diff --git a/CHANGELOG.md b/CHANGELOG.md
index fe2edffe8..a7b001187 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,12 +10,11 @@
 - **HunyuanVideo** optimizations: full offload, quantization and tiling support  
 - **LTXVideo** optimizations: full offload, quantization and tiling support  
 - VAE tiling granular options in *settings -> variable auto encoder*  
-- UI: live preview add sigma calculation, thanks @Disty0  
+- UI: live preview optimizations
 - UI: CSS optimizations when log view is disabled  
 - Samplers: add flow shift options and separate dynamic thresholding from dynamic shifting  
 - **Fixes**  
   - do not show disabled networks  
-  - live preview image sizes in modern and standard UI  
   - image width/height calculation when doing img2img  
   - flux pipeline switches: txt/img/inpaint  
   - interrogate caption with T5  
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index c5adbb81f..cd7875377 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -80,7 +80,6 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
     def create_item(self, name):
         l = networks.available_networks.get(name)
         if l is None:
-            print('HERE1')
             shared.log.warning(f'Networks: type=lora registered={len(list(networks.available_networks))} file="{name}" not registered')
             return None
         try:
diff --git a/javascript/progressBar.js b/javascript/progressBar.js
index dfd895f8e..afb9259dc 100644
--- a/javascript/progressBar.js
+++ b/javascript/progressBar.js
@@ -118,7 +118,7 @@ function requestProgress(id_task, progressEl, galleryEl, atEnd = null, onProgres
       const elapsedFromStart = (new Date() - dateStart) / 1000;
       hasStarted |= res.active;
       if (res.completed || (!res.active && (hasStarted || once)) || (elapsedFromStart > 30 && !res.queued && res.progress === prevProgress)) {
-        debug('onProgressEnd', res);
+        // debug('onProgressEnd', res);
         done();
         return;
       }
diff --git a/modules/model_flux.py b/modules/model_flux.py
index 4ba1bb556..7a3fa9e00 100644
--- a/modules/model_flux.py
+++ b/modules/model_flux.py
@@ -157,7 +157,7 @@ def load_quants(kwargs, repo_id, cache_dir):
     if 'transformer' not in kwargs and ('Model' in shared.opts.bnb_quantization or 'Model' in shared.opts.torchao_quantization):
         kwargs['transformer'] = diffusers.FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
         shared.log.debug(f'Quantization: module=transformer type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
-    if 'text_encoder_3' not in kwargs and ('Text Encoder' in shared.opts.bnb_quantization or 'Text Encoder' in shared.opts.torchao_quantization):
+    if 'text_encoder_2' not in kwargs and ('Text Encoder' in shared.opts.bnb_quantization or 'Text Encoder' in shared.opts.torchao_quantization):
         kwargs['text_encoder_2'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
         shared.log.debug(f'Quantization: module=t5 type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
     return kwargs
diff --git a/modules/model_sana.py b/modules/model_sana.py
index 7dc551a6f..79a13592d 100644
--- a/modules/model_sana.py
+++ b/modules/model_sana.py
@@ -18,12 +18,11 @@ def load_quants(kwargs, repo_id, cache_dir):
     if not quant_args:
         return kwargs
     load_args = kwargs.copy()
-    model_quant.load_bnb(f'Load model: type=SD3 quant={quant_args} args={load_args}')
-    if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
+    if 'transformer' not in kwargs and ('Model' in shared.opts.bnb_quantization or 'Model' in shared.opts.torchao_quantization):
         kwargs['transformer'] = diffusers.models.SanaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, **load_args, **quant_args)
         shared.log.debug(f'Quantization: module=transformer type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
-    if 'Text Encoder' in shared.opts.bnb_quantization and 'text_encoder_3' not in kwargs:
-        kwargs['text_encoder_3'] = transformers.AutoModelForCausalLM.from_pretrained(repo_id, subfolder="text_encoder", cache_dir=cache_dir, **load_args, **quant_args)
+    if 'text_encoder' not in kwargs and ('Text Encoder' in shared.opts.bnb_quantization or 'Text Encoder' in shared.opts.torchao_quantization):
+        kwargs['text_encoder'] = transformers.AutoModelForCausalLM.from_pretrained(repo_id, subfolder="text_encoder", cache_dir=cache_dir, **load_args, **quant_args)
         shared.log.debug(f'Quantization: module=t5 type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
     return kwargs
 
@@ -55,9 +54,9 @@ def load_sana(checkpoint_info, kwargs={}):
         kwargs['variant'] = 'fp16'
 
     if (fn is None) or (not os.path.exists(fn) or os.path.isdir(fn)):
-        kwargs = load_quants(kwargs, repo_id, cache_dir=shared.opts.diffusers_dir)
-    # kwargs = model_quant.create_bnb_config(kwargs)
-    # kwargs = model_quant.create_ao_config(kwargs)
+        # TODO sana: fails when quantized
+        # kwargs = load_quants(kwargs, repo_id, cache_dir=shared.opts.diffusers_dir)
+        pass
     shared.log.debug(f'Load model: type=Sana repo="{repo_id}" args={list(kwargs)}')
     t0 = time.time()
     pipe = diffusers.SanaPipeline.from_pretrained(repo_id, cache_dir=shared.opts.diffusers_dir, **kwargs)
diff --git a/modules/model_sd3.py b/modules/model_sd3.py
index d0e23026b..bf8644284 100644
--- a/modules/model_sd3.py
+++ b/modules/model_sd3.py
@@ -65,7 +65,7 @@ def load_quants(kwargs, repo_id, cache_dir):
     if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
         kwargs['transformer'] = diffusers.SD3Transformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
         shared.log.debug(f'Quantization: module=transformer type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
-    if 'Text Encoder' in shared.opts.bnb_quantization and 'text_encoder_3' not in kwargs:
+    if 'text_encoder_3' not in kwargs and ('Text Encoder' in shared.opts.bnb_quantization or 'Text Encoder' in shared.opts.torchao_quantization):
         kwargs['text_encoder_3'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_3", variant='fp16', cache_dir=cache_dir, torch_dtype=devices.dtype, **quant_args)
         shared.log.debug(f'Quantization: module=t5 type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
     return kwargs
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index a90ceec27..c4a2712f3 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -71,6 +71,8 @@ def single_sample_to_image(sample, approximation=None):
             warn_once(f"Unknown latent decode type: {approximation}")
             return Image.new(mode="RGB", size=(512, 512))
         try:
+            if x_sample.shape[0] > 4:
+                return Image.new(mode="RGB", size=(512, 512))
             if x_sample.dtype == torch.bfloat16:
                 x_sample.to(torch.float16)
             transform = T.ToPILImage()
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index a1959817c..4507ee3c8 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -153,8 +153,9 @@ def decode(latents):
         if not previous_warnings:
             previous_warnings = True
             shared.log.warning(f'TAESD unsupported model type: {model_class}')
-        return Image.new('RGB', (8, 8), color = (0, 0, 0))
-    vae = taesd_models[f'{model_class}-decoder']
+        # return Image.new('RGB', (8, 8), color = (0, 0, 0))
+        return latents
+    vae = taesd_models.get(f'{model_class}-decoder', None)
     if vae is None:
         model_path = os.path.join(paths.models_path, "TAESD", f"tae{model_class}_decoder.pth")
         download_model(model_path)
diff --git a/modules/shared_state.py b/modules/shared_state.py
index c773b36a9..024427f09 100644
--- a/modules/shared_state.py
+++ b/modules/shared_state.py
@@ -1,7 +1,7 @@
 import os
 import time
 import datetime
-from modules.errors import log
+from modules.errors import log, display
 
 
 class State:
@@ -164,17 +164,20 @@ class State:
         try:
             sample = self.current_latent
             self.current_image_sampling_step = self.sampling_step
+            """
             if self.current_noise_pred is not None and self.current_sigma is not None and self.current_sigma_next is not None:
                 original_sample = sample - (self.current_noise_pred * (self.current_sigma_next-self.current_sigma))
+                # RuntimeError: The size of tensor a (128) must match the size of tensor b (64) at non-singleton dimension 3
                 if self.prediction_type in {"epsilon", "flow_prediction"}:
                     sample = original_sample - (self.current_noise_pred * self.current_sigma)
                 elif self.prediction_type == "v_prediction":
                     sample = self.current_noise_pred * (-self.current_sigma / (self.current_sigma**2 + 1) ** 0.5) + (original_sample / (self.current_sigma**2 + 1)) # pylint: disable=invalid-unary-operand-type
+            """
             image = sd_samplers.samples_to_image_grid(sample) if shared.opts.show_progress_grid else sd_samplers.sample_to_image(sample)
             self.assign_current_image(image)
-        except Exception:
-            # log.error(f'Error setting current image: step={self.sampling_step} {e}')
-            pass
+        except Exception as e:
+            log.error(f'State image: last={self.id_live_preview} step={self.sampling_step} {e}')
+            display(e, 'State image')
         self.preview_busy = False
 
     def assign_current_image(self, image):
diff --git a/scripts/ltxvideo.py b/scripts/ltxvideo.py
index d4f62bc2b..7b12a3a86 100644
--- a/scripts/ltxvideo.py
+++ b/scripts/ltxvideo.py
@@ -26,10 +26,10 @@ def load_quants(kwargs, repo_id):
     if not quant_args:
         return kwargs
     model_quant.load_bnb(f'Load model: type=LTX quant={quant_args}')
-    if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
+    if 'transformer' not in kwargs and ('Model' in shared.opts.bnb_quantization or 'Model' in shared.opts.torchao_quantization):
         kwargs['transformer'] = diffusers.LTXVideoTransformer3DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=shared.opts.hfcache_dir, torch_dtype=devices.dtype, **quant_args)
         shared.log.debug(f'Quantization: module=transformer type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
-    if 'Text Encoder' in shared.opts.bnb_quantization and 'text_encoder_3' not in kwargs:
+    if 'text_encoder' not in kwargs and ('Text Encoder' in shared.opts.bnb_quantization or 'Text Encoder' in shared.opts.torchao_quantization):
         kwargs['text_encoder'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder", cache_dir=shared.opts.hfcache_dir, torch_dtype=devices.dtype, **quant_args)
         shared.log.debug(f'Quantization: module=t5 type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
     return kwargs