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import torch
from .nodes_registry import comfy_node
@comfy_node(
name="LTXVTiledVAEDecode",
)
class LTXVTiledVAEDecode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"vae": ("VAE",),
"latents": ("LATENT",),
"horizontal_tiles": ("INT", {"default": 1, "min": 1, "max": 6}),
"vertical_tiles": ("INT", {"default": 1, "min": 1, "max": 6}),
"overlap": ("INT", {"default": 1, "min": 1, "max": 8}),
"last_frame_fix": ("BOOLEAN", {"default": False}),
},
"optional": {
"working_device": (["cpu", "auto"], {"default": "auto"}),
"working_dtype": (["float16", "float32", "auto"], {"default": "auto"}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "decode"
CATEGORY = "latent"
def decode(
self,
vae,
latents,
horizontal_tiles,
vertical_tiles,
overlap,
last_frame_fix,
working_device="auto",
working_dtype="auto",
):
# Get the latent samples
samples = latents["samples"]
if last_frame_fix:
# Repeat the last frame along dimension 2 (frames)
# samples: [batch, channels, frames, height, width]
last_frame = samples[
:, :, -1:, :, :
] # shape: [batch, channels, 1, height, width]
samples = torch.cat([samples, last_frame], dim=2)
batch, channels, frames, height, width = samples.shape
time_scale_factor, width_scale_factor, height_scale_factor = (
vae.downscale_index_formula
)
image_frames = 1 + (frames - 1) * time_scale_factor
# Calculate output image dimensions
output_height = height * height_scale_factor
output_width = width * width_scale_factor
# Calculate tile sizes with overlap
base_tile_height = (height + (vertical_tiles - 1) * overlap) // vertical_tiles
base_tile_width = (width + (horizontal_tiles - 1) * overlap) // horizontal_tiles
# Initialize output tensor and weight tensor
# VAE decode returns images in format [batch, height, width, channels]
output = None
weights = None
target_device = samples.device if working_device == "auto" else working_device
if working_dtype == "auto":
target_dtype = samples.dtype
elif working_dtype == "float16":
target_dtype = torch.float16
elif working_dtype == "float32":
target_dtype = torch.float32
output = torch.zeros(
(
batch,
image_frames,
output_height,
output_width,
3,
),
device=target_device,
dtype=target_dtype,
)
weights = torch.zeros(
(batch, image_frames, output_height, output_width, 1),
device=target_device,
dtype=target_dtype,
)
# Process each tile
for v in range(vertical_tiles):
for h in range(horizontal_tiles):
# Calculate tile boundaries
h_start = h * (base_tile_width - overlap)
v_start = v * (base_tile_height - overlap)
# Adjust end positions for edge tiles
h_end = (
min(h_start + base_tile_width, width)
if h < horizontal_tiles - 1
else width
)
v_end = (
min(v_start + base_tile_height, height)
if v < vertical_tiles - 1
else height
)
# Calculate actual tile dimensions
tile_height = v_end - v_start
tile_width = h_end - h_start
logging.info(f"Processing VAE decode tile at row {v}, col {h}:")
logging.info(f" Position: ({v_start}:{v_end}, {h_start}:{h_end})")
logging.info(f" Size: {tile_height}x{tile_width}")
# Extract tile
tile = samples[:, :, :, v_start:v_end, h_start:h_end]
# Create tile latents dict
tile_latents = {"samples": tile}
# Decode the tile
decoded_tile = vae.decode(tile_latents["samples"])
# Calculate output tile boundaries
out_h_start = v_start * height_scale_factor
out_h_end = v_end * height_scale_factor
out_w_start = h_start * width_scale_factor
out_w_end = h_end * width_scale_factor
# Create weight mask for this tile
tile_out_height = out_h_end - out_h_start
tile_out_width = out_w_end - out_w_start
tile_weights = torch.ones(
(batch, image_frames, tile_out_height, tile_out_width, 1),
device=decoded_tile.device,
dtype=decoded_tile.dtype,
)
# Calculate overlap regions in output space
overlap_out_h = overlap * height_scale_factor
overlap_out_w = overlap * width_scale_factor
# Apply horizontal blending weights
if h > 0: # Left overlap
h_blend = torch.linspace(
0, 1, overlap_out_w, device=decoded_tile.device
)
tile_weights[:, :, :, :overlap_out_w, :] *= h_blend.view(
1, 1, 1, -1, 1
)
if h < horizontal_tiles - 1: # Right overlap
h_blend = torch.linspace(
1, 0, overlap_out_w, device=decoded_tile.device
)
tile_weights[:, :, :, -overlap_out_w:, :] *= h_blend.view(
1, 1, 1, -1, 1
)
# Apply vertical blending weights
if v > 0: # Top overlap
v_blend = torch.linspace(
0, 1, overlap_out_h, device=decoded_tile.device
)
tile_weights[:, :, :overlap_out_h, :, :] *= v_blend.view(
1, 1, -1, 1, 1
)
if v < vertical_tiles - 1: # Bottom overlap
v_blend = torch.linspace(
1, 0, overlap_out_h, device=decoded_tile.device
)
tile_weights[:, :, -overlap_out_h:, :, :] *= v_blend.view(
1, 1, -1, 1, 1
)
# Add weighted tile to output
output[:, :, out_h_start:out_h_end, out_w_start:out_w_end, :] += (
decoded_tile * tile_weights
).to(target_device, target_dtype)
# Add weights to weight tensor
weights[
:, :, out_h_start:out_h_end, out_w_start:out_w_end, :
] += tile_weights.to(target_device, target_dtype)
# Normalize by weights
output /= weights + 1e-8
# Reshape output to match expected format [batch * frames, height, width, channels]
output = output.view(
batch * image_frames, output_height, output_width, output.shape[-1]
)
if last_frame_fix:
output = output[:-time_scale_factor, :, :]
return (output,)
def compute_chunk_boundaries(
chunk_start: int,
temporal_tile_length: int,
temporal_overlap: int,
total_latent_frames: int,
):
"""Compute chunk boundaries for temporal tiling.
Args:
chunk_start: Starting frame index for the current chunk
temporal_tile_length: Length of each temporal tile
temporal_overlap: Number of frames to overlap between chunks
total_latent_frames: Total number of latent frames
Returns:
Tuple of (overlap_start, chunk_end)
"""
if chunk_start == 0:
# First chunk: no overlap needed
chunk_end = min(chunk_start + temporal_tile_length, total_latent_frames)
overlap_start = chunk_start
else:
# Subsequent chunks: include overlap from previous chunk
# -1 because we need one extra frame to overlap, which is decoded to a single frame
# never overlap with the first latent frame
overlap_start = max(1, chunk_start - temporal_overlap - 1)
extra_frames = chunk_start - overlap_start
chunk_end = min(
chunk_start + temporal_tile_length - extra_frames,
total_latent_frames,
)
return overlap_start, chunk_end
def calculate_temporal_output_boundaries(
overlap_start: int, time_scale_factor: int, tile_out_frames: int
):
"""Calculate temporal output boundaries for the decoded tile.
Args:
overlap_start: Starting frame index including overlap
time_scale_factor: Time scaling factor from VAE
tile_out_frames: Number of frames in the decoded tile
Returns:
Tuple of (out_t_start, out_t_end)
"""
# +1 for the first frame
out_t_start = 1 + overlap_start * time_scale_factor
# Calculate actual output temporal dimensions
out_t_end = out_t_start + tile_out_frames
return out_t_start, out_t_end
@comfy_node(
name="LTXVSpatioTemporalTiledVAEDecode",
)
class LTXVSpatioTemporalTiledVAEDecode(LTXVTiledVAEDecode):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"vae": ("VAE", {"tooltip": "The VAE to use."}),
"latents": ("LATENT", {"tooltip": "The latent samples to decode."}),
"spatial_tiles": (
"INT",
{
"default": 4,
"min": 1,
"max": 8,
"tooltip": "The number of spatial tiles to use, horizontal and vertical.",
},
),
"spatial_overlap": (
"INT",
{
"default": 1,
"min": 0,
"max": 8,
"tooltip": "The overlap between the spatial tiles. (in latent frames)",
},
),
"temporal_tile_length": (
"INT",
{
"default": 16,
"min": 2,
"max": 1000,
"tooltip": "The length of the temporal tile to use for the sampling, in latent frames, including the overlapping region.",
},
),
"temporal_overlap": (
"INT",
{
"default": 1,
"min": 0,
"max": 8,
"tooltip": "The overlap between the temporal tiles, in latent frames.",
},
),
"last_frame_fix": (
"BOOLEAN",
{
"default": False,
"tooltip": "If true, the last frame will be repeated and discarded after the decoding.",
},
),
"working_device": (
["cpu", "auto"],
{
"default": "auto",
"tooltip": "The device to use for the decoding. auto->same as the latents.",
},
),
"working_dtype": (
["float16", "float32", "auto"],
{
"default": "auto",
"tooltip": "The data type to use for the decoding. auto->same as the latents.",
},
),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "decode_spatial_temporal"
CATEGORY = "latent"
def decode_spatial_temporal(
self,
vae,
latents,
spatial_tiles=4,
spatial_overlap=1,
temporal_tile_length=16,
temporal_overlap=1,
last_frame_fix=False,
working_device="auto",
working_dtype="auto",
):
if temporal_tile_length < temporal_overlap + 1:
raise ValueError(
"Temporal tile length must be greater than temporal overlap + 1"
)
# Get the latent samples
samples = latents["samples"]
batch, channels, frames, height, width = samples.shape
time_scale_factor, width_scale_factor, height_scale_factor = (
vae.downscale_index_formula
)
image_frames = 1 + (frames - 1) * time_scale_factor
# Calculate output image dimensions
output_height = height * height_scale_factor
output_width = width * width_scale_factor
target_device = samples.device if working_device == "auto" else working_device
if working_dtype == "auto":
target_dtype = samples.dtype
elif working_dtype == "float16":
target_dtype = torch.float16
elif working_dtype == "float32":
target_dtype = torch.float32
# Initialize output tensor and weight tensor
output = torch.empty(
(
batch,
image_frames,
output_height,
output_width,
3,
),
device=target_device,
dtype=target_dtype,
)
# Process temporal chunks similar to reference function
total_latent_frames = frames
chunk_start = 0
while chunk_start < total_latent_frames:
# Calculate chunk boundaries
overlap_start, chunk_end = compute_chunk_boundaries(
chunk_start, temporal_tile_length, temporal_overlap, total_latent_frames
)
# units are latent frames
chunk_frames = chunk_end - overlap_start
logging.info(
f"Processing temporal chunk: {overlap_start}:{chunk_end} ({chunk_frames} latent frames)"
)
# Extract tile
tile = samples[:, :, overlap_start:chunk_end]
# Create tile latents dict
tile_latents = {"samples": tile}
# Decode the tile
decoded_tile = self.decode(
vae=vae,
latents=tile_latents,
vertical_tiles=spatial_tiles,
horizontal_tiles=spatial_tiles,
overlap=spatial_overlap,
last_frame_fix=last_frame_fix,
working_device=working_device,
working_dtype=working_dtype,
)[0][None]
if chunk_start == 0:
output[:, : decoded_tile.shape[1]] = decoded_tile
# Drop first frame if needed (overlap)
else:
if decoded_tile.shape[1] == 1:
raise ValueError("Dropping first frame but tile has only 1 frame")
decoded_tile = decoded_tile[:, 1:] # Drop first frame
# Calculate temporal output boundaries
out_t_start, out_t_end = calculate_temporal_output_boundaries(
overlap_start, time_scale_factor, decoded_tile.shape[1]
)
# Create weight mask for this tile
overlap_frames = temporal_overlap * time_scale_factor
frame_weights = torch.linspace(
0,
1,
overlap_frames + 2,
device=decoded_tile.device,
dtype=decoded_tile.dtype,
)[1:-1]
tile_weights = frame_weights.view(1, -1, 1, 1, 1)
after_overlap_frames_start = out_t_start + overlap_frames
# Add weighted tile to output
overlap_output = decoded_tile[:, :overlap_frames]
output[:, out_t_start:after_overlap_frames_start] *= 1 - tile_weights
output[:, out_t_start:after_overlap_frames_start] += (
tile_weights * overlap_output
)
output[:, after_overlap_frames_start:out_t_end] = decoded_tile[
:, overlap_frames:
]
# Move to next chunk
chunk_start = chunk_end
# Reshape output to match expected format [batch * frames, height, width, channels]
output = output.view(
batch * image_frames, output_height, output_width, output.shape[-1]
)
return (output,)
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