Merge branch 'main' into patch-1

README.md

@@ -148,6 +148,11 @@ not supported.
     pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
+    _For non-GPU systems:_
+    ```terminal
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
+    ``` 
+
     _For Macintoshes, either Intel or M1/M2:_
 
     ```sh

invokeai/app/invocations/latent.py

@@ -171,7 +171,7 @@ class TextToLatentsInvocation(BaseInvocation):
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
         self, context: InvocationContext, intermediate_state: PipelineIntermediateState
-    ) -> None:  
+    ) -> None:
         if (context.services.queue.is_canceled(context.graph_execution_state_id)):
             raise CanceledException
 
@@ -185,7 +185,7 @@ class TextToLatentsInvocation(BaseInvocation):
 
         diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
 
-    
+
     def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
         model_info = choose_model(model_manager, self.model)
         model_name = model_info['model_name']
@@ -195,7 +195,7 @@ class TextToLatentsInvocation(BaseInvocation):
             model=model,
             scheduler_name=self.scheduler
         )
-        
+
         if isinstance(model, DiffusionPipeline):
             for component in [model.unet, model.vae]:
                 configure_model_padding(component,
@@ -292,57 +292,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
             latent, device=model.device, dtype=latent.dtype
         )
-        
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
 
-        result_latents, result_attention_map_saver = model.latents_from_embeddings(
-            latents=initial_latents,
-            timesteps=timesteps,
-            noise=noise,
-            num_inference_steps=self.steps,
-            conditioning_data=conditioning_data,
-            callback=step_callback
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
-
-
-class LatentsToLatentsInvocation(TextToLatentsInvocation):
-    """Generates latents using latents as base image."""
-
-    type: Literal["l2l"] = "l2l"
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-        latent = context.services.latents.get(self.latents.latents_name)
-
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, state)
-
-        model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
-
-        # TODO: Verify the noise is the right size
-
-        initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
-            latent, device=model.device, dtype=latent.dtype
-        )
-        
         timesteps, _ = model.get_img2img_timesteps(
             self.steps,
             self.strength,

invokeai/app/services/graph.py

@@ -2,7 +2,6 @@
 
 import copy
 import itertools
-import traceback
 import uuid
 from types import NoneType
 from typing import (
@@ -26,7 +25,6 @@ from ..invocations.baseinvocation import (
     BaseInvocationOutput,
     InvocationContext,
 )
-from .invocation_services import InvocationServices
 
 
 class EdgeConnection(BaseModel):
@@ -215,7 +213,7 @@ InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()]
 
 
 class Graph(BaseModel):
-    id: str = Field(description="The id of this graph", default_factory=uuid.uuid4)
+    id: str = Field(description="The id of this graph", default_factory=lambda: uuid.uuid4().__str__())
     # TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
     nodes: dict[str, Annotated[InvocationsUnion, Field(discriminator="type")]] = Field(
         description="The nodes in this graph", default_factory=dict
@@ -750,9 +748,7 @@ class Graph(BaseModel):
 class GraphExecutionState(BaseModel):
     """Tracks the state of a graph execution"""
 
-    id: str = Field(
-        description="The id of the execution state", default_factory=uuid.uuid4
-    )
+    id: str = Field(description="The id of the execution state", default_factory=lambda: uuid.uuid4().__str__())
 
     # TODO: Store a reference to the graph instead of the actual graph?
     graph: Graph = Field(description="The graph being executed")
@@ -1171,7 +1167,7 @@ class LibraryGraph(BaseModel):
         if len(v) != len(set(i.alias for i in v)):
             raise ValueError("Duplicate exposed alias")
         return v
-    
+
     @root_validator
     def validate_exposed_nodes(cls, values):
         graph = values['graph']

invokeai/app/services/invocation_queue.py

@@ -1,30 +1,17 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import time
 from abc import ABC, abstractmethod
 from queue import Queue
-import time
+
+from pydantic import BaseModel, Field
 
 
-# TODO: make this serializable
-class InvocationQueueItem:
-    # session_id: str
-    graph_execution_state_id: str
-    invocation_id: str
-    invoke_all: bool
-    timestamp: float
-
-    def __init__(
-        self,
-        # session_id: str,
-        graph_execution_state_id: str,
-        invocation_id: str,
-        invoke_all: bool = False,
-    ):
-        # self.session_id = session_id
-        self.graph_execution_state_id = graph_execution_state_id
-        self.invocation_id = invocation_id
-        self.invoke_all = invoke_all
-        self.timestamp = time.time()
+class InvocationQueueItem(BaseModel):
+    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
+    invocation_id: str = Field(description="The ID of the node being invoked")
+    invoke_all: bool = Field(default=False)
+    timestamp: float = Field(default_factory=time.time)
 
 
 class InvocationQueueABC(ABC):

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -57,7 +57,7 @@ class HuggingFaceConceptsLibrary(object):
                 self.concept_list.extend(list(local_concepts_to_add))
                 return self.concept_list
             return self.concept_list
-        else:
+        elif Globals.internet_available is True:
             try:
                 models = self.hf_api.list_models(
                     filter=ModelFilter(model_name="sd-concepts-library/")
@@ -73,6 +73,8 @@ class HuggingFaceConceptsLibrary(object):
                     " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument."
                 )
             return self.concept_list
+        else: 
+            return self.concept_list
 
     def get_concept_model_path(self, concept_name: str) -> str:
         """