nodes/erosDiffusion-ComfyUI-Erosdiffusion-LTX2
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running sampler

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This commit is contained in:
Enrico
2026-01-19 15:57:02 +01:00
parent 3bd375205c
commit caef27e6da
1 changed files with 22 additions and 21 deletions
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scene_extender.py
+22 -21
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@@ -409,10 +409,14 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
if is_av_model and NestedTensor is not None:
audio_length = time_mgr.calculate_audio_latent_count(chunk_duration)
audio_latent = torch.zeros(
[1, 128, audio_length, 1, 1],
[1, 128, audio_length, 1], # 4D for Audio
device=mm.intermediate_device() if COMFY_AVAILABLE else "cpu"
)
input_latent = {"samples": NestedTensor((video_latent, audio_latent))}
print(f"DEBUG: First Chunk - Video Shape: {video_latent.shape}, Audio Shape: {audio_latent.shape}")
nt = NestedTensor((video_latent, audio_latent))
print(f"DEBUG: First Chunk - NestedTensor Audio Shape: {nt.tensors[1].shape}")
input_latent = {"samples": nt}
else:
input_latent = {"samples": video_latent}
@@ -446,7 +450,7 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
if is_av_model and NestedTensor is not None:
audio_length = time_mgr.calculate_audio_latent_count(total_duration)
new_audio = torch.zeros(
[1, 128, audio_length, 1, 1],
[1, 128, audio_length, 1], # 4D for Audio [B, C, T, D]
device=mm.intermediate_device() if COMFY_AVAILABLE else "cpu"
)
input_latent = {"samples": NestedTensor((new_video, new_audio))}
@@ -478,7 +482,7 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
# Audio overlap frames
audio_overlap = time_mgr.calculate_audio_latent_count(overlap_duration)
amask = torch.ones(
(1, 1, audio_length, 1, 1),
(1, 1, audio_length, 1), # 4D for Audio Mask
dtype=torch.float32,
device=mm.intermediate_device()
)
@@ -487,7 +491,11 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
# Audio doesn't have "Latent Guide" traditionally but masking works.
amask[:, :, :audio_overlap] = 1.0 - temporal_cond_strength
input_latent["noise_mask"] = NestedTensor((mask, amask))
print(f"DEBUG: Mask Shape: {mask.shape}, Amask Shape: {amask.shape}")
nt_mask = NestedTensor((mask, amask))
print(f"DEBUG: NestedTensor Tensors Shapes: {[t.shape for t in nt_mask.tensors]}")
input_latent["noise_mask"] = nt_mask
else:
input_latent["noise_mask"] = mask
@@ -655,9 +663,11 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
# Linear weights [0..1]
alpha = torch.linspace(0, 1, overlap, device=prev.device, dtype=prev.dtype)
# Reshape alpha for broadcasting [1, 1, overlap, 1, 1]
# Valid for both Video [B, C, T, H, W] and Audio [B, C, T, 1, 1]
alpha = alpha.view(1, 1, -1, 1, 1)
# Reshape alpha for broadcasting
# Valid for Video [B, C, T, H, W] (5D) and Audio [B, C, T, D] (4D)
# Dynamically append 1s based on dimensions
shape = [1, 1, -1] + [1] * (prev.ndim - 3)
alpha = alpha.view(*shape)
# Blend: prev_tail * (1-alpha) + next_head * alpha
# Wait, if we are appending NEXT to PREV.
@@ -683,12 +693,13 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
def _get_conds_from_guider(cls, guider):
"""Extract positive and negative conditioning from guider."""
conds = None
if hasattr(guider, "raw_conds"):
# Prefer 'conds' (Current/Active) over 'original_conds' (Historical/Input)
if hasattr(guider, "conds"):
conds = guider.conds
elif hasattr(guider, "raw_conds"):
conds = guider.raw_conds
elif hasattr(guider, "original_conds"):
conds = guider.original_conds
elif hasattr(guider, "conds"):
conds = guider.conds
if conds is not None:
if isinstance(conds, dict):
@@ -709,15 +720,8 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
# Fallback for some clip implementations (e.g. KJ LTXV)
conds = clip.encode_from_tokens_scheduled(tokens)
print(f" DEBUG: _encode_prompt Fallback. Type: {type(conds)}")
if conds and hasattr(conds, "__getitem__") and len(conds) > 0:
print(f" DEBUG: Item 0 Type: {type(conds[0])}")
if isinstance(conds[0], dict):
print(f" DEBUG: Item 0 Keys: {list(conds[0].keys())}")
# If result is List of Dicts (Non-Standard), Wrap it!
if conds and isinstance(conds, list) and len(conds) > 0 and isinstance(conds[0], dict):
print(" DEBUG: Detected Dict-based conditioning. Wrapping...")
new_conds = []
for c in conds:
# Attempt to find tensor
@@ -732,10 +736,7 @@ Guide refs: $0, $1, etc. reference guide_images batch by index"""
new_conds.append([tensor, c])
if new_conds:
print(f" DEBUG: Wrapped {len(new_conds)} items.")
return new_conds
else:
print(" DEBUG: Failed to find tensors to wrap.")
return conds