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97 lines
4.0 KiB
97 lines
4.0 KiB
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 = set(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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