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return live preview defaults to how they were

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only download TAESD model when it's needed
return calculations in single_sample_to_image to just if/elif/elif blocks
keep taesd model in its own directory
This commit is contained in:
AUTOMATIC
2023-05-17 09:24:01 +03:00
parent b217ebc490
commit 56a2672831
4 changed files with 31 additions and 29 deletions
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29
modules/sd_samplers_common.py
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@@ -22,28 +22,29 @@ def setup_img2img_steps(p, steps=None):
return steps, t_enc
approximation_indexes = {"Full": 0, "Tiny AE": 1, "Approx NN": 2, "Approx cheap": 3}
approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
def single_sample_to_image(sample, approximation=None):
if approximation is None or approximation not in approximation_indexes.keys():
approximation = approximation_indexes.get(opts.show_progress_type, 1)
if approximation == 1:
x_sample = sd_vae_taesd.decode()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample)
x_sample = torch.clamp((x_sample * 0.25) + 0.5, 0, 1)
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
x_sample = sd_vae_approx.cheap_approximation(sample)
elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
elif approximation == 3:
x_sample = sd_vae_taesd.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
x_sample = sd_vae_taesd.TAESD.unscale_latents(x_sample) # returns value in [-2, 2]
x_sample = x_sample * 0.5
else:
if approximation == 3:
x_sample = sd_vae_approx.cheap_approximation(sample)
elif approximation == 2:
x_sample = sd_vae_approx.model()(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 = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
return Image.fromarray(x_sample)