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Generate grid preview for progress image

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Unnoen
2022-10-19 21:38:10 +11:00
committed by AUTOMATIC1111
parent 24694e5983
commit 4fdb53c1e9
3 changed files with 30 additions and 2 deletions
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26
modules/sd_samplers.py
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@@ -7,7 +7,7 @@ import inspect
import k_diffusion.sampling
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
from modules import prompt_parser, devices, processing
from modules import prompt_parser, devices, processing, images
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -89,6 +89,30 @@ def sample_to_image(samples):
x_sample = x_sample.astype(np.uint8)
return Image.fromarray(x_sample)
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(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