Run tests on this branch

Co-authored-by: Lincoln Stein <lstein@gmail.com>

.github/workflows/python-tests.yml

@@ -9,6 +9,7 @@ on:
   push:
     branches:
       - 'main'
+      - 'bug-install-job-running-multiple-times'
   pull_request:
     types:
       - 'ready_for_review'

invokeai/app/invocations/ip_adapter.py

@@ -15,7 +15,7 @@ from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType, IPAdapterConfig, ModelType
+from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, IPAdapterConfig, ModelType
 
 
 class IPAdapterField(BaseModel):
@@ -89,17 +89,32 @@ class IPAdapterInvocation(BaseInvocation):
         assert isinstance(ip_adapter_info, IPAdapterConfig)
         image_encoder_model_id = ip_adapter_info.image_encoder_model_id
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
-        image_encoder_models = context.models.search_by_attrs(
+        image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
-            name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
-        )
-        assert len(image_encoder_models) == 1
         return IPAdapterOutput(
             ip_adapter=IPAdapterField(
                 image=self.image,
                 ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=ModelIdentifierField.from_config(image_encoder_models[0]),
+                image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
                 weight=self.weight,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,
             ),
         )
+
+    def _get_image_encoder(self, context: InvocationContext, image_encoder_model_name: str) -> AnyModelConfig:
+        found = False
+        while not found:
+            image_encoder_models = context.models.search_by_attrs(
+                name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
+            )
+            found = len(image_encoder_models) > 0
+            if not found:
+                context.logger.warning(
+                    f"The image encoder required by this IP Adapter ({image_encoder_model_name}) is not installed."
+                )
+                context.logger.warning("Downloading and installing now. This may take a while.")
+                installer = context._services.model_manager.install
+                job = installer.heuristic_import(f"InvokeAI/{image_encoder_model_name}")
+                installer.wait_for_job(job, timeout=600)  # wait up to 10 minutes - then raise a TimeoutException
+        assert len(image_encoder_models) == 1
+        return image_encoder_models[0]

invokeai/app/services/model_install/model_install_default.py

@@ -133,6 +133,14 @@ class ModelInstallService(ModelInstallServiceBase):
             self._download_cache.clear()
             self._running = False
 
+    def _put_in_queue(self, job: ModelInstallJob) -> None:
+        print(f'DEBUG: in _put_in_queue(job={job.id})')
+        if self._stop_event.is_set():
+            self.cancel_job(job)
+        else:
+            print(f'DEBUG: putting {job.id} into the install queue')
+            self._install_queue.put(job)
+
     def register_path(
         self,
         model_path: Union[Path, str],
@@ -218,7 +226,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
         if isinstance(source, LocalModelSource):
             install_job = self._import_local_model(source, config)
-            self._install_queue.put(install_job)  # synchronously install
+            self._put_in_queue(install_job)  # synchronously install
         elif isinstance(source, HFModelSource):
             install_job = self._import_from_hf(source, config)
         elif isinstance(source, URLModelSource):
@@ -328,7 +336,7 @@ class ModelInstallService(ModelInstallServiceBase):
         yaml_path.rename(yaml_path.with_suffix(".yaml.bak"))
 
     def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:  # noqa D102
-        self._cached_model_paths = {Path(x.path).absolute() for x in self.record_store.all_models()}
+        self._cached_model_paths = {Path(x.path).resolve() for x in self.record_store.all_models()}
         callback = self._scan_install if install else self._scan_register
         search = ModelSearch(on_model_found=callback)
         self._models_installed.clear()
@@ -342,7 +350,7 @@ class ModelInstallService(ModelInstallServiceBase):
         """Unregister the model. Delete its files only if they are within our models directory."""
         model = self.record_store.get_model(key)
         models_dir = self.app_config.models_path
-        model_path = Path(model.path)
+        model_path = models_dir / Path(model.path)  # handle legacy relative model paths
         if model_path.is_relative_to(models_dir):
             self.unconditionally_delete(key)
         else:
@@ -350,7 +358,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
     def unconditionally_delete(self, key: str) -> None:  # noqa D102
         model = self.record_store.get_model(key)
-        model_path = Path(model.path)
+        model_path = self.app_config.models_path / model.path
         if model_path.is_dir():
             rmtree(model_path)
         else:
@@ -403,10 +411,11 @@ class ModelInstallService(ModelInstallServiceBase):
                 done = True
                 continue
             try:
+                print(f'DEBUG: _install_next_item() checking for a job to install')
                 job = self._install_queue.get(timeout=1)
             except Empty:
                 continue
-
+            print(f'DEBUG: _install_next_item() got job {job.id}, status={job.status}')
             assert job.local_path is not None
             try:
                 if job.cancelled:
@@ -432,6 +441,7 @@ class ModelInstallService(ModelInstallServiceBase):
                     else:
                         key = self.install_path(job.local_path, job.config_in)
                     job.config_out = self.record_store.get_model(key)
+                    print(f'DEBUG: _install_next_item() signaling completion for job={job.id}, status={job.status}')
                     self._signal_job_completed(job)
 
             except InvalidModelConfigException as excp:
@@ -492,6 +502,8 @@ class ModelInstallService(ModelInstallServiceBase):
             for cur_base_model in BaseModelType:
                 for cur_model_type in ModelType:
                     models_dir = self._app_config.models_path / Path(cur_base_model.value, cur_model_type.value)
+                    if not models_dir.exists():
+                        continue
                     installed.update(self.scan_directory(models_dir))
             self._logger.info(f"{len(installed)} new models registered; {len(defunct_models)} unregistered")
 
@@ -518,7 +530,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
         new_path = models_dir / model.base.value / model.type.value / old_path.name
 
-        if old_path == new_path:
+        if old_path == new_path or new_path.exists() and old_path == new_path.resolve():
             return model
 
         self._logger.info(f"Moving {model.name} to {new_path}.")
@@ -779,14 +791,16 @@ class ModelInstallService(ModelInstallServiceBase):
 
     def _download_complete_callback(self, download_job: DownloadJob) -> None:
         self._logger.info(f"{download_job.source}: model download complete")
+        print(f'DEBUG: _download_complete_callback(download_job={download_job.source}')
         with self._lock:
-            install_job = self._download_cache[download_job.source]
+            install_job = self._download_cache.pop(download_job.source, None)
-            self._download_cache.pop(download_job.source, None)
+            print(f'DEBUG: download_job={download_job.source} / install_job={install_job}')
-
             # are there any more active jobs left in this task?
-            if install_job.downloading and all(x.complete for x in install_job.download_parts):
+            if install_job and install_job.downloading and all(x.complete for x in install_job.download_parts):
+                print(f'DEBUG: setting job {install_job.id} to DOWNLOADS_DONE')
                 install_job.status = InstallStatus.DOWNLOADS_DONE
-                self._install_queue.put(install_job)
+                print(f'DEBUG: putting {install_job.id} into the install queue')
+                self._put_in_queue(install_job)
 
             # Let other threads know that the number of downloads has changed
             self._downloads_changed_event.set()
@@ -828,7 +842,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
         if all(x.in_terminal_state for x in install_job.download_parts):
             # When all parts have reached their terminal state, we finalize the job to clean up the temporary directory and other resources
-            self._install_queue.put(install_job)
+            self._put_in_queue(install_job)
 
     # ------------------------------------------------------------------------------------------------
     # Internal methods that put events on the event bus

invokeai/backend/install/check_root.py

@@ -11,17 +11,6 @@ def check_invokeai_root(config: InvokeAIAppConfig):
     try:
         assert config.db_path.parent.exists(), f"{config.db_path.parent} not found"
         assert config.models_path.exists(), f"{config.models_path} not found"
-        if not config.ignore_missing_core_models:
-            for model in [
-                "CLIP-ViT-bigG-14-laion2B-39B-b160k",
-                "bert-base-uncased",
-                "clip-vit-large-patch14",
-                "sd-vae-ft-mse",
-                "stable-diffusion-2-clip",
-                "stable-diffusion-safety-checker",
-            ]:
-                path = config.models_path / f"core/convert/{model}"
-                assert path.exists(), f"{path} is missing"
     except Exception as e:
         print()
         print(f"An exception has occurred: {str(e)}")
@@ -32,10 +21,5 @@ def check_invokeai_root(config: InvokeAIAppConfig):
         print(
             '** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
         )
-        print(
-            '** (To skip this check completely, add "--ignore_missing_core_models" to your CLI args. Not installing '
-            "these core models will prevent the loading of some or all .safetensors and .ckpt files. However, you can "
-            "always come back and install these core models in the future.)"
-        )
         input("Press any key to continue...")
         sys.exit(0)

invokeai/backend/install/invokeai_configure.py

@@ -25,20 +25,20 @@ import npyscreen
 import psutil
 import torch
 import transformers
-from diffusers import AutoencoderKL, ModelMixin
+from diffusers import ModelMixin
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
 from omegaconf import DictConfig, OmegaConf
 from pydantic.error_wrappers import ValidationError
 from tqdm import tqdm
-from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+from transformers import AutoFeatureExtractor
 
 import invokeai.configs as configs
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.install.install_helper import InstallHelper, InstallSelections
 from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-from invokeai.backend.model_manager import BaseModelType, ModelType
+from invokeai.backend.model_manager import ModelType
 from invokeai.backend.util import choose_precision, choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm
@@ -210,51 +210,15 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
         print(traceback.format_exc(), file=sys.stderr)
 
 
-def download_conversion_models():
+def download_safety_checker():
     target_dir = config.models_path / "core/convert"
     kwargs = {}  # for future use
     try:
-        logger.info("Downloading core tokenizers and text encoders")
-
-        # bert
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=DeprecationWarning)
-            bert = BertTokenizerFast.from_pretrained("bert-base-uncased", **kwargs)
-            bert.save_pretrained(target_dir / "bert-base-uncased", safe_serialization=True)
-
-        # sd-1
-        repo_id = "openai/clip-vit-large-patch14"
-        hf_download_from_pretrained(CLIPTokenizer, repo_id, target_dir / "clip-vit-large-patch14")
-        hf_download_from_pretrained(CLIPTextModel, repo_id, target_dir / "clip-vit-large-patch14")
-
-        # sd-2
-        repo_id = "stabilityai/stable-diffusion-2"
-        pipeline = CLIPTokenizer.from_pretrained(repo_id, subfolder="tokenizer", **kwargs)
-        pipeline.save_pretrained(target_dir / "stable-diffusion-2-clip" / "tokenizer", safe_serialization=True)
-
-        pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs)
-        pipeline.save_pretrained(target_dir / "stable-diffusion-2-clip" / "text_encoder", safe_serialization=True)
-
-        # sd-xl - tokenizer_2
-        repo_id = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
-        _, model_name = repo_id.split("/")
-        pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs)
-        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
-
-        pipeline = CLIPTextConfig.from_pretrained(repo_id, **kwargs)
-        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
-
-        # VAE
-        logger.info("Downloading stable diffusion VAE")
-        vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", **kwargs)
-        vae.save_pretrained(target_dir / "sd-vae-ft-mse", safe_serialization=True)
-
         # safety checking
         logger.info("Downloading safety checker")
         repo_id = "CompVis/stable-diffusion-safety-checker"
         pipeline = AutoFeatureExtractor.from_pretrained(repo_id, **kwargs)
         pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
-
         pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id, **kwargs)
         pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
     except KeyboardInterrupt:
@@ -307,7 +271,7 @@ def download_lama():
 def download_support_models() -> None:
     download_realesrgan()
     download_lama()
-    download_conversion_models()
+    download_safety_checker()
 
 
 # -------------------------------------
@@ -744,12 +708,7 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
         shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
 
     dest = root / "models"
-    for model_base in BaseModelType:
+    dest.mkdir(parents=True, exist_ok=True)
-        for model_type in ModelType:
-            path = dest / model_base.value / model_type.value
-            path.mkdir(parents=True, exist_ok=True)
-    path = dest / "core"
-    path.mkdir(parents=True, exist_ok=True)
 
 
 # -------------------------------------

invokeai/backend/model_manager/load/model_loaders/controlnet.py

@@ -3,9 +3,6 @@
 
 from pathlib import Path
 
-import torch
-from safetensors.torch import load_file as safetensors_load_file
-
 from invokeai.backend.model_manager import (
     AnyModelConfig,
     BaseModelType,
@@ -37,27 +34,25 @@ class ControlNetLoader(GenericDiffusersLoader):
             return True
 
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
-        if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
+        assert isinstance(config, CheckpointConfigBase)
-            raise Exception(f"ControlNet conversion not supported for model type: {config.base}")
+        config_file = config.config_path
-        else:
-            assert isinstance(config, CheckpointConfigBase)
-            config_file = config.config_path
 
-        if model_path.suffix == ".safetensors":
+        image_size = (
-            checkpoint = safetensors_load_file(model_path, device="cpu")
+            512
-        else:
+            if config.base == BaseModelType.StableDiffusion1
-            checkpoint = torch.load(model_path, map_location="cpu")
+            else 768
-
+            if config.base == BaseModelType.StableDiffusion2
-        # sometimes weights are hidden under "state_dict", and sometimes not
+            else 1024
-        if "state_dict" in checkpoint:
-            checkpoint = checkpoint["state_dict"]
-
-        convert_controlnet_to_diffusers(
-            model_path,
-            output_path,
-            original_config_file=self._app_config.root_path / config_file,
-            image_size=512,
-            scan_needed=True,
-            from_safetensors=model_path.suffix == ".safetensors",
         )
+
+        self._logger.info(f"Converting {model_path} to diffusers format")
+        with open(self._app_config.root_path / config_file, "r") as config_stream:
+            convert_controlnet_to_diffusers(
+                model_path,
+                output_path,
+                original_config_file=config_stream,
+                image_size=image_size,
+                precision=self._torch_dtype,
+                from_safetensors=model_path.suffix == ".safetensors",
+            )
         return output_path

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -4,9 +4,6 @@
 from pathlib import Path
 from typing import Optional
 
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
-
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
@@ -14,7 +11,7 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelRepoVariant,
     ModelType,
-    ModelVariantType,
+    SchedulerPredictionType,
     SubModelType,
 )
 from invokeai.backend.model_manager.config import CheckpointConfigBase, MainCheckpointConfig
@@ -68,27 +65,31 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
 
     def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
         assert isinstance(config, MainCheckpointConfig)
-        variant = config.variant
         base = config.base
-        pipeline_class = (
-            StableDiffusionInpaintPipeline if variant == ModelVariantType.Inpaint else StableDiffusionPipeline
-        )
 
         config_file = config.config_path
+        prediction_type = config.prediction_type.value
+        upcast_attention = config.upcast_attention
+        image_size = (
+            1024
+            if base == BaseModelType.StableDiffusionXL
+            else 768
+            if config.prediction_type == SchedulerPredictionType.VPrediction and base == BaseModelType.StableDiffusion2
+            else 512
+        )
 
         self._logger.info(f"Converting {model_path} to diffusers format")
         convert_ckpt_to_diffusers(
             model_path,
             output_path,
             model_type=self.model_base_to_model_type[base],
-            model_version=base,
-            model_variant=variant,
             original_config_file=self._app_config.root_path / config_file,
             extract_ema=True,
-            scan_needed=True,
-            pipeline_class=pipeline_class,
             from_safetensors=model_path.suffix == ".safetensors",
             precision=self._torch_dtype,
+            prediction_type=prediction_type,
+            image_size=image_size,
+            upcast_attention=upcast_attention,
             load_safety_checker=False,
         )
         return output_path

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -57,12 +57,12 @@ class VAELoader(GenericDiffusersLoader):
 
         ckpt_config = OmegaConf.load(self._app_config.root_path / config_file)
         assert isinstance(ckpt_config, DictConfig)
-
+        self._logger.info(f"Converting {model_path} to diffusers format")
         vae_model = convert_ldm_vae_to_diffusers(
             checkpoint=checkpoint,
             vae_config=ckpt_config,
             image_size=512,
+            precision=self._torch_dtype,
         )
-        vae_model.to(self._torch_dtype)  # set precision appropriately
         vae_model.save_pretrained(output_path, safe_serialization=True)
         return output_path

invokeai/backend/model_manager/probe.py

@@ -132,11 +132,12 @@ class ModelProbe(object):
 
         format_type = ModelFormat.Diffusers if model_path.is_dir() else ModelFormat.Checkpoint
         model_info = None
-        model_type = None
+        model_type = ModelType(fields["type"]) if "type" in fields and fields["type"] else None
-        if format_type is ModelFormat.Diffusers:
+        if not model_type:
-            model_type = cls.get_model_type_from_folder(model_path)
+            if format_type is ModelFormat.Diffusers:
-        else:
+                model_type = cls.get_model_type_from_folder(model_path)
-            model_type = cls.get_model_type_from_checkpoint(model_path)
+            else:
+                model_type = cls.get_model_type_from_checkpoint(model_path)
         format_type = ModelFormat.ONNX if model_type == ModelType.ONNX else format_type
 
         probe_class = cls.PROBES[format_type].get(model_type)
@@ -156,7 +157,7 @@ class ModelProbe(object):
         fields["image_encoder_model_id"] = fields.get("image_encoder_model_id") or probe.get_image_encoder_model_id()
         fields["name"] = fields.get("name") or cls.get_model_name(model_path)
         fields["description"] = (
-            fields.get("description") or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
+            fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
         fields["format"] = fields.get("format") or probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
@@ -319,7 +320,7 @@ class ModelProbe(object):
     @classmethod
     def _scan_and_load_checkpoint(cls, model_path: Path) -> CkptType:
         with SilenceWarnings():
-            if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
+            if model_path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
                 cls._scan_model(model_path.name, model_path)
                 model = torch.load(model_path)
                 assert isinstance(model, dict)

invokeai/configs/INITIAL_MODELS.yaml

@@ -62,40 +62,72 @@ sd-1/main/trinart_stable_diffusion_v2:
    recommended: False
 sd-1/controlnet/qrcode_monster:
    source: monster-labs/control_v1p_sd15_qrcode_monster
+   description: Controlnet model that generates scannable creative QR codes
    subfolder: v2
 sd-1/controlnet/canny:
+   description: Controlnet weights trained on sd-1.5 with canny conditioning.
    source: lllyasviel/control_v11p_sd15_canny
    recommended: True
 sd-1/controlnet/inpaint:
    source: lllyasviel/control_v11p_sd15_inpaint
+   description: Controlnet weights trained on sd-1.5 with canny conditioning, inpaint version
 sd-1/controlnet/mlsd:
+   description: Controlnet weights trained on sd-1.5 with canny conditioning, MLSD version
    source: lllyasviel/control_v11p_sd15_mlsd
 sd-1/controlnet/depth:
+   description: Controlnet weights trained on sd-1.5 with depth conditioning
    source: lllyasviel/control_v11f1p_sd15_depth
    recommended: True
 sd-1/controlnet/normal_bae:
+   description: Controlnet weights trained on sd-1.5 with normalbae image conditioning
    source: lllyasviel/control_v11p_sd15_normalbae
 sd-1/controlnet/seg:
+   description: Controlnet weights trained on sd-1.5 with seg image conditioning
    source: lllyasviel/control_v11p_sd15_seg
 sd-1/controlnet/lineart:
+   description: Controlnet weights trained on sd-1.5 with lineart image conditioning
    source: lllyasviel/control_v11p_sd15_lineart
    recommended: True
 sd-1/controlnet/lineart_anime:
+   description: Controlnet weights trained on sd-1.5 with anime image conditioning
    source: lllyasviel/control_v11p_sd15s2_lineart_anime
 sd-1/controlnet/openpose:
+   description: Controlnet weights trained on sd-1.5 with openpose image conditioning
    source: lllyasviel/control_v11p_sd15_openpose
    recommended: True
 sd-1/controlnet/scribble:
    source: lllyasviel/control_v11p_sd15_scribble
+   description: Controlnet weights trained on sd-1.5 with scribble image conditioning
    recommended: False
 sd-1/controlnet/softedge:
    source: lllyasviel/control_v11p_sd15_softedge
+   description: Controlnet weights trained on sd-1.5 with soft edge conditioning
 sd-1/controlnet/shuffle:
    source: lllyasviel/control_v11e_sd15_shuffle
+   description: Controlnet weights trained on sd-1.5 with shuffle image conditioning
 sd-1/controlnet/tile:
    source: lllyasviel/control_v11f1e_sd15_tile
+   description: Controlnet weights trained on sd-1.5 with tiled image conditioning
 sd-1/controlnet/ip2p:
    source: lllyasviel/control_v11e_sd15_ip2p
+   description: Controlnet weights trained on sd-1.5 with ip2p conditioning.
+sdxl/controlnet/canny-sdxl:
+   description: Controlnet weights trained on sdxl-1.0 with canny conditioning.
+   source: diffusers/controlnet-canny-sdxl-1.0
+   recommended: True
+sdxl/controlnet/depth-sdxl:
+   description: Controlnet weights trained on sdxl-1.0 with depth conditioning.
+   source: diffusers/controlnet-depth-sdxl-1.0
+   recommended: True
+sdxl/controlnet/softedge-dexined-sdxl:
+   description: Controlnet weights trained on sdxl-1.0 with dexined soft edge preprocessing.
+   source: SargeZT/controlnet-sd-xl-1.0-softedge-dexined
+sdxl/controlnet/depth-16bit-zoe-sdxl:
+   description: Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).
+   source: SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe
+sdxl/controlnet/depth-zoe-sdxl:
+   description: Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).
+   source: diffusers/controlnet-zoe-depth-sdxl-1.0
 sd-1/t2i_adapter/canny-sd15:
    source: TencentARC/t2iadapter_canny_sd15v2
 sd-1/t2i_adapter/sketch-sd15:

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts

@@ -55,14 +55,13 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
     return;
   }
 
-  const isCurrentMainModelAvailable = currentModel ? models.some((m) => m.key === currentModel.key) : false;
+  const isCurrentMainModelAvailable = currentModel ? mainModels.some((m) => m.key === currentModel.key) : false;
-
   if (isCurrentMainModelAvailable) {
     return;
   }
 
   const defaultModel = state.config.sd.defaultModel;
-  const defaultModelInList = defaultModel ? models.find((m) => m.key === defaultModel) : false;
+  const defaultModelInList = defaultModel ? mainModels.find((m) => m.key === defaultModel) : false;
 
   if (defaultModelInList) {
     const result = zParameterModel.safeParse(defaultModelInList);
@@ -84,7 +83,7 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
     }
   }
 
-  const result = zParameterModel.safeParse(models[0]);
+  const result = zParameterModel.safeParse(mainModels[0]);
 
   if (!result.success) {
     log.error({ error: result.error.format() }, 'Failed to parse main model');

pyproject.toml

@@ -33,11 +33,11 @@ classifiers = [
 ]
 dependencies = [
   # Core generation dependencies, pinned for reproducible builds.
-  "accelerate==0.27.2",
+  "accelerate==0.28.0",
   "clip_anytorch==2.5.2",       # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
   "compel==2.0.2",
   "controlnet-aux==0.0.7",
-  "diffusers[torch]==0.26.3",
+  "diffusers[torch]==0.27.0",
   "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()
@@ -56,7 +56,7 @@ dependencies = [
   # Core application dependencies, pinned for reproducible builds.
   "fastapi-events==0.11.0",
   "fastapi==0.110.0",
-  "huggingface-hub==0.21.3",
+  "huggingface-hub==0.21.4",
   "pydantic-settings==2.2.1",
   "pydantic==2.6.3",
   "python-socketio==5.11.1",

tests/app/services/model_install/test_model_install.py

@@ -5,10 +5,11 @@ Test the model installer
 import platform
 import uuid
 from pathlib import Path
+from typing import Any, Dict
 
 import pytest
 from pydantic import ValidationError
-from pydantic.networks import Url
+from pydantic_core import Url
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.events.events_base import EventServiceBase
@@ -20,7 +21,7 @@ from invokeai.app.services.model_install import (
     URLModelSource,
 )
 from invokeai.app.services.model_records import UnknownModelException
-from invokeai.backend.model_manager.config import BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.config import BaseModelType, InvalidModelConfigException, ModelFormat, ModelType
 from tests.backend.model_manager.model_manager_fixtures import *  # noqa F403
 
 OS = platform.uname().system
@@ -53,7 +54,7 @@ def test_registration_meta(mm2_installer: ModelInstallServiceBase, embedding_fil
 
 def test_registration_meta_override_fail(mm2_installer: ModelInstallServiceBase, embedding_file: Path) -> None:
     key = None
-    with pytest.raises(ValidationError):
+    with pytest.raises((ValidationError, InvalidModelConfigException)):
         key = mm2_installer.register_path(embedding_file, {"name": "banana_sushi", "type": ModelType("lora")})
     assert key is None
 
@@ -263,3 +264,50 @@ def test_404_download(mm2_installer: ModelInstallServiceBase, mm2_app_config: In
     assert job.error
     assert "NOT FOUND" in job.error
     assert job.error_traceback.startswith("Traceback")
+
+
+@pytest.mark.parametrize(
+    "model_params",
+    [
+        # SDXL, Lora
+        {
+            "repo_id": "InvokeAI-test/textual_inversion_tests::learned_embeds-steps-1000.safetensors",
+            "name": "test_lora",
+            "type": "embedding",
+        },
+        # SDXL, Lora - incorrect type
+        {
+            "repo_id": "InvokeAI-test/textual_inversion_tests::learned_embeds-steps-1000.safetensors",
+            "name": "test_lora",
+            "type": "lora",
+        },
+    ],
+)
+@pytest.mark.timeout(timeout=40, method="thread")
+def test_heuristic_import_with_type(mm2_installer: ModelInstallServiceBase, model_params: Dict[str, str]):
+    """Test whether or not type is respected on configs when passed to heuristic import."""
+    assert "name" in model_params and "type" in model_params
+    config1: Dict[str, Any] = {
+        "name": f"{model_params['name']}_1",
+        "type": model_params["type"],
+        "hash": "placeholder1",
+    }
+    config2: Dict[str, Any] = {
+        "name": f"{model_params['name']}_2",
+        "type": ModelType(model_params["type"]),
+        "hash": "placeholder2",
+    }
+    assert "repo_id" in model_params
+    install_job1 = mm2_installer.heuristic_import(source=model_params["repo_id"], config=config1)
+    mm2_installer.wait_for_job(install_job1, timeout=20)
+    if model_params["type"] != "embedding":
+        assert install_job1.errored
+        assert install_job1.error_type == "InvalidModelConfigException"
+        return
+    assert install_job1.complete
+    assert install_job1.config_out if model_params["type"] == "embedding" else not install_job1.config_out
+
+    install_job2 = mm2_installer.heuristic_import(source=model_params["repo_id"], config=config2)
+    mm2_installer.wait_for_job(install_job2, timeout=20)
+    assert install_job2.complete
+    assert install_job2.config_out if model_params["type"] == "embedding" else not install_job2.config_out

tests/backend/model_manager/model_manager_fixtures.py

@@ -33,6 +33,7 @@ from invokeai.backend.model_manager.config import (
 from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache
 from invokeai.backend.util.logging import InvokeAILogger
 from tests.backend.model_manager.model_metadata.metadata_examples import (
+    HFTestLoraMetadata,
     RepoCivitaiModelMetadata1,
     RepoCivitaiVersionMetadata1,
     RepoHFMetadata1,
@@ -300,6 +301,20 @@ def mm2_session(embedding_file: Path, diffusers_dir: Path) -> Session:
             headers={"Content-Type": "application/json; charset=utf-8", "Content-Length": len(RepoHFMetadata1)},
         ),
     )
+    sess.mount(
+        "https://huggingface.co/api/models/InvokeAI-test/textual_inversion_tests?blobs=True",
+        TestAdapter(
+            HFTestLoraMetadata,
+            headers={"Content-Type": "application/json; charset=utf-8", "Content-Length": len(HFTestLoraMetadata)},
+        ),
+    )
+    sess.mount(
+        "https://huggingface.co/InvokeAI-test/textual_inversion_tests/resolve/main/learned_embeds-steps-1000.safetensors",
+        TestAdapter(
+            data,
+            headers={"Content-Type": "application/json; charset=utf-8", "Content-Length": len(data)},
+        ),
+    )
     for root, _, files in os.walk(diffusers_dir):
         for name in files:
             path = Path(root, name)