Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat(nodes): emit model loading events

Browse Source 
- remove dependency on having access to a `node` during emits, would need a bit of additional args passed through the system and I don't think its necessary at this point. this also allowed us to drop an extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the events are able to be emitted
- test all model loading events in the app and confirm socket events are received
This commit is contained in:
psychedelicious
2023-07-16 02:12:01 +10:00
parent 610c3a4512
commit 7b6159f8d6
3 changed files with 16 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
invokeai/app/invocations/latent.py
View File

@ -76,7 +76,7 @@ def get_scheduler(
scheduler_name, SCHEDULER_MAP['ddim']
)
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict()
**scheduler_info.dict(), context=context,
)
with orig_scheduler_info as orig_scheduler:
scheduler_config = orig_scheduler.config
@ -262,6 +262,7 @@ class TextToLatentsInvocation(BaseInvocation):
model_name=control_info.control_model.model_name,
model_type=ModelType.ControlNet,
base_model=control_info.control_model.base_model,
context=context,
)
)
@ -313,14 +314,14 @@ class TextToLatentsInvocation(BaseInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"})
**lora.dict(exclude={"weight"}), context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict()
**self.unet.unet.dict(), context=context,
)
with ExitStack() as exit_stack,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
@ -403,14 +404,14 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"})
**lora.dict(exclude={"weight"}), context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict()
**self.unet.unet.dict(), context=context,
)
with ExitStack() as exit_stack,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
@ -491,7 +492,7 @@ class LatentsToImageInvocation(BaseInvocation):
latents = context.services.latents.get(self.latents.latents_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.dict(), context=context,
)
with vae_info as vae:
@ -636,7 +637,7 @@ class ImageToLatentsInvocation(BaseInvocation):
#vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.dict(), context=context,
)
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))