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Fix tensor on wrong device error; slight change to logic
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@ -31,7 +31,7 @@ def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, posit
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#during each step, composite the reference latent back onto the partially sampled latent using the reference latent mask
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for i in range (min(last_step, len(sigmas) - 1) - start_step):
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if i > 1:
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if i > 0:
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#don't add new noise to samples after first loop iteration
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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@ -52,13 +52,13 @@ def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, posit
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process_latent_out = model.get_model_object("process_latent_out")
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process_latent_in = model.get_model_object("process_latent_in")
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scale = sigmas[temp_start + 1]
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scale = sigmas[temp_start + 1].to(noise.device)
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if torch.count_nonzero(reference_latent_image) > 0: #Don't shift the empty latent image.
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reference_latent_image = process_latent_in(reference_latent_image)
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noisy = model_sampling.noise_scaling(scale, noise, reference_latent_image)
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noisy = process_latent_out(noisy)
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noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0)
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noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0).to(samples.device)
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samples = video_latent_composite(samples, noisy, 0, 0, reference_latent_mask, multiplier=8, resize_source=True)
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