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Merge efa53c9e928f5d693ef4b75ebbc95cff8f2af6c1 into bc6dac4327a838f8583f6272cc3cc612b9b16134

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Alex "mcmonkey" Goodwin 2024-12-24 11:37:10 +08:00 committed by GitHub
commit 0e9e3f4e6b
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1 changed files with 8 additions and 5 deletions
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comfy/sample.py
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@ -10,12 +10,15 @@ def prepare_noise(latent_image, seed, noise_inds=None):
creates random noise given a latent image and a seed. creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed optional arg skip can be used to skip and discard x number of noise generations for a given seed
""" """
generator = torch.manual_seed(seed)
if noise_inds is None:
return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = [] noises = []
if noise_inds is None:
for i in range(latent_image.size()[0]):
generator = torch.manual_seed(seed + i)
noises.append(torch.randn(latent_image[i].size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu"))
return torch.stack(noises, axis=0)
generator = torch.manual_seed(seed)
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
for i in range(unique_inds[-1]+1): for i in range(unique_inds[-1]+1):
noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
if i in unique_inds: if i in unique_inds: