Generate grid preview for progress image

1 files changed, 25 insertions, 1 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index f58a29b9..74a480e5 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -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