add draft SD3 probing; there is an issue with FromOriginalControlNetMixin in backend.util.hotfixes due to new diffusers

invokeai/backend/model_manager/config.py

@@ -51,6 +51,7 @@ class BaseModelType(str, Enum):
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
+    StableDiffusion3 = "sd-3"
     # Kandinsky2_1 = "kandinsky-2.1"
 
 
@@ -74,8 +75,10 @@ class SubModelType(str, Enum):
     UNet = "unet"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
+    TextEncoder3 = "text_encoder_3"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
+    Tokenizer3 = "tokenizer_3"
     VAE = "vae"
     VAEDecoder = "vae_decoder"
     VAEEncoder = "vae_encoder"

invokeai/backend/model_manager/probe.py

@@ -100,6 +100,7 @@ class ModelProbe(object):
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
+        "StableDiffusion3Pipeline": ModelType.Main,
         "AutoencoderKL": ModelType.VAE,
         "AutoencoderTiny": ModelType.VAE,
         "ControlNetModel": ModelType.ControlNet,
@@ -298,10 +299,13 @@ class ModelProbe(object):
             return possible_conf.absolute()
 
         if model_type is ModelType.Main:
-            config_file = LEGACY_CONFIGS[base_type][variant_type]
+            if base_type is BaseModelType.StableDiffusion3:
-            if isinstance(config_file, dict):  # need another tier for sd-2.x models
+                config_file = "stable-diffusion/v3-inference.yaml"
-                config_file = config_file[prediction_type]
+            else:
-            config_file = f"stable-diffusion/{config_file}"
+                config_file = LEGACY_CONFIGS[base_type][variant_type]
+                if isinstance(config_file, dict):  # need another tier for sd-2.x models
+                    config_file = config_file[prediction_type]
+                config_file = f"stable-diffusion/{config_file}"
         elif model_type is ModelType.ControlNet:
             config_file = (
                 "controlnet/cldm_v15.yaml"
@@ -374,7 +378,7 @@ def get_default_settings_controlnet_t2i_adapter(model_name: str) -> Optional[Con
 def get_default_settings_main(model_base: BaseModelType) -> Optional[MainModelDefaultSettings]:
     if model_base is BaseModelType.StableDiffusion1 or model_base is BaseModelType.StableDiffusion2:
         return MainModelDefaultSettings(width=512, height=512)
-    elif model_base is BaseModelType.StableDiffusionXL:
+    elif model_base in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusion3]:
         return MainModelDefaultSettings(width=1024, height=1024)
     # We don't provide defaults for BaseModelType.StableDiffusionXLRefiner, as they are not standalone models.
     return None
@@ -398,7 +402,10 @@ class CheckpointProbeBase(ProbeBase):
         if model_type != ModelType.Main:
             return ModelVariantType.Normal
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
-        in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
+        key = "model.diffusion_model.input_blocks.0.0.weight"
+        if key not in state_dict:
+            return ModelVariantType.Normal
+        in_channels = state_dict[key].shape[1]
         if in_channels == 9:
             return ModelVariantType.Inpaint
         elif in_channels == 5:
@@ -425,6 +432,9 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusionXL
         elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
             return BaseModelType.StableDiffusionXLRefiner
+        key_name = "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight"
+        if key_name in state_dict:
+            return BaseModelType.StableDiffusion3
         else:
             raise InvalidModelConfigException("Cannot determine base type")
 
@@ -588,6 +598,10 @@ class FolderProbeBase(ProbeBase):
 
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
+        with open(self.model_path / "model_index.json", "r") as file:
+            index_conf = json.load(file)
+            if index_conf.get("_class_name") == "StableDiffusion3Pipeline":
+                return BaseModelType.StableDiffusion3
         with open(self.model_path / "unet" / "config.json", "r") as file:
             unet_conf = json.load(file)
         if unet_conf["cross_attention_dim"] == 768:

invokeai/backend/util/hotfixes.py

@@ -3,7 +3,12 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import diffusers
 import torch
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalControlNetMixin
+
+# The following import is
+# generating import errors with diffusers 028.2
+# tried diffusers.loaders.controlnet import FromOriginalControlNetMixin, but this
+# fails as well
+# from diffusers.loaders import FromOriginalControlNetMixin
 from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
 from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 from diffusers.models.embeddings import (
@@ -32,7 +37,7 @@ from invokeai.backend.util.logging import InvokeAILogger
 logger = InvokeAILogger.get_logger(__name__)
 
 
-class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
+class ControlNetModel(ModelMixin, ConfigMixin):
     """
     A ControlNet model.
 

pyproject.toml

@@ -37,7 +37,7 @@ dependencies = [
   "clip_anytorch==2.6.0",       # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
   "compel==2.0.2",
   "controlnet-aux==0.0.7",
-  "diffusers[torch]==0.27.2",
+  "diffusers[torch]",
   "invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "mediapipe==0.10.7",          # needed for "mediapipeface" controlnet model
   "numpy==1.26.4",              # >1.24.0 is needed to use the 'strict' argument to np.testing.assert_array_equal()
@@ -47,11 +47,11 @@ dependencies = [
   "pytorch-lightning==2.1.3",
   "safetensors==0.4.3",
   "timm==0.6.13",               # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
-  "torch==2.2.2",
+  "torch",
   "torchmetrics==0.11.4",
   "torchsde==0.2.6",
-  "torchvision==0.17.2",
+  "torchvision",
-  "transformers==4.41.1",
+  "transformers",
 
   # Core application dependencies, pinned for reproducible builds.
   "fastapi-events==0.11.0",