diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8e554bfd1..435df5506 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -123,6 +123,7 @@ For details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/master
   - installer dont cache installed state  
   - fix Cosmos-Predict2 retrying TAESD download  
   - better handle startup import errors  
+  - fix traceback width preventing copy&paste  
   - fix ansi controle output from scripts/extensions  
   - fix diffusers models non-unique hash  
   - fix loading of manually downloaded diffuser models  
diff --git a/installer.py b/installer.py
index ae508d6d2..63232d35c 100644
--- a/installer.py
+++ b/installer.py
@@ -1,3 +1,4 @@
+from typing import List, Optional
 import os
 import sys
 import json
@@ -104,6 +105,15 @@ def install_traceback(suppress: list = []):
 
 # setup console and file logging
 def setup_logging():
+    from functools import partial, partialmethod
+    from logging.handlers import RotatingFileHandler
+    from rich.theme import Theme
+    from rich.logging import RichHandler
+    from rich.console import Console
+    from rich.padding import Padding
+    from rich.segment import Segment
+    from rich import print as rprint
+    from rich.pretty import install as pretty_install
 
     class RingBuffer(logging.StreamHandler):
         def __init__(self, capacity):
@@ -128,6 +138,7 @@ def setup_logging():
         def get(self):
             return self.buffer
 
+
     class LogFilter(logging.Filter):
         def __init__(self):
             super().__init__()
@@ -135,14 +146,35 @@ def setup_logging():
         def filter(self, record):
             return len(record.getMessage()) > 2
 
+    def override_padding(self, console, options):
+        style = console.get_style(self.style)
+        width = options.max_width
+        self.left = 0
+        render_options = options.update_width(width - self.left - self.right)
+        if render_options.height is not None:
+            render_options = render_options.update_height(height=render_options.height - self.top - self.bottom)
+        lines = console.render_lines(self.renderable, render_options, style=style, pad=False)
+        _Segment = Segment
+        left = _Segment(" " * self.left, style) if self.left else None
+        right = [_Segment.line()]
+        blank_line: Optional[List[Segment]] = None
+        if self.top:
+            blank_line = [_Segment(f'{" " * width}\n', style)]
+            yield from blank_line * self.top
+        if left:
+            for line in lines:
+                yield left
+                yield from line
+                yield from right
+        else:
+            for line in lines:
+                yield from line
+                yield from right
+        if self.bottom:
+            blank_line = blank_line or [_Segment(f'{" " * width}\n', style)]
+            yield from blank_line * self.bottom
+
     t_start = time.time()
-    from functools import partial, partialmethod
-    from logging.handlers import RotatingFileHandler
-    from rich.theme import Theme
-    from rich.logging import RichHandler
-    from rich.console import Console
-    from rich import print as rprint
-    from rich.pretty import install as pretty_install
 
     if args.log:
         global log_file # pylint: disable=global-statement
@@ -159,12 +191,14 @@ def setup_logging():
     global console # pylint: disable=global-statement
     theme = Theme({
         "traceback.border": "black",
-        "traceback.border.syntax_error": "black",
         "inspect.value.border": "black",
+        "traceback.border.syntax_error": "dark_red",
         "logging.level.info": "blue_violet",
         "logging.level.debug": "purple4",
         "logging.level.trace": "dark_blue",
     })
+
+    Padding.__rich_console__ = override_padding
     console = Console(
         log_time=True,
         log_time_format='%H:%M:%S-%f',
@@ -173,6 +207,7 @@ def setup_logging():
         safe_box=True,
         theme=theme,
     )
+
     logging.basicConfig(level=logging.ERROR, format='%(asctime)s | %(name)s | %(levelname)s | %(module)s | %(message)s', handlers=[logging.NullHandler()]) # redirect default logger to null
     pretty_install(console=console)
     install_traceback()
diff --git a/modules/interrogate/vqa.py b/modules/interrogate/vqa.py
index 0457af7ea..cf3f5b5ee 100644
--- a/modules/interrogate/vqa.py
+++ b/modules/interrogate/vqa.py
@@ -130,7 +130,7 @@ def qwen(question: str, image: Image.Image, repo: str = None, system_prompt: str
         )
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
         if 'LLM' in shared.opts.cuda_compile:
-            model = compile_torch(model)
+            model = sd_models_compile.compile_torch(model)
         loaded = repo
         devices.torch_gc()
     sd_models.move_model(model, devices.device)
@@ -180,7 +180,7 @@ def gemma(question: str, image: Image.Image, repo: str = None, system_prompt: st
             **quant_args,
         )
         if 'LLM' in shared.opts.cuda_compile:
-            model = compile_torch(model)
+            model = sd_models_compile.compile_torch(model)
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
         loaded = repo
         devices.torch_gc()
@@ -307,7 +307,7 @@ def smol(question: str, image: Image.Image, repo: str = None, system_prompt: str
             )
         processor = transformers.AutoProcessor.from_pretrained(repo, cache_dir=shared.opts.hfcache_dir)
         if 'LLM' in shared.opts.cuda_compile:
-            model = compile_torch(model)
+            model = sd_models_compile.compile_torch(model)
         loaded = repo
         devices.torch_gc()
     sd_models.move_model(model, devices.device)
diff --git a/pipelines/model_chroma.py b/pipelines/model_chroma.py
index a8c5bad42..f8750782b 100644
--- a/pipelines/model_chroma.py
+++ b/pipelines/model_chroma.py
@@ -118,20 +118,18 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant): # pylint: disable=unus
         }
         if 'transformer' not in kwargs and model_quant.check_nunchaku('Model'):
             raise NotImplementedError('Nunchaku does not support Chroma Model yet. See https://github.com/mit-han-lab/nunchaku/issues/167')
-        elif 'transformer' not in kwargs and model_quant.check_quant('Model'):
+        if 'transformer' not in kwargs and model_quant.check_quant('Model'):
             load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model', device_map=True, modules_to_not_convert=["distilled_guidance_layer"])
-            if quant_args:
-                kwargs['transformer'] = diffusers.ChromaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", **load_args, **quant_args)
+            kwargs['transformer'] = diffusers.ChromaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", **load_args, **quant_args)
         if 'text_encoder' not in kwargs and model_quant.check_nunchaku('TE'):
             import nunchaku
             nunchaku_precision = nunchaku.utils.get_precision()
             nunchaku_repo = 'mit-han-lab/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors'
             shared.log.debug(f'Load module: quant=Nunchaku module=t5 repo="{nunchaku_repo}" precision={nunchaku_precision}')
             kwargs['text_encoder'] = nunchaku.NunchakuT5EncoderModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
-        elif 'text_encoder' not in kwargs and model_quant.check_quant('TE'):
+        if 'text_encoder' not in kwargs and model_quant.check_quant('TE'):
             load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
-            if quant_args:
-                kwargs['text_encoder'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder", **load_args, **quant_args)
+            kwargs['text_encoder'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder", **load_args, **quant_args)
     except Exception as e:
         shared.log.error(f'Quantization: {e}')
         errors.display(e, 'Quantization:')
diff --git a/pipelines/model_flux.py b/pipelines/model_flux.py
index 77c0e768e..6ac58a707 100644
--- a/pipelines/model_flux.py
+++ b/pipelines/model_flux.py
@@ -132,10 +132,9 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant): # pylint: disable=unus
                 kwargs['transformer'].quantization_method = 'SVDQuant'
                 if shared.opts.nunchaku_attention:
                     kwargs['transformer'].set_attention_impl("nunchaku-fp16")
-        elif 'transformer' not in kwargs and model_quant.check_quant('Model'):
+        if 'transformer' not in kwargs and model_quant.check_quant('Model'):
             load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model', device_map=True)
-            if quant_args:
-                kwargs['transformer'] = diffusers.FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", **load_args, **quant_args)
+            kwargs['transformer'] = diffusers.FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", **load_args, **quant_args)
         if 'text_encoder_2' not in kwargs and model_quant.check_nunchaku('TE'):
             import nunchaku
             nunchaku_precision = nunchaku.utils.get_precision()
@@ -143,10 +142,9 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant): # pylint: disable=unus
             shared.log.debug(f'Load module: quant=Nunchaku module=t5 repo="{nunchaku_repo}" precision={nunchaku_precision}')
             kwargs['text_encoder_2'] = nunchaku.NunchakuT5EncoderModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
             kwargs['text_encoder_2'].quantization_method = 'SVDQuant'
-        elif 'text_encoder_2' not in kwargs and model_quant.check_quant('TE'):
+        if 'text_encoder_2' not in kwargs and model_quant.check_quant('TE'):
             load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
-            if quant_args:
-                kwargs['text_encoder_2'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2", **load_args, **quant_args)
+            kwargs['text_encoder_2'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2", **load_args, **quant_args)
     except Exception as e:
         shared.log.error(f'Quantization: {e}')
         errors.display(e, 'Quantization:')
diff --git a/pipelines/model_sana.py b/pipelines/model_sana.py
index 7d0b65b62..c0fa486b7 100644
--- a/pipelines/model_sana.py
+++ b/pipelines/model_sana.py
@@ -15,12 +15,10 @@ def load_quants(kwargs, repo_id, cache_dir):
         kwargs['transformer'] = nunchaku.NunchakuSanaTransformer2DModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
     elif model_quant.check_quant('Model'):
         load_args, quant_args = model_quant.get_dit_args(kwargs_copy, module='Model')
-        if quant_args:
-            kwargs['transformer'] = diffusers.SanaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, **load_args, **quant_args)
+        kwargs['transformer'] = diffusers.SanaTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, **load_args, **quant_args)
     if model_quant.check_quant('TE'):
         load_args, quant_args = model_quant.get_dit_args(kwargs_copy, module='TE')
-        if quant_args:
-            kwargs['text_encoder'] = transformers.AutoModelForCausalLM.from_pretrained(repo_id, subfolder="text_encoder", cache_dir=cache_dir, **load_args, **quant_args)
+        kwargs['text_encoder'] = transformers.AutoModelForCausalLM.from_pretrained(repo_id, subfolder="text_encoder", cache_dir=cache_dir, **load_args, **quant_args)
     return kwargs