Update README.md

README.md

@@ -59,17 +59,17 @@ This is the dataset proposed in our paper **TIP-I2V: A Million-Scale Real Prompt
 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/TIP-I2V/TIP-I2V/resolve/main/assets/teasor.png" width="1000">
+  <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/TIP-I2V/TIP-I2V/resolve/main/assets/datapoint.png" width="1000">
+  <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/TIP-I2V/TIP-I2V/resolve/main/assets/stat.png" width="1000">
+  <img src="https://huggingface.co/datasets/tipi2v/TIP-I2V/resolve/main/assets/stat.png" width="1000">
 </p>
 
 # Download
@@ -80,7 +80,7 @@ For users in mainland China, try setting `export HF_ENDPOINT=https://hf-mirror.c
 ```python
 # Full (text and compressed image) prompts: ~13.4G
 from datasets import load_dataset
-ds = load_dataset("TIP-I2V/TIP-I2V", split='Full', streaming=True)
+ds = load_dataset("tipi2v/TIP-I2V", split='Full', streaming=True)
 
 # Convert to Pandas format (it may be slow)
 import pandas as pd
@@ -91,7 +91,7 @@ df = pd.DataFrame(ds)
 ```python
 # 100k subset (text and compressed image) prompts: ~0.8G
 from datasets import load_dataset
-ds = load_dataset("TIP-I2V/TIP-I2V", split='Subset', streaming=True)
+ds = load_dataset("tipi2v/TIP-I2V", split='Subset', streaming=True)
 
 # Convert to Pandas format (it may be slow)
 import pandas as pd
@@ -101,7 +101,7 @@ df = pd.DataFrame(ds)
 ```python
 # 10k TIP-Eval (text and compressed image) prompts: ~0.08G
 from datasets import load_dataset
-ds = load_dataset("TIP-I2V/TIP-I2V", split='Eval', streaming=True)
+ds = load_dataset("tipi2v/TIP-I2V", split='Eval', streaming=True)
 
 # Convert to Pandas format (it may be slow)
 import pandas as pd
@@ -113,22 +113,22 @@ 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="TIP-I2V/TIP-I2V", filename="Embedding/Full_Text_Embedding.parquet", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Full_Image_Embedding.parquet", repo_type="dataset")
+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="TIP-I2V/TIP-I2V", filename="Embedding/Subset_Text_Embedding.parquet", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Subset_Image_Embedding.parquet", repo_type="dataset")
+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="TIP-I2V/TIP-I2V", filename="Embedding/Eval_Text_Embedding.parquet", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Eval_Image_Embedding.parquet", repo_type="dataset")
+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
@@ -137,20 +137,20 @@ hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="Embedding/Eval_Image_Embedd
 # Full uncompressed image prompts: ~1T
 from huggingface_hub import hf_hub_download
 for i in range(1,52):
-    hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="image_prompt_tar/image_prompt_%d.tar"%i, repo_type="dataset")
+    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="TIP-I2V/TIP-I2V", filename="sub_image_prompt_tar/sub_image_prompt_%d.tar"%i, repo_type="dataset")
+    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="TIP-I2V/TIP-I2V", filename="eval_image_prompt_tar/eval_image_prompt.tar", repo_type="dataset")
+hf_hub_download(repo_id="tipi2v/TIP-I2V", filename="eval_image_prompt_tar/eval_image_prompt.tar", repo_type="dataset")
 ```
 
 ## Download generated videos
@@ -159,40 +159,40 @@ hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_image_prompt_tar/eval_
 # Full videos generated by Pika: ~1T
 from huggingface_hub import hf_hub_download
 for i in range(1,52):
-    hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="pika_videos_tar/pika_videos_%d.tar"%i, repo_type="dataset")
+    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="TIP-I2V/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_1.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/pika_videos_subset_2.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/svd_videos_subset.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/opensora_videos_subset.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_1.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/i2vgenxl_videos_subset_2.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="subset_videos_tar/cog_videos_subset.tar", repo_type="dataset")
+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="TIP-I2V/TIP-I2V", filename="eval_videos_tar/pika_videos_eval.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/svd_videos_eval.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/opensora_videos_eval.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/i2vgenxl_videos_eval.tar", repo_type="dataset")
-hf_hub_download(repo_id="TIP-I2V/TIP-I2V", filename="eval_videos_tar/cog_videos_eval.tar", repo_type="dataset")
+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/TIP-I2V/TIP-I2V/resolve/main/assets/table.png" width="1000">
+  <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/TIP-I2V/TIP-I2V/resolve/main/assets/comparison.png" width="1000">
+  <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%2FTIP-I2V%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_vidprom.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FTIP-I2V%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%2FTIP-I2V%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fdata_tip-i2v_diffusiondb.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FTIP-I2V%2FTIP-I2V%2Fresolve%2Fmain%2Ftip-i2v-visualize%2Fgrid_tip-i2v_diffusiondb.json)
+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.