Update README.md

README.md

@@ -1,3 +1,58 @@
----
-license: mit
----
+---
+license: mit
+base_model:
+- Wan-AI/Wan2.2-I2V-A14B-Diffusers
+- lightx2v/Wan2.2-Lightning
+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 diffusers:
+```python
+import torch
+import numpy as np
+from diffusers import WanImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+
+model_id = "FastDM/Wan2.2-I2V-A14B-Merge-Lightning-V1.0-Diffusers"
+dtype = torch.bfloat16
+device = "cuda"
+
+pipe = WanImageToVideoPipeline.from_pretrained(model_id, torch_dtype=dtype)
+pipe.to(device)
+
+
+image = load_image(
+    "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+)
+max_area = 480 * 832
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+image = image.resize((width, height))
+prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+
+negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+generator = torch.Generator(device=device).manual_seed(0)
+output = pipe(
+    image=image,
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    height=height,
+    width=width,
+    num_frames=81,
+    guidance_scale=3.5,
+    num_inference_steps=40,
+    generator=generator,
+).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 ./wan-a14b-lightningv1.1-fp8-guid1.mp4 --num-frames 81 --fps 16 --task i2v --prompts [PROMPTS] --image-path [PATH/TO/IMAGE]
+```