Search Results for 'DeepFaceLab'

Hi guys,

I’m very new to deepfake. I’m using DeepFaceLab_NVIDIA_RTX3000_series_build_11_20_2021. I managed to gather around 4200 pictures of the guy I d like to deepfake, I have all the heads aligned properly, guides were checked one by one and are ok, and I trained the masks using Xseg ( created around 200 manually).

When I use Quick96 it works, but when I try with SAEH or AMP I get the same error.

Here is when the console tells me:

Running trainer.

[new] No saved models found. Enter a name of a new model : WAYNE_KNIGHT_SAEHD
WAYNE_KNIGHT_SAEHD

Model first run.

Choose one or several GPU idxs (separated by comma).

[CPU] : CPU
[0] : NVIDIA GeForce RTX 4090

[0] Which GPU indexes to choose? : 0
0

[16] Autobackup every N hour ( 0..24 ?:help ) : 1
1
[y] Write preview history ( y/n ?:help ) :
y
[n] Choose image for the preview history ( y/n ) :
n
[1000000] Target iteration : 0
0
[n] Flip SRC faces randomly ( y/n ?:help ) :
n
[n] Flip DST faces randomly ( y/n ?:help ) :
n
[8] Batch_size ( ?:help ) : 12
12
[512] Resolution ( 64-640 ?:help ) : 2048
2048
[head] Face type ( h/mf/f/wf/head ?:help ) :
head
[liae-ud] AE architecture ( ?:help ) : df-ud
df-ud
[512] AutoEncoder dimensions ( 32-1024 ?:help ) : 2048
2048
[64] Encoder dimensions ( 16-256 ?:help ) : 256
256
[64] Decoder dimensions ( 16-256 ?:help ) : 256
256
[22] Decoder mask dimensions ( 16-256 ?:help ) : 256
256
[n] Masked training ( y/n ?:help ) : y
[y] Eyes and mouth priority ( y/n ?:help ) :
y
[y] Uniform yaw distribution of samples ( y/n ?:help ) : n
[y] Blur out mask ( y/n ?:help ) :
y
[y] Place models and optimizer on GPU ( y/n ?:help ) :
y
[y] Use AdaBelief optimizer? ( y/n ?:help ) :
y
[y] Use learning rate dropout ( n/y/cpu ?:help ) :
y
[y] Enable random warp of samples ( y/n ?:help ) :
y
[0.0] Random hue/saturation/light intensity ( 0.0 .. 0.3 ?:help ) : 0.05
0.05
[0.0] GAN power ( 0.0 .. 5.0 ?:help ) : 0.01
0.01
[64] GAN patch size ( 3-640 ?:help ) : 512
512
[16] GAN dimensions ( 4-512 ?:help ) :
16
[0.0] ‘True face’ power. ( 0.0000 .. 1.0 ?:help ) : 0.01
0.01
[0.0] Face style power ( 0.0..100.0 ?:help ) : 0
0.0
[0.0] Background style power ( 0.0..100.0 ?:help ) : 0
0.0
[none] Color transfer for src faceset ( none/rct/lct/mkl/idt/sot ?:help ) : rct
rct
[n] Enable gradient clipping ( y/n ?:help ) : y
[n] Enable pretraining mode ( y/n ?:help ) :
n
Error: Dimension 0 in both shapes must be equal, but are 5 and 6. Shapes are [5,5] and [6,6]. for ‘{{node concat_4}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](LeakyRelu_81, LeakyRelu_84, concat_4/axis)’ with input shapes: [?,512,5,5], [?,512,6,6], [] and with computed input tensors: input[2] = <1>.
Traceback (most recent call last):
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 1880, in _create_c_op
c_op = pywrap_tf_session.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 0 in both shapes must be equal, but are 5 and 6. Shapes are [5,5] and [6,6]. for ‘{{node concat_4}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](LeakyRelu_81, LeakyRelu_84, concat_4/axis)’ with input shapes: [?,512,5,5], [?,512,6,6], [] and with computed input tensors: input[2] = <1>.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\mainscripts\Trainer.py”, line 58, in trainerThread
debug=debug)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\ModelBase.py”, line 193, in __init__
self.on_initialize()
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\Model_SAEHD\Model.py”, line 518, in on_initialize
gpu_pred_src_src_d2 = self.D_src(gpu_pred_src_src_masked_opt)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\models\ModelBase.py”, line 117, in __call__
return self.forward(*args, **kwargs)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\leras\models\PatchDiscriminator.py”, line 184, in forward
x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\util\dispatch.py”, line 206, in wrapper
return target(*args, **kwargs)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\array_ops.py”, line 1769, in concat
return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\gen_array_ops.py”, line 1227, in concat_v2
“ConcatV2”, values=values, axis=axis, name=name)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\op_def_library.py”, line 750, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 3569, in _create_op_internal
op_def=op_def)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 2042, in __init__
control_input_ops, op_def)
File “T:\DEEPFAKE\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py”, line 1883, in _create_c_op
raise ValueError(str(e))
ValueError: Dimension 0 in both shapes must be equal, but are 5 and 6. Shapes are [5,5] and [6,6]. for ‘{{node concat_4}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](LeakyRelu_81, LeakyRelu_84, concat_4/axis)’ with input shapes: [?,512,5,5], [?,512,6,6], [] and with computed input tensors: input[2] = <1>.

Any chance someone here knows where are the wrong parameters? I tried to let all the default value too and still get the same error. I tried to change tons of parameters, going down on resolution, going from df to liae … I tried the rtx3000 serie and dx12 version of the software, nothing works.

export AMP and SAEHD as dfm both return an error about a 2gig limit too.

Thanks a lot for your help.

[deepfakeryKeymaster]

You’re using the wrong DFL build for your GPU. Download the DeepFaceLab_NVIDIA_up_to_RTX2080Ti_build

I followed all the steps but when running Train SAEHD it still shows this message: Running trainer.

Choose one of saved models, or enter a name to create a new model.
[r]: rename
[d]: delete

[0]: new – latest
:
0
Loading new_SAEHD model…

Choose one or several GPU idxs (separated by comma).

[0]: NVIDIA GeForce GTX 1080 Ti

[0] Which GPU indexes to choose?:
0

Initializing models: 100%|###############################################################| 5/5 [00:03<00:00, 1.43it/s]
Loaded 6005 packed faces from F:\DeepFaceLab_DirectX12\workspace\data_src\aligned
Sort by yaw: 100%|##################################################################| 128/128 [00:00<00:00, 885.13it/s]
Loaded 63012 packed faces from F:\DeepFaceLab_DirectX12\workspace\data_dst\aligned
Sort by yaw: 100%|###################################################################| 128/128 [00:01<00:00, 77.22it/s]

I also tried the method suggested by the admin but it didn’t work.
optimize your systemCan anyone help me with this?

when I proceed with the SAEHD merge, this error appears, how to fix it?

Running merger.

Choose one of saved models, or enter a name to create a new model.
[r] : rename
[d] : delete

[0] : TRAIN ALICE V1 – latest
: 0
0
Loading TRAIN ALICE V1_SAEHD model…

Choose one or several GPU idxs (separated by comma).

[CPU] : CPU
[0] : NVIDIA GeForce RTX 3090

[0] Which GPU indexes to choose? : 0
0

Traceback (most recent call last):
File “E:\DFL\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\mainscripts\Merger.py”, line 53, in main
cpu_only=cpu_only)
File “E:\DFL\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\ModelBase.py”, line 180, in __init__
self.on_initialize_options()
File “E:\DFL\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\models\Model_SAEHD\Model.py”, line 181, in on_initialize_options
raise Exception(“pretraining_data_path is not defined”)
Exception: pretraining_data_path is not defined

[prof.ogtParticipant]

SAME THING HERE:

Traceback (most recent call last):
File “<string>”, line 1, in <module>
File “multiprocessing\spawn.py”, line 105, in spawn_main
File “multiprocessing\spawn.py”, line 115, in _main
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\samplelib\__init__.py”, line 1, in <module>
from .Sample import Sample
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\samplelib\Sample.py”, line 7, in <module>
from core.cv2ex import *
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\cv2ex.py”, line 5, in <module>
from core import imagelib
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\imagelib\__init__.py”, line 9, in <module>
from .morph import morph_by_points
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\imagelib\morph.py”, line 3, in <module>
Traceback (most recent call last):
File “<string>”, line 1, in <module>
from scipy.spatial import Delaunay
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\__init__.py”, line 156, in <module>
File “multiprocessing\spawn.py”, line 105, in spawn_main
File “multiprocessing\spawn.py”, line 115, in _main
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\samplelib\__init__.py”, line 1, in <module>
from .Sample import Sample
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\samplelib\Sample.py”, line 7, in <module>
Traceback (most recent call last):
from . import fft File “<string>”, line 1, in <module>

from core.cv2ex import * File “multiprocessing\spawn.py”, line 105, in spawn_main
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\fft\__init__.py”, line 76, in <module>

File “multiprocessing\spawn.py”, line 115, in _main
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\cv2ex.py”, line 5, in <module>
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\samplelib\__init__.py”, line 1, in <module>
from ._basic import (from core import imagelib

File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\fft\_basic.py”, line 1, in <module>
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\imagelib\__init__.py”, line 9, in <module>
from .Sample import Sample
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\samplelib\Sample.py”, line 7, in <module>
from .morph import morph_by_points
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\imagelib\morph.py”, line 3, in <module>
from core.cv2ex import *
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\cv2ex.py”, line 5, in <module>
from scipy.spatial import Delaunay
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\__init__.py”, line 156, in <module>
from scipy._lib.uarray import generate_multimethod, Dispatchablefrom core import imagelib

File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\_lib\uarray.py”, line 27, in <module>
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\imagelib\__init__.py”, line 9, in <module>
from . import fft
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\fft\__init__.py”, line 76, in <module>
from ._uarray import *from .morph import morph_by_points

from ._basic import ( File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\_lib\_uarray\__init__.py”, line 114, in <module>
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\DeepFaceLab\core\imagelib\morph.py”, line 3, in <module>

File “<frozen importlib._bootstrap>”, line 971, in _find_and_load
File “<frozen importlib._bootstrap>”, line 955, in _find_and_load_unlocked
from scipy.spatial import Delaunayfrom ._backend import *

File “<frozen importlib._bootstrap>”, line 665, in _load_unlocked
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\__init__.py”, line 156, in <module>
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\_lib\_uarray\_backend.py”, line 1, in <module>
File “<frozen importlib._bootstrap_external>”, line 674, in exec_module
File “<frozen importlib._bootstrap_external>”, line 764, in get_code
File “<frozen importlib._bootstrap_external>”, line 833, in get_data
MemoryError
from . import fft
File “C:\Users\Personita\Desktop\DeepFaceLab_NVIDIA_RTX3000_series\_internal\python-3.6.8\lib\site-packages\scipy\fft\__init__.py”, line 76, in <module>
from typing import (
File “<frozen importlib._bootstrap>”, line 971, in _find_and_load
File “<frozen importlib._bootstrap>”, line 951, in _find_and_load_unlocked
File “<frozen importlib._bootstrap>”, line 894, in _find_spec
from ._basic import (
File “<frozen importlib._bootstrap_external>”, line 1157, in find_spec
File “<frozen importlib._bootstrap>”, line 971, in _find_and_load
File “<frozen importlib._bootstrap_external>”, line 1131, in _get_spec
File “<frozen importlib._bootstrap>”, line 955, in _find_and_load_unlocked
File “<frozen importlib._bootstrap_external>”, line 1112, in _legacy_get_spec
File “<frozen importlib._bootstrap>”, line 441, in spec_from_loader
File “<frozen importlib._bootstrap>”, line 665, in _load_unlocked
File “<frozen importlib._bootstrap_external>”, line 544, in spec_from_file_location
File “<frozen importlib._bootstrap_external>”, line 674, in exec_module
MemoryError
File “<frozen importlib._bootstrap_external>”, line 764, in get_code
File “<frozen importlib._bootstrap_external>”, line 833, in get_data
MemoryError

Hi, I’ve been using Deepfacelab for some time now and have produced some decent results. I just wondered what your method is for training. Do you have a set process? I have a 1080Ti so use 256 or 320 resolution and I always use a pre-trained model (a million iterations) but which options should I turn on at which iteration count to refine my model and improve the output quality?

It would be useful to know which settings make the most improvements, particularly those below.

== uniform_yaw: False ==
== lr_dropout: n ==
== random_warp: False ==
== gan_power: 0.0 ==
== true_face_power: 0.0 ==
== face_style_power: 0.0 ==
== bg_style_power: 0.0 ==
== clipgrad: True ==
== random_flip: True ==
== eyes_mouth_prio: False ==
== blur_out_mask: True ==
== adabelief: True ==
== random_hsv_power: 0.0 ==
== random_src_flip: False ==
== random_dst_flip: True ==
== gan_patch_size: 40 ==
== gan_dims: 16

[yeonslParticipant]

its error
reinstall deepfacelab and old workplace folder move to new install workplace folder
its gonna work ~

Please let me know if you are interested in setting up DeepFaceLab on a virtual Windows PC.

This is a simple script to append characters to be beginning of your filenames. Very useful for keeping faceset sources separated.

Step 1: Open Windows PowerShell.

Step 2: Navigate to the image folder. Examples:

cd "C:\DeepFaceLab\DeepFaceLab_NVIDIA_RTX3000_series\workspace\data_src"
cd "C:\DeepFaceLab\DeepFaceLab_NVIDIA_RTX3000_series\workspace\data_src\aligned"

Step 3: Copy this script. You may want to save this in a text file for later use.
Get-ChildItem *.* -Exclude "XXX_*" | where { ! $_.PSIsContainer } | rename-item -NewName { "XXX_" + $_.Name }

Step 4: Change both instances of “XXX_” to whatever you want to append. Keep it short.

Step 5: Paste script into PowerShell and hit Enter. Done.

Script Explained:
This script will loop through all files (not folders) in the current directory and append XXX_ to the beginning of all filenames. In order to avoid an infinite loop the script checks for the appended name first. Make sure to change both XXX_ near the beginning and the end.

Usage:
Every aligned faceset file takes its name from the parent frame or image.
If you rename your extracted frames or images before extracting the faceset then the filename will cross over to the faceset. You can recover the original filename at any time and it will be the appended filename.
If you rename your faceset files after extraction (or ones you have downloaded) then the name will not be permanent. If you use the recover original filename script all files will revert to their original parent frame filename and you will lose the appended filename.

[deepfakeryKeymaster]

Check out my tutorial, it should answer most of your questions:

DeepFaceLab 2.0 Faceset Extract Tutorial

[deepfakeryKeymaster]

In general the folder that’s going to grow is the model folder. The model will be several GB depending on your settings, maybe like 2GB – 8GB, but probably not 15. I want to say that your page file is also growing as memory is being consumed. TBH you really should have ALOT more headroom than 15GB. Here’s some things that can help optimize your system: https://www.deepfakevfx.com/guides/deepfacelab-2-0-guide/#system-optimization

[deepfakeryKeymaster]

The aligned images should be jpg files. Did you extract the aligned images after extracting the frames? Are you using a really old version of DeepFaceLab? The dst aligned files should be square jpg images, just like the source faceset you downloaded.

Hi!
I did all the previous steps and now i want to start SAEHD train but I get this error and it stops to work. I restarted the computer and start over again and same problem.
I apreciate the help!

[n] Enable gradient clipping ( y/n ?:help ) : n
[n] Enable pretraining mode ( y/n ?:help ) : n
Initializing models: 20%|############6 | 1/5 [00:11<00:46, 11.74s/it]
Error: OOM when allocating tensor with shape[576000,300] and type float on /job:localhost/replica:0/task:0/device:DML:0 by allocator DmlAllocator
[[node inter_AB/dense1/weight/Initializer/random_uniform/mul (defined at C:\Users\david.puluc\Documents\DeepFaceLab_DirectX12\_internal\python-3.6.8\lib\site-packages\tensorflow_core\python\framework\ops.py:1762) ]]
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.

Running trainer.

Loading XSeg model…

Choose one or several GPU idxs (separated by comma).

[CPU] : CPU
[0] : NVIDIA GeForce RTX 2080 Super
[1] : Intel(R) UHD Graphics 630

[1] Which GPU indexes to choose? : 1
1

Press enter in 2 seconds to override model settings.
[n] Restart training? ( y/n ?:help ) :
n
[8] Batch_size ( 2-16 ?:help ) :
8
[n] Enable pretraining mode ( y/n ) :
n
Loading samples: 100%|########################################################| 146146/146146 [06:15<00:00, 389.70it/s]
Loaded 63012 packed faces from C:\Users\sunni\OneDrive\Desktop\paul chang New\workspace\data_dst\aligned
Filtering: 100%|#############################################################| 209158/209158 [02:43<00:00, 1282.43it/s]
Using 288 segmented samples.
================= Model Summary ==================
== ==
== Model name: XSeg ==
== ==
== Current iteration: 4519 ==
== ==
==————— Model Options —————-==
== ==
== face_type: wf ==
== pretrain: False ==
== batch_size: 8 ==
== ==
==—————– Running On —————–==
== ==
== Device index: 1 ==
== Name: Intel(R) UHD Graphics 630 ==
== VRAM: 29.57GB ==
== ==
==================================================
Starting. Press “Enter” to stop training and save model.
Traceback (most recent call last):
File “C:\Users\sunni\OneDrive\Desktop\paul chang New\_internal\DeepFaceLab\main.py”, line 343, in <module>
arguments.func(arguments)
File “C:\Users\sunni\OneDrive\Desktop\paul chang New\_internal\DeepFaceLab\main.py”, line 132, in process_train
Trainer.main(**kwargs)
File “C:\Users\sunni\OneDrive\Desktop\paul chang New\_internal\DeepFaceLab\mainscripts\Trainer.py”, line 317, in main
lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, w, c)
File “C:\Users\sunni\OneDrive\Desktop\paul chang New\_internal\DeepFaceLab\models\ModelBase.py”, line 627, in get_loss_history_preview
ph_max = int ( (plist_max[col][p] / plist_abs_max) * (lh_height-1) )
ValueError: cannot convert float NaN to integer
[

I just installed and started using DeepFaceLab_DirectX12_build_05_04_2022 yesterday and things went smoothly until about 8 hours into the training. I got this message and I have no idea what it means or how to solve it. Nothing happens when I press a key to continue. I found a thread on another forum where a guy wrote that it can be fixed by doing a tensorflow update (in the project). He wrote: Simply go to the folder “DeepFake\ _internal\python-3.6.8”, open the terminal, and enter “python -m pip install tensorflow”. I assume that the “terminal” means to open the python application and type in “python -m pip install tensorflow” and press enter. I tried doing this but nothing happens or I am doing something wrong. I am obviously a beginner at both DFL, Python, and coding so would really appreciate some help from the pros. This is the message:

2022-09-14 00:41:00.440554: F tensorflow/core/common_runtime/dml/dml_upload_heap.cc:56] HRESULT failed with 0x887a0005: chunk->resource->Map(0, nullptr, &upload_heap_data)

Press any key to continue . . .