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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
fix(events): fix denoise progress percentage
- Restore calculation of step percentage but in the backend instead of client - Simplify signatures for denoise progress event callbacks - Clean up `step_callback.py` (types, do not recreate constant matrix on every step, formatting)
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psychedelicious
parent
ec4f10aed3
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0f733c42fc
@ -32,6 +32,7 @@ from invokeai.app.services.events.events_common import (
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SessionCompleteEvent,
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SessionStartedEvent,
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)
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from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
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if TYPE_CHECKING:
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from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
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@ -64,12 +65,11 @@ class EventServiceBase:
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self,
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queue_item: "SessionQueueItem",
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invocation: "BaseInvocation",
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step: int,
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total_steps: int,
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intermediate_state: PipelineIntermediateState,
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progress_image: "ProgressImage",
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) -> None:
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"""Emitted at each step during denoising of an invocation."""
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self.dispatch(InvocationDenoiseProgressEvent.build(queue_item, invocation, step, total_steps, progress_image))
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self.dispatch(InvocationDenoiseProgressEvent.build(queue_item, invocation, intermediate_state, progress_image))
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def emit_invocation_complete(
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self, queue_item: "SessionQueueItem", invocation: "BaseInvocation", output: "BaseInvocationOutput"
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@ -1,3 +1,4 @@
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from math import floor
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from typing import TYPE_CHECKING, Any, Coroutine, Optional, Protocol, TypeAlias, TypeVar
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from fastapi_events.handlers.local import local_handler
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@ -14,6 +15,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
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)
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from invokeai.app.util.misc import get_timestamp
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from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
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from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
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if TYPE_CHECKING:
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from invokeai.app.services.download.download_base import DownloadJob
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@ -68,7 +70,8 @@ def register_events(events: set[type[TEvent]], func: FastAPIEventFunc) -> None:
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:param func: The function to handle the events
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"""
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for event in events:
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local_handler.register(event_name=event.__event_name__, _func=func)
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assert hasattr(event, "__event_name__")
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local_handler.register(event_name=event.__event_name__, _func=func) # pyright: ignore [reportUnknownMemberType, reportUnknownArgumentType, reportAttributeAccessIssue]
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class QueueEventBase(EventBase):
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@ -128,16 +131,20 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
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progress_image: ProgressImage = Field(description="The progress image sent at each step during processing")
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step: int = Field(description="The current step of the invocation")
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total_steps: int = Field(description="The total number of steps in the invocation")
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order: int = Field(description="The order of the invocation in the session")
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percentage: float = Field(description="The percentage of completion of the invocation")
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@classmethod
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def build(
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cls,
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queue_item: SessionQueueItem,
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invocation: BaseInvocation,
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step: int,
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total_steps: int,
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intermediate_state: PipelineIntermediateState,
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progress_image: ProgressImage,
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) -> "InvocationDenoiseProgressEvent":
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step = intermediate_state.step
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total_steps = intermediate_state.total_steps
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order = intermediate_state.order
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return cls(
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queue_id=queue_item.queue_id,
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item_id=queue_item.item_id,
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@ -149,8 +156,20 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
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progress_image=progress_image,
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step=step,
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total_steps=total_steps,
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order=order,
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percentage=cls.calc_percentage(step, total_steps, order),
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)
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@staticmethod
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def calc_percentage(step: int, total_steps: int, scheduler_order: float) -> float:
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"""Calculate the percentage of completion of denoising."""
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if total_steps == 0:
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return 0.0
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if scheduler_order == 2:
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return floor((step + 1 + 1) / 2) / floor((total_steps + 1) / 2)
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# order == 1
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return (step + 1 + 1) / (total_steps + 1)
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class InvocationCompleteEvent(InvocationEventBase):
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"""Event model for invocation_complete"""
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@ -1,4 +1,4 @@
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from typing import TYPE_CHECKING, Callable
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from typing import TYPE_CHECKING, Callable, Optional
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import torch
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from PIL import Image
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@ -13,8 +13,36 @@ if TYPE_CHECKING:
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from invokeai.app.services.events.events_base import EventServiceBase
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from invokeai.app.services.shared.invocation_context import InvocationContextData
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# fast latents preview matrix for sdxl
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# generated by @StAlKeR7779
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SDXL_LATENT_RGB_FACTORS = [
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# R G B
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[0.3816, 0.4930, 0.5320],
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[-0.3753, 0.1631, 0.1739],
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[0.1770, 0.3588, -0.2048],
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[-0.4350, -0.2644, -0.4289],
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]
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SDXL_SMOOTH_MATRIX = [
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[0.0358, 0.0964, 0.0358],
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[0.0964, 0.4711, 0.0964],
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[0.0358, 0.0964, 0.0358],
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]
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def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
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# origingally adapted from code by @erucipe and @keturn here:
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# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
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# these updated numbers for v1.5 are from @torridgristle
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SD1_5_LATENT_RGB_FACTORS = [
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# R G B
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[0.3444, 0.1385, 0.0670], # L1
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[0.1247, 0.4027, 0.1494], # L2
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[-0.3192, 0.2513, 0.2103], # L3
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[-0.1307, -0.1874, -0.7445], # L4
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]
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def sample_to_lowres_estimated_image(
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samples: torch.Tensor, latent_rgb_factors: torch.Tensor, smooth_matrix: Optional[torch.Tensor] = None
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):
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latent_image = samples[0].permute(1, 2, 0) @ latent_rgb_factors
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if smooth_matrix is not None:
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@ -47,64 +75,12 @@ def stable_diffusion_step_callback(
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else:
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sample = intermediate_state.latents
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# TODO: This does not seem to be needed any more?
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# # txt2img provides a Tensor in the step_callback
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# # img2img provides a PipelineIntermediateState
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# if isinstance(sample, PipelineIntermediateState):
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# # this was an img2img
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# print('img2img')
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# latents = sample.latents
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# step = sample.step
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# else:
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# print('txt2img')
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# latents = sample
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# step = intermediate_state.step
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# TODO: only output a preview image when requested
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if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]:
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# fast latents preview matrix for sdxl
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# generated by @StAlKeR7779
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sdxl_latent_rgb_factors = torch.tensor(
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[
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# R G B
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[0.3816, 0.4930, 0.5320],
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[-0.3753, 0.1631, 0.1739],
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[0.1770, 0.3588, -0.2048],
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[-0.4350, -0.2644, -0.4289],
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],
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dtype=sample.dtype,
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device=sample.device,
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)
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sdxl_smooth_matrix = torch.tensor(
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[
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[0.0358, 0.0964, 0.0358],
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[0.0964, 0.4711, 0.0964],
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[0.0358, 0.0964, 0.0358],
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],
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dtype=sample.dtype,
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device=sample.device,
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)
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sdxl_latent_rgb_factors = torch.tensor(SDXL_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
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sdxl_smooth_matrix = torch.tensor(SDXL_SMOOTH_MATRIX, dtype=sample.dtype, device=sample.device)
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image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
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else:
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# origingally adapted from code by @erucipe and @keturn here:
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# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
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# these updated numbers for v1.5 are from @torridgristle
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v1_5_latent_rgb_factors = torch.tensor(
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[
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# R G B
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[0.3444, 0.1385, 0.0670], # L1
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[0.1247, 0.4027, 0.1494], # L2
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[-0.3192, 0.2513, 0.2103], # L3
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[-0.1307, -0.1874, -0.7445], # L4
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],
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dtype=sample.dtype,
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device=sample.device,
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)
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v1_5_latent_rgb_factors = torch.tensor(SD1_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
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image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
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(width, height) = image.size
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@ -116,7 +92,6 @@ def stable_diffusion_step_callback(
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events.emit_invocation_denoise_progress(
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context_data.queue_item,
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context_data.invocation,
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intermediate_state.step,
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intermediate_state.total_steps * intermediate_state.order,
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intermediate_state,
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ProgressImage(dataURL=dataURL, width=width, height=height),
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)
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