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

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):
+def single_sample_to_image(sample):
-    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[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)
 
+
+def sample_to_image(samples):
+    return single_sample_to_image(samples[0])
+
+
 def samples_to_image_grid(samples):
-    progress_images = []
+    return images.image_grid([single_sample_to_image(sample) for sample in samples])
-    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

modules/shared.py

@@ -294,7 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
-    "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"),
+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),

modules/ui.py

@@ -318,10 +318,10 @@ def check_progress_call(id_part):
         if shared.parallel_processing_allowed:
 
             if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if opts.progress_decode_combined:
+                if opts.show_progress_grid:
-                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent)
-                else:
                     shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
+                else:
+                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
                 shared.state.current_image_sampling_step = shared.state.sampling_step
 
         image = shared.state.current_image