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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Ryan's suggested changes to extension manager/extensions
Co-Authored-By: Ryan Dick <14897797+RyanJDick@users.noreply.github.com>
This commit is contained in:
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710dc6b487
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@ -57,6 +57,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
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)
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from invokeai.backend.stable_diffusion.diffusion.custom_atttention import CustomAttnProcessor2_0
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from invokeai.backend.stable_diffusion.diffusion_backend import StableDiffusionBackend
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from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
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from invokeai.backend.stable_diffusion.extensions.preview import PreviewExt
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from invokeai.backend.stable_diffusion.extensions_manager import ExtensionsManager
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from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
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@ -790,7 +791,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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ext_manager.add_extension(PreviewExt(step_callback))
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# ext: t2i/ip adapter
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ext_manager.callbacks.setup(denoise_ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)
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unet_info = context.models.load(self.unet.unet)
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assert isinstance(unet_info.model, UNet2DConditionModel)
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@ -8,6 +8,7 @@ from tqdm.auto import tqdm
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from invokeai.app.services.config.config_default import get_config
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from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, UNetKwargs
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningMode
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from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
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from invokeai.backend.stable_diffusion.extensions_manager import ExtensionsManager
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@ -41,23 +42,23 @@ class StableDiffusionBackend:
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# ext: inpaint[pre_denoise_loop, priority=normal] (maybe init, but not sure if it needed)
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# ext: preview[pre_denoise_loop, priority=low]
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ext_manager.callbacks.pre_denoise_loop(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.PRE_DENOISE_LOOP, ctx)
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for ctx.step_index, ctx.timestep in enumerate(tqdm(ctx.inputs.timesteps)): # noqa: B020
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# ext: inpaint (apply mask to latents on non-inpaint models)
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ext_manager.callbacks.pre_step(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.PRE_STEP, ctx)
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# ext: tiles? [override: step]
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ctx.step_output = self.step(ctx, ext_manager)
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# ext: inpaint[post_step, priority=high] (apply mask to preview on non-inpaint models)
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# ext: preview[post_step, priority=low]
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ext_manager.callbacks.post_step(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.POST_STEP, ctx)
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ctx.latents = ctx.step_output.prev_sample
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# ext: inpaint[post_denoise_loop] (restore unmasked part)
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ext_manager.callbacks.post_denoise_loop(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.POST_DENOISE_LOOP, ctx)
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return ctx.latents
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@torch.inference_mode()
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@ -80,7 +81,7 @@ class StableDiffusionBackend:
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# ext: cfg_rescale [modify_noise_prediction]
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# TODO: rename
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ext_manager.callbacks.post_apply_cfg(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.POST_APPLY_CFG, ctx)
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# compute the previous noisy sample x_t -> x_t-1
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step_output = ctx.scheduler.step(ctx.noise_pred, ctx.timestep, ctx.latents, **ctx.inputs.scheduler_step_kwargs)
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@ -120,14 +121,14 @@ class StableDiffusionBackend:
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ctx.inputs.conditioning_data.to_unet_kwargs(ctx.unet_kwargs, ctx.conditioning_mode)
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# ext: controlnet/ip/t2i [pre_unet]
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ext_manager.callbacks.pre_unet(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.PRE_UNET, ctx)
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# ext: inpaint [pre_unet, priority=low]
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# or
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# ext: inpaint [override: unet_forward]
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noise_pred = self._unet_forward(**vars(ctx.unet_kwargs))
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ext_manager.callbacks.post_unet(ctx, ext_manager)
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ext_manager.run_callback(ExtensionCallbackType.POST_UNET, ctx)
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# clean up locals
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ctx.unet_kwargs = None
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12
invokeai/backend/stable_diffusion/extension_callback_type.py
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12
invokeai/backend/stable_diffusion/extension_callback_type.py
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@ -0,0 +1,12 @@
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from enum import Enum
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class ExtensionCallbackType(Enum):
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SETUP = "setup"
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PRE_DENOISE_LOOP = "pre_denoise_loop"
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POST_DENOISE_LOOP = "post_denoise_loop"
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PRE_STEP = "pre_step"
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POST_STEP = "post_step"
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PRE_UNET = "pre_unet"
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POST_UNET = "post_unet"
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POST_APPLY_CFG = "post_apply_cfg"
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@ -2,44 +2,54 @@ from __future__ import annotations
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from contextlib import contextmanager
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Callable, Dict, List, Optional
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from typing import TYPE_CHECKING, Callable, Dict, List
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import torch
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from diffusers import UNet2DConditionModel
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if TYPE_CHECKING:
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from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
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from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
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@dataclass
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class InjectionInfo:
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type: str
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name: str
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order: Optional[int]
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function: Callable
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class CallbackMetadata:
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callback_type: ExtensionCallbackType
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order: int
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def callback(name: str, order: int = 0):
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def _decorator(func):
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func.__inj_info__ = {
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"type": "callback",
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"name": name,
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"order": order,
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}
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return func
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@dataclass
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class CallbackFunctionWithMetadata:
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metadata: CallbackMetadata
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function: Callable[[DenoiseContext], None]
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def callback(callback_type: ExtensionCallbackType, order: int = 0):
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def _decorator(function):
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function._ext_metadata = CallbackMetadata(
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callback_type=callback_type,
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order=order,
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)
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return function
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return _decorator
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class ExtensionBase:
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def __init__(self):
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self.injections: List[InjectionInfo] = []
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self._callbacks: Dict[ExtensionCallbackType, List[CallbackFunctionWithMetadata]] = {}
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# Register all of the callback methods for this instance.
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for func_name in dir(self):
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func = getattr(self, func_name)
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if not callable(func) or not hasattr(func, "__inj_info__"):
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continue
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metadata = getattr(func, "_ext_metadata", None)
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if metadata is not None and isinstance(metadata, CallbackMetadata):
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if metadata.callback_type not in self._callbacks:
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self._callbacks[metadata.callback_type] = []
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self._callbacks[metadata.callback_type].append(CallbackFunctionWithMetadata(metadata, func))
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self.injections.append(InjectionInfo(**func.__inj_info__, function=func))
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def get_callbacks(self):
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return self._callbacks
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@contextmanager
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def patch_extension(self, context: DenoiseContext):
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@ -5,11 +5,11 @@ from typing import TYPE_CHECKING, Callable, Optional
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import torch
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from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
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from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase, callback
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if TYPE_CHECKING:
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from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
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from invokeai.backend.stable_diffusion.extensions_manager import ExtensionsManager
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# TODO: change event to accept image instead of latents
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@ -29,8 +29,8 @@ class PreviewExt(ExtensionBase):
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self.callback = callback
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# do last so that all other changes shown
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@callback("pre_denoise_loop", order=1000)
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def initial_preview(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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@callback(ExtensionCallbackType.PRE_DENOISE_LOOP, order=1000)
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def initial_preview(self, ctx: DenoiseContext):
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self.callback(
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PipelineIntermediateState(
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step=-1,
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@ -42,8 +42,8 @@ class PreviewExt(ExtensionBase):
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)
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# do last so that all other changes shown
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@callback("post_step", order=1000)
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def step_preview(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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@callback(ExtensionCallbackType.POST_STEP, order=1000)
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def step_preview(self, ctx: DenoiseContext):
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if hasattr(ctx.step_output, "denoised"):
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predicted_original = ctx.step_output.denoised
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elif hasattr(ctx.step_output, "pred_original_sample"):
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@ -1,8 +1,6 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from contextlib import ExitStack, contextmanager
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from functools import partial
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from typing import TYPE_CHECKING, Callable, Dict, List, Optional
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import torch
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@ -12,102 +10,50 @@ from invokeai.app.services.session_processor.session_processor_common import Can
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if TYPE_CHECKING:
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from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
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from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
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class ExtCallbacksApi(ABC):
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@abstractmethod
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def setup(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def pre_denoise_loop(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def post_denoise_loop(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def pre_step(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def post_step(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def pre_unet(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def post_unet(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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@abstractmethod
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def post_apply_cfg(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
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pass
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class ProxyCallsClass:
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def __init__(self, handler):
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self._handler = handler
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def __getattr__(self, item):
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return partial(self._handler, item)
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class CallbackInjectionPoint:
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def __init__(self):
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self.handlers = {}
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def add(self, func: Callable, order: int):
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if order not in self.handlers:
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self.handlers[order] = []
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self.handlers[order].append(func)
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def __call__(self, *args, **kwargs):
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for order in sorted(self.handlers.keys(), reverse=True):
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for handler in self.handlers[order]:
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handler(*args, **kwargs)
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from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
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from invokeai.backend.stable_diffusion.extensions.base import CallbackFunctionWithMetadata, ExtensionBase
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class ExtensionsManager:
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def __init__(self, is_canceled: Optional[Callable[[], bool]] = None):
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self.extensions: List[ExtensionBase] = []
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self._is_canceled = is_canceled
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self._callbacks: Dict[str, CallbackInjectionPoint] = {}
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self.callbacks: ExtCallbacksApi = ProxyCallsClass(self.call_callback)
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self._extensions: List[ExtensionBase] = []
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self._ordered_callbacks: Dict[ExtensionCallbackType, List[CallbackFunctionWithMetadata]] = {}
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def add_extension(self, ext: ExtensionBase):
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self.extensions.append(ext)
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def add_extension(self, extension: ExtensionBase):
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self._extensions.append(extension)
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self._regenerate_ordered_callbacks()
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self._callbacks.clear()
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def _regenerate_ordered_callbacks(self):
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"""Regenerates self._ordered_callbacks. Intended to be called each time a new extension is added."""
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self._ordered_callbacks = {}
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for ext in self.extensions:
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for inj_info in ext.injections:
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if inj_info.type == "callback":
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if inj_info.name not in self._callbacks:
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self._callbacks[inj_info.name] = CallbackInjectionPoint()
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self._callbacks[inj_info.name].add(inj_info.function, inj_info.order)
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# Fill the ordered callbacks dictionary.
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for extension in self._extensions:
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for callback_type, callbacks in extension.get_callbacks().items():
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if callback_type not in self._ordered_callbacks:
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self._ordered_callbacks[callback_type] = []
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self._ordered_callbacks[callback_type].extend(callbacks)
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else:
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raise Exception(f"Unsupported injection type: {inj_info.type}")
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# Sort each callback list.
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for callback_type, callbacks in self._ordered_callbacks.items():
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self._ordered_callbacks[callback_type] = sorted(callbacks, key=lambda x: x.metadata.order)
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def call_callback(self, name: str, *args, **kwargs):
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def run_callback(self, callback_type: ExtensionCallbackType, ctx: DenoiseContext):
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# TODO: add to patchers too?
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# and if so, should it be only in beginning of function or in for loop
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if self._is_canceled and self._is_canceled():
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raise CanceledException
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if name in self._callbacks:
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self._callbacks[name](*args, **kwargs)
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callbacks = self._ordered_callbacks.get(callback_type, [])
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for cb in callbacks:
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cb.function(ctx)
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@contextmanager
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def patch_extensions(self, context: DenoiseContext):
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with ExitStack() as exit_stack:
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for ext in self.extensions:
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for ext in self._extensions:
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exit_stack.enter_context(ext.patch_extension(context))
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yield None
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