mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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125 lines
5.3 KiB
Python
125 lines
5.3 KiB
Python
import torch
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from PIL import Image
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import librosa
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from diffsynth.utils.data import VideoData, save_video_with_audio
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from diffsynth.pipelines.wan_video 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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with torch.no_grad():
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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_video = None
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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_tensor = 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_video,
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num_inference_steps=num_inference_steps,
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output_type="floatpoint",
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)
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# (B, C, T, H, W)
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current_clip_tensor = current_clip_tensor[:,:,-infer_frames:,:,:]
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if r == 0:
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current_clip_tensor = current_clip_tensor[:,:,3:,:,:]
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overlap_frames_num = min(motion_frames, current_clip_tensor.shape[2])
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motion_video = current_clip_tensor[:,:,-overlap_frames_num:,:,:].clone()
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else:
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overlap_frames_num = min(motion_frames, current_clip_tensor.shape[2])
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motion_video = torch.cat((motion_video[:,:,overlap_frames_num:,:,:], current_clip_tensor[:,:,-overlap_frames_num:,:,:]), dim=2)
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current_clip_quantized = pipe.vae_output_to_video(current_clip_tensor)
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video.extend(current_clip_quantized)
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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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tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
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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_full_Wan2.2-S2V-14B.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_clip_2_Wan2.2-S2V-14B.mp4",
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num_clip=2
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)
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