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---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 1M<n<10M
task_categories:
- image-to-video
- text-to-video
dataset_info:
features:
- name: UUID
dtype: string
- name: Text_Prompt
dtype: string
- name: Image_Prompt
dtype: image
- name: Subject
dtype: string
- name: Timestamp
dtype: string
- name: Text_NSFW
dtype: float32
- name: Image_NSFW
dtype: string
splits:
- name: Full
num_bytes: 13440652664.125
num_examples: 1701935
- name: Subset
num_bytes: 790710630
num_examples: 100000
- name: Eval
num_bytes: 78258893
num_examples: 10000
download_size: 27500759907
dataset_size: 27750274851.25
configs:
- config_name: default
data_files:
- split: Full
path: data/Full-*
- split: Subset
path: data/Subset-*
- split: Eval
path: data/Eval-*
tags:
- prompt
- image-to-video
- text-to-video
- visual-generation
- video-generation
pretty_name: TIP-I2V
---
# Summary
This is the dataset proposed in our paper **TIP-I2V: A Million-Scale Real Prompt-Gallery Dataset for Image-to-Video Diffusion Models**.
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.
<p align="center">
<img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/teasor.png" width="1000">
</p>
# Datapoint
<p align="center">
<img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/datapoint.png" width="1000">
</p>
# Statistics
<p align="center">
<img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/stat.png" width="1000">
</p>
# Download
For users in mainland China, try setting `export HF_ENDPOINT=https://hf-mirror.com` to successfully download the weights.
## Download the text and (compressed) image prompts with related information
```python
# Full (text and compressed image) prompts: ~13.4G
from datasets import load_dataset
ds = load_dataset("tipi2v/TIP-I2V", split='Full', streaming=True)
# Convert to Pandas format (it may be slow)
import pandas as pd
df = pd.DataFrame(ds)
```
```python
# 100k subset (text and compressed image) prompts: ~0.8G
from datasets import load_dataset
ds = load_dataset("tipi2v/TIP-I2V", split='Subset', streaming=True)
# Convert to Pandas format (it may be slow)
import pandas as pd
df = pd.DataFrame(ds)
```
```python
# 10k TIP-Eval (text and compressed image) prompts: ~0.08G
from datasets import load_dataset
ds = load_dataset("tipi2v/TIP-I2V", split='Eval', streaming=True)
# Convert to Pandas format (it may be slow)
import pandas as pd
df = pd.DataFrame(ds)
```
## Download the embeddings for text and image prompts
```python
# Embeddings for full text prompts (~21G) and image prompts (~3.5G)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="Embedding/Full_Text_Embedding.parquet", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="Embedding/Full_Image_Embedding.parquet", repo_type="dataset")
```
```python
# Embeddings for 100k subset text prompts (~1.2G) and image prompts (~0.2G)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="Embedding/Subset_Text_Embedding.parquet", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="Embedding/Subset_Image_Embedding.parquet", repo_type="dataset")
```
```python
# Embeddings for 10k TIP-Eval text prompts (~0.1G) and image prompts (~0.02G)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="Embedding/Eval_Text_Embedding.parquet", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="Embedding/Eval_Image_Embedding.parquet", repo_type="dataset")
```
## Download uncompressed image prompts
```python
# Full uncompressed image prompts: ~1T
from huggingface_hub import hf_hub_download
for i in range(1,52):
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="image_prompt_tar/image_prompt_%d.tar"%i, repo_type="dataset")
```
```python
# 100k subset uncompressed image prompts: ~69.6G
from huggingface_hub import hf_hub_download
for i in range(1,3):
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="sub_image_prompt_tar/sub_image_prompt_%d.tar"%i, repo_type="dataset")
```
```python
# 10k TIP-Eval uncompressed image prompts: ~6.5G
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_image_prompt_tar/eval_image_prompt.tar", repo_type="dataset")
```
## Download generated videos
```python
# Full videos generated by Pika: ~1T
from huggingface_hub import hf_hub_download
for i in range(1,52):
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="pika_videos_tar/pika_videos_%d.tar"%i, repo_type="dataset")
```
```python
# 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)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_1.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_2.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/svd_videos_subset.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/opensora_videos_subset.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_1.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_2.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="subset_videos_tar/cog_videos_subset.tar", repo_type="dataset")
```
```python
# 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)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_videos_tar/pika_videos_eval.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_videos_tar/svd_videos_eval.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_videos_tar/opensora_videos_eval.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_videos_tar/i2vgenxl_videos_eval.tar", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_videos_tar/cog_videos_eval.tar", repo_type="dataset")
```
# Comparison with VidProM and DiffusionDB
<p align="center">
<img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/table.png" width="1000">
</p>
<p align="center">
<img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/comparison.png" width="1000">
</p>
Click the [WizMap (TIP-I2V VS VidProM)](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Ftipi2v%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_vidprom.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Ftipi2v%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_vidprom.json) and [WizMap (TIP-I2V VS DiffusionDB)](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Ftipi2v%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_diffusiondb.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Ftipi2v%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_diffusiondb.json)
(wait for 5 seconds) for an interactive visualization of our 1.70 million prompts.
# License
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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