README.md

---
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: 790710630.0
    num_examples: 100000
  - name: Eval
    num_bytes: 78258893.0
    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
---

```python
# Full (text and compressed image) prompts: ~13.4G
from datasets import load_dataset
ds = load_dataset("WenhaoWang/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("WenhaoWang/TIP-I2V", split='Subset', streaming=True)

# Convert to Pandas format (it may be slow)
import pandas as pd
df = pd.DataFrame(ds)
```

```python
# 10k subset for TIP-Eval: ~0.08G
from datasets import load_dataset
ds = load_dataset("WenhaoWang/TIP-I2V", split='Eval', streaming=True)

# Convert to Pandas format (it may be slow)
import pandas as pd
df = pd.DataFrame(ds)
```

```python
# Embeddings for full text prompts (~21G) and image prompts (~3.5G)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Full_Text_Embedding.parquet", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="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="WenhaoWang/TIP-I2V", filename="Subset_Text_Embedding.parquet", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="Subset_Image_Embedding.parquet", repo_type="dataset")
```


```python
# Full uncompressed image prompts: ~1T
from huggingface_hub import hf_hub_download
for i in range(1,52):
    hf_hub_download(repo_id="WenhaoWang/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="WenhaoWang/TIP-I2V", filename="sub_image_prompt_tar/sub_image_prompt_%d.tar"%i, repo_type="dataset")
```

```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="WenhaoWang/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 (~xxG), I2VGen-XL (~xxG), and CogVideoX-5B (~xxG)
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_1.tar", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_2.tar", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/svd_videos_subset.tar", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/opensora_videos_subset.tar", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset.tar", repo_type="dataset")
hf_hub_download(repo_id="WenhaoWang/TIP-I2V", filename="subset_videos_tar/cog_videos_subset.tar", repo_type="dataset")
```