Diffusers 0.28 compatibility

configs/vae_config.yaml

@@ -0,0 +1,30 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.8.0",
+  "_name_or_path": "hf-models/stable-diffusion-v2-768x768/vae",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

nodes.py

@@ -74,15 +74,15 @@ class geowizard_model_loader:
             self.current_config = custom_config
             # setup pretrained models
             original_config = OmegaConf.load(os.path.join(script_directory, f"configs/v1-inference.yaml"))
+            vae_config = OmegaConf.load(os.path.join(script_directory, f"configs/vae_config.yaml"))
 
             dtype = convert_dtype(dtype)
             
-            from diffusers.loaders.single_file_utils import (convert_ldm_vae_checkpoint, create_vae_diffusers_config)
+            from diffusers.loaders.single_file_utils import (convert_ldm_vae_checkpoint)
             
             sd = vae.get_sd()
-            converted_vae_config = create_vae_diffusers_config(original_config, image_size=512)
-            converted_vae = convert_ldm_vae_checkpoint(sd, converted_vae_config)
-            self.vae = AutoencoderKL(**converted_vae_config)
+            converted_vae = convert_ldm_vae_checkpoint(sd, vae_config)
+            self.vae = AutoencoderKL(**vae_config)
             self.vae.load_state_dict(converted_vae, strict=False)