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