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simplify single_sample_to_image

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AUTOMATIC 2023-05-17 14:53:39 +03:00
parent 875ccc27f6
commit 1210548cba
1 changed files with 3 additions and 6 deletions
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9
modules/sd_samplers_common.py
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@ -26,22 +26,19 @@ approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD":
def single_sample_to_image(sample, approximation=None):
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
x_sample = sd_vae_approx.cheap_approximation(sample)
x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
elif approximation == 3:
x_sample = sample * 1.5
x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
else:
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
if approximation != 3:
x_sample = (x_sample + 1.0) / 2.0
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)