Instructions to use Nvidia-CMU25/DiffusionVideo2WorldGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nvidia-CMU25/DiffusionVideo2WorldGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Nvidia-CMU25/DiffusionVideo2WorldGeneration", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nvidia-CMU25/DiffusionVideo2WorldGeneration", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from __future__ import annotations | |
| from dataclasses import dataclass | |
| from typing import Optional | |
| import torch | |
| class DenoisePrediction: | |
| x0: torch.Tensor # clean data prediction | |
| eps: Optional[torch.Tensor] = None # noise prediction | |
| logvar: Optional[torch.Tensor] = None # log variance of noise prediction, can be used a confidence / uncertainty | |