Use FastH3 Omni name and add local AI recipes

README.md

@@ -9,12 +9,12 @@ tags:
 - text-to-audio-video
 - fastvideo
 - minimax-h3
-- omniref
+- fasth3-omni
 ---
 
-# FastH3 OmniRef — BF16 Release
+# FastH3 Omni — BF16 Release
 
-**This is the final BF16 release checkpoint of FastH3 OmniRef.** It generates video with synchronized audio from a text prompt and ordered image, video and audio references, using eight transformer evaluations.
+**This is the final BF16 release checkpoint of FastH3 Omni.** It generates video with synchronized audio from a text prompt and ordered image, video and audio references, using eight transformer evaluations.
 
 [All release variants](https://huggingface.co/collections/FastVideo/fasth3-omniref) · [UniServe](https://github.com/hao-ai-lab/uniserve) · [FastVideo](https://github.com/hao-ai-lab/FastVideo)
 
@@ -25,11 +25,26 @@ tags:
 | NVIDIA GB200 / B200 | [UniServe deployment guide](https://github.com/hao-ai-lab/uniserve/blob/main/docs/minimax_h3/minimax_h3.md#deployments) |
 | NVIDIA RTX PRO 6000 Blackwell | [UniServe deployment guide](https://github.com/hao-ai-lab/uniserve/blob/main/docs/minimax_h3/minimax_h3.md#deployments) |
 | NVIDIA Hopper, including H100 / H200 | [UniServe requirements and setup](https://github.com/hao-ai-lab/uniserve/blob/main/docs/minimax_h3/minimax_h3.md#requirements) |
-| NVIDIA DGX Spark (GB10) / RTX 5090 | [FastVideo local OmniRef guide](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md) with the [NVFP4 QAD variant](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD) |
+| NVIDIA DGX Spark (GB10) / RTX 5090 | [FastVideo local FastH3 Omni guide](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md) with the [NVFP4 QAD variant](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD) |
 
 For server deployment, use this BF16 checkpoint with UniServe's reference-conditioned (`ref2va`) configurations. The guide covers installation, model downloads, GPU placement, request capacities and the video/audio API. For the BF16 deployment, choose UniServe's `quality` precision preset. NVFP4 presets require Blackwell hardware.
 
-For local AI on Spark or RTX 5090, use FastVideo's device profiles and the NVFP4 QAD checkpoint. Follow the guide's memory, offload, AdaLN-table and decoder setup for your device. For visual workflows, use the [ComfyUI variants](https://huggingface.co/FastVideo/FastH3-OmniRef-Comfy).
+## Local AI on DGX Spark and RTX 5090
+
+Use **FastVideo** with the [NVFP4 QAD variant](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD) for local inference. QAD means quantization-aware distillation; these weights are trained for NVFP4 execution on Blackwell. Start with the [FastVideo cookbook](https://haoailab.com/FastVideo/cookbook/) and [FastH3 Omni recipe builder](https://haoailab.com/FastVideo/cookbook/minimax-h3/fasth3-omniref/).
+
+| Device | Setup and inference recipe |
+| --- | --- |
+| DGX Spark (GB10, 128 GB unified memory) | [Install FastVideo](https://haoailab.com/FastVideo/getting_started/installation/spark/) · [`spark` recipe](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_spark.yaml) |
+| RTX 5090 (32 GB) | [Install FastVideo](https://haoailab.com/FastVideo/getting_started/installation/gpu/) · [`rtx-5090` recipe](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_rtx5090.yaml) |
+
+Both profiles default to 480p. Spark keeps the model components resident; the RTX 5090 offloads the text encoder and requires a precomputed AdaLN table. Follow the [local inference guide](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md) to compose the model directory, prepare the table and choose a decoder. Keep the device recipe's attention and precision settings.
+
+For FP8, see the [FastH3 Omni FP8 weights](https://huggingface.co/FastVideo/FastH3-OmniRef-FP8) and [FP8 device setup](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md#device-profiles), including the [RTX 4090 recipe](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_rtx4090.yaml) for hardware without FP4 support. Its bundled NVFP4 text encoder requires Blackwell; non-FP4 hardware needs compatible BF16 encoder components. FP8 and NVFP4 QAD outputs can differ from BF16.
+
+The optional [NVFP4 VAE decoders](https://huggingface.co/FastVideo/FastH3-NVFP4-VAE-Decoder) offer 36-layer quality and 8-layer fast tiers. The guide documents decoder selection with `--decoder`. These local variants have their own memory and precision settings.
+
+For visual workflows, use the [ComfyUI variants](https://huggingface.co/FastVideo/FastH3-OmniRef-Comfy).
 
 ## Inputs and outputs
 
@@ -42,7 +57,7 @@ For local AI on Spark or RTX 5090, use FastVideo's device profiles and the NVFP4
 
 ```bash
 hf download FastVideo/FastVideo-FastH3-Omni-8-Step-V1 \
-  --local-dir ./FastH3-OmniRef-BF16
+  --local-dir ./FastH3-Omni-BF16
 ```
 
 The repository includes the BF16 reference transformer, schedulers and inference configuration. The runtime loads the shared text encoder, tokenizer, processor and video/audio VAEs from MiniMax-H3. Follow the runtime guide to prepare those components.