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Initial work to make downscale_ratio_temporal work. (#13972)

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comfyanonymous 2026-05-18 20:01:43 -07:00 committed by GitHub
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commit 990a7ae7f2
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3 changed files with 16 additions and 5 deletions
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12
comfy/sample.py
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@ -37,11 +37,12 @@ def prepare_noise(latent_image, seed, noise_inds=None):
return noises
def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None):
def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None, downscale_ratio_temporal=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 torch.count_nonzero(latent_image) == 0:
is_empty = torch.count_nonzero(latent_image) == 0
if is_empty:
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:
@ -51,6 +52,13 @@ def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None)
if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
latent_image = latent_image.unsqueeze(2)
if is_empty and downscale_ratio_temporal is not None:
if downscale_ratio_temporal != latent_format.temporal_downscale_ratio:
ratio = downscale_ratio_temporal / latent_format.temporal_downscale_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)
return latent_image
def prepare_sampling(model, noise_shape, positive, negative, noise_mask):

6
comfy_extras/nodes_custom_sampler.py
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@ -750,7 +750,7 @@ 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.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
latent["samples"] = latent_image
if not add_noise:
@ -770,6 +770,7 @@ class SamplerCustom(io.ComfyNode):
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out.pop("downscale_ratio_temporal", None)
out["samples"] = samples
if "x0" in x0_output:
x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
@ -949,7 +950,7 @@ 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.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
latent["samples"] = latent_image
noise_mask = None
@ -965,6 +966,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out.pop("downscale_ratio_temporal", None)
out["samples"] = samples
if "x0" in x0_output:
x0_out = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())

3
nodes.py
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@ -1524,7 +1524,7 @@ 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.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@ -1543,6 +1543,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
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.pop("downscale_ratio_temporal", None)
out["samples"] = samples
return (out, )