nodes/xhiroga-ComfyUI-FramePackWrapper_PlusOne
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feat: request! https://x.com/tori29umai/status/1953660674779091162

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Hiroaki Ogasawara
2025-08-08 12:54:10 +09:00
parent 5bef9b605c
commit fa6ac3bc79
9 changed files with 4498 additions and 1065 deletions
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diffusers_helper/models/hunyuan_video_packed.py
+67
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@@ -832,8 +832,33 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
hidden_states = hidden_states.flatten(2).transpose(1, 2)
# Generate rope frequencies for the full sequence
rope_freqs = self.rope(frame_indices=latent_indices, height=H, width=W, device=hidden_states.device)
rope_freqs = rope_freqs.flatten(2).transpose(1, 2)
# Fix rope_freqs size to match packed hidden_states
if rope_freqs.shape[1] != hidden_states.shape[1]:
# Calculate the ratio for expansion
hidden_seq_len = hidden_states.shape[1] # 23562
rope_seq_len = rope_freqs.shape[1] # 11781
if hidden_seq_len > rope_seq_len:
# Calculate how many times to repeat
repeat_factor = hidden_seq_len // rope_seq_len
remainder = hidden_seq_len % rope_seq_len
print(f"Expanding rope_freqs: {rope_seq_len} -> {hidden_seq_len} (repeat_factor={repeat_factor}, remainder={remainder})")
# Repeat rope_freqs to match hidden_states size
rope_freqs = rope_freqs.repeat(1, repeat_factor, 1)
if remainder > 0:
rope_freqs = torch.cat([rope_freqs, rope_freqs[:, :remainder]], dim=1)
print(f"Final rope_freqs shape: {rope_freqs.shape}")
elif hidden_seq_len < rope_seq_len:
# Trim rope_freqs to match hidden_states size
rope_freqs = rope_freqs[:, :hidden_seq_len, :]
print(f"Trimmed rope_freqs shape: {rope_freqs.shape}")
if clean_latents is not None and clean_latent_indices is not None:
clean_latents = clean_latents.to(hidden_states)
@@ -842,6 +867,20 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
clean_latent_rope_freqs = self.rope(frame_indices=clean_latent_indices, height=H, width=W, device=clean_latents.device)
clean_latent_rope_freqs = clean_latent_rope_freqs.flatten(2).transpose(1, 2)
# Ensure clean_latent_rope_freqs matches clean_latents size
if clean_latent_rope_freqs.shape[1] != clean_latents.shape[1]:
clean_seq_len = clean_latents.shape[1]
clean_rope_seq_len = clean_latent_rope_freqs.shape[1]
if clean_seq_len > clean_rope_seq_len:
repeat_factor = clean_seq_len // clean_rope_seq_len
remainder = clean_seq_len % clean_rope_seq_len
clean_latent_rope_freqs = clean_latent_rope_freqs.repeat(1, repeat_factor, 1)
if remainder > 0:
clean_latent_rope_freqs = torch.cat([clean_latent_rope_freqs, clean_latent_rope_freqs[:, :remainder]], dim=1)
elif clean_seq_len < clean_rope_seq_len:
clean_latent_rope_freqs = clean_latent_rope_freqs[:, :clean_seq_len, :]
hidden_states = torch.cat([clean_latents, hidden_states], dim=1)
rope_freqs = torch.cat([clean_latent_rope_freqs, rope_freqs], dim=1)
@@ -856,6 +895,20 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
clean_latent_2x_rope_freqs = pad_for_3d_conv(clean_latent_2x_rope_freqs, (2, 2, 2))
clean_latent_2x_rope_freqs = center_down_sample_3d(clean_latent_2x_rope_freqs, (2, 2, 2))
clean_latent_2x_rope_freqs = clean_latent_2x_rope_freqs.flatten(2).transpose(1, 2)
# Ensure clean_latent_2x_rope_freqs matches clean_latents_2x size
if clean_latent_2x_rope_freqs.shape[1] != clean_latents_2x.shape[1]:
clean_2x_seq_len = clean_latents_2x.shape[1]
clean_2x_rope_seq_len = clean_latent_2x_rope_freqs.shape[1]
if clean_2x_seq_len > clean_2x_rope_seq_len:
repeat_factor = clean_2x_seq_len // clean_2x_rope_seq_len
remainder = clean_2x_seq_len % clean_2x_rope_seq_len
clean_latent_2x_rope_freqs = clean_latent_2x_rope_freqs.repeat(1, repeat_factor, 1)
if remainder > 0:
clean_latent_2x_rope_freqs = torch.cat([clean_latent_2x_rope_freqs, clean_latent_2x_rope_freqs[:, :remainder]], dim=1)
elif clean_2x_seq_len < clean_2x_rope_seq_len:
clean_latent_2x_rope_freqs = clean_latent_2x_rope_freqs[:, :clean_2x_seq_len, :]
hidden_states = torch.cat([clean_latents_2x, hidden_states], dim=1)
rope_freqs = torch.cat([clean_latent_2x_rope_freqs, rope_freqs], dim=1)
@@ -870,6 +923,20 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
clean_latent_4x_rope_freqs = pad_for_3d_conv(clean_latent_4x_rope_freqs, (4, 4, 4))
clean_latent_4x_rope_freqs = center_down_sample_3d(clean_latent_4x_rope_freqs, (4, 4, 4))
clean_latent_4x_rope_freqs = clean_latent_4x_rope_freqs.flatten(2).transpose(1, 2)
# Ensure clean_latent_4x_rope_freqs matches clean_latents_4x size
if clean_latent_4x_rope_freqs.shape[1] != clean_latents_4x.shape[1]:
clean_4x_seq_len = clean_latents_4x.shape[1]
clean_4x_rope_seq_len = clean_latent_4x_rope_freqs.shape[1]
if clean_4x_seq_len > clean_4x_rope_seq_len:
repeat_factor = clean_4x_seq_len // clean_4x_rope_seq_len
remainder = clean_4x_seq_len % clean_4x_rope_seq_len
clean_latent_4x_rope_freqs = clean_latent_4x_rope_freqs.repeat(1, repeat_factor, 1)
if remainder > 0:
clean_latent_4x_rope_freqs = torch.cat([clean_latent_4x_rope_freqs, clean_latent_4x_rope_freqs[:, :remainder]], dim=1)
elif clean_4x_seq_len < clean_4x_rope_seq_len:
clean_latent_4x_rope_freqs = clean_latent_4x_rope_freqs[:, :clean_4x_seq_len, :]
hidden_states = torch.cat([clean_latents_4x, hidden_states], dim=1)
rope_freqs = torch.cat([clean_latent_4x_rope_freqs, rope_freqs], dim=1)