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Fix tensor on wrong device error; slight change to logic

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David Lee 2026-05-02 18:28:54 -04:00 committed by GitHub
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commit 0b7d56070d
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1 changed files with 3 additions and 3 deletions
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comfy_extras/nodes_custom_sampler.py
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@ -31,7 +31,7 @@ def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, posit
#during each step, composite the reference latent back onto the partially sampled latent using the reference latent mask #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, len(sigmas) - 1) - start_step):
if i > 1: if i > 0:
#don't add new noise to samples after first loop iteration #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") noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@ -52,13 +52,13 @@ def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, posit
process_latent_out = model.get_model_object("process_latent_out") process_latent_out = model.get_model_object("process_latent_out")
process_latent_in = model.get_model_object("process_latent_in") process_latent_in = model.get_model_object("process_latent_in")
scale = sigmas[temp_start + 1] scale = sigmas[temp_start + 1].to(noise.device)
if torch.count_nonzero(reference_latent_image) > 0: #Don't shift the empty latent image. if torch.count_nonzero(reference_latent_image) > 0: #Don't shift the empty latent image.
reference_latent_image = process_latent_in(reference_latent_image) reference_latent_image = process_latent_in(reference_latent_image)
noisy = model_sampling.noise_scaling(scale, noise, reference_latent_image) noisy = model_sampling.noise_scaling(scale, noise, reference_latent_image)
noisy = process_latent_out(noisy) noisy = process_latent_out(noisy)
noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0) noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0).to(samples.device)
samples = video_latent_composite(samples, noisy, 0, 0, reference_latent_mask, multiplier=8, resize_source=True) samples = video_latent_composite(samples, noisy, 0, 0, reference_latent_mask, multiplier=8, resize_source=True)