--- language: - en license: cc-by-nc-4.0 size_categories: - n>1T task_categories: - text-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.

# Datapoint

# Statistics

# Examples

# 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

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).