Add Cosmos3-Edge text-to-video notebook

.ci/ignore_treon_docker.txt

@@ -78,3 +78,4 @@ notebooks/omnivoice/omnivoice.ipynb
 notebooks/unlimited-ocr/unlimited-ocr.ipynb
 notebooks/qwen3.8-mtp/qwen3.8-mtp.ipynb
 notebooks/sam-3d-body/sam-3d-body.ipynb
+notebooks/cosmos3-edge-text-to-video/cosmos3-edge-text-to-video.ipynb

.ci/skipped_notebooks.yml

@@ -507,3 +507,10 @@
         - macos-13
         - ubuntu-22.04
         - windows-2022
+# Large model - 8 GB download and about 24 GB of host memory for conversion
+- notebook: notebooks/cosmos3-edge-text-to-video/cosmos3-edge-text-to-video.ipynb
+  skips:
+    - os:
+        - macos-13
+        - ubuntu-22.04
+        - windows-2022

.ci/spellcheck/.pyspelling.wordlist.txt

@@ -238,6 +238,7 @@ DeepStack
 demucs
 denoise
 denoised
+denoiser
 denoises
 denoising
 denormalization
@@ -815,6 +816,7 @@ OpenCV
 OpenCV's
 OpenDataLab
 openjourney
+OpenMDW
 OpenPose
 OpenShift
 OpenVINO
@@ -950,6 +952,7 @@ prithivMLmods
 proto
 protobuf
 PRs
+PSNR
 psychoacoustics
 PTQ
 px
@@ -975,6 +978,7 @@ quant
 quantized
 quantizer
 quantizers
+quantizes
 Quantizing
 quantizing
 QuartzNet
@@ -1253,6 +1257,7 @@ UNet
 Unet
 UNets
 Unimodal
+UniPC
 unsqueeze
 Uparrow
 uparrow

licensing/third-party-programs.txt

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notebooks/cosmos3-edge-text-to-video/README.md

@@ -0,0 +1,24 @@
+# Text-to-Video Generation with Cosmos3-Edge and OpenVINO
+
+[Cosmos3-Edge](https://huggingface.co/nvidia/Cosmos3-Edge) is the smallest model of NVIDIA's [Cosmos 3](https://research.nvidia.com/labs/cosmos-lab/cosmos3/) family of omnimodal world models for Physical AI ([technical report](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf)). It is a Mixture-of-Transformers: an autoregressive text tower reads the prompt, and a diffusion tower that attends to it denoises video latents, which a Wan VAE decodes into frames.
+
+![Frames of a video generated by Cosmos3-Edge with OpenVINO](cosmos3-edge-text-to-video.png)
+
+## Notebook Contents
+
+The tutorial generates 17-frame, 256×256 videos from a text prompt and consists of the following steps:
+
+1. **Download the model** - Download the text tokenizer, transformer, VAE and scheduler configuration of the public [checkpoint](https://huggingface.co/nvidia/Cosmos3-Edge) from Hugging Face.
+2. **Load the PyTorch model** - Rebuild the text tower and one denoising step as static-shape PyTorch modules (`cosmos3_edge_helper.py`) and run a reference generation with the UniPC scheduler.
+3. **Convert the models to OpenVINO IR** - Convert the text tower, the denoising step and the VAE decoder with static input shapes, then apply graph optimizations and build an FP16 VAE decoder (default) and an optional mixed INT8 decoder. The default balanced accuracy mode uses an FP32 text encoder and an FP32 timestep embedding in the otherwise FP16 denoiser.
+4. **Run OpenVINO inference** - Generate a video from a prompt with 4, 8, 16 or 30 denoising steps on the selected device and save it as an MP4.
+5. **Verify the conversion** - Compare the OpenVINO generator, decoder and full video with the PyTorch reference.
+
+The Cosmos3-Edge model is released under the [OpenMDW 1.1](https://openmdw.ai/license/1-1/) license.
+
+## Installation Instructions
+
+This is a self-contained example that relies solely on its own code.</br>
+We recommend running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/cosmos3-edge-text-to-video/README.md" />