adjusted regression tests to work with new SDModelTypes

invokeai/app/invocations/latent.py

@@ -2,10 +2,8 @@
 
 from typing import Literal, Optional, Union
 
-import diffusers
 import einops
 import torch
-from diffusers import DiffusionPipeline
 from diffusers.schedulers import SchedulerMixin as Scheduler
 from diffusers.image_processor import VaeImageProcessor
 from pydantic import BaseModel, Field
@@ -22,18 +20,16 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
     PostprocessingSettings
 from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.prompting.conditioning import get_uc_and_c_and_ec
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+from .baseinvocation import (
-import numpy as np
+    BaseInvocation, BaseInvocationOutput,
+    InvocationContext, InvocationConfig
+    )
 from ..services.image_storage import ImageType
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
-                             InvocationConfig, InvocationContext)
 from .compel import ConditioningField
 from .image import ImageField, ImageOutput, build_image_output
 
 from .model import ModelInfo, UNetField, VaeField
-from ...backend.model_management import SDModelType
 
 
 class LatentsField(BaseModel):
@@ -213,7 +209,7 @@ class TextToLatentsInvocation(BaseInvocation):
                 h_symmetry_time_pct=None,#h_symmetry_time_pct,
                 v_symmetry_time_pct=None#v_symmetry_time_pct,
             ),
-        ).add_scheduler_args_if_applicable(scheduler, eta=None)#ddim_eta)
+        ).add_scheduler_args_if_applicable(scheduler, eta=0.0)#ddim_eta)
         return conditioning_data
 
     def create_pipeline(self, unet, scheduler) -> StableDiffusionGeneratorPipeline:

tests/test_model_cache.py

@@ -2,7 +2,7 @@ import pytest
 import torch
 
 from enum import Enum
-from invokeai.backend.model_management.model_cache import ModelCache
+from invokeai.backend.model_management.model_cache import ModelCache, MODEL_CLASSES
 
 class DummyModelBase(object):
     '''Base class for dummy component of a diffusers model'''
@@ -32,13 +32,21 @@ class DummyPipeline(DummyModelBase):
     '''Dummy pipeline object is a composite of several types'''
     def __init__(self,repo_id):
         super().__init__(repo_id)
-        self.type1 = DummyModelType1('dummy/type1')
+        self.dummy_model_type1 = DummyModelType1('dummy/type1')
-        self.type2 = DummyModelType2('dummy/type2')
+        self.dummy_model_type2 = DummyModelType2('dummy/type2')
 
-class DMType(Enum):
+class DMType(str, Enum):
-    dummy_pipeline = DummyPipeline
+    dummy_pipeline = 'dummy_pipeline'
-    type1 = DummyModelType1
+    type1 = 'dummy_model_type1'
-    type2 = DummyModelType2
+    type2 = 'dummy_model_type2'
+
+MODEL_CLASSES.update(
+    {
+        DMType.dummy_pipeline: DummyPipeline,
+        DMType.type1: DummyModelType1,
+        DMType.type2: DummyModelType2,
+    }
+)
 
 cache = ModelCache(max_cache_size=4)
 
@@ -50,7 +58,7 @@ def test_pipeline_fetch():
         assert pipeline1 is not None, 'get_model() should not return None'
         assert pipeline1a is not None, 'get_model() should not return None'
         assert pipeline2 is not None, 'get_model() should not return None'
-        assert type(pipeline1)==DMType.dummy_pipeline.value,'get_model() did not return model of expected type'
+        assert type(pipeline1)==DummyPipeline,'get_model() did not return model of expected type'
         assert pipeline1==pipeline1a,'pipelines with the same repo_id should be the same'
         assert pipeline1!=pipeline2,'pipelines with different repo_ids should not be the same'
         assert len(cache.models)==2,'cache should uniquely cache models with same identity'
@@ -77,6 +85,6 @@ def test_submodel_fetch():
          cache.get_model(repo_id_or_path='dummy/pipeline2',model_type=DMType.dummy_pipeline,submodel=DMType.type1) as part2:
         assert type(part1)==DummyModelType1,'returned submodel is not of expected type'
         assert part1.device==torch.device('cuda'),'returned submodel should be in the GPU when in context'
-        assert pipeline.type1==part1,'returned submodel should match the corresponding subpart of parent model'
+        assert pipeline.dummy_model_type1==part1,'returned submodel should match the corresponding subpart of parent model'
-        assert pipeline.type1!=part2,'returned submodel should not match the subpart of a different parent'
+        assert pipeline.dummy_model_type1!=part2,'returned submodel should not match the subpart of a different parent'