feature: support TAESD - Tiny Autoencoder for Stable Diffusion (#4316)

[TAESD - Tiny Autoencoder for Stable
Diffusion](https://github.com/madebyollin/taesd) - is a tiny VAE that
provides significantly better results than my single-multiplication hack
but is still very fast.

The entire TAESD model weights are under 10 MB!

This PR requires diffusers 0.20:
- [x] #4311 

## To Do

Test with
- [x] SD 1.x
- [ ] SD 2.x: #4415 
- [x] SDXL

## Have you discussed this change with the InvokeAI team?
- See [TAESD Invocation
API](https://discord.com/channels/1020123559063990373/1137857402453119166)
      
## Have you updated all relevant documentation?
- [ ] No


## Related Tickets & Documents

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- Related Issue #
- Closes #

## QA Instructions, Screenshots, Recordings

Should be able to import these models:
- [madebyollin/taesd](https://huggingface.co/madebyollin/taesd)
- [madebyollin/taesdxl](https://huggingface.co/madebyollin/taesdxl)

and use them as VAE.

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Please provide steps on how to test changes, any hardware or 
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## Added/updated tests?

- [x] Some. There are new tests for VaeFolderProbe based on VAE
configurations, but no tests that require the full model weights.

invokeai/app/invocations/latent.py

@@ -1,12 +1,14 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
 from contextlib import ExitStack
+from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
 
 import einops
 import numpy as np
 import torch
 import torchvision.transforms as T
+from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models import UNet2DConditionModel
 from diffusers.models.attention_processor import (
@@ -857,8 +859,7 @@ class ImageToLatentsInvocation(BaseInvocation):
             # non_noised_latents_from_image
             image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
             with torch.inference_mode():
-                image_tensor_dist = vae.encode(image_tensor).latent_dist
-                latents = image_tensor_dist.sample().to(dtype=vae.dtype)  # FIXME: uses torch.randn. make reproducible!
+                latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
 
             latents = vae.config.scaling_factor * latents
             latents = latents.to(dtype=orig_dtype)
@@ -885,6 +886,18 @@ class ImageToLatentsInvocation(BaseInvocation):
         context.services.latents.save(name, latents)
         return build_latents_output(latents_name=name, latents=latents, seed=None)
 
+    @singledispatchmethod
+    @staticmethod
+    def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        image_tensor_dist = vae.encode(image_tensor).latent_dist
+        latents = image_tensor_dist.sample().to(dtype=vae.dtype)  # FIXME: uses torch.randn. make reproducible!
+        return latents
+
+    @_encode_to_tensor.register
+    @staticmethod
+    def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        return vae.encode(image_tensor).latents
+
 
 @invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
 class BlendLatentsInvocation(BaseInvocation):

invokeai/backend/model_management/model_probe.py

@@ -1,4 +1,5 @@
 import json
+import re
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Callable, Dict, Literal, Optional, Union
@@ -53,6 +54,7 @@ class ModelProbe(object):
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.Vae,
+        "AutoencoderTiny": ModelType.Vae,
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
     }
@@ -177,6 +179,7 @@ class ModelProbe(object):
         Get the model type of a hugging-face style folder.
         """
         class_name = None
+        error_hint = None
         if model:
             class_name = model.__class__.__name__
         else:
@@ -202,12 +205,18 @@ class ModelProbe(object):
                     class_name = conf["architectures"][0]
                 else:
                     class_name = None
+            else:
+                error_hint = f"No model_index.json or config.json found in {folder_path}."
 
         if class_name and (type := cls.CLASS2TYPE.get(class_name)):
             return type
+        else:
+            error_hint = f"class {class_name} is not one of the supported classes [{', '.join(cls.CLASS2TYPE.keys())}]"
 
         # give up
-        raise InvalidModelException(f"Unable to determine model type for {folder_path}")
+        raise InvalidModelException(
+            f"Unable to determine model type for {folder_path}" + (f"; {error_hint}" if error_hint else "")
+        )
 
     @classmethod
     def _scan_and_load_checkpoint(cls, model_path: Path) -> dict:
@@ -461,16 +470,32 @@ class PipelineFolderProbe(FolderProbeBase):
 
 class VaeFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
+        if self._config_looks_like_sdxl():
+            return BaseModelType.StableDiffusionXL
+        elif self._name_looks_like_sdxl():
+            # but SD and SDXL VAE are the same shape (3-channel RGB to 4-channel float scaled down
+            # by a factor of 8), we can't necessarily tell them apart by config hyperparameters.
+            return BaseModelType.StableDiffusionXL
+        else:
+            return BaseModelType.StableDiffusion1
+
+    def _config_looks_like_sdxl(self) -> bool:
+        # config values that distinguish Stability's SD 1.x VAE from their SDXL VAE.
         config_file = self.folder_path / "config.json"
         if not config_file.exists():
             raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
         with open(config_file, "r") as file:
             config = json.load(file)
-        return (
-            BaseModelType.StableDiffusionXL
-            if config.get("scaling_factor", 0) == 0.13025 and config.get("sample_size") in [512, 1024]
-            else BaseModelType.StableDiffusion1
-        )
+        return config.get("scaling_factor", 0) == 0.13025 and config.get("sample_size") in [512, 1024]
+
+    def _name_looks_like_sdxl(self) -> bool:
+        return bool(re.search(r"xl\b", self._guess_name(), re.IGNORECASE))
+
+    def _guess_name(self) -> str:
+        name = self.folder_path.name
+        if name == "vae":
+            name = self.folder_path.parent.name
+        return name
 
 
 class TextualInversionFolderProbe(FolderProbeBase):

tests/test_model_probe.py

@@ -0,0 +1,22 @@
+from pathlib import Path
+
+import pytest
+
+from invokeai.backend import BaseModelType
+from invokeai.backend.model_management.model_probe import VaeFolderProbe
+
+
+@pytest.mark.parametrize(
+    "vae_path,expected_type",
+    [
+        ("sd-vae-ft-mse", BaseModelType.StableDiffusion1),
+        ("sdxl-vae", BaseModelType.StableDiffusionXL),
+        ("taesd", BaseModelType.StableDiffusion1),
+        ("taesdxl", BaseModelType.StableDiffusionXL),
+    ],
+)
+def test_get_base_type(vae_path: str, expected_type: BaseModelType, datadir: Path):
+    sd1_vae_path = datadir / "vae" / vae_path
+    probe = VaeFolderProbe(sd1_vae_path)
+    base_type = probe.get_base_type()
+    assert base_type == expected_type

tests/test_model_probe/vae/sd-vae-ft-mse/config.json

@@ -0,0 +1,29 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.4.2",
+  "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": 256,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

tests/test_model_probe/vae/sdxl-vae/config.json

@@ -0,0 +1,31 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.18.0.dev0",
+  "_name_or_path": ".",
+  "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": 1024,
+  "scaling_factor": 0.13025,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

tests/test_model_probe/vae/taesd/config.json

@@ -0,0 +1,37 @@
+{
+  "_class_name": "AutoencoderTiny",
+  "_diffusers_version": "0.20.0.dev0",
+  "act_fn": "relu",
+  "decoder_block_out_channels": [
+    64,
+    64,
+    64,
+    64
+  ],
+  "encoder_block_out_channels": [
+    64,
+    64,
+    64,
+    64
+  ],
+  "force_upcast": false,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "latent_magnitude": 3,
+  "latent_shift": 0.5,
+  "num_decoder_blocks": [
+    3,
+    3,
+    3,
+    1
+  ],
+  "num_encoder_blocks": [
+    1,
+    3,
+    3,
+    3
+  ],
+  "out_channels": 3,
+  "scaling_factor": 1.0,
+  "upsampling_scaling_factor": 2
+}

tests/test_model_probe/vae/taesdxl/config.json

@@ -0,0 +1,37 @@
+{
+  "_class_name": "AutoencoderTiny",
+  "_diffusers_version": "0.20.0.dev0",
+  "act_fn": "relu",
+  "decoder_block_out_channels": [
+    64,
+    64,
+    64,
+    64
+  ],
+  "encoder_block_out_channels": [
+    64,
+    64,
+    64,
+    64
+  ],
+  "force_upcast": false,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "latent_magnitude": 3,
+  "latent_shift": 0.5,
+  "num_decoder_blocks": [
+    3,
+    3,
+    3,
+    1
+  ],
+  "num_encoder_blocks": [
+    1,
+    3,
+    3,
+    3
+  ],
+  "out_channels": 3,
+  "scaling_factor": 1.0,
+  "upsampling_scaling_factor": 2
+}