ltxv: add noise to guidance image to ensure generated motion. (#5937)
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@@ -379,6 +379,7 @@ class LTXVModel(torch.nn.Module):
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positional_embedding_max_pos=[20, 2048, 2048],
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dtype=None, device=None, operations=None, **kwargs):
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super().__init__()
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self.generator = None
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self.dtype = dtype
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self.out_channels = in_channels
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self.inner_dim = num_attention_heads * attention_head_dim
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@@ -417,6 +418,7 @@ class LTXVModel(torch.nn.Module):
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def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, transformer_options={}, **kwargs):
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patches_replace = transformer_options.get("patches_replace", {})
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image_noise_scale = transformer_options.get("image_noise_scale", 0.15)
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indices_grid = self.patchifier.get_grid(
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orig_num_frames=x.shape[2],
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@@ -435,6 +437,17 @@ class LTXVModel(torch.nn.Module):
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timestep = self.patchifier.patchify(ts)
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input_x = x.clone()
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x[:, :, 0] = guiding_latent[:, :, 0]
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if image_noise_scale > 0:
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if self.generator is None:
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self.generator = torch.Generator(device=x.device).manual_seed(42)
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elif self.generator.device != x.device:
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self.generator = torch.Generator(device=x.device).set_state(self.generator.get_state())
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noise_shape = [guiding_latent.shape[0], guiding_latent.shape[1], 1, guiding_latent.shape[3], guiding_latent.shape[4]]
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guiding_noise = image_noise_scale * (input_ts ** 2) * torch.randn(size=noise_shape, device=x.device, generator=self.generator)
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x[:, :, 0] += guiding_noise[:, :, 0]
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orig_shape = list(x.shape)
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