move convert_vae to inline, restore comments

library/model_util.py

@@ -632,7 +632,7 @@ def convert_ldm_clip_checkpoint_v2(checkpoint, max_length):
     del new_sd[ANOTHER_POSITION_IDS_KEY]
   else:
     position_ids = torch.Tensor([list(range(max_length))]).to(torch.int64)
-  
+
   new_sd["text_model.embeddings.position_ids"] = position_ids
   return new_sd
 
@@ -864,7 +864,6 @@ def load_checkpoint_with_text_encoder_conversion(ckpt_path):
   return checkpoint, state_dict
 
 
-
 # TODO dtype指定の動作が怪しいので確認する text_encoderを指定形式で作れるか未確認
 def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
   _, state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path)
@@ -887,7 +886,7 @@ def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
 
   vae = AutoencoderKL(**vae_config)
   info = vae.load_state_dict(converted_vae_checkpoint)
-  print("loadint vae:", info)
+  print("loading vae:", info)
 
   # convert text_model
   if v2:
@@ -1089,11 +1088,6 @@ def save_diffusers_checkpoint(v2, output_dir, text_encoder, unet, pretrained_mod
 VAE_PREFIX = "first_stage_model."
 
 
-def convert_vae(vae_sd, vae_config):
-  converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_sd, vae_config)
-  vae = AutoencoderKL(**vae_config)
-  vae.load_state_dict(converted_vae_checkpoint)
-
 def load_vae(vae_id, dtype):
   print(f"load VAE: {vae_id}")
   if os.path.isdir(vae_id) or not os.path.isfile(vae_id):
@@ -1109,27 +1103,34 @@ def load_vae(vae_id, dtype):
   # local
   vae_config = create_vae_diffusers_config()
 
-  if vae_id.endswith(".bin"): return convert_vae(torch.load(vae_id, map_location="cpu"), vae_config)
+  if vae_id.endswith(".bin"):
+    # SD 1.5 VAE on Huggingface
+    converted_vae_checkpoint = torch.load(vae_id, map_location="cpu")
+  else:
+    # StableDiffusion
+    vae_model = (load_file(vae_id, "cpu") if is_safetensors(vae_id)
+                 else torch.load(vae_id, map_location="cpu"))
+    vae_sd = vae_model['state_dict'] if 'state_dict' in vae_model else vae_model
 
-  vae_model = (load_file(vae_id, "cpu") if is_safetensors(vae_id)
-              else torch.load(vae_id, map_location="cpu"))
-  vae_sd = vae_model['state_dict'] if 'state_dict' in vae_model else vae_model
-  
-  full_model = False
-  for vae_key in vae_sd:
-    if vae_key.startswith(VAE_PREFIX):
-      full_model = True
-      break
-  if not full_model:
-    sd = {}
-    for key, value in vae_sd.items():
-      sd[VAE_PREFIX + key] = value
-    vae_sd = sd
-    del sd
+    # vae only or full model
+    full_model = False
+    for vae_key in vae_sd:
+      if vae_key.startswith(VAE_PREFIX):
+        full_model = True
+        break
+    if not full_model:
+      sd = {}
+      for key, value in vae_sd.items():
+        sd[VAE_PREFIX + key] = value
+      vae_sd = sd
+      del sd
 
-  
-  return convert_vae(vae_sd, vae_config)
+    # Convert the VAE model.
+    converted_vae_checkpoint = convert_ldm_vae_checkpoint(vae_sd, vae_config)
 
+  vae = AutoencoderKL(**vae_config)
+  vae.load_state_dict(converted_vae_checkpoint)
+  return vae
 
 # endregion