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Fix lint: whitespace and unused vars
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@ -711,19 +711,9 @@ class HeliosModel(torch.nn.Module):
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)
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)
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f_long = self._rope_downsample_3d(f_long, (long_t, hs, ws), (4, 4, 4))
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f_long = self._rope_downsample_3d(f_long, (long_t, hs, ws), (4, 4, 4))
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hidden_states = torch.cat([x_long, hidden_states], dim=1)
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hidden_states = torch.cat([x_long, hidden_states], dim=1)
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freqs = torch.cat([f_long, freqs], dim=1)
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freqs = torch.cat([f_long, freqs], dim=1)
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history_context_length = hidden_states.shape[1] - original_context_length
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history_context_length = hidden_states.shape[1] - original_context_length
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mismatch = hidden_states.shape[1] != freqs.shape[1]
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summary_key = (
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int(post_t),
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int(post_h),
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int(post_w),
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int(original_context_length),
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int(hidden_states.shape[1]),
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int(freqs.shape[1]),
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int(history_context_length),
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)
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if timestep.ndim == 0:
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if timestep.ndim == 0:
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timestep = timestep.unsqueeze(0)
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timestep = timestep.unsqueeze(0)
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@ -412,7 +412,6 @@ def _helios_dmd_sample(
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for i in range(len(sigmas) - 1):
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for i in range(len(sigmas) - 1):
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sigma = sigmas[i]
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sigma = sigmas[i]
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sigma_next = sigmas[i + 1]
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timestep = all_timesteps[i] if i < len(all_timesteps) else i
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timestep = all_timesteps[i] if i < len(all_timesteps) else i
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denoised = model(x, sigma * s_in, **extra_args)
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denoised = model(x, sigma * s_in, **extra_args)
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