---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 1M<n<10M
task_categories:
- image-to-video
- text-to-video
- text-to-image
- image-to-image
pretty_name: TIP-I2V
tags:
- prompt
- image-to-video
- visual-generation
- video-generation
dataset_info:
  features:
  - name: UUID
    dtype: string
  - name: UserID
    dtype: string
  - name: Text_Prompt
    dtype: string
  - name: Image_Prompt
    dtype: image
  - name: Subject
    dtype: string
  - name: Direction
    dtype: string
  - name: Timestamp
    dtype: string
  - name: Text_NSFW
    dtype: float32
  - name: Image_NSFW
    dtype: string
  splits:
  - name: Full
    num_bytes: 13538959055.45
    num_examples: 1701935
  - name: Subset
    num_bytes: 796512047
    num_examples: 100000
  - name: Eval
    num_bytes: 78836541
    num_examples: 10000
  download_size: 14247800861
  dataset_size: 14414307643.45
configs:
- config_name: default
  data_files:
  - split: Full
    path: data/Full-*
  - split: Subset
    path: data/Subset-*
  - split: Eval
    path: data/Eval-*
---

# News
🌟 Downloaded **10,000+** times on Hugging Face after one month of release.

✨ Ranked **Top 1** in the Hugging Face Dataset Trending List for the visual generation community (image-to-video, text-to-video, text-to-image, and image-to-image) on November 10, 2024.

# Summary
This is the dataset proposed in our paper **TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation**.

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>

# Examples
<p align="center">
  <img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/example_a.png" width="1000">
</p>
<p align="center">
  <img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/example_b.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")
```

## Download original HTML files
```python
# 10 files (~32G)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-1 [1123665843365093487].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-2 [1126318113038798948].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-3 [1129173119609876580].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-4 [1129173161527750727].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-5 [1129173449592553564].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-6 [1134375192890712074].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-7 [1134375328442224690].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-8 [1134375370590802051].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-9 [1134375412189908992].html", repo_type="dataset")
hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="raw_html/Pika - Creations - generate-10 [1134375457236725770].html", 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. (The WizMap visualization website is maintained by its official team rather than by us, ensuring that the anonymity requirement is not violated.)


# 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).