Run tests on this branch

.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/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):
@@ -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:
@@ -781,14 +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]
-            self._download_cache.pop(download_job.source, None)
-
+            install_job = 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()
@@ -830,7 +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/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
-        if format_type is ModelFormat.Diffusers:
-            model_type = cls.get_model_type_from_folder(model_path)
-        else:
-            model_type = cls.get_model_type_from_checkpoint(model_path)
+        model_type = ModelType(fields["type"]) if "type" in fields and fields["type"] else None
+        if not model_type:
+            if format_type is ModelFormat.Diffusers:
+                model_type = cls.get_model_type_from_folder(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)

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)