xinyun/ComfyUI
1
0
Fork 0
mirror of https://git.datalinker.icu/comfyanonymous/ComfyUI synced 2026-09-03 13:37:08 +08:00
Code Issues Packages Projects Releases Wiki Activity

fixed tensor shape errors

Browse Source
This commit is contained in:
Pratik-Doshi-99 2025-07-17 01:25:29 +00:00
parent 596c342668
commit aa33d7978e
3 changed files with 35 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
comfy/ldm/models/autoencoder.py
View File

@ -200,6 +200,8 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
return z return z
def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
print('Decoding hunyuan latent. Received tensor:',z.shape)
if self.max_batch_size is None: if self.max_batch_size is None:
dec = self.post_quant_conv(z) dec = self.post_quant_conv(z)
dec = self.decoder(dec, **decoder_kwargs) dec = self.decoder(dec, **decoder_kwargs)

1
comfy/sd.py
View File

@ -409,6 +409,7 @@ class VAE:
self.downscale_index_formula = (4, 8, 8) self.downscale_index_formula = (4, 8, 8)
self.latent_dim = 3 self.latent_dim = 3
self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
print('Loading Hunyuan VAE. Latent channels = ',self.latent_channels)
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype) self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)

47
comfy/taesd/taehv.py
View File

@ -62,6 +62,7 @@ def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
""" """
assert x.ndim == 5, f"TAEHV operates on NTCHW tensors, but got {x.ndim}-dim tensor" assert x.ndim == 5, f"TAEHV operates on NTCHW tensors, but got {x.ndim}-dim tensor"
N, T, C, H, W = x.shape N, T, C, H, W = x.shape
print('Received tensor of shape:',x.shape)
if parallel: if parallel:
x = x.reshape(N*T, C, H, W) x = x.reshape(N*T, C, H, W)
# parallel over input timesteps, iterate over blocks # parallel over input timesteps, iterate over blocks
@ -70,9 +71,11 @@ def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
NT, C, H, W = x.shape NT, C, H, W = x.shape
T = NT // N T = NT // N
_x = x.reshape(N, T, C, H, W) _x = x.reshape(N, T, C, H, W)
mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape) mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape)
print('Intermediate shape:',x.shape)
x = b(x, mem) x = b(x, mem)
else: else:
print('Intermediate shape:',x.shape)
x = b(x) x = b(x)
NT, C, H, W = x.shape NT, C, H, W = x.shape
T = NT // N T = NT // N
@ -89,6 +92,7 @@ def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
mem = [None] * len(model) mem = [None] * len(model)
while work_queue: while work_queue:
xt, i = work_queue.pop(0) xt, i = work_queue.pop(0)
print('Intermediate shape:', xt.shape)
if i == len(model): if i == len(model):
# reached end of the graph, append result to output list # reached end of the graph, append result to output list
out.append(xt) out.append(xt)
@ -150,7 +154,7 @@ class TAEHV(nn.Module):
decoder_space_upscale: whether spatial upsampling is enabled for each block. upsampling can be disabled for a cheaper preview. decoder_space_upscale: whether spatial upsampling is enabled for each block. upsampling can be disabled for a cheaper preview.
""" """
super().__init__() super().__init__()
self.taehv_encoder = nn.Sequential( self.encoder = nn.Sequential(
conv(TAEHV.image_channels, 64), nn.ReLU(inplace=True), conv(TAEHV.image_channels, 64), nn.ReLU(inplace=True),
TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64), TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64),
TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64), TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64),
@ -159,7 +163,7 @@ class TAEHV(nn.Module):
) )
n_f = [256, 128, 64, 64] n_f = [256, 128, 64, 64]
self.frames_to_trim = 2**sum(decoder_time_upscale) - 1 self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
self.taehv_decoder = nn.Sequential( self.decoder = nn.Sequential(
Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True), Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True),
MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
@ -167,10 +171,10 @@ class TAEHV(nn.Module):
nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels), nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels),
) )
if checkpoint_path is not None: if checkpoint_path is not None:
self.load_state_dict((checkpoint_path, safe_load=True)) self.load_state_dict(comfy.utils.load_torch_file(checkpoint_path, safe_load=True))
def load_state_dict(state_dict, strict=True): def load_state_dict(self, state_dict, strict=True):
return super().load_state_dict(self.patch_tgrow_layers(state_dict), strict=strict) return super().load_state_dict(self.patch_tgrow_layers(state_dict), strict=strict)
@ -217,7 +221,7 @@ class TAEHV(nn.Module):
sd[key] = sd[key][-new_sd[key].shape[0]:] sd[key] = sd[key][-new_sd[key].shape[0]:]
return sd return sd
def encode_video(self, x, parallel=True, show_progress_bar=False): def encode_video(self, x, parallel=False, show_progress_bar=False):
"""Encode a sequence of frames. """Encode a sequence of frames.
Args: Args:
x: input NTCHW RGB (C=3) tensor with values in [0, 1]. x: input NTCHW RGB (C=3) tensor with values in [0, 1].
@ -228,17 +232,26 @@ class TAEHV(nn.Module):
""" """
return apply_model_with_memblocks(self.encoder, x, parallel, show_progress_bar) return apply_model_with_memblocks(self.encoder, x, parallel, show_progress_bar)
def decode_video(self, x, parallel=True, show_progress_bar=False): def decode_video(self, x, parallel=False, show_progress_bar=False):
"""Decode a sequence of frames. """Decode a sequence of frames.
Args: Args:
x: input NTCHW latent (C=16) tensor with ~Gaussian values. x: input NCTHW latent (C=16) tensor with ~Gaussian values.
parallel: if True, all frames will be processed at once. parallel: if True, all frames will be processed at once.
(this is faster but may require more memory). (this is faster but may require more memory).
if False, frames will be processed sequentially. if False, frames will be processed sequentially.
Returns NTCHW RGB tensor with ~[0, 1] values. Returns NTCHW RGB tensor with ~[0, 1] values.
""" """
#converting NCTHW to NTCHW
x = x.permute(0,2,1,3,4)
x = apply_model_with_memblocks(self.decoder, x, parallel, show_progress_bar) x = apply_model_with_memblocks(self.decoder, x, parallel, show_progress_bar)
return x[:, self.frames_to_trim:]
x = x[:, self.frames_to_trim:] # trim the time dimension
#converting NTCHW to NCTHW
x = x.permute(0,2,1,3,4)
return x
def decode(self, x): def decode(self, x):
"""Decode a single frame or batch of frames for preview.""" """Decode a single frame or batch of frames for preview."""
@ -247,13 +260,17 @@ class TAEHV(nn.Module):
x = x.unsqueeze(1) x = x.unsqueeze(1)
# For preview, we'll just take the first frame after decoding # For preview, we'll just take the first frame after decoding
decoded = self.decode_video(x, parallel=True, show_progress_bar=False) decoded = self.decode_video(x, parallel=False, show_progress_bar=False)
print(decoded.shape)
#converting
return decoded
# Return single frame for preview # Return single frame for preview
if decoded.shape[1] > 0: # if decoded.shape[1] > 0:
return decoded[:, 0] # return decoded[:, 0]
else: # else:
return decoded.squeeze(1) # return decoded.squeeze(1)
def encode(self, x): def encode(self, x):
"""Encode a single frame or batch of frames.""" """Encode a single frame or batch of frames."""
@ -261,7 +278,7 @@ class TAEHV(nn.Module):
# Add temporal dimension for single frame # Add temporal dimension for single frame
x = x.unsqueeze(1) x = x.unsqueeze(1)
encoded = self.encode_video(x, parallel=True, show_progress_bar=False) encoded = self.encode_video(x, parallel=False, show_progress_bar=False)
# Return single frame # Return single frame
return encoded.squeeze(1) return encoded.squeeze(1)