Spaces:
Running
on
Zero
Running
on
Zero
NIRVANALAN
commited on
Commit
•
76e9ff7
1
Parent(s):
e0ba37d
update
Browse files- dit/dit_i23d.py +1 -1
- dit/dit_models_xformers.py +1 -2
- dit/norm.py +0 -18
- requirements.txt +2 -1
dit/dit_i23d.py
CHANGED
@@ -16,7 +16,7 @@ try:
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from apex.normalization import FusedRMSNorm as RMSNorm
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except:
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from torch.nn import LayerNorm
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from
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# from vit.vit_triplane import XYZPosEmbed
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from apex.normalization import FusedRMSNorm as RMSNorm
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except:
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from torch.nn import LayerNorm
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from diffusers.models.normalization import RMSNorm
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# from vit.vit_triplane import XYZPosEmbed
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dit/dit_models_xformers.py
CHANGED
@@ -29,8 +29,7 @@ try:
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from apex.normalization import FusedLayerNorm as LayerNorm
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except:
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from torch.nn import LayerNorm
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-
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from dit.norm import RMSNorm
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# from torch.nn import LayerNorm
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# from xformers import triton
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from apex.normalization import FusedLayerNorm as LayerNorm
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except:
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from torch.nn import LayerNorm
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from diffusers.models.normalization import RMSNorm
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# from torch.nn import LayerNorm
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# from xformers import triton
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dit/norm.py
DELETED
@@ -1,18 +0,0 @@
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import torch
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def rms_norm(x, weight=None, eps=1e-05):
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output = x / torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
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return output * weight if weight is not None else output
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class RMSNorm(torch.nn.Module):
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def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, dtype=None, device=None):
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super().__init__()
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self.eps = eps
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if elementwise_affine:
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self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
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else:
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self.register_parameter('weight', None)
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def forward(self, x):
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return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
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requirements.txt
CHANGED
@@ -31,4 +31,5 @@ safetensors
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matplotlib
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git+https://github.com/nupurkmr9/vision-aided-gan
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PyMCubes
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trimesh
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matplotlib
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git+https://github.com/nupurkmr9/vision-aided-gan
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PyMCubes
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trimesh
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diffusers
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