remove xfuser in taylorseer

ppdiffusers/examples/Fast-Diffusers/Training-Free/taylorseer/cache_functions/force_scheduler.py

@@ -11,8 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-import torch
+import paddle
 
 
 def force_scheduler(cache_dic, current):
@@ -22,10 +21,10 @@ def force_scheduler(cache_dic, current):
     else:
         # TokenCache
         linear_step_weight = 0.0
-    step_factor = torch.tensor(
+    step_factor = paddle.to_tensor(
         1 - linear_step_weight + 2 * linear_step_weight * current["step"] / current["num_steps"]
     )
-    threshold = torch.round(cache_dic["fresh_threshold"] / step_factor)
+    threshold = paddle.round(cache_dic["fresh_threshold"] / step_factor)
 
     # no force constrain for sensitive steps, cause the performance is good enough.
     # you may have a try.

ppdiffusers/examples/Fast-Diffusers/Training-Free/taylorseer/forwards/__init__.py

@@ -15,4 +15,3 @@
 from .double_transformer_forward import taylorseer_flux_double_block_forward
 from .flux_forward import taylorseer_flux_forward
 from .single_transformer_forward import taylorseer_flux_single_block_forward
-from .xfuser_flux_forward import taylorseer_xfuser_flux_forward