Merge branch 'v2.3' into bugfix/detect-running-invoke-before-updating

Author: lstein

ldm/invoke/conditioning.py

@@ -12,7 +12,8 @@
 from transformers import CLIPTokenizer
 
 from compel import Compel
-from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser
+from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser, \
+    Conjunction
 from .devices import torch_dtype
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ldm.invoke.globals import Globals
@@ -55,22 +56,25 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
+
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_prompt: FlattenedPrompt|Blend
-    lora_conditions = None
+    positive_conjunction: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
         positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-        positive_prompt = positive_conjunction.prompts[0]
-        should_use_lora_manager = True
-        lora_weights = positive_conjunction.lora_weights
-        if model.peft_manager:
-            should_use_lora_manager = model.peft_manager.should_use(lora_weights)
-            if not should_use_lora_manager:
-                model.peft_manager.set_loras(lora_weights)
-        if model.lora_manager and should_use_lora_manager:
-            lora_conditions = model.lora_manager.set_loras_conditions(lora_weights)
+    positive_prompt = positive_conjunction.prompts[0]
+
+    should_use_lora_manager = True
+    lora_weights = positive_conjunction.lora_weights
+    lora_conditions = None
+    if model.peft_manager:
+        should_use_lora_manager = model.peft_manager.should_use(lora_weights)
+        if not should_use_lora_manager:
+            model.peft_manager.set_loras(lora_weights)
+    if model.lora_manager and should_use_lora_manager:
+        lora_conditions = model.lora_manager.set_loras_conditions(lora_weights)
+
     negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
     negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
 
@@ -93,12 +97,12 @@ def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = Fals
     Union[FlattenedPrompt, Blend], FlattenedPrompt):
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_prompt: FlattenedPrompt|Blend
+    positive_conjunction: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
         positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-        positive_prompt = positive_conjunction.prompts[0]
+    positive_prompt = positive_conjunction.prompts[0]
     negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
     negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
 
@@ -217,18 +221,26 @@ def log_tokenization_for_text(text, tokenizer, display_label=None):
         print(f'{discarded}\x1b[0m')
 
 
-def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Blend]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Conjunction]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
-    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
-
-    return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)
+    flattened_prompts = []
+    weights = []
+    loras = []
+    for i, x in enumerate(parsed_conjunctions):
+        if len(x.prompts)>0:
+            flattened_prompts.append(x.prompts[0])
+            weights.append(weighted_subprompts[i][1])
+        if len(x.lora_weights)>0:
+            loras.extend(x.lora_weights)
+
+    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)],
+                       lora_weights = loras)
 
 
 def split_weighted_subprompts(text, skip_normalize=False)->list: