hotfix to 2.3.4 (#3188)

- Pin diffusers to 0.14
- Small fix to LoRA loading routine that was preventing placement of
LoRA files in subdirectories.
- Bump version to 2.3.4.post1

ldm/invoke/_version.py

@@ -1 +1 @@
-__version__='2.3.4'
+__version__='2.3.4.post1'

ldm/invoke/conditioning.py

@@ -12,7 +12,8 @@ from typing import Union, Optional, Any
 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]
+    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 Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)
+    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:

ldm/invoke/config/invokeai_update.py

@@ -4,14 +4,13 @@ pip install <path_to_git_source>.
 '''
 import os
 import platform
+import psutil
 import requests
 from rich import box, print
-from rich.console import Console, Group, group
+from rich.console import Console, group
 from rich.panel import Panel
 from rich.prompt import Prompt
 from rich.style import Style
-from rich.syntax import Syntax
-from rich.text import Text
 
 from ldm.invoke import __version__
 
@@ -32,6 +31,19 @@ else:
 def get_versions()->dict:
     return requests.get(url=INVOKE_AI_REL).json()
 
+def invokeai_is_running()->bool:
+    for p in psutil.process_iter():
+        try:
+            cmdline = p.cmdline()
+            matches = [x for x in cmdline if x.endswith(('invokeai','invokeai.exe'))]
+            if matches:
+                print(f':exclamation: [bold red]An InvokeAI instance appears to be running as process {p.pid}[/red bold]')
+                return True
+        except psutil.AccessDenied:
+            continue
+    return False
+        
+    
 def welcome(versions: dict):
     
     @group()
@@ -62,6 +74,10 @@ def welcome(versions: dict):
 
 def main():
     versions = get_versions()
+    if invokeai_is_running():
+        print(f':exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]')
+        return
+
     welcome(versions)
 
     tag = None

ldm/invoke/dynamic_prompts.py

@@ -100,8 +100,8 @@ def expand_prompts(
             for command in commands:
                 sequence += 1
                 format = _get_fn_format(outdir, sequence)
-                parent_conn.send(
-                    command + f' --fnformat="{format}"'
+                parent_conn.send_bytes(
+                    (command + f' --fnformat="{format}"').encode('utf-8')
                 )
             parent_conn.close()
         else:
@@ -116,7 +116,10 @@ def _get_fn_format(directory:str, sequence:int)->str:
     Get a filename that doesn't exceed filename length restrictions
     on the current platform.
     """
-    max_length = os.pathconf(directory,'PC_NAME_MAX')
+    try:
+        max_length = os.pathconf(directory,'PC_NAME_MAX')
+    except:
+        max_length = 255
     prefix = f'dp.{sequence:04}.'
     suffix = '.png'
     max_length -= len(prefix)+len(suffix)
@@ -130,7 +133,7 @@ class MessageToStdin(object):
     def readline(self) -> str:
         try:
             if len(self.linebuffer) == 0:
-                message = self.connection.recv()
+                message = self.connection.recv_bytes().decode('utf-8')
                 self.linebuffer = message.split("\n")
             result = self.linebuffer.pop(0)
             return result

ldm/modules/kohya_lora_manager.py

@@ -339,12 +339,14 @@ class KohyaLoraManager:
         return lora
 
     def apply_lora_model(self, name, mult: float = 1.0):
+        path_file = None
         for suffix in ["ckpt", "safetensors", "pt"]:
-            path_file = Path(self.lora_path, f"{name}.{suffix}")
-            if path_file.exists():
+            path_files = [x for x in Path(self.lora_path).glob(f"**/{name}.{suffix}")]
+            if len(path_files):
+                path_file = path_files[0]
                 print(f"   | Loading lora {path_file.name} with weight {mult}")
                 break
-        if not path_file.exists():
+        if not path_file:
             print(f"   ** Unable to find lora: {name}")
             return
 

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
   "clip_anytorch",
   "compel~=1.1.0",
   "datasets",
-  "diffusers[torch]~=0.14",
+  "diffusers[torch]==0.14",
   "dnspython==2.2.1",
   "einops",
   "eventlet",