Running a 5090 and getting OOM errors on 720p resolution

I tried export PYTORCH_ALLOC_CONF=expandable_segments:True to see if that would resolve the OOM errors on my 5090 but it does not.
I've been at this for hours and havent been able to determine the right combination to use and not get OOM

I am also utilizing the "--quant_linear" argument

export PYTHONPATH=turbodiffusion
export PYTORCH_ALLOC_CONF=expandable_segments:True
export TOKENIZERS_PARALLELISM=false

python turbodiffusion/inference/wan2.1_t2v_infer.py \
    --model Wan2.1-14B \
    --dit_path checkpoints/TurboWan2.1-T2V-14B-720P-quant.pth \
    --prompt "A photo realistic stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about." \
    --resolution 720p \
    --aspect_ratio 16:9 \
    --num_steps 4 \
    --seed 297308 \
    --attention_type sagesla \
    --sla_topk 0.15 \
    --num_samples 1 \
    --num_frames 77 \
    --sigma_max 80 \
    --save_path /home/wedwards/Documents/turbo_diffusion_ouput/t2v_20251226_104112.mp4 \
    --quant_linear

Console Information
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[12-26 10:48:47|INFO|turbodiffusion/inference/wan2.1_t2v_infer.py:61:] Computing embedding for prompt: A photo realistic stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.
[12-26 10:48:47|INFO|turbodiffusion/rcm/utils/umt5.py:495:init] loading checkpoints/models_t5_umt5-xxl-enc-bf16.pth
[12-26 10:48:57|INFO|turbodiffusion/inference/wan2.1_t2v_infer.py:66:] Loading DiT model from checkpoints/TurboWan2.1-T2V-14B-720P-quant.pth
[12-26 10:48:57|INFO|turbodiffusion/rcm/networks/wan2pt1.py:829:enable_selective_checkpoint] Enable selective checkpoint with mm_only, for every 1 blocks. Total blocks: 40
[12-26 10:49:13|SUCCESS|turbodiffusion/inference/wan2.1_t2v_infer.py:69:] Successfully loaded DiT model.
[12-26 10:49:13|INFO|turbodiffusion/rcm/tokenizers/wan2pt1.py:590:_video_vae] loading checkpoints/Wan2.1_VAE.pth
[12-26 10:49:13|INFO|turbodiffusion/inference/wan2.1_t2v_infer.py:75:] Generating with prompt: A photo realistic stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.
Sampling: 100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:32<00:00, 8.11s/it]
Traceback (most recent call last):
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/inference/wan2.1_t2v_infer.py", line 132, in
video = tokenizer.decode(samples)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/rcm/tokenizers/wan2pt1.py", line 737, in decode
return self.model.decode(
^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/utils/contextlib.py", line 120, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/rcm/tokenizers/wan2pt1.py", line 685, in decode
video_recon = self.model.decode(zs, self.scale)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/rcm/tokenizers/wan2pt1.py", line 532, in decode
out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/rcm/tokenizers/wan2pt1.py", line 416, in forward
x = layer(x, feat_cache, feat_idx)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/rcm/tokenizers/wan2pt1.py", line 202, in forward
x = layer(x, feat_cache[idx])
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/Documents/Programs/TurboDiffusion/turbodiffusion/rcm/tokenizers/wan2pt1.py", line 55, in forward
return super().forward(x)
^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 717, in forward
return self._conv_forward(input, self.weight, self.bias)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wedwards/anaconda3/envs/turbodiffusion/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 712, in _conv_forward
return F.conv3d(
^^^^^^^^^
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 17.80 GiB. GPU 0 has a total capacity of 31.35 GiB of which 2.64 GiB is free. Including non-PyTorch memory, this process has 27.65 GiB memory in use. Of the allocated memory 7.80 GiB is allocated by PyTorch, and 19.26 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
(turbodiffusion) wedwards@Inf-Imagine-Linux:~/Documents/Programs/TurboDiffusion$

Start with turbodiffusion/inference/wan2.1_t2v_infer.py at the tokenizer.decode call around line 132, then trace the VAE path in turbodiffusion/rcm/tokenizers/wan2pt1.py around decode and the failing Conv3D layer. Reproduce the command with the reported 720p, 77-frame, and --quant_linear settings and determine a supported configuration or code-level resolution for the CUDA OOM. Done means the reported workload completes without the decode allocation failure or the limitation is clearly documented.