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
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

View File

@ -71,6 +71,7 @@ sampler_extra_params = {
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'], 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
} }
def setup_img2img_steps(p, steps=None): def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not 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 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 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 = 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 = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8) x_sample = x_sample.astype(np.uint8)
return Image.fromarray(x_sample) return Image.fromarray(x_sample)
def sample_to_image(samples):
return single_sample_to_image(samples[0])
def samples_to_image_grid(samples): 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): def store_latent(decoded):
state.current_latent = decoded state.current_latent = decoded

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@ -294,7 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
options_templates.update(options_section(('ui', "User interface"), { options_templates.update(options_section(('ui', "User interface"), {
"show_progressbar": OptionInfo(True, "Show progressbar"), "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}), "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"), "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"), "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"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),

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@ -318,10 +318,10 @@ def check_progress_call(id_part):
if shared.parallel_processing_allowed: 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 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) 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 shared.state.current_image_sampling_step = shared.state.sampling_step
image = shared.state.current_image image = shared.state.current_image