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Fix compositing error, change input arguments

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David Lee 2026-05-04 13:50:39 -04:00 committed by GitHub
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commit ae54d7a987
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1 changed files with 37 additions and 58 deletions
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comfy_extras/nodes_custom_sampler.py
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@ -15,9 +15,7 @@ def video_latent_composite(destination, source, x, y, mask=None, multiplier=8, r
# destination/source shape: [B, C, F, H, W]
source = source.to(destination.device)
# 1. Spatial Resizing for Source
if resize_source:
# size=(Frames, Height, Width). We keep source's F, but match destination's H, W
target_size = (source.shape[2], destination.shape[3], destination.shape[4])
source = torch.nn.functional.interpolate(
source,
@ -26,22 +24,14 @@ def video_latent_composite(destination, source, x, y, mask=None, multiplier=8, r
align_corners=False
)
# 2. Coordinate Scaling
x_latent = x // multiplier
y_latent = y // multiplier
# 3. Mask Processing (Input: [F, H, W])
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
# Convert [F, H, W] -> [1, 1, F, H, W]
# This allows it to broadcast across any Batch or Channel in 'source'
mask = mask.unsqueeze(0).unsqueeze(0)
# Resize mask spatially, preserving its frame count
# size=(mask_frames, source_height, source_width)
mask_target_size = (mask.shape[2], source.shape[3], source.shape[4])
mask = torch.nn.functional.interpolate(
mask,
@ -50,97 +40,87 @@ def video_latent_composite(destination, source, x, y, mask=None, multiplier=8, r
align_corners=False
)
# 4. Dimension Calculations for Spatial Slicing
dst_h, dst_w = destination.shape[3], destination.shape[4]
src_h, src_w = source.shape[3], source.shape[4]
# Calculate visible overlap region
visible_h = max(0, min(y_latent + src_h, dst_h) - max(0, y_latent))
visible_w = max(0, min(x_latent + src_w, dst_w) - max(0, x_latent))
if visible_h <= 0 or visible_w <= 0:
return destination
# Determine slicing offsets
src_top = max(0, -y_latent)
src_left = max(0, -x_latent)
dst_top = max(0, y_latent)
dst_left = max(0, x_latent)
# 5. Slicing and Blending
# destination/source/mask are now all 5D: [B, C, F, H, W]
# We slice only the H and W dimensions (indices 3 and 4)
m = mask[:, :, :, src_top:src_top+visible_h, src_left:src_left+visible_w]
s = source[:, :, :, src_top:src_top+visible_h, src_left:src_left+visible_w]
d = destination[:, :, :, dst_top:dst_top+visible_h, dst_left:dst_left+visible_w]
# Combine using the mask
destination[:, :, :, dst_top:dst_top+visible_h, dst_left:dst_left+visible_w] = (m * s) + ((1.0 - m) * d)
return destination
def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask, denoise=1.0, start_step=None, time_to_move_last_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask, denoise=1.0, start_step=None, time_to_move_last_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
sigmas = sampler.sigmas
sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
model_sampling = model.get_model_object("model_sampling")
process_latent_out = model.get_model_object("process_latent_out")
process_latent_in = model.get_model_object("process_latent_in")
if last_step == None:
reference_latent_image = latent_image.clone()
reference_sigmas = sampler.sigmas
reference_noise = noise.clone()
if last_step == None or last_step > steps:
last_step = steps
if time_to_move_last_step == None:
time_to_move_last_step = last_step
if time_to_move_last_step > last_step:
if time_to_move_last_step == None or time_to_move_last_step > last_step:
time_to_move_last_step = last_step
if start_step == None:
start_step = 0
#during each step, composite the reference latent back onto the partially sampled latent using the reference latent mask
for i in range (min(last_step, len(sigmas) - 1) - start_step):
for i in range (min(last_step, steps) - start_step):
if i > 0:
#don't add new noise to samples after first loop iteration
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
#don't add new noise to samples after first step taken
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
if i < last_step - 1:
temp_start = start_step + i
if temp_start < last_step - 1:
temp_force_full_denoise = False
else:
temp_force_full_denoise = force_full_denoise
temp_start = start_step + i
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=temp_start, last_step=temp_start + 1, force_full_denoise=temp_force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
#add noise to the reference latent image (referenced from AddNoise node)
if temp_start < time_to_move_last_step:
model_sampling = model.get_model_object("model_sampling")
process_latent_out = model.get_model_object("process_latent_out")
process_latent_in = model.get_model_object("process_latent_in")
scale = sigmas[temp_start + 1].to(noise.device)
scale = reference_sigmas[temp_start + 1].to(noise.device)
if torch.count_nonzero(reference_latent_image) > 0: #Don't shift the empty latent image.
reference_latent_image = process_latent_in(reference_latent_image)
noisy = model_sampling.noise_scaling(scale, noise, reference_latent_image)
noisy = process_latent_out(noisy)
noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0).to(samples.device)
noisy = model_sampling.noise_scaling(scale, reference_noise, process_latent_in(reference_latent_image))
noisy = model_sampling.inverse_noise_scaling(scale, noisy)
noisy = process_latent_out(noisy)
else:
noisy = reference_latent_image
noisy.to(samples.device)
samples = video_latent_composite(samples, noisy, 0, 0, latent_mask, multiplier=1, resize_source=True)
samples = video_latent_composite(samples, noisy, 0, 0, reference_latent_mask, multiplier=8, resize_source=True)
latent_image = samples
samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return samples
def time_to_move_common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, reference_latent, reference_latent_mask, denoise=1.0, disable_noise=False, start_step=None, time_to_move_last_step = None, last_step=None, force_full_denoise=False):
def time_to_move_common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, latent_mask, denoise=1.0, disable_noise=False, start_step=None, time_to_move_last_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))
reference_latent_image = reference_latent["samples"]
reference_latent_image = comfy.sample.fix_empty_latent_channels(model, reference_latent_image, reference_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")
else:
@ -153,7 +133,7 @@ def time_to_move_common_ksampler(model, seed, steps, cfg, sampler_name, schedule
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask,
samples = time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask,
denoise=denoise, start_step=start_step, time_to_move_last_step = time_to_move_last_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()
@ -1127,7 +1107,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
return io.NodeOutput(out, out_denoised)
sample = execute
class TimeToMoveKSamplerAdvanced(io.ComfyNode):
@classmethod
@ -1145,9 +1125,8 @@ class TimeToMoveKSamplerAdvanced(io.ComfyNode):
io.Combo.Input("scheduler", options = comfy.samplers.KSampler.SCHEDULERS),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent_image", tooltip = "Generally should be the same as reference_latent_image."),
io.Latent.Input("reference_latent_image"),
io.Mask.Input("reference_latent_mask", tooltip = "Make sure mask is the same length as the latents rather than the original video."),
io.Latent.Input("latent_image"),
io.Mask.Input("latent_mask", tooltip = "Make sure mask is the same length as the latents rather than the original video."),
io.Int.Input("start_at_step", default = 0, min = 0, max = 10000, advanced = True, tooltip = "Generally should set at a step greater than 0."),
io.Int.Input("time_to_move_end_at_step", default = 0, min = 0, max = 10000, advanced = True, tooltip = "Generally should set at a step greater than 0 and less than total number of steps."),
io.Int.Input("end_at_step", default = 10000, min = 0, max = 10000, advanced = True, tooltip = "Use just like typical end_at_step with normal KSamplerAdvanced"),
@ -1159,7 +1138,7 @@ class TimeToMoveKSamplerAdvanced(io.ComfyNode):
)
@classmethod
def execute(cls, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask, start_at_step, time_to_move_end_at_step, end_at_step, return_with_leftover_noise, denoise=1.0) -> io.NodeOutput:
def execute(cls, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask, start_at_step, time_to_move_end_at_step, end_at_step, return_with_leftover_noise, denoise=1.0) -> io.NodeOutput:
force_full_denoise = True
if return_with_leftover_noise == "enable":
force_full_denoise = False
@ -1167,7 +1146,7 @@ class TimeToMoveKSamplerAdvanced(io.ComfyNode):
if add_noise == "disable":
disable_noise = True
return time_to_move_common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, time_to_move_last_step = time_to_move_end_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
return time_to_move_common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, latent_mask, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, time_to_move_last_step = time_to_move_end_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
class AddNoise(io.ComfyNode):
@classmethod