llm

backend/text_processing/gemma_engine.py

@@ -119,7 +119,7 @@ class GemmaTextProcessingEngine:
 
             zs.extend(line_z_values)
 
-        return torch.stack(zs)
+        return zs
 
     def process_embeds(self, batch_tokens):
         device = memory_management.text_encoder_device()

backend/text_processing/qwen3_engine.py

@@ -86,7 +86,7 @@ class Qwen3TextProcessingEngine:
 
             zs.extend(line_z_values)
 
-        return torch.stack(zs)
+        return zs
 
     def process_embeds(self, batch_tokens):
         device = memory_management.text_encoder_device()

backend/text_processing/qwen_engine.py

@@ -99,7 +99,7 @@ class QwenTextProcessingEngine:
 
             zs.extend(line_z_values)
 
-        return torch.stack(zs)
+        return zs
 
     def strip_template(self, out, tokens):
         template_end = 0

backend/text_processing/umt5_engine.py

@@ -113,18 +113,18 @@ class UMT5TextProcessingEngine:
                 line_z_values = []
 
                 # pad all chunks to length of longest chunk
-                # max_tokens = 0
-                # for chunk in chunks:
-                #     max_tokens = max(len(chunk.tokens), max_tokens)
+                max_tokens = 0
+                for chunk in chunks:
+                    max_tokens = max(len(chunk.tokens), max_tokens)
 
                 for chunk in chunks:
                     tokens = chunk.tokens
                     multipliers = chunk.multipliers
 
-                    # remaining_count = max_tokens - len(tokens)
-                    # if remaining_count > 0:
-                    #     tokens += [self.pad_token] * remaining_count
-                    #     multipliers += [1.0] * remaining_count
+                    remaining_count = max_tokens - len(tokens)
+                    if remaining_count > 0:
+                        tokens += [self.id_pad] * remaining_count
+                        multipliers += [1.0] * remaining_count
 
                     z = self.process_tokens([tokens], [multipliers])[0]
                     line_z_values.append(z)

modules/prompt_parser.py

@@ -199,12 +199,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps,
             continue
 
         texts = SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts)
-        try:
-            conds = model.get_learned_conditioning(texts)
-        except RuntimeError as e:
-            if "stack expects each tensor to be equal size" in repr(e):
-                raise NotImplementedError("Prompt Editing/Alternating is not supported in LLM Text Encoders")
-            raise e
+        conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
         for i, (end_at_step, _) in enumerate(prompt_schedule):

modules/sd_models.py

@@ -383,6 +383,8 @@ def forge_model_reload():
         _cond = sd_model.get_learned_conditioning(SdConditioning([opts.empty_prompt_template]))
         if isinstance(_cond, dict):
             _cond = DictWithShape(_cond)
+        elif isinstance(_cond, list):
+            _cond = torch.stack(_cond)
 
         sd_model.empty_cond = _cond.to(devices.cpu)
         timer.record("calculate empty prompt")