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News
🌟 Downloaded 10,000+ times on HF Mirror after one month of release.
✨ Ranked Top 1 in the HF Mirror 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.
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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
# 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)
# 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)
# 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
# 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")
# 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")
# 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
# 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")
# 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")
# 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
# 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")
# 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")
# 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
# 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
Click the WizMap (TIP-I2V VS VidProM) and WizMap (TIP-I2V VS DiffusionDB) (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.
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