Update README.md

README.md

@@ -7,6 +7,12 @@ pipeline_tag: text-to-video
 ---
 This model is a merger of [Wan-AI/Wan2.2-I2V-A14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers) and [Wan2.2-Lightning v1 model](https://huggingface.co/lightx2v/Wan2.2-Lightning/tree/main/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1), it can be run with diffusers pipeline.
 
+Running with [FastDM](https://github.com/KE-AI-ENG/FastDM):
+
+```
+python gen.py --model-path FastDM/Wan2.2-I2V-A14B-Merge-Lightning-V1.0-Diffusers --architecture wan --guidance-scale 1.0 --height 480 --width 832 --steps 4 --use-fp8 --output-path ./output.mp4 --num-frames 81 --fps 16 --task i2v --prompts [PROMPTS] --image-path [PATH/TO/IMAGE]
+```
+
 Running with diffusers:
 ```python
 import torch
@@ -48,11 +54,4 @@ output = pipe(
 ).frames[0]
 export_to_video(output, "i2v_output.mp4", fps=16)
 
-```
-
-**For speedup infer**, you can use [FastDM](https://github.com/KE-AI-ENG/FastDM), which generate a 720x1280 vedio with H20 only cost 120s.
-
-test command:
-```
-python gen.py --model-path FastDM/Wan2.2-I2V-A14B-Merge-Lightning-V1.0-Diffusers --architecture wan --guidance-scale 1.0 --height 480 --width 832 --steps 4 --use-fp8 --output-path ./output.mp4 --num-frames 81 --fps 16 --task i2v --prompts [PROMPTS] --image-path [PATH/TO/IMAGE]
 ```