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Merge branch 'master' into fix/ltx2-regression-tests

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This commit is contained in:
Christian Byrne
2026-08-16 18:58:18 -07:00
committed by GitHub
parent 49b6313322 0696f61dce
commit 7b577ea666
2 changed files with 21 additions and 1 deletions
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comfy/latent_formats.py
+17
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@@ -1,4 +1,5 @@
import torch import torch
import comfy.nested_tensor
class LatentFormat: class LatentFormat:
scale_factor = 1.0 scale_factor = 1.0
@@ -17,6 +18,9 @@ class LatentFormat:
def process_out(self, latent): def process_out(self, latent):
return latent / self.scale_factor return latent / self.scale_factor
def fix_empty_latent(self, latent):
return latent
class SD15(LatentFormat): class SD15(LatentFormat):
def __init__(self, scale_factor=0.18215): def __init__(self, scale_factor=0.18215):
self.scale_factor = scale_factor self.scale_factor = scale_factor
@@ -606,6 +610,19 @@ class MiniMaxH3AV(MiniMaxH3Video):
# max channels across the two streams (video 24, audio 32) so per-stream slices keep both streams whole # max channels across the two streams (video 24, audio 32) so per-stream slices keep both streams whole
latent_channels = 32 latent_channels = 32
def fix_empty_latent(self, latent):
video_latent_channels = MiniMaxH3Video.latent_channels
audio_latent_channels = 32
audio_channels = 2
frames_per_token = (1, 4, 4, 4, 4)
audio_frame_rescale = 5.0 / 3.0
video = latent[:, :video_latent_channels].clone()
frame_count = sum(frames_per_token[i % len(frames_per_token)] for i in range(video.shape[2]))
audio_t = round(frame_count * audio_frame_rescale)
audio = latent.new_zeros((latent.shape[0], audio_latent_channels, audio_channels, audio_t))
return comfy.nested_tensor.NestedTensor((video, audio))
class HunyuanVideo(LatentFormat): class HunyuanVideo(LatentFormat):
latent_channels = 16 latent_channels = 16
latent_dimensions = 3 latent_dimensions = 3
comfy/sample.py
+4 -1
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@@ -45,7 +45,7 @@ def prepare_empty_noise(latent_image):
def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None, downscale_ratio_temporal=None): def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None, downscale_ratio_temporal=None):
if latent_image.is_nested: if latent_image.is_nested:
return latent_image return latent_image
latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels latent_format = model.get_model_object("latent_format")
is_empty = torch.count_nonzero(latent_image) == 0 is_empty = torch.count_nonzero(latent_image) == 0
if is_empty: if is_empty:
if latent_format.latent_channels != latent_image.shape[1]: if latent_format.latent_channels != latent_image.shape[1]:
@@ -64,6 +64,9 @@ def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None,
new_t = max(1, round(latent_image.shape[2] * ratio)) new_t = max(1, round(latent_image.shape[2] * ratio))
latent_image = comfy.utils.repeat_to_batch_size(latent_image, new_t, dim=2) latent_image = comfy.utils.repeat_to_batch_size(latent_image, new_t, dim=2)
if is_empty:
latent_image = latent_format.fix_empty_latent(latent_image)
return latent_image return latent_image
def prepare_sampling(model, noise_shape, positive, negative, noise_mask): def prepare_sampling(model, noise_shape, positive, negative, noise_mask):