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removed the option to use 2x more memory when generating previews

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added an option to always only show one image in previews
removed duplicate code
This commit is contained in:
AUTOMATIC
2022-10-22 20:48:13 +03:00
parent 4fdb53c1e9
commit d213d6ca6f
3 changed files with 13 additions and 28 deletions
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33
modules/sd_samplers.py
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@@ -71,6 +71,7 @@ sampler_extra_params = {
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
}
def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not None:
steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
@@ -82,37 +83,21 @@ def setup_img2img_steps(p, steps=None):
return steps, t_enc
def sample_to_image(samples):
x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
def single_sample_to_image(sample):
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)
def sample_to_image(samples):
return single_sample_to_image(samples[0])
def samples_to_image_grid(samples):
progress_images = []
for i in range(len(samples)):
# Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[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)
progress_images.append(Image.fromarray(x_sample))
return images.image_grid([single_sample_to_image(sample) for sample in samples])
return images.image_grid(progress_images)
def samples_to_image_grid_combined(samples):
progress_images = []
# Decode all samples at once to increase speed at the cost of VRAM usage.
x_samples = processing.decode_first_stage(shared.sd_model, samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples:
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
progress_images.append(Image.fromarray(x_sample))
return images.image_grid(progress_images)
def store_latent(decoded):
state.current_latent = decoded