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Convert the Cosmos3-Edge text tower and 4-step CFG denoiser into one static-shape OpenVINO IR, apply GPU graph optimizations and a mixed INT8 VAE decoder, and verify the generator, decoder and full video against PyTorch. Also add Diffusers to third-party programs and domain terms to the spellcheck word list.
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The notebook downloads an 8 GB checkpoint and needs about 24 GB of host memory for conversion, which exhausts the CI runners. Skip it on all OS runners and in the docker job, as done for other large video notebooks.
Export the text tower and a single denoising step instead of one graph with four unrolled steps, and drive the steps with the UniPC scheduler at runtime. Users pick 4, 8, 16 or 30 steps; the default is now 8. With one step per graph no weight is reused, so the GPU workaround for shared fully connected weights is no longer needed and is removed. The converted models shrink from 16.4 GB to 7.8 GB.
Run four denoising steps by default and use the balanced accuracy mode: an FP32 text encoder and an FP32 timestep embedding in the otherwise FP16 denoiser. Keep fast and reference modes available, and include the optimized generator and FP16 two-chunk VAE decoder path used by the notebook. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Format the new notebook and helper with the repository's Black version and add the reported domain terms to the spelling dictionary. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Use the standard helper_files setup cell that calls setup_notebook_venv(), as required by .ci/check_notebooks.py, and move package installation to the following cell. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Added Cosmos3-edge text2video openvino notebook