVIDIA DGX Spark through CUDA 13, including two-Spark inference. Follow the [FastH3 recipes](https://haoailab.com/FastVideo/cookbook/minimax-h3/) and read the [Blog](https://haoailab.com/blogs/fasth3-local/). - `2026/08/27`: [FastH3 Preview v1](https://haoailab.com/blogs/fasth3-preview/) is an open-weight 4-step sparse-distilled MiniMax-H3 model for synchronized video-and-audio generation, developed in collaboration with [Nuva Lab](https://nuvalab.ai/) and the [NVIDIA FastGen team](https://github.com/NVlabs/FastGen). Download the recommended [VSA / Data-Free weights](https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree), or see the [full FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3). -- `2026/08/19`: FastVideo now supports MLX on Apple Silicon with [FastMetal-QAD](https://huggingface.co/collections/FastVideo/fastmetal), a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/) and read the [Blog](https://haoailab.com/blogs/fastmetal/). +- `2026/08/19`: FastVideo now supports MLX on Apple Silicon with [FastMetal-QAD](https://huggingface.co/collections/FastVideo/fastmetal), a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the [MLX install guide](https://haoailab.com/FastVideo/getting_started/installation/mlx/) and read the [Blog](https://haoailab.com/blogs/fastmetal/). ### More News -- [All announcements and earlier milestones](https://hao-ai-lab.github.io/FastVideo/news/). +- [All announcements and earlier milestones](https://haoailab.com/FastVideo/news/). ## Key Features @@ -34,16 +34,16 @@ FastVideo has the following features: - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing. - Causal distillation through Self-Forcing - - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for the supported training workflows, and the [support matrix](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for supported models. + - See this [page](https://haoailab.com/FastVideo/training/overview/) for the supported training workflows, and the [support matrix](https://haoailab.com/FastVideo/inference/support_matrix/) for supported models. - State-of-the-art performance optimizations for inference - Sequence Parallelism for distributed inference - Multiple state-of-the-art attention backends - User-friendly CLI and Python API - - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations. + - See this [page](https://haoailab.com/FastVideo/inference/optimizations/) for full list of supported optimizations. - Diverse hardware and OS support - Support H100, A100, 4090 - Support Linux, Windows, MacOS - - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for full list of supported models, hardware assumptions, and optimization compatibility. + - See this [page](https://haoailab.com/FastVideo/inference/support_matrix/) for full list of supported models, hardware assumptions, and optimization compatibility. - Realtime video generation & editing - [Dreamverse](apps/dreamverse/README.md): stream and "vibe direct" video in realtime ([live demo](https://dreamverse.fastvideo.org/)), deployable on local GPU, a self-hosted B200 server, Docker, or serverless Modal @@ -59,13 +59,13 @@ source .venv/bin/activate UV_TORCH_BACKEND=cu126 uv pip install fastvideo ``` -Platform guides: [NVIDIA GPUs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/gpu/), [DGX Spark / GB10](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/spark/), [Apple Silicon MLX](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/), [AMD / ROCm](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/amd/) — or the [full installation guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/). +Platform guides: [NVIDIA GPUs](https://haoailab.com/FastVideo/getting_started/installation/gpu/), [DGX Spark / GB10](https://haoailab.com/FastVideo/getting_started/installation/spark/), [Apple Silicon MLX](https://haoailab.com/FastVideo/getting_started/installation/mlx/), [AMD / ROCm](https://haoailab.com/FastVideo/getting_started/installation/amd/) — or the [full installation guide](https://haoailab.com/FastVideo/getting_started/installation/). -To have a coding agent do the install, give it the [agent install prompt](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/#install-with-an-ai-coding-agent). +To have a coding agent do the install, give it the [agent install prompt](https://haoailab.com/FastVideo/getting_started/installation/#install-with-an-ai-coding-agent). ## Sparse Distillation -Sparse distillation reaches >50× denoising speedup. See the [distillation guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and the [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/); the training recipes live under [`examples/distill/`](examples/distill/) with the [FastVideo Synthetic datasets](https://huggingface.co/FastVideo). +Sparse distillation reaches >50× denoising speedup. See the [distillation guide](https://haoailab.com/FastVideo/distillation/dmd/) and the [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/); the training recipes live under [`examples/distill/`](examples/distill/) with the [FastVideo Synthetic datasets](https://huggingface.co/FastVideo). ## Dreamverse — Realtime Video Generation & Editing @@ -84,7 +84,7 @@ serverless [Modal](apps/dreamverse/scripts/modal/README.md) — see the ### Generating Your First Video -Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/attention/vsa/#installation). Create a file called `example.py` with the following code: +Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://haoailab.com/FastVideo/attention/vsa/#installation). Create a file called `example.py` with the following code: ```python import os @@ -121,13 +121,13 @@ Run the script with: python example.py ``` -For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/). +For a more detailed guide, please see our [inference quick start](https://haoailab.com/FastVideo/inference/inference_quick_start/). ## More Guides -- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/) -- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) -- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/) +- [Design Overview](https://haoailab.com/FastVideo/design/overview/) +- [Distillation Guide](https://haoailab.com/FastVideo/distillation/dmd/) +- [Contribution Guide](https://haoailab.com/FastVideo/contributing/overview/) ## Awesome work using FastVideo or our research projects @@ -142,7 +142,7 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai ## 🤝 Contributing -We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/). +We welcome all contributions. Please check out our guide [here](https://haoailab.com/FastVideo/contributing/overview/). See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899). ## Acknowledgement diff --git a/docs-site/scripts/transfer-docs.mjs b/docs-site/scripts/transfer-docs.mjs index a3350cf98..e60808ec7 100644 --- a/docs-site/scripts/transfer-docs.mjs +++ b/docs-site/scripts/transfer-docs.mjs @@ -265,7 +265,7 @@ export async function transferDocs() { .replace(/\[([^\]]+)\]\([^)]+\)/g, "$1") .split(/:\s|\.\s/)[0] .replace(/\.$/, ""); - const href = links.at(-1)?.[2] ?? "https://hao-ai-lab.github.io/FastVideo/news/"; + const href = links.at(-1)?.[2] ?? "https://haoailab.com/FastVideo/news/"; const iso = date.replaceAll("/", "-"); const label = new Intl.DateTimeFormat("en", { month: "short", @@ -277,7 +277,7 @@ export async function transferDocs() { }); await writeFile( join(content, "_snippets/home-news.mdx"), - `
\n

Recent news

All announcements →
\n\n
\n`, + `
\n

Recent news

All announcements →
\n\n
\n`, ); await writeFile(join(content, "_snippets/home-projects.mdx"), asMdx(projects.trim())); await rm(join(content, "index.md")); diff --git a/docs/inference/support_matrix.md b/docs/inference/support_matrix.md index a6d68eee2..4c0cbcf19 100644 --- a/docs/inference/support_matrix.md +++ b/docs/inference/support_matrix.md @@ -16,8 +16,8 @@ actively maintained `main` inference path. ## Registered Model IDs -Every Hugging Face model ID registered in `fastvideo/registry.py` on `main` -(commit `8d89f30d`), grouped by family. Any ID below can +Every Hugging Face model ID registered in `fastvideo/registry.py` on `main`, +grouped by family. Any ID below can be passed to `VideoGenerator.from_pretrained(...)`; FastVideo resolves the matching pipeline and sampling defaults. The **Family** column is a documentation grouping: it follows each registration's declared `model_family`, @@ -36,6 +36,7 @@ column links a runnable script in `examples/inference/basic/` where one exists. | flux2 | `black-forest-labs/FLUX.2-klein-4B`
`black-forest-labs/FLUX.2-klein-9B` | T2I | [basic_flux2_klein.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2_klein.py) | | flux2 | `black-forest-labs/FLUX.2-dev` | T2I | [basic_flux2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2.py) | | gen3c | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | T2V | [basic_gen3c.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gen3c.py) | +| helios | `BestWishYsh/Helios-Distilled` | T2V | [basic_helios_distilled_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_helios_distilled_t2v.py) | | hunyuan | `hunyuanvideo-community/HunyuanVideo` | T2V | — | | hunyuan | `FastVideo/FastHunyuan-diffusers` | T2V | — | | hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | T2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) | @@ -60,14 +61,16 @@ column links a runnable script in `examples/inference/basic/` where one exists. | lingbotworld2 | `robbyant/lingbot-world-v2-14b-causal-fast` | I2V | [basic_lingbotworld2_causal_fast.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld2_causal_fast.py) | | lingbotworld_fast | `FastVideo/LingBot-World-Fast-Diffusers` | I2V | [basic_lingbotworld_fast.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld_fast.py) | | ltx2 | `FastVideo/LTX2-Distilled-Diffusers`
`FastVideo/LTX2.3-Distilled-Diffusers`
`FastVideo/LTX-2.3-Distilled-Diffusers` | T2V | [basic_ltx2_distilled.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_distilled.py) | -| ltx2 | `FastVideo/LTX-2.5-Distilled-Diffusers` | T2V, I2V | [T2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_t2av.py)
[I2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_i2av.py) | -| ltx2 | `FastVideo/LTX-2.5-Dev-Diffusers` | T2V, I2V | [T2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_t2av.py)
[I2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_i2av.py) | +| ltx2 | `FastVideo/LTX-2.5-Distilled-Diffusers`
`FastVideo/LTX2.5-Distilled-Diffusers` | T2V, I2V | [T2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_t2av.py)
[I2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_i2av.py) | +| ltx2 | `FastVideo/LTX-2.5-Dev-Diffusers`
`FastVideo/LTX2.5-Dev-Diffusers` | T2V, I2V | [T2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_t2av.py)
[I2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_i2av.py) | | ltx2 | `Lightricks/LTX-2.3`
`FastVideo/LTX2.3-base`
`FastVideo/LTX2.3-Diffusers` | T2V | [basic_ltx2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2.py) | | ltx2 | `Lightricks/LTX-2`
`FastVideo/LTX2-base`
`FastVideo/LTX2-Diffusers` | T2V | [basic_ltx2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2.py) | -| mmaudio | `FastVideo/MMAudio-large-44k-v2-Diffusers` | V2A, T2A | [basic_mmaudio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_mmaudio.py) | +| mmaudio | `FastVideo/MMAudio-small-16k-Diffusers`
`FastVideo/MMAudio-small-44k-Diffusers`
`FastVideo/MMAudio-medium-44k-Diffusers`
`FastVideo/MMAudio-large-44k-v2-Diffusers`
`FastVideo/MMAudio-large-44k-Diffusers` | V2A, T2A | [basic_mmaudio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_mmaudio.py) | | matrixgame | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers`
`FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers`
`FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers`
`FastVideo/Matrix-Game-2.0-Base-Diffusers`
`FastVideo/Matrix-Game-2.0-GTA-Diffusers`
`FastVideo/Matrix-Game-2.0-TempleRun-Diffusers`
`mignonjia/mg_longtuning_distilled_zelda`
`mignonjia/mg_sf_distilled_zelda_1k_steps`
`mignonjia/mg_sf_distilled_zelda`
`mignonjia/mg_causal_zelda`
`mignonjia/mg_bidirectional_zelda` | I2V | [basic_matrixgame2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame2.py) | | matrixgame | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | I2V | [basic_matrixgame3.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame3.py) | | minimax_h3 | `MiniMaxAI/MiniMax-H3` | T2V, I2V | [T2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_t2v.py)
[FL2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_fl2va.py)
[Ref2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_ref2va.py) | +| minimax_h3 | `FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2` | T2V, I2V | [basic_fasth3.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3.py) | +| minimax_h3 | `FastVideo/FastVideo-FastH3-8-Step-V2` | T2V, I2V | [basic_fasth3_8step.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_8step.py) | | pika_h3_flex | `Pika-Labs/Pika-H3-Flex` | T2V, I2V | [T2VA/I2VA/L2VA/FL2VA/Ref2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_pika_h3_flex.py) | | open_h3_regenerate_2k | `Efficient-Large-Model/Open-H3-Regenerate-2K` | — | [basic_open_h3_regenerate_2k.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_open_h3_regenerate_2k.py) | | pan2 | `IFM/PAN2` | T2V, I2V | [basic_pan2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_pan2.py) | @@ -97,7 +100,7 @@ column links a runnable script in `examples/inference/basic/` where one exists. (`StableAudioT2AConfig` / `StableAudioOpenSmallConfig`); they are registered under the generic T2V workload option in the registry. -**Note (MMAudio)**: the registered Hugging Face model ID is reserved but not +**Note (MMAudio)**: the registered Hugging Face model IDs are reserved but not yet public. Follow the [MMAudio inference guide](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/pipelines/basic/mmaudio/README.md) to convert the official weights locally and set `MMAUDIO_MODEL_PATH`.