Add pipeline tag, library name and set inference to true

This PR improves the model card by:

- Adding the `pipeline_tag`, enabling people to find your model at https://huggingface.co/models?pipeline_tag=text-to-video
- Adding the `library_name` which allows easier usage of the model
- Setting inference to `true`

README.md

@@ -1,13 +1,15 @@
 ---
+language:
+- en
 license: other
 license_link: https://huggingface.co/THUDM/CogVideoX-5b-I2V/blob/main/LICENSE
-language:
-  - en
 tags:
-  - video-generation
-  - thudm
-  - image-to-video
-inference: false
+- video-generation
+- thudm
+- image-to-video
+pipeline_tag: text-to-video
+library_name: diffusers
+inference: true
 ---
 
 # CogVideoX1.5-5B-I2V
@@ -118,8 +120,6 @@ conversion to get a better experience.**
 
 1. Install the required dependencies
 
-
-
 ```shell
 # diffusers (from source)
 # transformers>=4.46.2
@@ -164,7 +164,7 @@ export_to_video(video, "output.mp4", fps=8)
 
 [PytorchAO](https://github.com/pytorch/ao) and [Optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be
 used to quantize the text encoder, transformer, and VAE modules to reduce CogVideoX's memory requirements. This allows
-the model to run on free T4 Colab or GPUs with lower VRAM! Also, note that TorchAO quantization is fully compatible
+the model to run on free T4 Colab or GPUs with smaller VRAM! Also, note that TorchAO quantization is fully compatible
 with `torch.compile`, which can significantly accelerate inference.
 
 ```python
@@ -248,5 +248,4 @@ This model is released under the [CogVideoX LICENSE](LICENSE).
   journal={arXiv preprint arXiv:2408.06072},
   year={2024}
 }
-```
-
+```