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Auto reshape 2d to 3d latent for single image generation on video model.

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This commit is contained in:
comfyanonymous 2024-12-29 02:26:49 -05:00
parent e1dec3c792
commit a618f768e0
2 changed files with 11 additions and 3 deletions
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6
comfy/latent_formats.py
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@ -3,6 +3,7 @@ import torch
class LatentFormat:
scale_factor = 1.0
latent_channels = 4
latent_dimensions = 2
latent_rgb_factors = None
latent_rgb_factors_bias = None
taesd_decoder_name = None
@ -143,6 +144,7 @@ class SD3(LatentFormat):
class StableAudio1(LatentFormat):
latent_channels = 64
latent_dimensions = 1
class Flux(SD3):
latent_channels = 16
@ -178,6 +180,7 @@ class Flux(SD3):
class Mochi(LatentFormat):
latent_channels = 12
latent_dimensions = 3
def __init__(self):
self.scale_factor = 1.0
@ -219,6 +222,8 @@ class Mochi(LatentFormat):
class LTXV(LatentFormat):
latent_channels = 128
latent_dimensions = 3
def __init__(self):
self.latent_rgb_factors = [
[ 1.1202e-02, -6.3815e-04, -1.0021e-02],
@ -355,6 +360,7 @@ class LTXV(LatentFormat):
class HunyuanVideo(LatentFormat):
latent_channels = 16
latent_dimensions = 3
scale_factor = 0.476986
latent_rgb_factors = [
[-0.0395, -0.0331, 0.0445],

8
comfy/sample.py
View File

@ -25,9 +25,11 @@ def prepare_noise(latent_image, seed, noise_inds=None):
return noises
def fix_empty_latent_channels(model, latent_image):
latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
latent_image = latent_image.unsqueeze(2)
return latent_image
def prepare_sampling(model, noise_shape, positive, negative, noise_mask):