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e0aabb3edf
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2
.github/workflows/test-build.yml
vendored
2
.github/workflows/test-build.yml
vendored
@ -18,7 +18,7 @@ jobs:
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strategy:
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strategy:
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fail-fast: false
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fail-fast: false
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matrix:
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matrix:
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python-version: ["3.8", "3.9", "3.10", "3.11"]
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python-version: ["3.9", "3.10", "3.11", "3.12"]
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steps:
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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- name: Set up Python ${{ matrix.python-version }}
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@ -1,11 +1,8 @@
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import torch
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import torch
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import math
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from math import lcm
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import comfy.utils
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import comfy.utils
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def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
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return abs(a*b) // math.gcd(a, b)
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class CONDRegular:
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class CONDRegular:
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def __init__(self, cond):
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def __init__(self, cond):
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self.cond = cond
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self.cond = cond
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@ -456,9 +456,8 @@ class LTXVModel(torch.nn.Module):
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x = self.patchify_proj(x)
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x = self.patchify_proj(x)
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timestep = timestep * 1000.0
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timestep = timestep * 1000.0
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attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1]))
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if attention_mask is not None and not torch.is_floating_point(attention_mask):
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attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf")) # not sure about this
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attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
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# attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype)
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pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
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pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
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