Play literally any other smut game, this has been asked a billion times by a billion normies just literally go play something else. You are so annoying. There are absolutely zero other games of this quality that appeal to the niche of people who basically just want dickgirls and femboys. Your taste is more broad, and you have a wide selection of games to choose from. Not everything needs to appeal to you
TheVanessii
Creator of
Recent community posts
How do I disable Ultra-violence+? My source wads don't have unity update garbage in them, I don't want that in my smooshed wad :(
Could maybe put a thing that checks whether you're using unity versions or not, so that it doesn't put it there regardless? It just kinda irks me a bit, especially because the UV+ menu text glaringly sticks out like a red thumb due to it's font color not matching the other difficulties
Cool! Last one I found was under Progression I think. It reads "When characters return from their adventures with loot, treasures of with some quest/goal completed, they gain 1 Level." and I think it's supposed to read "When characters return from their adventures with loot, treasures [or] with some quest/goal completed, they gain 1 Level.
Sorry I wasn't able to put this all together in a single post with the previous one!
Hey there! I was curious about the reasoning behind daggers requiring strength, but a longsword can use 2 dex to wield? This would mean that a full dex character can't use a dagger in the beginning, but a full strength character could use daggers easily? This also means a full dex build can't use daggers, but they can use axes, longswords, and swords? I would assume that a dagger would allow one to use maybe 1 Dex / 1 Dex or Str to wield it, since I wouldn't really imagine them being fully strength weapons,,,? I guess this is a suggestion I dunno, but I'm just kinda curious on what the intent here is I suppose? There's also hammers and maces which can use 1 Dex, but Daggers can't,,, which I find very confusing. If anything, I'd think the staffs, maces, and hammers would require 1 Str, while the daggers, dirks, and sickles would require 1 Dex or something. I guess it just seems kinda backwards in a way?
I get the same error 90% of the time
cudnn is used
OK
D:/Hentai/Pics/1566840918519.gif
Input FPS: 12.5
QWindowsNativeFileDialogBase::shellItem : Unhandled scheme: "data"
C:/Users/Deej/Desktop
D:/Hentai/Pics/1566840918519.gif
C:/Users/Deej/Desktop/1566840918519//1566840918519.mp4
12.5
0
0
1
Interpolate 1 frames
The testing model weight is: ./model_weights/best.pth
Framerate Index: 0
D:\DAIN_APP Alpha\torch\nn\functional.py:2494: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
D:\DAIN_APP Alpha\torch\nn\functional.py:2693: UserWarning: Default grid_sample and affine_grid behavior will be changed to align_corners=False from 1.4.0. See the documentation of grid_sample for details.
warnings.warn("Default grid_sample and affine_grid behavior will be changed "
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
..\torch\csrc\autograd\python_function.cpp:622: UserWarning: Legacy autograd function with non-static forward method is deprecated and will be removed in 1.3. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)
Traceback (most recent call last):
File "my_design.py", line 79, in render
File "my_DAIN_class.py", line 469, in RenderVideo
File "my_DAIN_class.py", line 202, in interpolate
File "site-packages\torch\nn\modules\module.py", line 541, in __call__
File "networks\DAIN_slowmotion.py", line 182, in forward
File "site-packages\torch\nn\modules\module.py", line 541, in __call__
File "Resblock\BasicBlock.py", line 81, in forward
File "site-packages\torch\nn\modules\module.py", line 541, in __call__
File "site-packages\torch\nn\modules\container.py", line 92, in forward
File "site-packages\torch\nn\modules\module.py", line 541, in __call__
File "site-packages\torch\nn\modules\conv.py", line 345, in forward
File "site-packages\torch\nn\modules\conv.py", line 342, in conv2d_forward
RuntimeError: CUDA out of memory. Tried to allocate 280.00 MiB (GPU 0; 4.00 GiB total capacity; 2.92 GiB already allocated; 0 bytes free; 35.32 MiB cached)