theluyuan/DiffSynth-Studio
1
0
Fork 0
mirror of https://github.com/modelscope/DiffSynth-Studio.git synced 2026-03-18 22:08:13 +00:00
Code Issues Packages Projects Releases Wiki Activity

wans2v lowvram

Browse Source
This commit is contained in:
mi804
2025-08-27 13:05:53 +08:00
parent b541b9bed2
commit 8a0bd7c377
2 changed files with 9 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
diffsynth/models/wav2vec.py
View File

@@ -99,19 +99,17 @@ class WanS2VAudioEncoder(torch.nn.Module):
self.model = Wav2Vec2ForCTC(Wav2Vec2Config(**config))
self.video_rate = 30
def extract_audio_feat(self, input_audio, sample_rate, processor, return_all_layers=False, dtype=torch.float32):
input_values = processor(input_audio, sampling_rate=sample_rate, return_tensors="pt").input_values.to(self.model.dtype)
def extract_audio_feat(self, input_audio, sample_rate, processor, return_all_layers=False, dtype=torch.float32, device='cpu'):
input_values = processor(input_audio, sampling_rate=sample_rate, return_tensors="pt").input_values.to(dtype=dtype, device=device)
# retrieve logits & take argmax
res = self.model(input_values.to(self.model.device), output_hidden_states=True)
res = self.model(input_values, output_hidden_states=True)
if return_all_layers:
feat = torch.cat(res.hidden_states)
else:
feat = res.hidden_states[-1]
feat = linear_interpolation(feat, input_fps=50, output_fps=self.video_rate)
z = feat.to(dtype)
return z
return feat
def get_audio_embed_bucket(self, audio_embed, stride=2, batch_frames=12, m=2):
num_layers, audio_frame_num, audio_dim = audio_embed.shape

9
diffsynth/pipelines/wan_video_new.py
View File

@@ -50,9 +50,9 @@ class WanVideoPipeline(BasePipeline):
self.units = [
WanVideoUnit_ShapeChecker(),
WanVideoUnit_NoiseInitializer(),
WanVideoUnit_PromptEmbedder(),
WanVideoUnit_S2V(),
WanVideoUnit_InputVideoEmbedder(),
WanVideoUnit_PromptEmbedder(),
WanVideoUnit_ImageEmbedderVAE(),
WanVideoUnit_ImageEmbedderCLIP(),
WanVideoUnit_ImageEmbedderFused(),
@@ -266,13 +266,14 @@ class WanVideoPipeline(BasePipeline):
module_map = {
torch.nn.Linear: AutoWrappedLinear,
torch.nn.LayerNorm: AutoWrappedModule,
torch.nn.Conv1d: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device="cpu",
computation_dtype=dtype,
computation_dtype=self.torch_dtype,
computation_device=self.device,
),
)
@@ -905,14 +906,14 @@ class WanVideoUnit_S2V(PipelineUnit):
def __init__(self):
super().__init__(
take_over=True,
onload_model_names=("audio_encoder", "vae", )
onload_model_names=("audio_encoder", "vae",)
)
def process_audio(self, pipe: WanVideoPipeline, input_audio, audio_sample_rate, num_frames):
if input_audio is None or pipe.audio_encoder is None or pipe.audio_processor is None:
return {}
pipe.load_models_to_device(["audio_encoder"])
z = pipe.audio_encoder.extract_audio_feat(input_audio, audio_sample_rate, pipe.audio_processor, return_all_layers=True)
z = pipe.audio_encoder.extract_audio_feat(input_audio, audio_sample_rate, pipe.audio_processor, return_all_layers=True, dtype=pipe.torch_dtype, device=pipe.device)
audio_embed_bucket, num_repeat = pipe.audio_encoder.get_audio_embed_bucket_fps(
z, fps=16, batch_frames=num_frames - 1, m=0
)