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mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
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optimize live preview

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This commit is contained in:
Vladimir Mandic
2023-06-09 12:29:50 -04:00
parent 0109052fdd
commit e8e260408f
2 changed files with 9 additions and 7 deletions
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javascript/progressbar.js
+2 -1
View File
@@ -116,6 +116,7 @@ function requestProgress(id_task, progressEl, galleryEl, atEnd = null, onProgres
};
const start = (id_task, id_live_preview) => {
console.log('HERE start', id_task, id_live_preview, opts.live_preview_refresh_period)
request('./internal/progress', { id_task, id_live_preview }, (res) => {
lastState = res;
const elapsedFromStart = (new Date() - dateStart) / 1000;
@@ -128,7 +129,7 @@ function requestProgress(id_task, progressEl, galleryEl, atEnd = null, onProgres
if (res.live_preview && !livePreview) init();
if (res.live_preview && galleryEl) img.src = res.live_preview;
if (onProgress) onProgress(res);
setTimeout(() => start(id_task, res.id_live_preview), opts.live_preview_refresh_period || 250);
setTimeout(() => start(id_task, id_live_preview), opts.live_preview_refresh_period || 250);
}, done);
};
start(id_task, 0);
modules/sd_samplers_common.py
+7 -6
View File
@@ -28,7 +28,9 @@ approximation_indexes = {"Full VAE": 0, "Approximate NN": 1, "Approximate simple
def single_sample_to_image(sample, approximation=None):
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 1:
if approximation == 0:
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
elif approximation == 2:
x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
@@ -36,10 +38,10 @@ def single_sample_to_image(sample, approximation=None):
x_sample = sample * 1.5
x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
else:
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
shared.log.warning(f"Unknown image decode type: {approximation}")
return Image.new(mode="RGB", size=(512, 512))
x_sample = torch.clamp(255 * x_sample, min=0.0, max=255).cpu()
x_sample = np.moveaxis(x_sample.numpy(), 0, 2).astype(np.uint8)
return Image.fromarray(x_sample)
@@ -53,7 +55,6 @@ def samples_to_image_grid(samples, approximation=None):
def store_latent(decoded):
state.current_latent = decoded
if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
if not shared.parallel_processing_allowed:
shared.state.assign_current_image(sample_to_image(decoded))