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asdasdasd1Best Settings For 1050ti
#1
İ have 1050ti 4gb.i was using fakeapp last year.it was working ok .i installed yours but it gives errors probably allocation.

so what options do you recommend for my device ?

Code:
===== Model summary =====
== Model name: SAE
==
== Current iteration: 0
==
== Model options:
== |== batch_size : 8
== |== sort_by_yaw : False
== |== random_flip : False
== |== resolution : 128
== |== face_type : f
== |== learn_mask : True
== |== optimizer_mode : 1
== |== archi : df
== |== ae_dims : 512
== |== e_ch_dims : 42
== |== d_ch_dims : 21
== |== multiscale_decoder : False
== |== ca_weights : False
== |== pixel_loss : False
== |== face_style_power : 10.0
== |== bg_style_power : 10.0
== |== apply_random_ct : False
== Running on:
== |== [0 : GeForce GTX 1050 Ti]
=========================
Starting. Press "Enter" to stop training and save model.
Error: OOM when allocating tensor with shape[64512,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
        [[{{node mul_67}} = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Adam/beta_1/read, Variable_8/read)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

        [[{{node add_29/_1117}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_7871_add_29", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Traceback (most recent call last):
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\DeepFaceLab\mainscripts\Trainer.py", line 107, in trainerThread
   iter, iter_time = model.train_one_iter()
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\DeepFaceLab\models\ModelBase.py", line 404, in train_one_iter
   losses = self.onTrainOneIter(sample, self.generator_list)
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\DeepFaceLab\models\Model_SAE\Model.py", line 423, in onTrainOneIter
   src_loss, dst_loss, = self.src_dst_train (feed)
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\python-3.6.8\lib\site-packages\keras\backend\tensorflow_backend.py", line 2715, in __call__
   return self._call(inputs)
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\python-3.6.8\lib\site-packages\keras\backend\tensorflow_backend.py", line 2675, in _call
   fetched = self._callable_fn(*array_vals)
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1439, in __call__
   run_metadata_ptr)
 File "D:\Downloads\DeepFaceLabCUDA9.2SSE\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in __exit__
   c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[64512,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
        [[{{node mul_67}} = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Adam/beta_1/read, Variable_8/read)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

        [[{{node add_29/_1117}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_7871_add_29", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Done.

Working ok with h64.what settings you guys recommend for h64.
#2
Just play around with settings until it no longer gives OOM errors.

== |== resolution : 128 decrease this
also decrease batch size

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