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Fix nondeterministic results when add_noise==disable

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Pam 2024-08-21 09:41:42 +05:00
parent f257fc999f
commit fa87f263ce
3 changed files with 8 additions and 8 deletions
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5
comfy/sample.py
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@ -5,12 +5,15 @@ import comfy.utils
import numpy as np import numpy as np
import logging import logging
def prepare_noise(latent_image, seed, noise_inds=None): def prepare_noise(latent_image, seed, noise_inds=None, disable_noise=False):
""" """
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) generator = torch.manual_seed(seed)
if disable_noise:
return torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
if noise_inds is None: if noise_inds is None:
return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")

2
comfy_extras/nodes_custom_sampler.py
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@ -397,7 +397,7 @@ class Noise_EmptyNoise:
def generate_noise(self, input_latent): def generate_noise(self, input_latent):
latent_image = input_latent["samples"] latent_image = input_latent["samples"]
return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") return comfy.sample.prepare_noise(latent_image, self.seed, disable_noise=True)
class Noise_RandomNoise: class Noise_RandomNoise:

5
nodes.py
View File

@ -1381,11 +1381,8 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
latent_image = latent["samples"] latent_image = latent["samples"]
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds, disable_noise)
noise_mask = None noise_mask = None
if "noise_mask" in latent: if "noise_mask" in latent: