add wans2v example

examples/wanvideo/model_inference/Wan2.2-S2V-14B.py

@@ -1,8 +1,9 @@
 import torch
 from PIL import Image
 import librosa
-from diffsynth import save_video, VideoData, save_video_with_audio
+from diffsynth import VideoData, save_video_with_audio
 from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
 
 pipe = WanVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
@@ -15,24 +16,54 @@ pipe = WanVideoPipeline.from_pretrained(
     ],
     audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"),
 )
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_video_dataset",
+    local_dir="./data/example_video_dataset",
+    allow_file_pattern=f"wans2v/*"
+)
+
+num_frames = 81 # 4n+1
+height = 448
+width = 832
 
 prompt = "a person is singing"
-input_image = Image.open("/mnt/nas1/zhanghong/project/aigc/Wan2.2_s2v/examples/pose.png").convert("RGB").resize((width, height))
+negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，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"
+input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height))
 # s2v audio input, recommend 16kHz sampling rate
-audio_path = '/mnt/nas1/zhanghong/project/aigc/Wan2.2_s2v/examples/sing.MP3'
+audio_path = 'data/example_video_dataset/wans2v/sing.MP3'
 input_audio, sample_rate = librosa.load(audio_path, sr=16000)
 
 # Speech-to-video
 video = pipe(
     prompt=prompt,
     input_image=input_image,
-    negative_prompt="",
+    negative_prompt=negative_prompt,
     seed=0,
-    num_frames=81,
-    height=1280,
-    width=720,
+    num_frames=num_frames,
+    height=height,
+    width=width,
     audio_sample_rate=sample_rate,
     input_audio=input_audio,
     num_inference_steps=40,
 )
-save_video_with_audio(video, "video_with_audio.mp4", audio_path, fps=16, quality=5)
+save_video_with_audio(video[1:], "video_with_audio.mp4", audio_path, fps=16, quality=5)
+
+# s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps.
+pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4'
+pose_video = VideoData(pose_video_path, height=height, width=width)
+
+# Speech-to-video with pose
+video = pipe(
+    prompt=prompt,
+    input_image=input_image,
+    negative_prompt=negative_prompt,
+    seed=0,
+    num_frames=num_frames,
+    height=height,
+    width=width,
+    audio_sample_rate=sample_rate,
+    input_audio=input_audio,
+    s2v_pose_video=pose_video,
+    num_inference_steps=40,
+)
+save_video_with_audio(video[1:], "video_pose_with_audio.mp4", audio_path, fps=16, quality=5)