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

added an option to always only show one image in previews
removed duplicate code

1 files changed, 10 insertions, 25 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 74a480e5..0b408a70 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -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(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)

+    return images.image_grid([single_sample_to_image(sample) for sample in samples])

+

 

 def store_latent(decoded):

     state.current_latent = decoded