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@@ -338,13 +338,14 @@ def slerp(val, low, high):
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def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
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+ eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
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xs = []
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# if we have multiple seeds, this means we are working with batch size>1; this then
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# enables the generation of additional tensors with noise that the sampler will use during its processing.
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# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
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# produce the same images as with two batches [100], [101].
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- if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
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+ if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
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sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
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else:
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sampler_noises = None
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@@ -384,8 +385,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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if sampler_noises is not None:
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cnt = p.sampler.number_of_needed_noises(p)
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- if opts.eta_noise_seed_delta > 0:
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- torch.manual_seed(seed + opts.eta_noise_seed_delta)
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+ if eta_noise_seed_delta > 0:
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+ torch.manual_seed(seed + eta_noise_seed_delta)
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for j in range(cnt):
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sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
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AUTOMATIC