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--- |
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language: |
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- en |
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license: cc-by-nc-4.0 |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- image-to-video |
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- text-to-video |
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dataset_info: |
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features: |
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- name: UUID |
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dtype: string |
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- name: Text_Prompt |
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dtype: string |
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- name: Image_Prompt |
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dtype: image |
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- name: Subject |
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dtype: string |
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- name: Timestamp |
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dtype: string |
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- name: Text_NSFW |
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dtype: float32 |
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- name: Image_NSFW |
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dtype: string |
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splits: |
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- name: Full |
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num_bytes: 13440652664.125 |
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num_examples: 1701935 |
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- name: Subset |
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num_bytes: 790710630 |
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num_examples: 100000 |
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- name: Eval |
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num_bytes: 78258893 |
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num_examples: 10000 |
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download_size: 27500759907 |
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dataset_size: 27750274851.25 |
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configs: |
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- config_name: default |
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data_files: |
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- split: Full |
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path: data/Full-* |
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- split: Subset |
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path: data/Subset-* |
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- split: Eval |
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path: data/Eval-* |
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tags: |
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- prompt |
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- image-to-video |
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- text-to-video |
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- visual-generation |
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- video-generation |
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pretty_name: TIP-I2V |
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--- |
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# Summary |
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This is the dataset proposed in our paper **TIP-I2V: A Million-Scale Real Prompt-Gallery Dataset for Image-to-Video Diffusion Models**. |
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TIP-I2V is the first dataset comprising over 1.70 million unique user-provided text and image prompts. Besides the prompts, TIP-I2V also includes videos generated by five state-of-the-art image-to-video models (Pika, Stable Video Diffusion, Open-Sora, I2VGen-XL, and CogVideoX-5B). The TIP-I2V contributes to the development of better and safer image-to-video models. |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/TIP-I2V/TIP-I2V/resolve/main/assets/teasor.png" width="1000"> |
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</p> |
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# Datapoint |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/TIP-I2V/TIP-I2V/resolve/main/assets/datapoint.png" width="1000"> |
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</p> |
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# Statistics |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/TIP-I2V/TIP-I2V/resolve/main/assets/stat.png" width="1000"> |
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</p> |
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# Download |
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For users in mainland China, try setting `export HF_ENDPOINT=https://hf-mirror.com` to successfully download the weights. |
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## Download the text and (compressed) image prompts with related information |
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```python |
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# Full (text and compressed image) prompts: ~13.4G |
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from datasets import load_dataset |
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ds = load_dataset("TIP-I2V/TIP-I2V", split='Full', streaming=True) |
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# Convert to Pandas format (it may be slow) |
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import pandas as pd |
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df = pd.DataFrame(ds) |
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``` |
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```python |
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# 100k subset (text and compressed image) prompts: ~0.8G |
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from datasets import load_dataset |
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ds = load_dataset("TIP-I2V/TIP-I2V", split='Subset', streaming=True) |
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# Convert to Pandas format (it may be slow) |
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import pandas as pd |
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df = pd.DataFrame(ds) |
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``` |
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```python |
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# 10k TIP-Eval (text and compressed image) prompts: ~0.08G |
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from datasets import load_dataset |
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ds = load_dataset("TIP-I2V/TIP-I2V", split='Eval', streaming=True) |
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# Convert to Pandas format (it may be slow) |
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import pandas as pd |
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df = pd.DataFrame(ds) |
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``` |
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## Download the embeddings for text and image prompts |
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```python |
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# Embeddings for full text prompts (~21G) and image prompts (~3.5G) |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Full_Text_Embedding.parquet", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Full_Image_Embedding.parquet", repo_type="dataset") |
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``` |
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```python |
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# Embeddings for 100k subset text prompts (~1.2G) and image prompts (~0.2G) |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Subset_Text_Embedding.parquet", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Subset_Image_Embedding.parquet", repo_type="dataset") |
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``` |
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```python |
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# Embeddings for 10k TIP-Eval text prompts (~0.1G) and image prompts (~0.02G) |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Eval_Text_Embedding.parquet", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Eval_Image_Embedding.parquet", repo_type="dataset") |
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``` |
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## Download uncompressed image prompts |
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```python |
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# Full uncompressed image prompts: ~1T |
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from huggingface_hub import hf_hub_download |
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for i in range(1,52): |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="image_prompt_tar/image_prompt_%d.tar"%i, repo_type="dataset") |
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``` |
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```python |
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# 100k subset uncompressed image prompts: ~69.6G |
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from huggingface_hub import hf_hub_download |
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for i in range(1,3): |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="sub_image_prompt_tar/sub_image_prompt_%d.tar"%i, repo_type="dataset") |
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``` |
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```python |
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# 10k TIP-Eval uncompressed image prompts: ~6.5G |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_image_prompt_tar/eval_image_prompt.tar", repo_type="dataset") |
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``` |
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## Download generated videos |
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```python |
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# Full videos generated by Pika: ~1T |
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from huggingface_hub import hf_hub_download |
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for i in range(1,52): |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="pika_videos_tar/pika_videos_%d.tar"%i, repo_type="dataset") |
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``` |
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```python |
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# 100k subset videos generated by Pika (~57.6G), Stable Video Diffusion (~38.9G), Open-Sora (~47.2G), I2VGen-XL (~54.4G), and CogVideoX-5B (~36.7G) |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_1.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_2.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/svd_videos_subset.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/opensora_videos_subset.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_1.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_2.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/cog_videos_subset.tar", repo_type="dataset") |
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``` |
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```python |
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# 10k TIP-Eval videos generated by Pika (~5.8G), Stable Video Diffusion (~3.9G), Open-Sora (~4.7G), I2VGen-XL (~5.4G), and CogVideoX-5B (~3.6G) |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/pika_videos_eval.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/svd_videos_eval.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/opensora_videos_eval.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/i2vgenxl_videos_eval.tar", repo_type="dataset") |
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hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/cog_videos_eval.tar", repo_type="dataset") |
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``` |
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# Comparison with VidProM and DiffusionDB |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/TIP-I2V/TIP-I2V/resolve/main/assets/table.png" width="1000"> |
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</p> |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/TIP-I2V/TIP-I2V/resolve/main/assets/comparison.png" width="1000"> |
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</p> |
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Click the [WizMap (TIP-I2V VS VidProM)](x) and [WizMap (TIP-I2V VS DiffusionDB)](x) |
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(wait for 5 seconds) for an interactive visualization of our 1.70 million prompts. |
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# License |
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The prompts and videos in our TIP-I2V are licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). |
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