import torch import ldm_patched.modules.model_management import ldm_patched.modules.samplers import ldm_patched.modules.conds import ldm_patched.modules.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 = ldm_patched.modules.utils.repeat_to_batch_size(noise_mask, shape[0]) noise_mask = noise_mask.to(device) return noise_mask def get_models_from_cond(cond, model_type): models = [] for c in cond: if model_type in c: models += [c[model_type]] return models def convert_cond(cond): out = [] for c in cond: temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove temp["cross_attn"] = c[0] temp["model_conds"] = model_conds out.append(temp) return out 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 prepare_sampling(model, noise_shape, positive, negative, noise_mask): device = model.load_device positive = convert_cond(positive) negative = convert_cond(negative) 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()) ldm_patched.modules.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) real_model = model.model return real_model, positive, negative, noise_mask, models 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): real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) sampler = ldm_patched.modules.samplers.KSampler(real_model, steps=steps, device=model.load_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.to(ldm_patched.modules.model_management.intermediate_device()) cleanup_additional_models(models) cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) noise = noise.to(model.load_device) latent_image = latent_image.to(model.load_device) sigmas = sigmas.to(model.load_device) samples = ldm_patched.modules.samplers.sample(real_model, noise, positive_copy, negative_copy, 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(ldm_patched.modules.model_management.intermediate_device()) cleanup_additional_models(models) cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) return samples