98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
import torch
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import comfy.model_management
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import comfy.samplers
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import math
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import numpy as np
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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 prepare_mask(noise_mask, shape, device):
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"""ensures noise mask is of proper dimensions"""
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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")
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noise_mask = noise_mask.round()
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noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
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if noise_mask.shape[0] < shape[0]:
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noise_mask = noise_mask.repeat(math.ceil(shape[0] / noise_mask.shape[0]), 1, 1, 1)[:shape[0]]
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noise_mask = noise_mask.to(device)
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return noise_mask
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def broadcast_cond(cond, batch, device):
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"""broadcasts conditioning to the batch size"""
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copy = []
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for p in cond:
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t = p[0]
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if t.shape[0] < batch:
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t = torch.cat([t] * batch)
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t = t.to(device)
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copy += [[t] + p[1:]]
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return copy
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def get_models_from_cond(cond, model_type):
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models = []
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for c in cond:
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if model_type in c[1]:
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models += [c[1][model_type]]
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return models
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def get_additional_models(positive, negative):
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"""loads additional models in positive and negative conditioning"""
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control_nets = get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")
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control_models = []
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for m in control_nets:
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control_models += m.get_models()
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gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
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gligen = [x[1] for x in gligen]
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models = control_models + gligen
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return models
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def cleanup_additional_models(models):
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"""cleanup additional models that were loaded"""
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for m in models:
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if hasattr(m, 'cleanup'):
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m.cleanup()
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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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device = comfy.model_management.get_torch_device()
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if noise_mask is not None:
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noise_mask = prepare_mask(noise_mask, noise.shape, device)
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real_model = None
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models = get_additional_models(positive, negative)
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comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[2] * noise.shape[3]))
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real_model = model.model
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noise = noise.to(device)
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latent_image = latent_image.to(device)
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positive_copy = broadcast_cond(positive, noise.shape[0], device)
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negative_copy = broadcast_cond(negative, noise.shape[0], device)
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sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
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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)
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samples = samples.cpu()
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cleanup_additional_models(models)
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return samples
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