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add docstrings

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BlenderNeko 2023-04-23 20:09:09 +02:00
parent 8d2de420d3
commit 5818539743
2 changed files with 19 additions and 12 deletions
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25
comfy/sample.py
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@ -2,22 +2,21 @@ import torch
import comfy.model_management import comfy.model_management
def prepare_noise(latent, seed, disable_noise): def prepare_noise(latent, seed):
"""creates random noise given a LATENT and a seed"""
latent_image = latent["samples"] latent_image = latent["samples"]
if disable_noise: batch_index = 0
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") if "batch_index" in latent:
else: batch_index = latent["batch_index"]
batch_index = 0
if "batch_index" in latent:
batch_index = latent["batch_index"]
generator = torch.manual_seed(seed) generator = torch.manual_seed(seed)
for i in range(batch_index): for i in range(batch_index):
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")
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
return noise return noise
def create_mask(latent, noise): def create_mask(latent, noise):
"""creates a mask for a given LATENT and noise"""
noise_mask = None noise_mask = None
device = comfy.model_management.get_torch_device() device = comfy.model_management.get_torch_device()
if "noise_mask" in latent: if "noise_mask" in latent:
@ -30,6 +29,7 @@ def create_mask(latent, noise):
return noise_mask return noise_mask
def broadcast_cond(cond, noise): def broadcast_cond(cond, noise):
"""broadcasts conditioning to the noise batch size"""
device = comfy.model_management.get_torch_device() device = comfy.model_management.get_torch_device()
copy = [] copy = []
for p in cond: for p in cond:
@ -41,6 +41,7 @@ def broadcast_cond(cond, noise):
return copy return copy
def load_c_nets(positive, negative): def load_c_nets(positive, negative):
"""loads control nets in positive and negative conditioning"""
def get_models(cond): def get_models(cond):
models = [] models = []
for c in cond: for c in cond:
@ -53,10 +54,12 @@ def load_c_nets(positive, negative):
return get_models(positive) + get_models(negative) return get_models(positive) + get_models(negative)
def load_additional_models(positive, negative): def load_additional_models(positive, negative):
"""loads additional models in positive and negative conditioning"""
models = load_c_nets(positive, negative) models = load_c_nets(positive, negative)
comfy.model_management.load_controlnet_gpu(models) comfy.model_management.load_controlnet_gpu(models)
return models return models
def cleanup_additional_models(models): def cleanup_additional_models(models):
"""cleanup additional models that were loaded"""
for m in models: for m in models:
m.cleanup() m.cleanup()

6
nodes.py
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@ -744,7 +744,11 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
device = comfy.model_management.get_torch_device() device = comfy.model_management.get_torch_device()
latent_image = latent["samples"] latent_image = latent["samples"]
noise = comfy.sample.prepare_noise(latent, seed, disable_noise) if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
noise = comfy.sample.prepare_noise(latent, seed)
noise_mask = comfy.sample.create_mask(latent, noise) noise_mask = comfy.sample.create_mask(latent, noise)
real_model = None real_model = None