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Not really tested WAN Phantom Support. (#8321)
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@@ -539,13 +539,20 @@ class WanModel(torch.nn.Module):
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x = self.unpatchify(x, grid_sizes)
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return x
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def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs):
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def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
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bs, c, t, h, w = x.shape
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x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
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patch_size = self.patch_size
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t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
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h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
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w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
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if time_dim_concat is not None:
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time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
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x = torch.cat([x, time_dim_concat], dim=2)
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t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0])
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img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
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img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
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img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
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