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import torch
import comfy.model_management
import comfy.samplers
import comfy.utils
import math
import numpy as np
def prepare_noise(latent_image, seed, noise_inds=None):
"""
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
"""
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 = []
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")
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises
def prepare_mask(noise_mask, shape, device):
"""ensures noise mask is of proper dimensions"""
noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
noise_mask = noise_mask.round()
noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
noise_mask = noise_mask.to(device)
return noise_mask
def broadcast_cond(cond, batch, device):
"""broadcasts conditioning to the batch size"""
copy = []
for p in cond:
t = comfy.utils.repeat_to_batch_size(p[0], batch)
t = t.to(device)
copy += [[t] + p[1:]]
return copy
def get_models_from_cond(cond, model_type):
models = []
for c in cond:
if model_type in c[1]:
models += [c[1][model_type]]
return models
def get_additional_models(positive, negative, dtype):
"""loads additional models in positive and negative conditioning"""
control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))
inference_memory = 0
control_models = []
for m in control_nets:
control_models += m.get_models()
inference_memory += m.inference_memory_requirements(dtype)
gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
gligen = [x[1] for x in gligen]
models = control_models + gligen
return models, inference_memory
def cleanup_additional_models(models):
"""cleanup additional models that were loaded"""
for m in models:
if hasattr(m, 'cleanup'):
m.cleanup()
def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
device = comfy.model_management.get_torch_device()
if noise_mask is not None:
noise_mask = prepare_mask(noise_mask, noise.shape, device)
real_model = None
models, inference_memory = get_additional_models(positive, negative, model.model_dtype())
comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory)
real_model = model.model
noise = noise.to(device)
latent_image = latent_image.to(device)
positive_copy = broadcast_cond(positive, noise.shape[0], device)
negative_copy = broadcast_cond(negative, noise.shape[0], device)
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
samples = samples.cpu()
cleanup_additional_models(models)
return samples
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