remove redundant prediction_type and attention_upscaling flags

invokeai/backend/install/invokeai_configure.py

@@ -631,8 +631,8 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
                                    }
                                   )
                         )
-    with open(root / 'invokeai.yaml','w') as f:
-        f.write('#empty invokeai.yaml initialization file')
+#    with open(root / 'invokeai.yaml','w') as f:
+#        f.write('#empty invokeai.yaml initialization file')
 
 # -------------------------------------
 def run_console_ui(

invokeai/backend/install/model_install_backend.py

@@ -3,8 +3,6 @@ Utility (backend) functions used by model_install.py
 """
 import os
 import shutil
-import sys
-import traceback
 import warnings
 from dataclasses import dataclass,field
 from pathlib import Path
@@ -12,10 +10,9 @@ from tempfile import TemporaryDirectory
 from typing import List, Dict, Callable, Union, Set
 
 import requests
-from diffusers import AutoencoderKL, StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
-from omegaconf.dictconfig import DictConfig
 from tqdm import tqdm
 
 import invokeai.configs as configs
@@ -24,7 +21,6 @@ from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType
 from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo
 from invokeai.backend.util import download_with_resume
-from ..stable_diffusion import StableDiffusionGeneratorPipeline
 from ..util.logging import InvokeAILogger
 
 warnings.filterwarnings("ignore")
@@ -290,7 +286,7 @@ class ModelInstall(object):
                         location = self._download_hf_model(repo_id, files, staging)
                         break
                     elif f'learned_embeds.{suffix}' in files:
-                        location = self._download_hf_model(repo_id, [f'learned_embeds.suffix'], staging)
+                        location = self._download_hf_model(repo_id, ['learned_embeds.suffix'], staging)
                         break
             if not location:
                 logger.warning(f'Could not determine type of repo {repo_id}. Skipping install.')
@@ -307,7 +303,6 @@ class ModelInstall(object):
             self._install_path(dest, info)
 
     def _make_attributes(self, path: Path, info: ModelProbeInfo)->dict:
-
         # convoluted way to retrieve the description from datasets
         description = f'{info.base_type.value} {info.model_type.value} model'
         if key := self.reverse_paths.get(self.current_id):
@@ -320,18 +315,7 @@ class ModelInstall(object):
             model_format = info.format,
             )
         if info.model_type == ModelType.Pipeline:
-            attributes.update(
-                              dict(
-                                  variant = info.variant_type,
-                              )
-            )
-            if info.base_type == BaseModelType.StableDiffusion2:
-                attributes.update(
-                    dict(
-                        prediction_type = info.prediction_type,
-                        upcast_attention = info.prediction_type == SchedulerPredictionType.VPrediction,
-                    )
-                )
+            attributes.update(dict(variant = info.variant_type,))
             if info.format=="checkpoint":
                 try:
                     legacy_conf = LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type] if info.base_type == BaseModelType.StableDiffusion2 \

invokeai/backend/model_management/models/stable_diffusion.py

@@ -131,17 +131,12 @@ class StableDiffusion2Model(DiffusersModel):
         model_format: Literal[StableDiffusion2ModelFormat.Diffusers]
         vae: Optional[str] = Field(None)
         variant: ModelVariantType
-        prediction_type: SchedulerPredictionType
-        upcast_attention: bool
 
     class CheckpointConfig(ModelConfigBase):
         model_format: Literal[StableDiffusion2ModelFormat.Checkpoint]
         vae: Optional[str] = Field(None)
-        config: Optional[str] = Field(None)
+        config: str
         variant: ModelVariantType
-        prediction_type: SchedulerPredictionType
-        upcast_attention: bool
-
 
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         assert base_model == BaseModelType.StableDiffusion2