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update variable

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This commit is contained in:
mi804
2025-07-23 11:20:06 +08:00
parent f1f00c4255
commit 3aed244c6f
2 changed files with 10 additions and 10 deletions
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6
diffsynth/models/wan_video_dit.py
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@@ -287,14 +287,14 @@ class WanModel(torch.nn.Module):
has_ref_conv: bool = False,
add_control_adapter: bool = False,
in_dim_control_adapter: int = 24,
is_5b: bool = False,
seperated_timestep: bool = False,
):
super().__init__()
self.dim = dim
self.freq_dim = freq_dim
self.has_image_input = has_image_input
self.patch_size = patch_size
self.is_5b = is_5b
self.seperated_timestep = seperated_timestep
self.patch_embedding = nn.Conv3d(
in_dim, dim, kernel_size=patch_size, stride=patch_size)
@@ -685,7 +685,7 @@ class WanModelStateDictConverter:
"num_heads": 24,
"num_layers": 30,
"eps": 1e-6,
"is_5b": True,
"seperated_timestep": True,
}
else:
config = {}

14
diffsynth/pipelines/wan_video_new.py
View File

@@ -237,7 +237,7 @@ class WanVideoPipeline(BasePipeline):
WanVideoUnit_InputVideoEmbedder(),
WanVideoUnit_PromptEmbedder(),
WanVideoUnit_ImageEmbedder(),
WanVideoUnit_ImageEmbedder5B(),
WanVideoUnit_ImageVaeEmbedder(),
WanVideoUnit_FunControl(),
WanVideoUnit_FunReference(),
WanVideoUnit_FunCameraControl(),
@@ -737,7 +737,7 @@ class WanVideoUnit_ImageEmbedder(PipelineUnit):
)
def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride):
if input_image is None or pipe.dit.is_5b:
if input_image is None or pipe.dit.seperated_timestep:
return {}
pipe.load_models_to_device(self.onload_model_names)
image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
@@ -766,7 +766,7 @@ class WanVideoUnit_ImageEmbedder(PipelineUnit):
return {"clip_feature": clip_context, "y": y}
class WanVideoUnit_ImageEmbedder5B(PipelineUnit):
class WanVideoUnit_ImageVaeEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("input_image", "noise", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"),
@@ -774,7 +774,7 @@ class WanVideoUnit_ImageEmbedder5B(PipelineUnit):
)
def process(self, pipe: WanVideoPipeline, input_image, noise, num_frames, height, width, tiled, tile_size, tile_stride):
if input_image is None or not pipe.dit.is_5b:
if input_image is None or not pipe.dit.seperated_timestep:
return {}
pipe.load_models_to_device(self.onload_model_names)
image = pipe.preprocess_image(input_image.resize((width, height))).transpose(0, 1).to(pipe.device)
@@ -789,7 +789,7 @@ class WanVideoUnit_ImageEmbedder5B(PipelineUnit):
import math
seq_len = int(math.ceil(seq_len / pipe.sp_size)) * pipe.sp_size
return {"latents": latents, "mask_5b": mask2[0].unsqueeze(0), "seq_len": seq_len}
return {"latents": latents, "latent_mask_for_timestep": mask2[0].unsqueeze(0), "seq_len": seq_len}
@staticmethod
def masks_like(tensor, zero=False, generator=None, p=0.2):
@@ -1162,8 +1162,8 @@ def model_fn_wan_video(
get_sequence_parallel_world_size,
get_sp_group)
if dit.is_5b and "mask_5b" in kwargs:
temp_ts = (kwargs["mask_5b"][0][0][:, ::2, ::2] * timestep).flatten()
if dit.seperated_timestep and "latent_mask_for_timestep" in kwargs:
temp_ts = (kwargs["latent_mask_for_timestep"][0][0][:, ::2, ::2] * timestep).flatten()
temp_ts= torch.cat([temp_ts, temp_ts.new_ones(kwargs["seq_len"] - temp_ts.size(0)) * timestep])
timestep = temp_ts.unsqueeze(0).flatten()
t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep).unflatten(0, (latents.size(0), kwargs["seq_len"])))