Merge branch 'invoke-ai:main' into main

ldm/invoke/_version.py

@@ -1 +1 @@
-__version__='2.3.0-rc5'
+__version__='2.3.0-rc6'

ldm/invoke/config/invokeai_configure.py

@@ -10,6 +10,7 @@ print("Loading Python libraries...\n")
 import argparse
 import io
 import os
+import re
 import shutil
 import sys
 import traceback
@@ -320,7 +321,7 @@ You may re-run the configuration script again in the future if you do not wish t
         while again:
             try:
                 access_token = getpass_asterisk.getpass_asterisk(prompt="HF Token ❯ ")
-                if access_token is None or len(access_token)==0:
+                if access_token is None or len(access_token) == 0:
                     raise EOFError
                 HfLogin(access_token)
                 access_token = HfFolder.get_token()
@@ -379,7 +380,7 @@ def download_weight_datasets(
     migrate_models_ckpt()
     successful = dict()
     for mod in models.keys():
-        print(f"{mod}...", file=sys.stderr, end="")
+        print(f"Downloading {mod}:")
         successful[mod] = _download_repo_or_file(
             Datasets[mod], access_token, precision=precision
         )
@@ -532,7 +533,7 @@ def update_config_file(successfully_downloaded: dict, opt: dict):
         configs_dest = Default_config_file.parent
         shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
 
-    yaml = new_config_file_contents(successfully_downloaded, config_file)
+    yaml = new_config_file_contents(successfully_downloaded, config_file, opt)
 
     try:
         backup = None
@@ -568,7 +569,7 @@ def update_config_file(successfully_downloaded: dict, opt: dict):
 
 
 # ---------------------------------------------
-def new_config_file_contents(successfully_downloaded: dict, config_file: Path) -> str:
+def new_config_file_contents(successfully_downloaded: dict, config_file: Path, opt: dict) -> str:
     if config_file.exists():
         conf = OmegaConf.load(str(config_file.expanduser().resolve()))
     else:
@@ -576,7 +577,14 @@ def new_config_file_contents(successfully_downloaded: dict, config_file: Path) -
 
     default_selected = None
     for model in successfully_downloaded:
-        stanza = conf[model] if model in conf else {}
+
+        # a bit hacky - what we are doing here is seeing whether a checkpoint
+        # version of the model was previously defined, and whether the current
+        # model is a diffusers (indicated with a path)
+        if conf.get(model) and Path(successfully_downloaded[model]).is_dir():
+            offer_to_delete_weights(model, conf[model], opt.yes_to_all)
+            
+        stanza = {}
         mod = Datasets[model]
         stanza["description"] = mod["description"]
         stanza["repo_id"] = mod["repo_id"]
@@ -599,8 +607,8 @@ def new_config_file_contents(successfully_downloaded: dict, config_file: Path) -
                 )
             else:
                 stanza["vae"] = mod["vae"]
-        if mod.get('default',False):
-            stanza['default'] = True
+        if mod.get("default", False):
+            stanza["default"] = True
             default_selected = True
 
         conf[model] = stanza
@@ -612,7 +620,22 @@ def new_config_file_contents(successfully_downloaded: dict, config_file: Path) -
 
     return OmegaConf.to_yaml(conf)
 
-
+# ---------------------------------------------
+def offer_to_delete_weights(model_name: str, conf_stanza: dict, yes_to_all: bool):
+    if not (weights := conf_stanza.get('weights')):
+        return
+    if re.match('/VAE/',conf_stanza.get('config')):
+        return
+    if yes_to_all or \
+       yes_or_no(f'\n** The checkpoint version of {model_name} is superseded by the diffusers version. Delete the original file {weights}?', default_yes=False):
+        weights = Path(weights)
+        if not weights.is_absolute():
+            weights = Path(Globals.root) / weights
+        try:
+            weights.unlink()
+        except OSError as e:
+            print(str(e))
+    
 # ---------------------------------------------
 # this will preload the Bert tokenizer fles
 def download_bert():
@@ -641,7 +664,8 @@ def download_from_hf(
         resume_download=True,
         **kwargs,
     )
-    return path if model else None
+    model_name = '--'.join(('models',*model_name.split('/')))
+    return path / model_name if model else None
 
 
 # ---------------------------------------------

ldm/invoke/generator/diffusers_pipeline.py

@@ -317,7 +317,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 # fix is in https://github.com/kulinseth/pytorch/pull/222 but no idea when it will get merged to pytorch mainline.
                 pass
             else:
-                self.enable_attention_slicing(slice_size='auto')
+                self.enable_attention_slicing(slice_size='max')
 
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               conditioning_data: ConditioningData,