Merge branch 'main' into bugfix/trim-whitespace-from-urls

invokeai/frontend/install/model_install.py

@@ -20,6 +20,7 @@ from multiprocessing import Process
 from multiprocessing.connection import Connection, Pipe
 from pathlib import Path
 from shutil import get_terminal_size
+from typing import Optional
 
 import npyscreen
 import torch
@@ -630,21 +631,23 @@ def ask_user_for_prediction_type(model_path: Path, tui_conn: Connection = None)
         return _ask_user_for_pt_cmdline(model_path)
 
 
-def _ask_user_for_pt_cmdline(model_path: Path) -> SchedulerPredictionType:
+def _ask_user_for_pt_cmdline(model_path: Path) -> Optional[SchedulerPredictionType]:
     choices = [SchedulerPredictionType.Epsilon, SchedulerPredictionType.VPrediction, None]
     print(
         f"""
-Please select the type of the V2 checkpoint named {model_path.name}:
-[1] A model based on Stable Diffusion v2 trained on 512 pixel images (SD-2-base)
-[2] A model based on Stable Diffusion v2 trained on 768 pixel images (SD-2-768)
-[3] Skip this model and come back later.
+Please select the scheduler prediction type of the checkpoint named {model_path.name}:
+[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
+[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
+[3] Accept the best guess;  you can fix it in the Web UI later
 """
     )
     choice = None
     ok = False
     while not ok:
         try:
-            choice = input("select> ").strip()
+            choice = input("select [3]> ").strip()
+            if not choice:
+                return None
             choice = choices[int(choice) - 1]
             ok = True
         except (ValueError, IndexError):
@@ -655,7 +658,6 @@ Please select the type of the V2 checkpoint named {model_path.name}:
 
 
 def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPredictionType:
-    try:
     tui_conn.send_bytes(f"*need v2 config for:{model_path}".encode("utf-8"))
     # note that we don't do any status checking here
     response = tui_conn.recv_bytes().decode("utf-8")
@@ -665,12 +667,9 @@ def _ask_user_for_pt_tui(model_path: Path, tui_conn: Connection) -> SchedulerPre
         return SchedulerPredictionType.epsilon
     elif response == "v":
         return SchedulerPredictionType.VPrediction
-        elif response == "abort":
-            logger.info("Conversion aborted")
+    elif response == "guess":
         return None
     else:
-            return response
-    except Exception:
         return None
 
 

invokeai/frontend/install/widgets.py

@@ -381,12 +381,12 @@ def select_stable_diffusion_config_file(
     wrap: bool = True,
     model_name: str = "Unknown",
 ):
-    message = f"Please select the correct base model for the V2 checkpoint named '{model_name}'. Press <CANCEL> to skip installation."
+    message = f"Please select the correct prediction type for the checkpoint named '{model_name}'. Press <CANCEL> to skip installation."
     title = "CONFIG FILE SELECTION"
     options = [
-        "An SD v2.x base model (512 pixels; no 'parameterization:' line in its yaml file)",
-        "An SD v2.x v-predictive model (768 pixels; 'parameterization: \"v\"' line in its yaml file)",
-        "Skip installation for now and come back later",
+        "'epsilon' - most v1.5 models and v2 models trained on 512 pixel images",
+        "'vprediction' - v2 models trained on 768 pixel images and a few v1.5 models)",
+        "Accept the best guess; you can fix it in the Web UI later",
     ]
 
     F = ConfirmCancelPopup(
@@ -410,7 +410,7 @@ def select_stable_diffusion_config_file(
     choice = F.add(
         npyscreen.SelectOne,
         values=options,
-        value=[0],
+        value=[2],
         max_height=len(options) + 1,
         scroll_exit=True,
     )
@@ -420,5 +420,5 @@ def select_stable_diffusion_config_file(
     if not F.value:
         return None
     assert choice.value[0] in range(0, 3), "invalid choice"
-    choices = ["epsilon", "v", "abort"]
+    choices = ["epsilon", "v", "guess"]
     return choices[choice.value[0]]