| 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 = 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])
|
| 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
|
|
|
|
|
