mirror of https://github.com/vladmandic/automatic
46 lines
2.2 KiB
Python
46 lines
2.2 KiB
Python
import torch
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from diffusers.utils.torch_utils import randn_tensor
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from modules import scripts, processing, shared, devices
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from modules.processing_helpers import slerp
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class Script(scripts.Script):
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standalone = False
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def title(self):
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return 'Init Latents'
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def show(self, is_img2img):
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return scripts.AlwaysVisible if shared.backend == shared.Backend.DIFFUSERS else False
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@staticmethod
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def get_latents(p):
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generator_device = devices.cpu if shared.opts.diffusers_generator_device == "CPU" else shared.device
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generator = [torch.Generator(generator_device).manual_seed(s) for s in p.seeds]
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shape = (len(generator), shared.sd_model.unet.config.in_channels, p.height // shared.sd_model.vae_scale_factor,
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p.width // shared.sd_model.vae_scale_factor)
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latents = randn_tensor(shape, generator=generator, device=shared.sd_model._execution_device, dtype=shared.sd_model.unet.dtype) # pylint: disable=protected-access
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var_generator = [torch.Generator(generator_device).manual_seed(ss) for ss in p.subseeds]
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var_latents = randn_tensor(shape, generator=var_generator, device=shared.sd_model._execution_device, dtype=shared.sd_model.unet.dtype) # pylint: disable=protected-access
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return latents, var_latents, generator, var_generator
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@staticmethod
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def set_slerp(p, latents, var_latents, generator, var_generator):
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if p.subseed_strength < 1:
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p.init_latent = slerp(p.subseed_strength, latents, var_latents)
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if p.subseed_strength == 1:
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p.init_latent = var_latents
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if 0 < p.subseed_strength <= 0.5:
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p.generator = generator
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if 0.5 < p.subseed_strength <= 1:
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p.generator = var_generator
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def process_batch(self, p: processing.StableDiffusionProcessing, *args, **kwargs): # pylint: disable=arguments-differ
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if shared.backend != shared.Backend.DIFFUSERS:
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return
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args = list(args)
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if p.subseed_strength != 0 and getattr(shared.sd_model, '_execution_device', None) is not None:
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latents, var_latents, generator, var_generator = self.get_latents(p)
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self.set_slerp(p, latents, var_latents, generator, var_generator)
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