diff options
| -rw-r--r-- | modules/sd_samplers_common.py | 9 |
1 files changed, 3 insertions, 6 deletions
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index d99c933d..763829f1 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -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)
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