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
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7e47687 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # 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
@dataclass
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
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