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https://github.com/modelscope/DiffSynth-Studio.git
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support s2v framepack
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mi804
@@ -48,7 +48,7 @@ save_video(video, "video1.mp4", fps=15, quality=5)
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| Model ID | Extra Parameters | Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
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|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B.py)|-|-|-|-|
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|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B_multi_clips.py)|-|-|-|-|
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|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./model_inference/Wan2.2-I2V-A14B.py)|[code](./model_training/full/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-I2V-A14B.py)|
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|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./model_inference/Wan2.2-T2V-A14B.py)|[code](./model_training/full/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-T2V-A14B.py)|
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|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./model_inference/Wan2.2-TI2V-5B.py)|[code](./model_training/full/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_lora/Wan2.2-TI2V-5B.py)|
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@@ -48,7 +48,7 @@ save_video(video, "video1.mp4", fps=15, quality=5)
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|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
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|-|-|-|-|-|-|-|
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|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B.py)|-|-|-|-|
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|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B_multi_clips.py)|-|-|-|-|
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|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./model_inference/Wan2.2-I2V-A14B.py)|[code](./model_training/full/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-I2V-A14B.py)|
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|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./model_inference/Wan2.2-T2V-A14B.py)|[code](./model_training/full/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-T2V-A14B.py)|
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|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./model_inference/Wan2.2-TI2V-5B.py)|[code](./model_training/full/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_lora/Wan2.2-TI2V-5B.py)|
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116
examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py
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116
examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py
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import torch
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from PIL import Image
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import librosa
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from diffsynth import VideoData, save_video_with_audio
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig, WanVideoUnit_S2V
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from modelscope import dataset_snapshot_download
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def speech_to_video(
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prompt,
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input_image,
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audio_path,
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negative_prompt="",
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num_clip=None,
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audio_sample_rate=16000,
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pose_video_path=None,
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infer_frames=80,
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height=448,
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width=832,
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num_inference_steps=40,
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fps=16, # recommend fixing fps as 16 for s2v
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motion_frames=73, # hyperparameter of wan2.2-s2v
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save_path=None,
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):
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# s2v audio input, recommend 16kHz sampling rate
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input_audio, sample_rate = librosa.load(audio_path, sr=audio_sample_rate)
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# 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.
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pose_video = VideoData(pose_video_path, height=height, width=width) if pose_video_path is not None else None
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audio_embeds, pose_latents, num_repeat = WanVideoUnit_S2V.pre_calculate_audio_pose(
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pipe=pipe,
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input_audio=input_audio,
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audio_sample_rate=sample_rate,
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s2v_pose_video=pose_video,
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num_frames=infer_frames + 1,
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height=height,
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width=width,
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fps=fps,
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)
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num_repeat = min(num_repeat, num_clip) if num_clip is not None else num_repeat
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print(f"Generating {num_repeat} video clips...")
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motion_videos = []
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video = []
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for r in range(num_repeat):
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s2v_pose_latents = pose_latents[r] if pose_latents is not None else None
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current_clip = pipe(
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prompt=prompt,
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input_image=input_image,
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negative_prompt=negative_prompt,
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seed=0,
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num_frames=infer_frames + 1,
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height=height,
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width=width,
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audio_embeds=audio_embeds[r],
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s2v_pose_latents=s2v_pose_latents,
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motion_video=motion_videos,
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num_inference_steps=num_inference_steps,
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)
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current_clip = current_clip[-infer_frames:]
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if r == 0:
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current_clip = current_clip[3:]
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overlap_frames_num = min(motion_frames, len(current_clip))
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motion_videos = motion_videos[overlap_frames_num:] + current_clip[-overlap_frames_num:]
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video.extend(current_clip)
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save_video_with_audio(video, save_path, audio_path, fps=16, quality=5)
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print(f"processed the {r+1}th clip of total {num_repeat} clips.")
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return video
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"),
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"),
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],
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audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"),
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)
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/example_video_dataset",
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local_dir="./data/example_video_dataset",
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allow_file_pattern=f"wans2v/*",
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)
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infer_frames = 80 # 4n
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height = 448
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width = 832
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prompt = "a person is singing"
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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"
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input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height))
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video_with_audio = speech_to_video(
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prompt=prompt,
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input_image=input_image,
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audio_path='data/example_video_dataset/wans2v/sing.MP3',
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negative_prompt=negative_prompt,
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pose_video_path='data/example_video_dataset/wans2v/pose.mp4',
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save_path="video_with_audio_full.mp4",
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infer_frames=infer_frames,
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height=height,
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width=width,
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)
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# num_clip means generating only the first n clips with n * infer_frames frames.
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video_with_audio_pose = speech_to_video(
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prompt=prompt,
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input_image=input_image,
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audio_path='data/example_video_dataset/wans2v/sing.MP3',
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negative_prompt=negative_prompt,
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pose_video_path='data/example_video_dataset/wans2v/pose.mp4',
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save_path="video_with_audio_pose_clip_2.mp4",
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num_clip=2
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)
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