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Merge upstream/master, keep local README.md

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GitHub Actions 2026-01-25 00:43:36 +00:00
commit db70874eff
7 changed files with 45 additions and 22 deletions
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5
comfy/latent_formats.py
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@ -8,6 +8,7 @@ class LatentFormat:
latent_rgb_factors_bias = None
latent_rgb_factors_reshape = None
taesd_decoder_name = None
spacial_downscale_ratio = 8
def process_in(self, latent):
return latent * self.scale_factor
@ -181,6 +182,7 @@ class Flux(SD3):
class Flux2(LatentFormat):
latent_channels = 128
spacial_downscale_ratio = 16
def __init__(self):
self.latent_rgb_factors =[
@ -592,6 +594,7 @@ class Wan22(Wan21):
class HunyuanImage21(LatentFormat):
latent_channels = 64
latent_dimensions = 2
spacial_downscale_ratio = 32
scale_factor = 0.75289
latent_rgb_factors = [
@ -725,6 +728,7 @@ class HunyuanVideo15(LatentFormat):
latent_rgb_factors_bias = [ 0.0456, -0.0202, -0.0644]
latent_channels = 32
latent_dimensions = 3
spacial_downscale_ratio = 16
scale_factor = 1.03682
taesd_decoder_name = "lighttaehy1_5"
@ -749,6 +753,7 @@ class ACEAudio(LatentFormat):
class ChromaRadiance(LatentFormat):
latent_channels = 3
spacial_downscale_ratio = 1
def __init__(self):
self.latent_rgb_factors = [

27
comfy/ldm/wan/vae.py
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@ -5,7 +5,7 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from comfy.ldm.modules.diffusionmodules.model import vae_attention
from comfy.ldm.modules.diffusionmodules.model import vae_attention, torch_cat_if_needed
import comfy.ops
ops = comfy.ops.disable_weight_init
@ -20,22 +20,29 @@ class CausalConv3d(ops.Conv3d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._padding = (self.padding[2], self.padding[2], self.padding[1],
self.padding[1], 2 * self.padding[0], 0)
self.padding = (0, 0, 0)
self._padding = 2 * self.padding[0]
self.padding = (0, self.padding[1], self.padding[2])
def forward(self, x, cache_x=None, cache_list=None, cache_idx=None):
if cache_list is not None:
cache_x = cache_list[cache_idx]
cache_list[cache_idx] = None
padding = list(self._padding)
if cache_x is not None and self._padding[4] > 0:
cache_x = cache_x.to(x.device)
x = torch.cat([cache_x, x], dim=2)
padding[4] -= cache_x.shape[2]
if cache_x is None and x.shape[2] == 1:
#Fast path - the op will pad for use by truncating the weight
#and save math on a pile of zeros.
return super().forward(x, autopad="causal_zero")
if self._padding > 0:
padding_needed = self._padding
if cache_x is not None:
cache_x = cache_x.to(x.device)
padding_needed = max(0, padding_needed - cache_x.shape[2])
padding_shape = list(x.shape)
padding_shape[2] = padding_needed
padding = torch.zeros(padding_shape, device=x.device, dtype=x.dtype)
x = torch_cat_if_needed([padding, cache_x, x], dim=2)
del cache_x
x = F.pad(x, padding)
return super().forward(x)

10
comfy/ops.py
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@ -203,7 +203,9 @@ class disable_weight_init:
def reset_parameters(self):
return None
def _conv_forward(self, input, weight, bias, *args, **kwargs):
def _conv_forward(self, input, weight, bias, autopad=None, *args, **kwargs):
if autopad == "causal_zero":
weight = weight[:, :, -input.shape[2]:, :, :]
if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):
out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)
if bias is not None:
@ -212,15 +214,15 @@ class disable_weight_init:
else:
return super()._conv_forward(input, weight, bias, *args, **kwargs)
def forward_comfy_cast_weights(self, input):
def forward_comfy_cast_weights(self, input, autopad=None):
weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
x = self._conv_forward(input, weight, bias)
x = self._conv_forward(input, weight, bias, autopad=autopad)
uncast_bias_weight(self, weight, bias, offload_stream)
return x
def forward(self, *args, **kwargs):
run_every_op()
if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0 or "autopad" in kwargs:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)

12
comfy/sample.py
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@ -37,12 +37,18 @@ def prepare_noise(latent_image, seed, noise_inds=None):
return noises
def fix_empty_latent_channels(model, latent_image):
def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None):
if latent_image.is_nested:
return latent_image
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 torch.count_nonzero(latent_image) == 0:
if latent_format.latent_channels != latent_image.shape[1]:
latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
if downscale_ratio_spacial is not None:
if downscale_ratio_spacial != latent_format.spacial_downscale_ratio:
ratio = downscale_ratio_spacial / latent_format.spacial_downscale_ratio
latent_image = comfy.utils.common_upscale(latent_image, round(latent_image.shape[-1] * ratio), round(latent_image.shape[-2] * ratio), "nearest-exact", crop="disabled")
if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
latent_image = latent_image.unsqueeze(2)
return latent_image

6
comfy_extras/nodes_custom_sampler.py
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@ -741,7 +741,7 @@ class SamplerCustom(io.ComfyNode):
latent = latent_image
latent_image = latent["samples"]
latent = latent.copy()
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
latent["samples"] = latent_image
if not add_noise:
@ -760,6 +760,7 @@ class SamplerCustom(io.ComfyNode):
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out["samples"] = samples
if "x0" in x0_output:
x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
@ -939,7 +940,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
latent = latent_image
latent_image = latent["samples"]
latent = latent.copy()
latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image, latent.get("downscale_ratio_spacial", None))
latent["samples"] = latent_image
noise_mask = None
@ -954,6 +955,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
samples = samples.to(comfy.model_management.intermediate_device())
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out["samples"] = samples
if "x0" in x0_output:
x0_out = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())

2
comfy_extras/nodes_sd3.py
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@ -55,7 +55,7 @@ class EmptySD3LatentImage(io.ComfyNode):
@classmethod
def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples":latent})
return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
generate = execute # TODO: remove

5
nodes.py
View File

@ -1230,7 +1230,7 @@ class EmptyLatentImage:
def generate(self, width, height, batch_size=1):
latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
return ({"samples":latent}, )
return ({"samples": latent, "downscale_ratio_spacial": 8}, )
class LatentFromBatch:
@ -1538,7 +1538,7 @@ class SetLatentNoiseMask:
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@ -1556,6 +1556,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out["samples"] = samples
return (out, )