diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 58f111c95..424736233 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -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);
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index e76e7d4e8..07495f586 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -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))