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Fix lint: whitespace and unused vars

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qqingzheng 2026-03-10 15:36:37 +08:00
parent 457e7000e2
commit b476d5e62b
2 changed files with 7 additions and 18 deletions
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24
comfy/ldm/helios/model.py
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@ -711,19 +711,9 @@ class HeliosModel(torch.nn.Module):
) )
f_long = self._rope_downsample_3d(f_long, (long_t, hs, ws), (4, 4, 4)) f_long = self._rope_downsample_3d(f_long, (long_t, hs, ws), (4, 4, 4))
hidden_states = torch.cat([x_long, hidden_states], dim=1) hidden_states = torch.cat([x_long, hidden_states], dim=1)
freqs = torch.cat([f_long, freqs], dim=1) freqs = torch.cat([f_long, freqs], dim=1)
history_context_length = hidden_states.shape[1] - original_context_length history_context_length = hidden_states.shape[1] - original_context_length
mismatch = hidden_states.shape[1] != freqs.shape[1]
summary_key = (
int(post_t),
int(post_h),
int(post_w),
int(original_context_length),
int(hidden_states.shape[1]),
int(freqs.shape[1]),
int(history_context_length),
)
if timestep.ndim == 0: if timestep.ndim == 0:
timestep = timestep.unsqueeze(0) timestep = timestep.unsqueeze(0)
@ -770,28 +760,28 @@ class HeliosModel(torch.nn.Module):
def unpatchify(self, x, grid_sizes): def unpatchify(self, x, grid_sizes):
""" """
Unpatchify the output from proj_out back to video format. Unpatchify the output from proj_out back to video format.
Args: Args:
x: [batch, num_patches, out_dim * prod(patch_size)] x: [batch, num_patches, out_dim * prod(patch_size)]
grid_sizes: (num_frames, height, width) in patch space grid_sizes: (num_frames, height, width) in patch space
Returns: Returns:
[batch, out_dim, num_frames, height, width] in pixel space [batch, out_dim, num_frames, height, width] in pixel space
""" """
b = x.shape[0] b = x.shape[0]
post_t, post_h, post_w = grid_sizes post_t, post_h, post_w = grid_sizes
p_t, p_h, p_w = self.patch_size p_t, p_h, p_w = self.patch_size
# Reshape: [B, T*H*W, out_dim*p_t*p_h*p_w] -> [B, T, H, W, p_t, p_h, p_w, out_dim] # Reshape: [B, T*H*W, out_dim*p_t*p_h*p_w] -> [B, T, H, W, p_t, p_h, p_w, out_dim]
# Use -1 to let PyTorch infer the channel dimension (out_dim) # Use -1 to let PyTorch infer the channel dimension (out_dim)
hidden_states = x.reshape(b, post_t, post_h, post_w, p_t, p_h, p_w, -1) hidden_states = x.reshape(b, post_t, post_h, post_w, p_t, p_h, p_w, -1)
# Permute: [B, T, H, W, p_t, p_h, p_w, C] -> [B, C, T, p_t, H, p_h, W, p_w] # Permute: [B, T, H, W, p_t, p_h, p_w, C] -> [B, C, T, p_t, H, p_h, W, p_w]
hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6) hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
# Flatten patches: [B, C, T, p_t, H, p_h, W, p_w] -> [B, C, T*p_t, H*p_h, W*p_w] # Flatten patches: [B, C, T, p_t, H, p_h, W, p_w] -> [B, C, T*p_t, H*p_h, W*p_w]
output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3) output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
return output return output
def _rope_downsample_3d(self, freqs, grid_sizes, kernel_size): def _rope_downsample_3d(self, freqs, grid_sizes, kernel_size):
b, _, one, d, i2, j2 = freqs.shape b, _, one, d, i2, j2 = freqs.shape

1
comfy_extras/nodes_helios.py
View File

@ -412,7 +412,6 @@ def _helios_dmd_sample(
for i in range(len(sigmas) - 1): for i in range(len(sigmas) - 1):
sigma = sigmas[i] sigma = sigmas[i]
sigma_next = sigmas[i + 1]
timestep = all_timesteps[i] if i < len(all_timesteps) else i timestep = all_timesteps[i] if i < len(all_timesteps) else i
denoised = model(x, sigma * s_in, **extra_args) denoised = model(x, sigma * s_in, **extra_args)