use diffusers 0.14 cache layout

This PR ports the `main` PR #2871 to the v2.3 branch. This adjusts
the global diffusers model cache to work with the 0.14 diffusers
layout of placing models in HF_HOME/hub rather than HF_HOME/diffusers.

ldm/invoke/CLI.py

@@ -960,7 +960,6 @@ def prepare_image_metadata(
         wildcards["seed"] = seed
         wildcards["model_id"] = model_id
         try:
-            print(f'DEBUG: fnformat={opt.fnformat}')
             filename = opt.fnformat.format(**wildcards)
         except KeyError as e:
             print(

ldm/invoke/config/invokeai_configure.py

@@ -290,7 +290,7 @@ def download_vaes():
         # first the diffusers version
         repo_id = "stabilityai/sd-vae-ft-mse"
         args = dict(
-            cache_dir=global_cache_dir("diffusers"),
+            cache_dir=global_cache_dir("hub"),
         )
         if not AutoencoderKL.from_pretrained(repo_id, **args):
             raise Exception(f"download of {repo_id} failed")

ldm/invoke/config/model_install_backend.py

@@ -262,7 +262,6 @@ def _download_diffusion_weights(
             path = download_from_hf(
                 model_class,
                 repo_id,
-                cache_subdir="diffusers",
                 safety_checker=None,
                 **extra_args,
             )

ldm/invoke/globals.py

@@ -88,16 +88,13 @@ def global_cache_dir(subdir:Union[str,Path]='')->Path:
     '''
     Returns Path to the model cache directory. If a subdirectory
     is provided, it will be appended to the end of the path, allowing
-    for huggingface-style conventions:
+    for Hugging Face-style conventions. Currently, Hugging Face has
-         global_cache_dir('diffusers')
+    moved all models into the "hub" subfolder, so for any pretrained
+    HF model, use:
          global_cache_dir('hub')
-    Current HuggingFace documentation (mid-Jan 2023) indicates that
+
-    transformers models will be cached into a "transformers" subdirectory,
+    The legacy location for transformers used to be global_cache_dir('transformers')
-    but in practice they seem to go into "hub". But if needed:
+    and global_cache_dir('diffusers') for diffusers.
-         global_cache_dir('transformers')
-    One other caveat is that HuggingFace is moving some diffusers models
-    into the "hub" subdirectory as well, so this will need to be revisited
-    from time to time.
     '''
     home: str = os.getenv('HF_HOME')
 

ldm/invoke/merge_diffusers.py

@@ -437,10 +437,10 @@ def main():
     args = _parse_args()
     global_set_root(args.root_dir)
 
-    cache_dir = str(global_cache_dir("diffusers"))
+    cache_dir = str(global_cache_dir("hub"))
     os.environ[
         "HF_HOME"
-    ] = cache_dir  # because not clear the merge pipeline is honoring cache_dir
+    ] = str(global_cache_dir())  # because not clear the merge pipeline is honoring cache_dir
     args.cache_dir = cache_dir
 
     try:

ldm/invoke/model_manager.py

@@ -507,7 +507,7 @@ class ModelManager(object):
             if vae := self._load_vae(mconfig["vae"]):
                 pipeline_args.update(vae=vae)
         if not isinstance(name_or_path, Path):
-            pipeline_args.update(cache_dir=global_cache_dir("diffusers"))
+            pipeline_args.update(cache_dir=global_cache_dir("hub"))
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
@@ -1093,9 +1093,12 @@ class ModelManager(object):
         to the 2.3.0 "diffusers" version. This should be a one-time operation, called at
         script startup time.
         """
-        # Three transformer models to check: bert, clip and safety checker
+        # Three transformer models to check: bert, clip and safety checker, and
+        # the diffusers as well
+        models_dir = Path(Globals.root, "models")
         legacy_locations = [
             Path(
+                models_dir,
                 "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker"
             ),
             Path("bert-base-uncased/models--bert-base-uncased"),
@@ -1103,17 +1106,26 @@ class ModelManager(object):
                 "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14"
             ),
         ]
-        models_dir = Path(Globals.root, "models")
+        legacy_locations.extend(list(global_cache_dir("diffusers").glob('*')))
         legacy_layout = False
         for model in legacy_locations:
-            legacy_layout = legacy_layout or Path(models_dir, model).exists()
+            legacy_layout = legacy_layout or model.exists()
         if not legacy_layout:
             return
 
         print(
-            "** Legacy version <= 2.2.5 model directory layout detected. Reorganizing."
+            """
+>> ALERT:
+>> The location of your previously-installed diffusers models needs to move from
+>> invokeai/models/diffusers to invokeai/models/hub due to a change introduced by
+>> diffusers version 0.14. InvokeAI will now move all models from the "diffusers" directory
+>> into "hub" and then remove the diffusers directory. This is a quick, safe, one-time
+>> operation. However if you have customized either of these directories and need to
+>> make adjustments, please press ctrl-C now to abort and relaunch InvokeAI when you are ready.
+>> Otherwise press <enter> to continue."""
         )
         print("** This is a quick one-time operation.")
+        input("continue> ")
 
         # transformer files get moved into the hub directory
         if cls._is_huggingface_hub_directory_present():
@@ -1125,33 +1137,20 @@ class ModelManager(object):
         for model in legacy_locations:
             source = models_dir / model
             dest = hub / model.stem
+            if dest.exists() and not source.exists():
+                continue
             print(f"** {source} => {dest}")
             if source.exists():
-                if dest.exists():
+                if dest.is_symlink():
-                    rmtree(source)
+                    print(f"** Found symlink at {dest.name}. Not migrating.")
+                elif dest.exists():
+                    if source.is_dir():
+                        rmtree(source)
+                    else:
+                        source.unlink()
                 else:
                     move(source, dest)
-
+                    
-        # anything else gets moved into the diffusers directory
-        if cls._is_huggingface_hub_directory_present():
-            diffusers = global_cache_dir("diffusers")
-        else:
-            diffusers = models_dir / "diffusers"
-
-        os.makedirs(diffusers, exist_ok=True)
-        for root, dirs, _ in os.walk(models_dir, topdown=False):
-            for dir in dirs:
-                full_path = Path(root, dir)
-                if full_path.is_relative_to(hub) or full_path.is_relative_to(diffusers):
-                    continue
-                if Path(dir).match("models--*--*"):
-                    dest = diffusers / dir
-                    print(f"** {full_path} => {dest}")
-                    if dest.exists():
-                        rmtree(full_path)
-                    else:
-                        move(full_path, dest)
-
         # now clean up by removing any empty directories
         empty = [
             root
@@ -1249,7 +1248,7 @@ class ModelManager(object):
             path = name_or_path
         else:
             owner, repo = name_or_path.split("/")
-            path = Path(global_cache_dir("diffusers") / f"models--{owner}--{repo}")
+            path = Path(global_cache_dir("hub") / f"models--{owner}--{repo}")
         if not path.exists():
             return None
         hashpath = path / "checksum.sha256"
@@ -1310,7 +1309,7 @@ class ModelManager(object):
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=global_cache_dir("diffusers"),
+            cache_dir=global_cache_dir("hug"),
             local_files_only=not Globals.internet_available,
         )
 

ldm/invoke/training/textual_inversion_training.py

@@ -634,7 +634,7 @@ def do_textual_inversion_training(
     assert (
         pretrained_model_name_or_path
     ), f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
-    pipeline_args = dict(cache_dir=global_cache_dir("diffusers"))
+    pipeline_args = dict(cache_dir=global_cache_dir("hub"))
 
     # Load tokenizer
     if tokenizer_name:

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
   "clip_anytorch",
   "compel==0.1.7",
   "datasets",
-  "diffusers[torch]~=0.13",
+  "diffusers[torch]~=0.14",
   "dnspython==2.2.1",
   "einops",
   "eventlet",