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| import torch | |
| import comfy.model_management | |
| import comfy.samplers | |
| import comfy.utils | |
| import numpy as np | |
| import logging | |
| 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 fix_empty_latent_channels(model, latent_image): | |
| latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels | |
| if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: | |
| latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1) | |
| return latent_image | |
| def prepare_sampling(model, noise_shape, positive, negative, noise_mask): | |
| logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") | |
| return model, positive, negative, noise_mask, [] | |
| def cleanup_additional_models(models): | |
| logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") | |
| 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): | |
| sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) | |
| 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) | |
| samples = samples.to(comfy.model_management.intermediate_device()) | |
| return samples | |
| def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): | |
| 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) | |
| samples = samples.to(comfy.model_management.intermediate_device()) | |
| return samples | |