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Merge branch 'main' into lstein/new-model-manager

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This commit is contained in:
Lincoln Stein
2023-05-13 22:01:34 -04:00
committed by GitHub
parent b31a6ff605 47a088d685
commit baf5451fa0
79 changed files with 717 additions and 550 deletions
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2
invokeai/app/invocations/generate.py
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@ -51,7 +51,7 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
width: int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
height: int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
model: str = Field(default="", description="The model to use (currently ignored)")
# fmt: on

4
invokeai/app/invocations/image.py
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@ -33,8 +33,8 @@ class ImageOutput(BaseInvocationOutput):
# fmt: off
type: Literal["image"] = "image"
image: ImageField = Field(default=None, description="The output image")
width: Optional[int] = Field(default=None, description="The width of the image in pixels")
height: Optional[int] = Field(default=None, description="The height of the image in pixels")
width: int = Field(description="The width of the image in pixels")
height: int = Field(description="The height of the image in pixels")
# fmt: on
class Config:

95
invokeai/app/invocations/latent.py
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@ -14,6 +14,7 @@ from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from ...backend.image_util.seamless import configure_model_padding
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import (
ConditioningData, StableDiffusionGeneratorPipeline,
@ -21,6 +22,10 @@ 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
import numpy as np
from ..services.image_storage import ImageType
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
@ -42,48 +47,59 @@ class LatentsField(BaseModel):
class LatentsOutput(BaseInvocationOutput):
"""Base class for invocations that output latents"""
#fmt: off
type: Literal["latent_output"] = "latent_output"
latents: LatentsField = Field(default=None, description="The output latents")
type: Literal["latents_output"] = "latents_output"
# Inputs
latents: LatentsField = Field(default=None, description="The output latents")
width: int = Field(description="The width of the latents in pixels")
height: int = Field(description="The height of the latents in pixels")
#fmt: on
def build_latents_output(latents_name: str, latents: torch.Tensor):
return LatentsOutput(
latents=LatentsField(latents_name=latents_name),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
class NoiseOutput(BaseInvocationOutput):
"""Invocation noise output"""
#fmt: off
type: Literal["noise_output"] = "noise_output"
type: Literal["noise_output"] = "noise_output"
# Inputs
noise: LatentsField = Field(default=None, description="The output noise")
width: int = Field(description="The width of the noise in pixels")
height: int = Field(description="The height of the noise in pixels")
#fmt: on
# TODO: this seems like a hack
scheduler_map = dict(
ddim=diffusers.DDIMScheduler,
dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_dpm_2=diffusers.KDPM2DiscreteScheduler,
k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
k_euler=diffusers.EulerDiscreteScheduler,
k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
k_heun=diffusers.HeunDiscreteScheduler,
k_lms=diffusers.LMSDiscreteScheduler,
plms=diffusers.PNDMScheduler,
)
def build_noise_output(latents_name: str, latents: torch.Tensor):
return NoiseOutput(
noise=LatentsField(latents_name=latents_name),
width=latents.size()[3] * 8,
height=latents.size()[2] * 8,
)
SAMPLER_NAME_VALUES = Literal[
tuple(list(scheduler_map.keys()))
tuple(list(SCHEDULER_MAP.keys()))
]
def get_scheduler(
context: InvocationContext,
scheduler_info: ModelInfo,
scheduler_name: str,
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
orig_scheduler_info = context.services.model_manager.get_model(**scheduler_info.dict())
with orig_scheduler_info as orig_scheduler:
scheduler_config = orig_scheduler.config
scheduler_class = scheduler_map.get(scheduler_name,'ddim')
if "_backup" in scheduler_config:
scheduler_config = scheduler_config["_backup"]
scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
scheduler = scheduler_class.from_config(scheduler_config)
# hack copied over from generate.py
if not hasattr(scheduler, 'uses_inpainting_model'):
@ -139,9 +155,7 @@ class NoiseInvocation(BaseInvocation):
name = f'{context.graph_execution_state_id}__{self.id}'
context.services.latents.set(name, noise)
return NoiseOutput(
noise=LatentsField(latents_name=name)
)
return build_noise_output(latents_name=name, latents=noise)
# Text to image
@ -157,7 +171,8 @@ class TextToLatentsInvocation(BaseInvocation):
noise: Optional[LatentsField] = Field(description="The noise to use")
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
model: str = Field(default="", description="The model to use (currently ignored)")
seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
@ -264,9 +279,7 @@ class TextToLatentsInvocation(BaseInvocation):
name = f'{context.graph_execution_state_id}__{self.id}'
context.services.latents.set(name, result_latents)
return LatentsOutput(
latents=LatentsField(latents_name=name)
)
return build_latents_output(latents_name=name, latents=result_latents)
class LatentsToLatentsInvocation(TextToLatentsInvocation):
@ -337,9 +350,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
name = f'{context.graph_execution_state_id}__{self.id}'
context.services.latents.set(name, result_latents)
return LatentsOutput(
latents=LatentsField(latents_name=name)
)
return build_latents_output(latents_name=name, latents=result_latents)
# Latent to image
@ -417,11 +428,11 @@ class ResizeLatentsInvocation(BaseInvocation):
type: Literal["lresize"] = "lresize"
# Inputs
latents: Optional[LatentsField] = Field(description="The latents to resize")
width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
mode: Optional[LATENTS_INTERPOLATION_MODE] = Field(default="bilinear", description="The interpolation mode")
antialias: Optional[bool] = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
latents: Optional[LatentsField] = Field(description="The latents to resize")
width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
antialias: bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
@ -438,7 +449,7 @@ class ResizeLatentsInvocation(BaseInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.set(name, resized_latents)
return LatentsOutput(latents=LatentsField(latents_name=name))
return build_latents_output(latents_name=name, latents=resized_latents)
class ScaleLatentsInvocation(BaseInvocation):
@ -447,10 +458,10 @@ class ScaleLatentsInvocation(BaseInvocation):
type: Literal["lscale"] = "lscale"
# Inputs
latents: Optional[LatentsField] = Field(description="The latents to scale")
scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
mode: Optional[LATENTS_INTERPOLATION_MODE] = Field(default="bilinear", description="The interpolation mode")
antialias: Optional[bool] = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
latents: Optional[LatentsField] = Field(description="The latents to scale")
scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
antialias: bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
@ -468,7 +479,7 @@ class ScaleLatentsInvocation(BaseInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.set(name, resized_latents)
return LatentsOutput(latents=LatentsField(latents_name=name))
return build_latents_output(latents_name=name, latents=resized_latents)
class ImageToLatentsInvocation(BaseInvocation):
@ -522,4 +533,4 @@ class ImageToLatentsInvocation(BaseInvocation):
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.set(name, latents)
return LatentsOutput(latents=LatentsField(latents_name=name))
return build_latents_output(latents_name=name, latents=latents)

10
invokeai/app/invocations/math.py
View File

@ -3,6 +3,7 @@
from typing import Literal
from pydantic import BaseModel, Field
import numpy as np
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
@ -73,3 +74,12 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
def invoke(self, context: InvocationContext) -> IntOutput:
return IntOutput(a=int(self.a / self.b))
class RandomIntInvocation(BaseInvocation):
"""Outputs a single random integer."""
#fmt: off
type: Literal["rand_int"] = "rand_int"
#fmt: on
def invoke(self, context: InvocationContext) -> IntOutput:
return IntOutput(a=np.random.randint(0, np.iinfo(np.int32).max))

7
invokeai/app/services/default_graphs.py
View File

@ -48,13 +48,14 @@ def create_text_to_image() -> LibraryGraph:
def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
"""Creates the default system graphs, or adds new versions if the old ones don't match"""
# TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
graphs: list[LibraryGraph] = list()
# text_to_image = graph_library.get(default_text_to_image_graph_id)
# TODO: Check if the graph is the same as the default one, and if not, update it
#if text_to_image is None:
# # TODO: Check if the graph is the same as the default one, and if not, update it
# #if text_to_image is None:
text_to_image = create_text_to_image()
graph_library.set(text_to_image)

21
invokeai/app/services/processor.py
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@ -1,3 +1,4 @@
import time
import traceback
from threading import Event, Thread, BoundedSemaphore
@ -6,6 +7,7 @@ from .invocation_queue import InvocationQueueItem
from .invoker import InvocationProcessorABC, Invoker
from ..models.exceptions import CanceledException
import invokeai.backend.util.logging as logger
class DefaultInvocationProcessor(InvocationProcessorABC):
__invoker_thread: Thread
__stop_event: Event
@ -34,8 +36,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
try:
self.__threadLimit.acquire()
while not stop_event.is_set():
queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
try:
queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
except Exception as e:
logger.debug("Exception while getting from queue: %s" % e)
if not queue_item: # Probably stopping
# do not hammer the queue
time.sleep(0.5)
continue
graph_execution_state = (
@ -124,7 +132,16 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
# Queue any further commands if invoking all
is_complete = graph_execution_state.is_complete()
if queue_item.invoke_all and not is_complete:
self.__invoker.invoke(graph_execution_state, invoke_all=True)
try:
self.__invoker.invoke(graph_execution_state, invoke_all=True)
except Exception as e:
logger.error("Error while invoking: %s" % e)
self.__invoker.services.events.emit_invocation_error(
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
source_node_id=source_node_id,
error=traceback.format_exc()
)
elif is_complete:
self.__invoker.services.events.emit_graph_execution_complete(
graph_execution_state.id