62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
import torch
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import comfy.model_management
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def prepare_noise(latent, seed, disable_noise):
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latent_image = latent["samples"]
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_index = 0
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if "batch_index" in latent:
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batch_index = latent["batch_index"]
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generator = torch.manual_seed(seed)
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for i in range(batch_index):
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noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
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noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
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return noise
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def create_mask(latent, noise):
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noise_mask = None
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device = comfy.model_management.get_torch_device()
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if "noise_mask" in latent:
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noise_mask = latent['noise_mask']
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noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
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noise_mask = noise_mask.round()
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noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
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noise_mask = torch.cat([noise_mask] * noise.shape[0])
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noise_mask = noise_mask.to(device)
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return noise_mask
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def broadcast_cond(cond, noise):
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device = comfy.model_management.get_torch_device()
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copy = []
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for p in cond:
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t = p[0]
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if t.shape[0] < noise.shape[0]:
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t = torch.cat([t] * noise.shape[0])
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t = t.to(device)
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copy += [[t] + p[1:]]
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return copy
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def load_c_nets(positive, negative):
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def get_models(cond):
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models = []
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for c in cond:
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if 'control' in c[1]:
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models += [c[1]['control']]
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if 'gligen' in c[1]:
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models += [c[1]['gligen'][1]]
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return models
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return get_models(positive) + get_models(negative)
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def load_additional_models(positive, negative):
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models = load_c_nets(positive, negative)
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comfy.model_management.load_controlnet_gpu(models)
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return models
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def cleanup_additional_models(models):
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for m in models:
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m.cleanup() |