Fix Inpainting Issues (#3744)

- fix: Inpaint not working with some schedulers: Resolves #3732
- fix: LoRA's not working at all while inpainting.

invokeai/app/invocations/generate.py

@@ -154,40 +154,42 @@ class InpaintInvocation(BaseInvocation):
 
     @contextmanager
     def load_model_old_way(self, context, scheduler):
+        def _lora_loader():
+            for lora in self.unet.loras:
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
+        
         unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
         vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
 
-        #unet = unet_info.context.model
-        #vae = vae_info.context.model
+        with vae_info as vae,\
+                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
+                unet_info as unet:
 
-        with ExitStack() as stack:
-            loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
+            device = context.services.model_manager.mgr.cache.execution_device
+            dtype = context.services.model_manager.mgr.cache.precision
 
-            with vae_info as vae,\
-                 unet_info as unet,\
-                 ModelPatcher.apply_lora_unet(unet, loras):
+            pipeline = StableDiffusionGeneratorPipeline(
+                vae=vae,
+                text_encoder=None,
+                tokenizer=None,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=None,
+                feature_extractor=None,
+                requires_safety_checker=False,
+                precision="float16" if dtype == torch.float16 else "float32",
+                execution_device=device,
+            )
 
-                device = context.services.model_manager.mgr.cache.execution_device
-                dtype = context.services.model_manager.mgr.cache.precision
-
-                pipeline = StableDiffusionGeneratorPipeline(
-                    vae=vae,
-                    text_encoder=None,
-                    tokenizer=None,
-                    unet=unet,
-                    scheduler=scheduler,
-                    safety_checker=None,
-                    feature_extractor=None,
-                    requires_safety_checker=False,
-                    precision="float16" if dtype == torch.float16 else "float32",
-                    execution_device=device,
-                )
-
-                yield OldModelInfo(
-                    name=self.unet.unet.model_name,
-                    hash="<NO-HASH>",
-                    model=pipeline,
-                )
+            yield OldModelInfo(
+                name=self.unet.unet.model_name,
+                hash="<NO-HASH>",
+                model=pipeline,
+            )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
@@ -226,21 +228,21 @@ class InpaintInvocation(BaseInvocation):
                 ),  # Shorthand for passing all of the parameters above manually
             )
 
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
+            # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
+            # each time it is called. We only need the first one.
+            generator_output = next(outputs)
 
-        image_dto = context.services.images.create(
-            image=generator_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-        )
+            image_dto = context.services.images.create(
+                image=generator_output.image,
+                image_origin=ResourceOrigin.INTERNAL,
+                image_category=ImageCategory.GENERAL,
+                session_id=context.graph_execution_state_id,
+                node_id=self.id,
+                is_intermediate=self.is_intermediate,
+            )
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+            return ImageOutput(
+                image=ImageField(image_name=image_dto.image_name),
+                width=image_dto.width,
+                height=image_dto.height,
+            )

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -127,7 +127,7 @@ class AddsMaskGuidance:
 
     def _t_for_field(self, field_name: str, t):
         if field_name == "pred_original_sample":
-            return torch.zeros_like(t, dtype=t.dtype)  # it represents t=0
+            return self.scheduler.timesteps[-1]
         return t
 
     def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor: