use heuristic to select RAM cache size during headless install; blackified

installer/lib/installer.py

@@ -344,12 +344,12 @@ class InvokeAiInstance:
                 auto_install = True
         sys.argv = new_argv
 
-        import requests  # to catch download exceptions
         import messages
+        import requests  # to catch download exceptions
 
         auto_install = auto_install or messages.user_wants_auto_configuration()
         if auto_install:
-            sys.argv.append('--yes')
+            sys.argv.append("--yes")
         else:
             messages.introduction()
 

installer/lib/messages.py

@@ -7,7 +7,7 @@ import os
 import platform
 from pathlib import Path
 
-from prompt_toolkit import prompt, HTML
+from prompt_toolkit import HTML, prompt
 from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
@@ -97,13 +97,17 @@ def user_wants_auto_configuration() -> bool:
             padding=(1, 1),
         )
     )
-    choice = prompt(HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
+    choice = (
+        prompt(
+            HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
             validator=Validator.from_callable(
-                        lambda n: n=='' or n.startswith(('a', 'A', 'm', 'M')),
-                        error_message="Please select 'a' or 'm'"
+                lambda n: n == "" or n.startswith(("a", "A", "m", "M")), error_message="Please select 'a' or 'm'"
             ),
-                ) or 'a'
-    return choice.lower().startswith('a')
+        )
+        or "a"
+    )
+    return choice.lower().startswith("a")
+
 
 def dest_path(dest=None) -> Path:
     """

invokeai/backend/install/invokeai_configure.py

@@ -70,7 +70,6 @@ def get_literal_fields(field) -> list[Any]:
 config = InvokeAIAppConfig.get_config()
 
 Model_dir = "models"
-
 Default_config_file = config.model_conf_path
 SD_Configs = config.legacy_conf_path
 
@@ -458,7 +457,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
         )
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
-            name="Model RAM cache size (GB). Make this at least large enough to hold a single full model.",
+            name="Model RAM cache size (GB). Make this at least large enough to hold a single full model (2GB for SD-1, 6GB for SDXL).",
             begin_entry_at=0,
             editable=False,
             color="CONTROL",
@@ -651,8 +650,19 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
     return editApp.new_opts()
 
 
+def default_ramcache() -> float:
+    """Run a heuristic for the default RAM cache based on installed RAM."""
+
+    # Note that on my 64 GB machine, psutil.virtual_memory().total gives 62 GB,
+    # So we adjust everthing down a bit.
+    return (
+        15.0 if MAX_RAM >= 60 else 7.5 if MAX_RAM >= 30 else 4 if MAX_RAM >= 14 else 2.1
+    )  # 2.1 is just large enough for sd 1.5 ;-)
+
+
 def default_startup_options(init_file: Path) -> Namespace:
     opts = InvokeAIAppConfig.get_config()
+    opts.ram = default_ramcache()
     return opts
 
 

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -175,10 +175,7 @@ class InvokeAIDiffuserComponent:
                                 dim=0,
                             ),
                         }
-                    (
-                        encoder_hidden_states,
-                        encoder_attention_mask,
-                    ) = self._concat_conditionings_for_batch(
+                    (encoder_hidden_states, encoder_attention_mask,) = self._concat_conditionings_for_batch(
                         conditioning_data.unconditioned_embeddings.embeds,
                         conditioning_data.text_embeddings.embeds,
                     )
@@ -240,10 +237,7 @@ class InvokeAIDiffuserComponent:
         wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
 
         if wants_cross_attention_control:
-            (
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_cross_attention_controlled_conditioning(
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_cross_attention_controlled_conditioning(
                 sample,
                 timestep,
                 conditioning_data,
@@ -251,10 +245,7 @@ class InvokeAIDiffuserComponent:
                 **kwargs,
             )
         elif self.sequential_guidance:
-            (
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_standard_conditioning_sequentially(
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning_sequentially(
                 sample,
                 timestep,
                 conditioning_data,
@@ -262,10 +253,7 @@ class InvokeAIDiffuserComponent:
             )
 
         else:
-            (
-                unconditioned_next_x,
-                conditioned_next_x,
-            ) = self._apply_standard_conditioning(
+            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning(
                 sample,
                 timestep,
                 conditioning_data,

invokeai/backend/training/textual_inversion_training.py

@@ -470,10 +470,7 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
+            (h, w,) = (
                 img.shape[0],
                 img.shape[1],
             )