fix lora unload and improve preview error handler

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2026-02-06
+## Update for 2026-02-07
 
 - **Upscalers**
   - add support for [spandrel](https://github.com/chaiNNer-org/spandrel)  
@@ -19,6 +19,7 @@
   - fix: improve wildcard weights parsing, thanks @Tillerz  
   - fix: ui gallery cace recursive cleanup, thanks @awsr  
   - fix: `anima` model detection  
+  - fix: lora unwanted unload  
 
 ## Update for 2026-02-04
 

extensions-builtin/sdnext-modernui

@@ -1 +1 @@
-Subproject commit ead16e14410ff177e2e4e105bcbec3eaa737de7d
+Subproject commit 2b9f7c293f3a2146b991a943397e7567059e73c8

modules/lora/extra_networks_lora.py

@@ -175,6 +175,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
 
     def changed(self, requested: List[str], include: List[str] = None, exclude: List[str] = None) -> bool:
         if shared.opts.lora_force_reload:
+            debug_log(f'Network check: type=LoRA key="{key}" requested={requested} loaded={loaded} status=forced')
             return True
         sd_model = shared.sd_model.pipe if hasattr(shared.sd_model, 'pipe') else shared.sd_model
         if not hasattr(sd_model, 'loaded_loras'):
@@ -185,14 +186,16 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
             exclude = ['none']
         key = f'include={",".join(include)}:exclude={",".join(exclude)}'
         loaded = sd_model.loaded_loras.get(key, [])
-        debug_log(f'Network check: type=LoRA key="{key}" requested={requested} loaded={loaded}')
         if len(requested) != len(loaded):
             sd_model.loaded_loras[key] = requested
+            debug_log(f'Network check: type=LoRA key="{key}" requested={requested} loaded={loaded} status=changed')
             return True
         for req, load in zip(requested, loaded):
             if req != load:
                 sd_model.loaded_loras[key] = requested
+                debug_log(f'Network check: type=LoRA key="{key}" requested={requested} loaded={loaded} status=changed')
                 return True
+        debug_log(f'Network check: type=LoRA key="{key}" requested={requested} loaded={loaded} status=same')
         return False
 
     def activate(self, p, params_list, step=0, include=[], exclude=[]):
@@ -245,9 +248,8 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
                 shared.log.info(f'Network load: type=LoRA networks={[n.name for n in l.loaded_networks]} method={load_method} mode={"fuse" if shared.opts.lora_fuse_native else "backup"} te={te_multipliers} unet={unet_multipliers} time={l.timer.summary}')
 
     def deactivate(self, p, force=False):
-        if len(lora_diffusers.diffuser_loaded) > 0:
+        if len(lora_diffusers.diffuser_loaded) > 0 and (shared.opts.lora_force_reload or force):
-            if not (shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled is True):
+            unload_diffusers()
-                unload_diffusers()
         if force:
             networks.network_deactivate()
         if self.active and l.debug:

modules/sd_samplers_common.py

@@ -2,7 +2,7 @@ import time
 import threading
 from collections import namedtuple
 import torch
-import torchvision.transforms as T
+import torchvision.transforms.functional as TF
 from PIL import Image
 from modules import shared, devices, processing, images, sd_samplers, timer
 from modules.vae import sd_vae_approx, sd_vae_taesd, sd_vae_stablecascade
@@ -80,12 +80,11 @@ def single_sample_to_image(sample, approximation=None):
             else:
                 if x_sample.shape[0] > 4 or x_sample.shape[0] == 4:
                     return Image.new(mode="RGB", size=(512, 512))
-                if x_sample.dtype == torch.bfloat16:
+                x_sample = torch.nan_to_num(x_sample, nan=0.0, posinf=1, neginf=0)
-                    x_sample = x_sample.to(torch.float16)
+                x_sample = (255.0 * x_sample).to(torch.uint8)
                 if len(x_sample.shape) == 4:
                     x_sample = x_sample[0]
-                transform = T.ToPILImage()
+                image = TF.to_pil_image(x_sample)
-                image = transform(x_sample)
         except Exception as e:
             warn_once(f'Preview: {e}')
             image = Image.new(mode="RGB", size=(512, 512))

modules/vae/sd_vae_taesd.py

@@ -158,13 +158,13 @@ def decode(latents):
         try:
             with devices.inference_context():
                 t0 = time.time()
-                dtype = devices.dtype_vae if devices.dtype_vae != torch.bfloat16 else torch.float16 # taesd does not support bf16
+                dtype = devices.dtype_vae if (devices.dtype_vae != torch.bfloat16) else torch.float16 # taesd does not support bf16
                 tensor = latents.unsqueeze(0) if len(latents.shape) == 3 else latents
                 tensor = tensor.detach().clone().to(devices.device, dtype=dtype)
                 if debug:
                     shared.log.debug(f'Decode: type="taesd" variant="{variant}" input={latents.shape} tensor={tensor.shape}')
                 # Fallback: reshape packed 128-channel latents to 32 channels if not already unpacked
-                if variant == 'TAE FLUX.2' and len(tensor.shape) == 4 and tensor.shape[1] == 128:
+                if (variant == 'TAE FLUX.2') and (len(tensor.shape) == 4) and (tensor.shape[1] == 128):
                     b, _c, h, w = tensor.shape
                     tensor = tensor.reshape(b, 32, h * 2, w * 2)
                 if variant.startswith('TAESD') or variant in {'TAE FLUX.1', 'TAE FLUX.2', 'TAE SD3'}: