| 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 | |