diff --git a/README.md b/README.md index a737e1907..ca51193da 100644 --- a/README.md +++ b/README.md @@ -8,24 +8,18 @@ **FastVideo is a unified post-training and real-time inference framework for accelerated video generation.** -## NEWS -- `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/). +## Recent news +- `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/). - `2026/10/06`: 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/). -- `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. See the [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), [Attn-QAT training guide](https://haoailab.com/FastVideo/training/attn_qat/), and [blog](https://haoailab.com/blogs/fastwan-qad/). -- `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/). -- `2026/03/13`: Release demo: Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU, check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/). -- `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py). -- `2025/08/04`: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/). ### More News -- `2025/06/14`: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389). -- `2025/04/24`: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released! -- `2025/02/18`: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/). +- [All announcements and earlier milestones](https://hao-ai-lab.github.io/FastVideo/news/). ## Key Features @@ -54,59 +48,23 @@ FastVideo has the following features: ## Getting Started -We recommend using [uv](https://docs.astral.sh/uv/) to create a clean environment. If you previously used Conda, switching to uv generally gives faster and more stable installs. +Install FastVideo with [uv](https://docs.astral.sh/uv/): ```bash -# Create and activate a new uv environment uv venv --python 3.12 --seed source .venv/bin/activate -# Install FastVideo on NVIDIA CUDA 12 +# NVIDIA CUDA 12; use cu130 on CUDA 13 UV_TORCH_BACKEND=cu126 uv pip install fastvideo ``` -Use `UV_TORCH_BACKEND=cu130` on CUDA 13. Apple silicon users should follow the -[MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/). +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/). -> **On an Apple Silicon Mac?** Install with `uv pip install -e '.[mlx]'` from -> a clone, then pick a recipe in the -> [cookbook](https://haoailab.com/FastVideo/cookbook/). See the -> [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/). - -Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions. - -> **On an NVIDIA DGX Spark (GB10 / ARM64 + CUDA 13)?** There's no prebuilt ARM wheel for the FastVideo CUDA kernel, so it's an editable from-source install (`UV_TORCH_BACKEND=cu130 uv pip install -e .`, which compiles that kernel for you) rather than `UV_TORCH_BACKEND=cu130 uv pip install fastvideo`. A compatible prebuilt ARM64 FlashAttention wheel is available separately. Follow the [DGX Spark install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/spark/). - -### Install with an AI coding agent - -FastVideo is a monorepo with rich agent guidance (see [`AGENTS.md`](AGENTS.md)). If you use Claude Code, Cursor, or another coding agent, paste the prompt below — it detects your platform and follows the matching guide: - -```text -Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment. - -1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`. -2. Read and follow the matching install guide exactly (in this repo, or at - https://hao-ai-lab.github.io/FastVideo/getting_started/installation/): - - NVIDIA GPU, x86_64 -> docs/getting_started/installation/gpu.md - - NVIDIA DGX Spark / GB10, aarch64, CUDA 13 -> docs/getting_started/installation/spark.md - - Apple Silicon, macOS -> docs/getting_started/installation/mlx.md -3. Use uv for every step. If a command fails, debug it and tell me what you changed. -4. Verify the result: - python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())" - fastvideo --help -5. Report which platform you detected and any deviations you had to make. -``` +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). ## Sparse Distillation -For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/). - -See below for recipes and datasets: - -| Model | Sparse Distillation | Dataset | -| ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | -| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P) | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) | -| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) | +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). ## Dreamverse — Realtime Video Generation & Editing diff --git a/docs-site/lib/readme.mjs b/docs-site/lib/readme.mjs index ae08e930f..7023b8bb2 100644 --- a/docs-site/lib/readme.mjs +++ b/docs-site/lib/readme.mjs @@ -1,4 +1,4 @@ -const NEWS_HEADINGS = ["news", "recent updates", "what's new", "latest news", "announcements"]; +const NEWS_HEADINGS = ["news", "recent news", "recent updates", "what's new", "latest news", "announcements"]; const PROJECT_HEADINGS = [ "awesome work using fastvideo or our research projects", diff --git a/docs-site/navigation.json b/docs-site/navigation.json index 489ed306f..8885794eb 100644 --- a/docs-site/navigation.json +++ b/docs-site/navigation.json @@ -8,6 +8,10 @@ "href": "/fast-models", "icon": "boxes" }, + { + "label": "News", + "href": "/news" + }, { "label": "Get started", "icon": "rocket", diff --git a/docs-site/scripts/transfer-docs.mjs b/docs-site/scripts/transfer-docs.mjs index e18303fc0..a3350cf98 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://github.com/hao-ai-lab/FastVideo#news"; + const href = links.at(-1)?.[2] ?? "https://hao-ai-lab.github.io/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-site/tests/readme-sections.test.mjs b/docs-site/tests/readme-sections.test.mjs index 91449ee51..b6457eaa9 100644 --- a/docs-site/tests/readme-sections.test.mjs +++ b/docs-site/tests/readme-sections.test.mjs @@ -59,6 +59,15 @@ test("stops at the next heading and ignores other sections", () => { assert.doesNotMatch(news[1].text, /Awesome work/); }); +test("accepts the Recent news heading used by the README", () => { + const { news } = readReadmeSections(legacy.replace("## NEWS", "## Recent news")); + assert.equal(news.length, 2); + assert.deepEqual( + news.map((item) => item.date), + ["2026/10/06", "2026/09/15"], + ); +}); + test("returns null when a section is missing", () => { assert.deepEqual(readReadmeSections("# FastVideo\n\n## Overview\n"), { news: null, projects: null }); }); diff --git a/docs/getting_started/installation.md b/docs/getting_started/installation.md index 1e0280372..31c8167ed 100644 --- a/docs/getting_started/installation.md +++ b/docs/getting_started/installation.md @@ -60,6 +60,29 @@ UV_TORCH_BACKEND=cu126 uv pip install -e . uv pip install flash-attn --no-build-isolation -v ``` +## Install with an AI coding agent + +FastVideo is a monorepo with rich agent guidance (see +[`AGENTS.md`](https://github.com/hao-ai-lab/FastVideo/blob/main/AGENTS.md)). +If you use Claude Code, Cursor, or another coding agent, paste the prompt +below; it detects your platform and follows the matching guide: + +```text +Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment. + +1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`. +2. Read and follow the matching install guide exactly (in this repo, or at + https://hao-ai-lab.github.io/FastVideo/getting_started/installation/): + - NVIDIA GPU, x86_64 -> docs/getting_started/installation/gpu.md + - NVIDIA DGX Spark / GB10, aarch64, CUDA 13 -> docs/getting_started/installation/spark.md + - Apple Silicon, macOS -> docs/getting_started/installation/mlx.md +3. Use uv for every step. If a command fails, debug it and tell me what you changed. +4. Verify the result: + python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())" + fastvideo --help +5. Report which platform you detected and any deviations you had to make. +``` + ## Requirements - **Python**: 3.10-3.12 is the tested and recommended range (the commands diff --git a/docs/news.md b/docs/news.md new file mode 100644 index 000000000..041152052 --- /dev/null +++ b/docs/news.md @@ -0,0 +1,27 @@ +# News + +All FastVideo announcements, newest first. The README carries the latest items; this page keeps the full history. + +## Recent news + +- `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/). +- `2026/10/06`: 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/). + +## Earlier news + +- `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. See the [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), [Attn-QAT training guide](https://haoailab.com/FastVideo/training/attn_qat/), and [blog](https://haoailab.com/blogs/fastwan-qad/). +- `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/). +- `2026/03/13`: Release demo: Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU, check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/). +- `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py). +- `2025/08/04`: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/). + +## Earlier milestones + +- `2025/06/14`: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389). +- `2025/04/24`: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released! +- `2025/02/18`: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).