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fixed teacache bug

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This commit is contained in:
DeepBeepMeep 2025-05-04 20:08:23 +02:00
parent f0099e0e4b
commit e718245407
3 changed files with 11 additions and 6 deletions
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4
README.md
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@ -233,6 +233,10 @@ If you want to manage in different areas Loras for the 1.3B model and the 14B as
- loras/1.3B - loras/1.3B
- loras/14B - loras/14B
You can also put all the loras in the same place by launching the app with following command line (*path* is a path to shared loras directory):
```
python wgp.exe --lora-dir path --lora-dir-i2v path
```
For each activated Lora, you may specify a *multiplier* that is one float number that corresponds to its weight (default is 1.0) .The multipliers for each Lora should be separated by a space character or a carriage return. For instance:\ For each activated Lora, you may specify a *multiplier* that is one float number that corresponds to its weight (default is 1.0) .The multipliers for each Lora should be separated by a space character or a carriage return. For instance:\
*1.2 0.8* means that the first lora will have a 1.2 multiplier and the second one will have 0.8. *1.2 0.8* means that the first lora will have a 1.2 multiplier and the second one will have 0.8.

8
wan/modules/attention.py
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@ -56,10 +56,10 @@ def sageattn_wrapper(
attention_length attention_length
): ):
q,k, v = qkv_list q,k, v = qkv_list
padding_length = q.shape[0] -attention_length padding_length = q.shape[1] -attention_length
q = q[:attention_length, :, : ] q = q[:, :attention_length, :, : ]
k = k[:attention_length, :, : ] k = k[:, :attention_length, :, : ]
v = v[:attention_length, :, : ] v = v[:, :attention_length, :, : ]
if True: if True:
qkv_list = [q,k,v] qkv_list = [q,k,v]
del q, k ,v del q, k ,v

5
wan/modules/model.py
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@ -797,11 +797,12 @@ class WanModel(ModelMixin, ConfigMixin):
def compute_teacache_threshold(self, start_step, timesteps = None, speed_factor =0): def compute_teacache_threshold(self, start_step, timesteps = None, speed_factor =0):
rescale_func = np.poly1d(self.coefficients) modulation_dtype = self.time_projection[1].weight.dtype
rescale_func = np.poly1d(self.coefficients)
e_list = [] e_list = []
for t in timesteps: for t in timesteps:
t = torch.stack([t]) t = torch.stack([t])
time_emb = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(self.patch_embedding.weight.dtype) ) # b, dim time_emb = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(modulation_dtype) ) # b, dim
e_list.append(time_emb) e_list.append(time_emb)
best_deltas = None best_deltas = None
best_threshold = 0.01 best_threshold = 0.01