fix vae decode for backend original

.vscode/settings.json

@@ -8,5 +8,6 @@
     "./repositories/taming"
   ],
   "python.analysis.typeCheckingMode": "off",
-  "editor.formatOnSave": false
+  "editor.formatOnSave": false,
+  "python.REPL.enableREPLSmartSend": false
 }

CHANGELOG.md

@@ -38,7 +38,10 @@ Other improvements:
   improves quality of the flow-matching samplers  
 - t5 support manually downloaded models  
   applies to all models that use t5 transformer  
+
+Fixes:
 - fix handling of model configs if offline config is not available  
+- fix vae decode in backend original
 
 Work-in-progress:
 - flux controlnet support: (*1)

modules/processing_helpers.py

@@ -196,7 +196,8 @@ def decode_first_stage(model, x, full_quality=True):
         try:
             if full_quality:
                 if hasattr(model, 'decode_first_stage'):
-                    x_sample = model.decode_first_stage(x) * 0.5 + 0.5
+                    # x_sample = model.decode_first_stage(x) * 0.5 + 0.5
+                    x_sample = model.decode_first_stage(x)
                 elif hasattr(model, 'vae'):
                     x_sample = processing_vae.vae_decode(latents=x, model=model, output_type='np', full_quality=full_quality)
                 else:

modules/sd_hijack.py

@@ -265,7 +265,6 @@ class EmbeddingsWithFixes(torch.nn.Module):
     def forward(self, input_ids):
         batch_fixes = self.embeddings.fixes
         self.embeddings.fixes = None
-
         inputs_embeds = self.wrapped(input_ids)
 
         if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:

modules/sd_models.py

@@ -737,6 +737,9 @@ def set_diffuser_options(sd_model, vae = None, op: str = 'model', offload=True):
         set_diffuser_offload(sd_model, op)
 
 def set_diffuser_offload(sd_model, op: str = 'model'):
+    if not shared.native:
+        shared.log.warning('Attempting to use offload with backend=original')
+        return
     if sd_model is None:
         shared.log.warning(f'{op} is not loaded')
         return
@@ -837,6 +840,8 @@ def apply_balanced_offload(sd_model):
                     shared.log.error(f'Balanced offload: module={module_name} {e}')
                 devices.torch_gc(fast=True)
 
+    if not shared.native:
+        return
     apply_balanced_offload_to_module(sd_model)
     if hasattr(sd_model, "prior_pipe"):
         apply_balanced_offload_to_module(sd_model.prior_pipe)
@@ -859,6 +864,19 @@ def normalize_device(device):
 def move_model(model, device=None, force=False):
     if model is None or device is None:
         return
+
+    if not shared.native:
+        if type(model).__name__ == 'LatentDiffusion':
+            model = model.to(device)
+            if hasattr(model, 'model'):
+                model.model = model.model.to(device)
+            if hasattr(model, 'first_stage_model'):
+                model.first_stage_model = model.first_stage_model.to(device)
+            if hasattr(model, 'cond_stage_model'):
+                model.cond_stage_model = model.cond_stage_model.to(device)
+        devices.torch_gc()
+        return
+
     if getattr(model, 'vae', None) is not None and get_diffusers_task(model) != DiffusersTaskType.TEXT_2_IMAGE:
         if device == devices.device and model.vae.device.type != "meta": # force vae back to gpu if not in txt2img mode
             model.vae.to(device)