error handling of meta embeds

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

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2025-12-22
+## Update for 2025-12-23
 
 - **Models**
   - [LongCat Image](https://github.com/meituan-longcat/LongCat-Image) in *Image* and *Image Edit* variants  
@@ -31,6 +31,7 @@
   - torch.compile skip offloading steps  
   - kanvas css with standardui  
   - control input media with non-english locales  
+  - handle embeds when on meta device  
 
 ## Update for 2025-12-11
 

modules/processing_args.py

@@ -237,6 +237,11 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
             embeds = prompt_parser_diffusers.embedder('prompt_embeds')
             if embeds is None:
                 shared.log.warning('Prompt parser encode: empty prompt embeds')
+                prompt_parser_diffusers.embedder = None
+                args['prompt'] = prompts
+            elif embeds.device == torch.device('meta'):
+                shared.log.warning('Prompt parser encode: embeds on meta device')
+                prompt_parser_diffusers.embedder = None
                 args['prompt'] = prompts
             else:
                 args['prompt_embeds'] = embeds
@@ -273,6 +278,7 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
                 args['negative_prompt'] = negative_prompts[0]
             else:
                 args['negative_prompt'] = negative_prompts
+
     if 'complex_human_instruction' in possible:
         chi = shared.opts.te_complex_human_instruction
         p.extra_generation_params["CHI"] = chi

modules/sd_models.py

@@ -184,6 +184,23 @@ def set_diffuser_options(sd_model, vae=None, op:str='model', offload:bool=True,
 
 
 def move_model(model, device=None, force=False):
+    def set_execution_device(module, device):
+        if device == torch.device('cpu'):
+            return
+        if hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device"): # pylint: disable=protected-access
+            try:
+                """
+                for k, v in module.named_parameters(recurse=True):
+                    if v.device == torch.device('meta'):
+                        from accelerate.utils import set_module_tensor_to_device
+                        set_module_tensor_to_device(module, k, device, tied_params_map=module._hf_hook.tied_params_map)
+                """
+                module._hf_hook.execution_device = device # pylint: disable=protected-access
+                # module._hf_hook.offload = True
+            except Exception as e:
+                if os.environ.get('SD_MOVE_DEBUG', None):
+                    shared.log.error(f'Model move execution device: device={device} {e}')
+
     if model is None or device is None:
         return
 
@@ -204,20 +221,20 @@ def move_model(model, device=None, force=False):
             if not isinstance(m, torch.nn.Module) or name in model._exclude_from_cpu_offload: # pylint: disable=protected-access
                 continue
             for module in m.modules():
-                if (hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None): # pylint: disable=protected-access
-                    try:
-                        module._hf_hook.execution_device = device # pylint: disable=protected-access
-                    except Exception as e:
-                        if os.environ.get('SD_MOVE_DEBUG', None):
-                            shared.log.error(f'Model move execution device: device={device} {e}')
+                set_execution_device(module, device)
+    # set_execution_device(model, device)
+
     if getattr(model, 'has_accelerate', False) and not force:
         return
     if hasattr(model, "device") and devices.normalize_device(model.device) == devices.normalize_device(device) and not force:
         return
+
     try:
         t0 = time.time()
         try:
-            if hasattr(model, 'to'):
+            if model.device == torch.device('meta'):
+                set_execution_device(model, device)
+            elif hasattr(model, 'to'):
                 model.to(device)
             if hasattr(model, "prior_pipe"):
                 model.prior_pipe.to(device)

pipelines/generic.py

@@ -56,6 +56,7 @@ def load_transformer(repo_id, cls_name, load_config=None, subfolder="transformer
             shared.log.debug(f'Load model: transformer="{local_file}" cls={cls_name.__name__} quant="{quant_type}" loader={_loader("diffusers")} args={load_args}')
             if dtype is not None:
                 load_args['torch_dtype'] = dtype
+            load_args.pop('device_map', None) # single-file uses different syntax
             loader = cls_name.from_single_file if hasattr(cls_name, 'from_single_file') else cls_name.from_pretrained
             transformer = loader(
                 local_file,