enhance model_manager support for converting inpainting ckpt files

Previously conversions of .ckpt and .safetensors files to diffusers
models were failing with channel mismatch errors. This is corrected
with this PR.

- The model_manager convert_and_import() method now accepts the path
  to the checkpoint file's configuration file, using the parameter
  `original_config_file`. For inpainting files this should be set to
  the full path to `v1-inpainting-inference.yaml`.

- If no configuration file is provided in the call, then the presence
  of an inpainting file will be inferred at the
  `ldm.ckpt_to_diffuser.convert_ckpt_to_diffUser()` level by looking
  for the string "inpaint" in the path. AUTO1111 does something
  similar to this, but it is brittle and not recommended.

- This PR also changes the model manager model_names() method to return
  the model names in case folded sort order.

ldm/invoke/CLI.py

@@ -712,10 +712,12 @@ def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_des
 def optimize_model(model_name_or_path:str, gen, opt, completer):
     manager = gen.model_manager
     ckpt_path = None
+    original_config_file = None
 
     if (model_info := manager.model_info(model_name_or_path)):
         if 'weights' in model_info:
             ckpt_path = Path(model_info['weights'])
+            original_config_file = Path(model_info['config'])
             model_name = model_name_or_path
             model_description = model_info['description']
         else:
@@ -723,12 +725,18 @@ def optimize_model(model_name_or_path:str, gen, opt, completer):
             return
     elif os.path.exists(model_name_or_path):
         ckpt_path = Path(model_name_or_path)
-        model_name,model_description = _get_model_name_and_desc(
+        model_name, model_description = _get_model_name_and_desc(
             manager,
             completer,
             ckpt_path.stem,
             f'Converted model {ckpt_path.stem}'
         )
+        is_inpainting = input('Is this an inpainting model? [n] ').startswith(('y','Y'))
+        original_config_file = Path(
+            'configs',
+            'stable-diffusion',
+            'v1-inpainting-inference.yaml' if is_inpainting else 'v1-inference.yaml'
+        )
     else:
         print(f'** {model_name_or_path} is neither an existing model nor the path to a .ckpt file')
         return
@@ -736,6 +744,9 @@ def optimize_model(model_name_or_path:str, gen, opt, completer):
     if not ckpt_path.is_absolute():
         ckpt_path = Path(Globals.root,ckpt_path)
 
+    if original_config_file and not original_config_file.is_absolute():
+        original_config_file = Path(Globals.root,original_config_file)
+
     diffuser_path = Path(Globals.root, 'models',Globals.converted_ckpts_dir,model_name)
     if diffuser_path.exists():
         print(f'** {model_name_or_path} is already optimized. Will not overwrite. If this is an error, please remove the directory {diffuser_path} and try again.')
@@ -751,6 +762,7 @@ def optimize_model(model_name_or_path:str, gen, opt, completer):
         model_name=model_name,
         model_description=model_description,
         vae = vae,
+        original_config_file = original_config_file,
         commit_to_conf=opt.conf,
     )
     if not new_config:

ldm/invoke/ckpt_to_diffuser.py

@@ -22,7 +22,11 @@ import re
 import torch
 import warnings
 from pathlib import Path
-from ldm.invoke.globals import Globals, global_cache_dir
+from ldm.invoke.globals import (
+    Globals,
+    global_cache_dir,
+    global_config_dir,
+    )
 from safetensors.torch import load_file
 from typing import Union
 
@@ -826,6 +830,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param upcast_attention: Whether the attention computation should always be upcasted. This is necessary when
     running stable diffusion 2.1.
     '''
+
+    print(f'DEBUG: original_config_file={original_config_file}')
     
     with warnings.catch_warnings():
         warnings.simplefilter('ignore')
@@ -852,13 +858,16 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
 
             if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
-                original_config_file = os.path.join(Globals.root,'configs','stable-diffusion','v2-inference-v.yaml')
+                original_config_file = global_config_dir() / 'stable-diffusion' / 'v2-inference-v.yaml'
 
                 if global_step == 110000:
                     # v2.1 needs to upcast attention
                     upcast_attention = True
+            elif str(checkpoint_path).lower().find('inpaint') >= 0: # brittle - please pass original_config_file parameter!
+                print(f'  | checkpoint has "inpaint" in name, assuming an inpainting model')
+                original_config_file = global_config_dir() / 'stable-diffusion' / 'v1-inpainting-inference.yaml'
             else:
-                original_config_file = os.path.join(Globals.root,'configs','stable-diffusion','v1-inference.yaml')
+                original_config_file = global_config_dir() / 'stable-diffusion' / 'v1-inference.yaml'
 
         original_config = OmegaConf.load(original_config_file)