Prepare FastH3 8-Step V2 release documentation and notices

Simplify the model card, add FastVideo branding and linked acknowledgements, document the exact VSA80 8-forward shift10/3 inference contract and public-runtime compatibility hold, and add MiniMax/Qwen notices. Keep weights and original provenance unchanged. Public release, GPU validation, and license/territory approval remain pending; repository stays private.

INFERENCE.md

@@ -0,0 +1,61 @@
+# FastH3 8-Step V2 inference
+
+## Compatibility status
+
+Public FastVideo `main` was checked at commit
+`a943220c115228ade5d57b3bab9a6a87fd600a10` on 2026-09-08.
+Its H3 pipeline rejects video scheduler shifts other than 12. This checkpoint
+requires 10. Its public `basic_fasth3.py` also lacks `--video-scheduler-shift`
+and `--audio-scheduler-shift`. Do not advertise that unchanged example as a
+working quickstart for this model.
+
+The existing internal FastVideo checkout at
+`0f2334f6b5d2ca2fafad15b95932df0e3d1a77c0` exposes both options and applies
+them to the scheduler instances. The command below is for reviewers who
+already have that compatible environment. It is not a public installation
+recipe, and no new end-to-end GPU run was performed during release preparation.
+
+## Exact sampling contract
+
+- Unshifted student inputs: `[999, 874, 749, 624, 500, 375, 250, 125]`.
+- The terminal clean endpoint is zero: eight model evaluations, nine grid nodes.
+- Video shift: `10`; audio shift: `3`; guidance: `1.0`.
+- Backend: `VIDEO_SPARSE_ATTN_H3`; sparsity: `0.8`; tile size: `64`.
+- Sampling: ODE updates, without stochastic re-noising.
+- Full H3 video and audio VAEs; preserve the output audio track.
+
+`--steps 9` alone is not equivalent to the explicit trained ladder.
+The environment variable below supplies the exact integer rungs; the two
+scheduler shifts must also be applied. `fastvideo_inference.json` records
+the same settings; verify that your launcher applies them rather than
+assuming its defaults match this file.
+
+## Private-review command
+
+Run in an already configured compatible FastVideo checkout, on a GPU worker:
+
+```bash
+export FASTVIDEO_DMD_DENOISING_STEPS=999,874,749,624,500,375,250,125
+unset FASTVIDEO_DMD_STOCHASTIC_RENOISE
+
+python examples/inference/basic/basic_fasth3.py \
+  --model-path FastVideo/FastVideo-FastH3-8-Step-V2 \
+  --prompt 'integrated_multimodal_description: A red fox runs through fresh snow at dawn. overall_soundscape: Fast pawsteps in snow, winter wind, and distant birds.' \
+  --height 768 --width 1344 --num-frames 124 \
+  --steps 9 \
+  --video-scheduler-shift 10 --audio-scheduler-shift 3 \
+  --num-gpus 4 \
+  --vsa-sparsity 0.8 --vsa-tile-size 64 --vsa-kernel sm100a \
+  --output outputs/fasth3-v2 \
+  --repeats 1
+```
+
+This example requests 1344×768, 124 frames at 24 fps (about 5.17 seconds),
+and writes `outputs/fasth3-v2/fasth3.mp4`. The `sm100a` route needs a compatible
+Blackwell GPU and kernel build. Triton is a different kernel option, not a
+license to change the trained sparsity, tile geometry, or shifts.
+
+Before public release, verify a clean public install, eight actual student
+forwards, video/audio shifts 10/3, VSA80 tile64, successful MP4 audio muxing,
+and representative high-motion output quality. Report any latency with the
+exact hardware, resolution, frame count, attention path and warmup policy.

LICENSE-Qwen3-VL

@@ -0,0 +1,201 @@
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NOTICE

@@ -0,0 +1,18 @@
+MiniMax H3 is licensed under the MiniMax H3 Community License Agreement, Copyright © 2026 MiniMax. All Rights Reserved.
+
+FastVideo-FastH3-8-Step-V2 is a modified MiniMax H3 model from the FastVideo team.
+The transformer/*.safetensors files contain the DMD2-distilled, VSA-H3 student
+at training step 1300, not the original MiniMax H3 transformer weights.
+The accompanying transformer configuration and index describe that student.
+The scheduler configurations and fastvideo_inference.json describe its trained
+eight-forward schedule, video/audio shifts 10/3, and 80% tile-64 VSA policy.
+The modular model index, model card, and release/provenance documentation were
+prepared by FastVideo for this derivative.
+
+The bundled text encoder, tokenizer, processor, video VAE and audio VAE weights
+are unmodified MiniMax H3 components. The Qwen3-VL encoder is licensed under
+Apache License 2.0; a copy is included as LICENSE-Qwen3-VL.
+Source: https://github.com/QwenLM/Qwen3-VL
+
+The model license is provided in LICENSE. These notices do not replace or
+modify its terms.

README.md

@@ -3,7 +3,7 @@ license: other
 license_name: minimax-h3-community
 license_link: LICENSE
 base_model: MiniMaxAI/MiniMax-H3
-library_name: diffusers
+library_name: fastvideo
 pipeline_tag: text-to-video
 tags:
 - text-to-video
@@ -12,96 +12,112 @@ tags:
 - text-to-audio-video
 - distillation
 - dmd2
+- vsa
 - few-step
+- 8-step
 - minimax-h3
 - fastvideo
 - fasth3
 ---
 
+<p align="center">
+  <a href="https://github.com/hao-ai-lab/FastVideo"><img src="https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/assets/logos/logo.svg" width="320" alt="FastVideo"></a>
+</p>
+
 # FastVideo-FastH3-8-Step-V2
 
-Private FastVideo evaluation snapshot of the `dmd2_sp4_fsdp64_v14_datafree_mixed_vsa80_8step_vshift10` MiniMax-H3
-student at training step 1300. It generates synchronized video and audio
-with 8 transformer forwards.
+FastH3 8-Step V2 is a text-to-audio-video checkpoint from
+[FastVideo](https://github.com/hao-ai-lab/FastVideo), distilled from
+[MiniMax H3](https://huggingface.co/MiniMaxAI/MiniMax-H3). It generates video
+and stereo audio in eight transformer forwards using data-free DMD2 and
+Video Sparse Attention (VSA).
 
-This repository is intentionally private. It is an internal checkpoint for
-evaluation and UI integration, not a public release recommendation.
+[FastVideo](https://github.com/hao-ai-lab/FastVideo) ·
+[FastH3 blog](https://haoailab.com/blogs/fasth3-preview/) ·
+[FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3)
 
-## Checkpoint identity
+Powered by MiniMax H3.
 
-- Training run: `20260829T210445Z-h3-v14-8step-datafree-vsa80-vshift10`
-- Lineage parent: `20260829T023805Z-h3-8step-datafree-vsa80-vshift8`
-- W&B: [euce1zuw](https://wandb.ai/wlsaidhi/h3-dmd2-vsa/runs/euce1zuw)
-- Step: 1300
-- Transformer content SHA-256: `516323fa396fa5dff4e82669d4e9a08a5791692a3d3b98ff6bc3de3fc6a33d11`
-- Checkpoint metadata SHA-256: `ca9f2d609c05742ba465d24989981ec02cca26acb6ca2f163dc0f6dc8d11c27b`
-- FastVideo execution commit: `24bbe7fddd05ca6f2c34b3dbed06ac1c75b72086`
-- Data commit: `b92be606a439c67006220459df236d18c48fd38c3e11f4771d21fe0d078a3371`
+> Private release candidate. Public release is pending the compatibility and
+> review checks in [RELEASE_CHECKLIST.md](RELEASE_CHECKLIST.md).
 
-`checkpoint_content.json` records every indexed transformer shard and digest.
-`checkpoint_metadata.json` preserves the source training configuration.
+## Model
 
-## Contents
+| Setting | Value |
+|---|---|
+| Task | Text to video with stereo audio (T2AV) |
+| Method | Data-free [DMD2](https://arxiv.org/abs/2405.14867), carried backward simulation |
+| Student attention | [VSA-H3](https://arxiv.org/abs/2505.13389), 80% sparsity, 64-token tiles |
+| Denoising | 8 transformer forwards |
+| Video / audio shifts | 10 / 3 |
+| Guidance scale | 1.0 |
+| Checkpoint | Training step 1300, bf16 full weights |
 
-This is a self-contained Diffusers modular-pipeline package. The distilled
-bf16 student is under `transformer/`; the text encoder, tokenizer, processor,
-video VAE and audio VAE are unmodified MiniMax-H3 components. The scheduler
-configs are pinned to this student's trained video/audio shifts.
+Training used prompt conditioning and student-generated latents, not target
+video latents. The corpus used mixed resolutions and aspect ratios. This is
+an eight-step DMD2 checkpoint, not a PDD model.
 
-The `transformer_ref` component is not bundled because this student targets
-T2AV. Its modular-index entry remains on `MiniMaxAI/MiniMax-H3`.
+**This checkpoint requires FastVideo's VSA-H3 backend and Video Sparse
+Attention kernel.** The four-step models' default 90% sparsity and 12/3 shifts
+are not this model's sampling recipe. No matching eight-step LoRA is included.
 
-## Exact FastVideo inference contract
+## Run with FastVideo
 
-The student was trained on the explicit ladder `[999, 874, 749, 624, 500, 375, 250, 125]`. These
-are 8 transformer forwards; do not replace the ladder
-with an arbitrary scheduler grid.
+Start with the [installation guide](https://haoailab.com/FastVideo/getting_started/installation/)
+or [agent-guided installation](https://github.com/hao-ai-lab/FastVideo#install-with-an-ai-coding-agent).
+For NVIDIA B200 with CUDA 13, the public FastVideo environment is installed with:
 
 ```bash
-export FASTVIDEO_DMD_DENOISING_STEPS=999,874,749,624,500,375,250,125
-
-python examples/inference/basic/basic_fasth3.py \
-  --model-path FastVideo/FastVideo-FastH3-8-Step-V2 \
-  --prompt '<MiniMax-H3 T2AV prompt>' \
-  --steps 9 \
-  --video-scheduler-shift 10 \
-  --audio-scheduler-shift 3 \
-  --num-gpus 4 \
-  --vsa-sparsity 0.8 \
-  --vsa-tile-size 64 \
-  --vsa-kernel sm100a
+git clone https://github.com/hao-ai-lab/FastVideo.git
+cd FastVideo
+uv venv --python 3.12 --seed
+source .venv/bin/activate
+UV_TORCH_BACKEND=cu130 uv pip install \
+  --no-sources-package fastvideo-kernel \
+  -e ".[fasth3]"
 ```
 
-The 9 scheduler grid points execute the 8
-trained jumps. Use video/audio scheduler shifts
-`10/3`, guidance scale 1.0,
-and VSA-H3 at 80% sparsity with 64-token tiles. Compilation is an optional runtime optimization; first validate output
-parity on the pinned FastVideo commit.
-
-## Training summary
-
-- Method: data-free DMD2 with carried backward-simulation ODE rollouts. Training consumes text conditioning and student-generated latents rather than target video latents.
-- Student grid: `[999, 874, 749, 624, 500, 375, 250, 125]`.
-- Video/audio scheduler shifts: `10` /
-  `3`.
-- Student attention: VSA-H3 at 80% sparsity with 64-token tiles.
-- Global batch size: 64.
-- Student and critic learning rate: `2e-06` and
-  `2e-06`.
-- Precision: fp32 training state and bf16 inference export.
-- Training topology: 64 GB200 GPUs, SP=4,
-  HSDP replicate=1 / shard=64.
-- Strict continuous score clock: `True`;
-  shift `2.4`, warp max `0.999`.
-
-## Limitations
-
-- Intermediate checkpoint at step 1300 of a planned 4000-step run.
-- T2AV only; the reference-conditioned transformer was not distilled here.
-- Quality remains below base MiniMax-H3 on difficult motion, fine structure,
-  and some audio details.
-- Sampling outside the trained ladder or attention geometry is
-  off-distribution.
-- Inherits the MiniMax H3 Community License and base-model limitations.
-
-Indexed student: 688 bf16 tensors / 70,099,502,592 logical bytes.
+**Compatibility hold:** the public H3 pipeline currently enforces video shift
+12 and its `basic_fasth3.py` example does not expose scheduler-shift overrides.
+It cannot yet run this shift-10 checkpoint unchanged. Installing FastVideo is
+not sufficient until that support lands; do not substitute shift 12 or the
+four-step defaults. See [INFERENCE.md](INFERENCE.md) for the exact contract
+and the private-review command for a compatible checkout.
+
+While the repo is private, reviewers need an account with access:
+
+```bash
+hf auth login
+hf download FastVideo/FastVideo-FastH3-8-Step-V2 \
+  --local-dir ./FastH3-8-Step-V2
+```
+
+The complete package is approximately 148 GB. It includes the distilled
+transformer, text encoder, tokenizer, processor, video VAE and audio VAE.
+It is stored in a modular Diffusers layout, but the supported runtime is
+FastVideo with VSA-H3, not an unmodified dense Diffusers pipeline.
+
+## Scope and limitations
+
+- T2AV only. FL2VA and Ref2VA were not distilled or validated for this release.
+- Difficult motion, small details and some audio can remain below Base H3.
+- Sampling outside the trained schedule or attention policy is an ablation.
+- The four-step blog's latency and evaluation numbers do not establish this
+  eight-step model's performance. Release-specific measurements are pending.
+
+This model inherits the [MiniMax H3 Community License](LICENSE), including
+its territorial, use and commercial restrictions. Review it before use or
+redistribution. The bundled Qwen3-VL encoder is covered by
+[Apache 2.0](LICENSE-Qwen3-VL). See [NOTICE](NOTICE) for attribution and modifications.
+
+Training and checkpoint provenance are preserved in
+[provenance.json](provenance.json), [checkpoint_metadata.json](checkpoint_metadata.json)
+and [checkpoint_content.json](checkpoint_content.json).
+
+## Acknowledgements
+
+We thank [Nuva Lab](https://nuvalab.ai/) for bringing production grounding to FastH3 through its experience with real-world creative video-agent workloads. Its production-aligned post-training insights help bridge open-source research to practical data-assisted distillation for commercial video workflows, with Omni Ref as the next focus.
+
+We thank the [NVIDIA FastGen](https://github.com/NVlabs/FastGen) team for the [DMD2](https://arxiv.org/abs/2405.14867) framework and H3 reference experiment that helped us align the score clock, modality shifts, and backward simulation.
+
+We also thank [MiniMax](https://huggingface.co/MiniMaxAI/MiniMax-H3) for releasing H3-Base, and the [vLLM project](https://vllm.ai/), [NVIDIA](https://www.nvidia.com/en-us/), and [MBZUAI](https://mbzuai.ac.ae/) for their continued sponsorship and support of [FastVideo](https://github.com/hao-ai-lab/FastVideo).

RELEASE_CHECKLIST.md

@@ -0,0 +1,48 @@
+# FastH3 8-Step V2 release checklist
+
+Prepared 2026-09-08. Keep the repository private until the author approves release.
+
+## Verified packaging
+
+- [x] Canonical repo: `FastVideo/FastVideo-FastH3-8-Step-V2`; private visibility retained.
+- [x] Model card identifies the step-1300 V14 data-free DMD2 model, VSA80/tile64,
+  eight forwards and video/audio shifts 10/3.
+- [x] FastVideo logo, blog, code, method citations and linked acknowledgements included.
+- [x] All indexed shards exist: 14 transformer, 14 text-encoder, 3 video-VAE;
+  the single-file audio VAE is also present.
+- [x] All 14 student shard sizes and SHA-256 values in `checkpoint_content.json`
+  match the Hub file/LFS metadata. No large weight download was required.
+- [x] The preserved `checkpoint_metadata.json` SHA-256 matches provenance.
+- [x] `LICENSE` matches the pinned MiniMax H3 base revision exactly.
+- [x] Added the MiniMax redistribution `NOTICE` and Qwen3-VL Apache-2.0 license.
+- [x] Model card and embedded repository references use the V2 name.
+
+The audited package revision before these documentation changes was
+`a001692841117d7d04707951db28b12187f81c66` (67 files, 147,870,260,063 bytes).
+The student contains 688 bf16 tensors. Documentation updates must not alter
+checkpoint weights, scheduler settings or original provenance hashes.
+
+## Required before publishing
+
+- [ ] Land or provide a reviewed public FastVideo inference path for video shift
+  10 and the exact eight-rung ladder. Public `main` at
+  `a943220c115228ade5d57b3bab9a6a87fd600a10` still hard-codes video shift 12;
+  the old README's scheduler-shift CLI options are absent there.
+- [ ] Replace the compatibility hold with a verified public quickstart.
+- [ ] Run one end-to-end GPU generation using the final public installation;
+  confirm eight forwards, shifts 10/3, VSA80/tile64 and playable video with audio.
+- [ ] Inspect representative high-motion samples. Do not copy the four-step
+  models' evaluation results or speed claims onto this checkpoint.
+- [ ] Complete author/legal review of the MiniMax H3 territory, redistribution
+  and commercial terms, including any required separate authorization.
+  Adding license notices is not legal clearance for worldwide distribution.
+- [ ] Review the historical cluster paths and run identifiers in the preserved
+  provenance files before publication. No credentials were found in the
+  reviewed metadata, but provenance sanitization would require updating the
+  corresponding integrity references deliberately.
+- [ ] Obtain explicit approval to change visibility. No public collection edit,
+  announcement, or visibility change is part of this preparation.
+
+Qwen license source:
+`https://github.com/QwenLM/Qwen3-VL/blob/96588727e44c78b25ba03ea03b8e12f7e64fd0da/LICENSE`.
+Runtime details and the compatible private-review command are in `INFERENCE.md`.