Update README to include the diffusers integration

README.md

@@ -238,7 +238,47 @@ In preparation.
 
 Our codebase essentially supports all the commonly used components in video generation. You can manage your experiments flexibly by adding corresponding registration classes, including `ENGINE, MODEL, DATASETS, EMBEDDER, AUTO_ENCODER, DISTRIBUTION, VISUAL, DIFFUSION, PRETRAIN`, and can be compatible with all our open-source algorithms according to your own needs. If you have any questions, feel free to give us your feedback at any time.
 
+## Integration of I2VGenXL with 🧨 diffusers
 
+I2VGenXL is supported in the 🧨 diffusers library. Here's how to use it:
+
+```python
+import torch
+from diffusers import I2VGenXLPipeline
+from diffusers.utils import load_image, export_to_gif
+
+repo_id = "ali-vilab/i2vgen-xl" 
+pipeline = I2VGenXLPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16").to("cuda")
+
+image_url = "https://github.com/ali-vilab/i2vgen-xl/blob/main/data/test_images/img_0009.png?download=true"
+image = load_image(image_url).convert("RGB")
+prompt = "Papers were floating in the air on a table in the library"
+
+generator = torch.manual_seed(8888)
+frames = pipeline(
+    prompt=prompt,
+    image=image,
+    generator=generator
+).frames[0]
+
+print(export_to_gif(frames))
+```
+
+Find the official documentation [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/i2vgenxl). 
+
+Sample output with I2VGenXL:
+
+<table>
+    <tr>
+        <td><center>
+        masterpiece, bestquality, sunset.
+        <br>
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/i2vgen-xl-example.gif"
+            alt="library"
+            style="width: 300px;" />
+        </center></td>
+    </tr>
+</table>
 
 ## BibTeX