add support for loading VAE autoencoders

To add a VAE autoencoder to an existing model:

1. Download the appropriate autoencoder and put it into
   models/ldm/stable-diffusion

   Note that you MUST use a VAE that was written for the
   original CompViz Stable Diffusion codebase. For v1.4,
   that would be the file named vae-ft-mse-840000-ema-pruned.ckpt
   that you can download from https://huggingface.co/stabilityai/sd-vae-ft-mse-original

2. Edit config/models.yaml to contain the following stanza, modifying `weights`
   and `vae` as required to match the weights and vae model file names. There is
   no requirement to rename the VAE file.

~~~
stable-diffusion-1.4:
  weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
  description: Stable Diffusion v1.4
  config: configs/stable-diffusion/v1-inference.yaml
  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
  width: 512
  height: 512
~~~

3. Alternatively from within the `invoke.py` CLI, you may use the command
   `!editmodel stable-diffusion-1.4` to bring up a simple editor that will
   allow you to add the path to the VAE.

4. If you are just installing InvokeAI for the first time, you can also
   use `!import_model models/ldm/stable-diffusion/sd-v1.4.ckpt` instead
   to create the configuration from scratch.

5. That's it!

ldm/invoke/model_cache.py

@@ -13,6 +13,7 @@ import gc
 import hashlib
 import psutil
 import transformers
+import os
 from sys import getrefcount
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
@@ -193,6 +194,7 @@ class ModelCache(object):
         mconfig = self.config[model_name]
         config = mconfig.config
         weights = mconfig.weights
+        vae = mconfig.get('vae',None)
         width = mconfig.width
         height = mconfig.height
 
@@ -222,9 +224,17 @@ class ModelCache(object):
         else:
             print('   | Using more accurate float32 precision')
 
+        # look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
+        if vae and os.path.exists(vae):
+            print(f'   | Loading VAE weights from: {vae}')
+            vae_ckpt = torch.load(vae, map_location="cpu")
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            model.first_stage_model.load_state_dict(vae_dict, strict=False)
+
         model.to(self.device)
         # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
         model.cond_stage_model.device = self.device
+        
         model.eval()
 
         for m in model.modules():