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import torch
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import comfy.model_management
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import comfy.samplers
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import comfy.utils
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import numpy as np
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import logging
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def prepare_noise(latent_image, seed, noise_inds=None):
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"""
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creates random noise given a latent image and a seed.
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optional arg skip can be used to skip and discard x number of noise generations for a given seed
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"""
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generator = torch.manual_seed(seed)
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if noise_inds is None:
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return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
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unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
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noises = []
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for i in range(unique_inds[-1]+1):
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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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if i in unique_inds:
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noises.append(noise)
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noises = [noises[i] for i in inverse]
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noises = torch.cat(noises, axis=0)
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return noises
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def fix_empty_latent_channels(model, latent_image):
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latent_channels = model.get_model_object("latent_format").latent_channels
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if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
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latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
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return latent_image
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def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
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logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
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return model, positive, negative, noise_mask, []
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def cleanup_additional_models(models):
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logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed")
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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):
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sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
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samples = sampler.sample(noise, positive, negative, 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)
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samples = samples.to(comfy.model_management.intermediate_device())
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return samples
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def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
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samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
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samples = samples.to(comfy.model_management.intermediate_device())
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return samples
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