mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'lstein/default-model-install' into release/invokeai-3-0-beta
This commit is contained in:
Lincoln Stein
commit
e95cb3aa71
@ -20,7 +20,6 @@ from invokeai.version.invokeai_version import __version__
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from ..services.default_graphs import create_system_graphs
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from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
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from ..services.restoration_services import RestorationServices
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from ..services.graph import GraphExecutionState, LibraryGraph
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from ..services.image_file_storage import DiskImageFileStorage
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from ..services.invocation_queue import MemoryInvocationQueue
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@ -58,7 +57,7 @@ class ApiDependencies:
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@staticmethod
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def initialize(config, event_handler_id: int, logger: Logger = logger):
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logger.debug(f'InvokeAI version {__version__}')
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logger.debug(f"InvokeAI version {__version__}")
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logger.debug(f"Internet connectivity is {config.internet_available}")
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events = FastAPIEventService(event_handler_id)
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@ -117,7 +116,7 @@ class ApiDependencies:
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)
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services = InvocationServices(
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model_manager=ModelManagerService(config,logger),
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model_manager=ModelManagerService(config, logger),
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events=events,
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latents=latents,
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images=images,
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@ -129,7 +128,6 @@ class ApiDependencies:
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),
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graph_execution_manager=graph_execution_manager,
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processor=DefaultInvocationProcessor(),
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restoration=RestorationServices(config, logger),
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configuration=config,
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logger=logger,
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)
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@ -54,7 +54,6 @@ from .services.invocation_services import InvocationServices
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from .services.invoker import Invoker
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from .services.model_manager_service import ModelManagerService
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from .services.processor import DefaultInvocationProcessor
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from .services.restoration_services import RestorationServices
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from .services.sqlite import SqliteItemStorage
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import torch
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@ -295,7 +294,6 @@ def invoke_cli():
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),
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graph_execution_manager=graph_execution_manager,
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processor=DefaultInvocationProcessor(),
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restoration=RestorationServices(config,logger=logger),
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logger=logger,
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configuration=config,
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)
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@ -1,55 +0,0 @@
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from typing import Literal, Optional
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from pydantic import Field
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from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
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from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
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from .image import ImageOutput
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class RestoreFaceInvocation(BaseInvocation):
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"""Restores faces in an image."""
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# fmt: off
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type: Literal["restore_face"] = "restore_face"
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# Inputs
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image: Optional[ImageField] = Field(description="The input image")
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strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration" )
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# fmt: on
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"tags": ["restoration", "image"],
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},
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}
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get_pil_image(self.image.image_name)
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results = context.services.restoration.upscale_and_reconstruct(
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image_list=[[image, 0]],
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upscale=None,
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strength=self.strength, # GFPGAN strength
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save_original=False,
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image_callback=None,
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)
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# Results are image and seed, unwrap for now
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# TODO: can this return multiple results?
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image_dto = context.services.images.create(
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image=results[0][0],
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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is_intermediate=self.is_intermediate,
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)
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return ImageOutput(
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image=ImageField(image_name=image_dto.image_name),
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width=image_dto.width,
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height=image_dto.height,
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)
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@ -1,48 +1,112 @@
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# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Literal, Optional
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# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
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from pathlib import Path
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from typing import Literal, Union, cast
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import cv2 as cv
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import numpy as np
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from PIL import Image
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from pydantic import Field
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from realesrgan import RealESRGANer
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from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
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from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
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from .baseinvocation import BaseInvocation, InvocationContext
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from .image import ImageOutput
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# TODO: Populate this from disk?
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# TODO: Use model manager to load?
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REALESRGAN_MODELS = Literal[
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"RealESRGAN_x4plus.pth",
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"RealESRGAN_x4plus_anime_6B.pth",
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"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
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]
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class UpscaleInvocation(BaseInvocation):
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"""Upscales an image."""
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# fmt: off
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type: Literal["upscale"] = "upscale"
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class RealESRGANInvocation(BaseInvocation):
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"""Upscales an image using RealESRGAN."""
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# Inputs
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image: Optional[ImageField] = Field(description="The input image", default=None)
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strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
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level: Literal[2, 4] = Field(default=2, description="The upscale level")
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# fmt: on
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"tags": ["upscaling", "image"],
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},
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}
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type: Literal["realesrgan"] = "realesrgan"
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image: Union[ImageField, None] = Field(default=None, description="The input image")
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model_name: REALESRGAN_MODELS = Field(
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default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use"
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)
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.services.images.get_pil_image(self.image.image_name)
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results = context.services.restoration.upscale_and_reconstruct(
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image_list=[[image, 0]],
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upscale=(self.level, self.strength),
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strength=0.0, # GFPGAN strength
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save_original=False,
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image_callback=None,
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image = context.services.images.get_pil_image(self.image.image_name) # type: ignore
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models_dir = cast(Path, context.services.configuration.root_dir) / Path("models/") # type: ignore
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rrdbnet_model = None
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netscale = None
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model_path = None
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if self.model_name in [
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"RealESRGAN_x4plus.pth",
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"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
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]:
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# x4 RRDBNet model
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rrdbnet_model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=4,
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)
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netscale = 4
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elif self.model_name == "RealESRGAN_x4plus_anime_6B.pth":
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# x4 RRDBNet model, 6 blocks
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rrdbnet_model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=6, # 6 blocks
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num_grow_ch=32,
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scale=4,
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)
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netscale = 4
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# TODO: add x2 models handling?
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# elif self.model_name in ["RealESRGAN_x2plus"]:
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# # x2 RRDBNet model
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# model = RRDBNet(
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# num_in_ch=3,
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# num_out_ch=3,
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# num_feat=64,
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# num_block=23,
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# num_grow_ch=32,
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# scale=2,
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# )
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# model_path = Path()
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# netscale = 2
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else:
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msg = f"Invalid RealESRGAN model: {self.model_name}"
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context.services.logger.error(msg)
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raise ValueError(msg)
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model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=str(models_dir / model_path),
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model=rrdbnet_model,
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half=False,
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)
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# Results are image and seed, unwrap for now
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# TODO: can this return multiple results?
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# prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
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cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
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# We can pass an `outscale` value here, but it just resizes the image by that factor after
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# upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
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# upscaling, you'll need to add a resize node after this one.
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upscaled_image, img_mode = upsampler.enhance(cv_image)
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# back to PIL
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pil_image = Image.fromarray(
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cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)
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).convert("RGBA")
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image_dto = context.services.images.create(
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image=results[0][0],
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image=pil_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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@ -271,13 +271,13 @@ class InvokeAISettings(BaseSettings):
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@classmethod
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def _excluded(self)->List[str]:
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# combination of deprecated parameters and internal ones that shouldn't be exposed
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# internal fields that shouldn't be exposed as command line options
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return ['type','initconf']
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@classmethod
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def _excluded_from_yaml(self)->List[str]:
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# combination of deprecated parameters and internal ones that shouldn't be exposed
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return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model']
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# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
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return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore']
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class Config:
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env_file_encoding = 'utf-8'
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@ -366,7 +366,7 @@ setting environment variables INVOKEAI_<setting>.
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log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
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nsfw_checker : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
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patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
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restore : bool = Field(default=True, description="Enable/disable face restoration code", category='Features')
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restore : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
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always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
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free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
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@ -10,7 +10,6 @@ if TYPE_CHECKING:
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from invokeai.app.services.model_manager_service import ModelManagerServiceBase
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from invokeai.app.services.events import EventServiceBase
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from invokeai.app.services.latent_storage import LatentsStorageBase
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from invokeai.app.services.restoration_services import RestorationServices
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from invokeai.app.services.invocation_queue import InvocationQueueABC
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from invokeai.app.services.item_storage import ItemStorageABC
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from invokeai.app.services.config import InvokeAISettings
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@ -34,7 +33,6 @@ class InvocationServices:
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model_manager: "ModelManagerServiceBase"
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processor: "InvocationProcessorABC"
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queue: "InvocationQueueABC"
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restoration: "RestorationServices"
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def __init__(
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self,
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@ -50,7 +48,6 @@ class InvocationServices:
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model_manager: "ModelManagerServiceBase",
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processor: "InvocationProcessorABC",
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queue: "InvocationQueueABC",
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restoration: "RestorationServices",
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):
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self.board_images = board_images
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self.boards = boards
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@ -65,4 +62,3 @@ class InvocationServices:
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self.model_manager = model_manager
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self.processor = processor
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self.queue = queue
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self.restoration = restoration
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@ -1,113 +0,0 @@
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import sys
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import traceback
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import torch
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from typing import types
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from ...backend.restoration import Restoration
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from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
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# This should be a real base class for postprocessing functions,
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# but right now we just instantiate the existing gfpgan, esrgan
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# and codeformer functions.
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class RestorationServices:
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'''Face restoration and upscaling'''
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def __init__(self,args,logger:types.ModuleType):
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try:
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gfpgan, codeformer, esrgan = None, None, None
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if args.restore or args.esrgan:
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restoration = Restoration()
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# TODO: redo for new model structure
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if False and args.restore:
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gfpgan, codeformer = restoration.load_face_restore_models(
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args.gfpgan_model_path
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)
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else:
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logger.info("Face restoration disabled")
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if False and args.esrgan:
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esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
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else:
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logger.info("Upscaling disabled")
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else:
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logger.info("Face restoration and upscaling disabled")
|
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except (ModuleNotFoundError, ImportError):
|
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print(traceback.format_exc(), file=sys.stderr)
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logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
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self.device = torch.device(choose_torch_device())
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self.gfpgan = gfpgan
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self.codeformer = codeformer
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self.esrgan = esrgan
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self.logger = logger
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self.logger.info('Face restoration initialized')
|
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# note that this one method does gfpgan and codepath reconstruction, as well as
|
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# esrgan upscaling
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# TO DO: refactor into separate methods
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def upscale_and_reconstruct(
|
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self,
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image_list,
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facetool="gfpgan",
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upscale=None,
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upscale_denoise_str=0.75,
|
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strength=0.0,
|
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codeformer_fidelity=0.75,
|
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save_original=False,
|
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image_callback=None,
|
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prefix=None,
|
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):
|
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results = []
|
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for r in image_list:
|
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image, seed = r
|
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try:
|
||||
if strength > 0:
|
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if self.gfpgan is not None or self.codeformer is not None:
|
||||
if facetool == "gfpgan":
|
||||
if self.gfpgan is None:
|
||||
self.logger.info(
|
||||
"GFPGAN not found. Face restoration is disabled."
|
||||
)
|
||||
else:
|
||||
image = self.gfpgan.process(image, strength, seed)
|
||||
if facetool == "codeformer":
|
||||
if self.codeformer is None:
|
||||
self.logger.info(
|
||||
"CodeFormer not found. Face restoration is disabled."
|
||||
)
|
||||
else:
|
||||
cf_device = (
|
||||
CPU_DEVICE if self.device == MPS_DEVICE else self.device
|
||||
)
|
||||
image = self.codeformer.process(
|
||||
image=image,
|
||||
strength=strength,
|
||||
device=cf_device,
|
||||
seed=seed,
|
||||
fidelity=codeformer_fidelity,
|
||||
)
|
||||
else:
|
||||
self.logger.info("Face Restoration is disabled.")
|
||||
if upscale is not None:
|
||||
if self.esrgan is not None:
|
||||
if len(upscale) < 2:
|
||||
upscale.append(0.75)
|
||||
image = self.esrgan.process(
|
||||
image,
|
||||
upscale[1],
|
||||
seed,
|
||||
int(upscale[0]),
|
||||
denoise_str=upscale_denoise_str,
|
||||
)
|
||||
else:
|
||||
self.logger.info("ESRGAN is disabled. Image not upscaled.")
|
||||
except Exception as e:
|
||||
self.logger.info(
|
||||
f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
|
||||
)
|
||||
|
||||
if image_callback is not None:
|
||||
image_callback(image, seed, upscaled=True, use_prefix=prefix)
|
||||
else:
|
||||
r[0] = image
|
||||
|
||||
results.append([image, seed])
|
||||
|
||||
return results
|
||||
@ -30,8 +30,6 @@ from huggingface_hub import login as hf_hub_login
|
||||
from omegaconf import OmegaConf
|
||||
from tqdm import tqdm
|
||||
from transformers import (
|
||||
AutoProcessor,
|
||||
CLIPSegForImageSegmentation,
|
||||
CLIPTextModel,
|
||||
CLIPTokenizer,
|
||||
AutoFeatureExtractor,
|
||||
@ -45,7 +43,6 @@ from invokeai.app.services.config import (
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
|
||||
from invokeai.frontend.install.widgets import (
|
||||
SingleSelectColumns,
|
||||
CenteredButtonPress,
|
||||
IntTitleSlider,
|
||||
set_min_terminal_size,
|
||||
@ -226,64 +223,30 @@ def download_conversion_models():
|
||||
|
||||
# ---------------------------------------------
|
||||
def download_realesrgan():
|
||||
logger.info("Installing models from RealESRGAN...")
|
||||
model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth"
|
||||
wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
|
||||
|
||||
model_dest = config.root_path / "models/core/upscaling/realesrgan/realesr-general-x4v3.pth"
|
||||
wdn_model_dest = config.root_path / "models/core/upscaling/realesrgan/realesr-general-wdn-x4v3.pth"
|
||||
|
||||
download_with_progress_bar(model_url, str(model_dest), "RealESRGAN")
|
||||
download_with_progress_bar(wdn_model_url, str(wdn_model_dest), "RealESRGANwdn")
|
||||
|
||||
|
||||
def download_gfpgan():
|
||||
logger.info("Installing GFPGAN models...")
|
||||
for model in (
|
||||
[
|
||||
"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",
|
||||
"./models/core/face_restoration/gfpgan/GFPGANv1.4.pth",
|
||||
],
|
||||
[
|
||||
"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",
|
||||
"./models/core/face_restoration/gfpgan/weights/detection_Resnet50_Final.pth",
|
||||
],
|
||||
[
|
||||
"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",
|
||||
"./models/core/face_restoration/gfpgan/weights/parsing_parsenet.pth",
|
||||
],
|
||||
):
|
||||
model_url, model_dest = model[0], config.root_path / model[1]
|
||||
download_with_progress_bar(model_url, str(model_dest), "GFPGAN weights")
|
||||
|
||||
logger.info("Installing RealESRGAN models...")
|
||||
URLs = [
|
||||
dict(
|
||||
url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
||||
dest = "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
|
||||
description = "RealESRGAN_x4plus.pth",
|
||||
),
|
||||
dict(
|
||||
url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
||||
dest = "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
|
||||
description = "RealESRGAN_x4plus_anime_6B.pth",
|
||||
),
|
||||
dict(
|
||||
url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
||||
dest= "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
||||
description = "ESRGAN_SRx4_DF2KOST_official.pth",
|
||||
),
|
||||
]
|
||||
for model in URLs:
|
||||
download_with_progress_bar(model['url'], config.models_path / model['dest'], model['description'])
|
||||
|
||||
# ---------------------------------------------
|
||||
def download_codeformer():
|
||||
logger.info("Installing CodeFormer model file...")
|
||||
model_url = (
|
||||
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
|
||||
)
|
||||
model_dest = config.root_path / "models/core/face_restoration/codeformer/codeformer.pth"
|
||||
download_with_progress_bar(model_url, str(model_dest), "CodeFormer")
|
||||
|
||||
|
||||
# ---------------------------------------------
|
||||
def download_clipseg():
|
||||
logger.info("Installing clipseg model for text-based masking...")
|
||||
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
|
||||
try:
|
||||
hf_download_from_pretrained(AutoProcessor, CLIPSEG_MODEL, config.root_path / 'models/core/misc/clipseg')
|
||||
hf_download_from_pretrained(CLIPSegForImageSegmentation, CLIPSEG_MODEL, config.root_path / 'models/core/misc/clipseg')
|
||||
except Exception:
|
||||
logger.info("Error installing clipseg model:")
|
||||
logger.info(traceback.format_exc())
|
||||
|
||||
|
||||
def download_support_models():
|
||||
download_realesrgan()
|
||||
download_gfpgan()
|
||||
download_codeformer()
|
||||
download_clipseg()
|
||||
download_conversion_models()
|
||||
|
||||
# -------------------------------------
|
||||
@ -858,9 +821,9 @@ def main():
|
||||
download_support_models()
|
||||
|
||||
if opt.skip_sd_weights:
|
||||
logger.info("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
|
||||
logger.warning("SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST")
|
||||
elif models_to_download:
|
||||
logger.info("\n** DOWNLOADING DIFFUSION WEIGHTS **")
|
||||
logger.info("DOWNLOADING DIFFUSION WEIGHTS")
|
||||
process_and_execute(opt, models_to_download)
|
||||
|
||||
postscript(errors=errors)
|
||||
|
||||
@ -117,6 +117,7 @@ class ModelInstall(object):
|
||||
|
||||
# supplement with entries in models.yaml
|
||||
installed_models = self.mgr.list_models()
|
||||
|
||||
for md in installed_models:
|
||||
base = md['base_model']
|
||||
model_type = md['model_type']
|
||||
@ -134,6 +135,12 @@ class ModelInstall(object):
|
||||
)
|
||||
return {x : model_dict[x] for x in sorted(model_dict.keys(),key=lambda y: model_dict[y].name.lower())}
|
||||
|
||||
def list_models(self, model_type):
|
||||
installed = self.mgr.list_models(model_type=model_type)
|
||||
print(f'Installed models of type `{model_type}`:')
|
||||
for i in installed:
|
||||
print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}")
|
||||
|
||||
def starter_models(self)->Set[str]:
|
||||
models = set()
|
||||
for key, value in self.datasets.items():
|
||||
|
||||
@ -908,7 +908,6 @@ class ModelManager(object):
|
||||
from invokeai.backend.install.model_install_backend import ModelInstall
|
||||
from invokeai.frontend.install.model_install import ask_user_for_prediction_type
|
||||
|
||||
|
||||
class ScanAndImport(ModelSearch):
|
||||
def __init__(self, directories, logger, ignore: Set[Path], installer: ModelInstall):
|
||||
super().__init__(directories, logger)
|
||||
|
||||
@ -1,4 +0,0 @@
|
||||
"""
|
||||
Initialization file for the invokeai.backend.restoration package
|
||||
"""
|
||||
from .base import Restoration
|
||||
@ -1,45 +0,0 @@
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
class Restoration:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def load_face_restore_models(
|
||||
self, gfpgan_model_path="./models/core/face_restoration/gfpgan/GFPGANv1.4.pth"
|
||||
):
|
||||
# Load GFPGAN
|
||||
gfpgan = self.load_gfpgan(gfpgan_model_path)
|
||||
if gfpgan.gfpgan_model_exists:
|
||||
logger.info("GFPGAN Initialized")
|
||||
else:
|
||||
logger.info("GFPGAN Disabled")
|
||||
gfpgan = None
|
||||
|
||||
# Load CodeFormer
|
||||
codeformer = self.load_codeformer()
|
||||
if codeformer.codeformer_model_exists:
|
||||
logger.info("CodeFormer Initialized")
|
||||
else:
|
||||
logger.info("CodeFormer Disabled")
|
||||
codeformer = None
|
||||
|
||||
return gfpgan, codeformer
|
||||
|
||||
# Face Restore Models
|
||||
def load_gfpgan(self, gfpgan_model_path):
|
||||
from .gfpgan import GFPGAN
|
||||
|
||||
return GFPGAN(gfpgan_model_path)
|
||||
|
||||
def load_codeformer(self):
|
||||
from .codeformer import CodeFormerRestoration
|
||||
|
||||
return CodeFormerRestoration()
|
||||
|
||||
# Upscale Models
|
||||
def load_esrgan(self, esrgan_bg_tile=400):
|
||||
from .realesrgan import ESRGAN
|
||||
|
||||
esrgan = ESRGAN(esrgan_bg_tile)
|
||||
logger.info("ESRGAN Initialized")
|
||||
return esrgan
|
||||
@ -1,120 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
pretrained_model_url = (
|
||||
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
|
||||
)
|
||||
|
||||
|
||||
class CodeFormerRestoration:
|
||||
def __init__(
|
||||
self, codeformer_dir="./models/core/face_restoration/codeformer", codeformer_model_path="codeformer.pth"
|
||||
) -> None:
|
||||
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
codeformer_dir = self.globals.root_dir / codeformer_dir
|
||||
self.model_path = codeformer_dir / codeformer_model_path
|
||||
self.codeformer_model_exists = self.model_path.exists()
|
||||
|
||||
if not self.codeformer_model_exists:
|
||||
logger.error(f"NOT FOUND: CodeFormer model not found at {self.model_path}")
|
||||
sys.path.append(os.path.abspath(codeformer_dir))
|
||||
|
||||
def process(self, image, strength, device, seed=None, fidelity=0.75):
|
||||
if seed is not None:
|
||||
logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}")
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
from basicsr.utils import img2tensor, tensor2img
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
|
||||
from PIL import Image
|
||||
from torchvision.transforms.functional import normalize
|
||||
|
||||
from .codeformer_arch import CodeFormer
|
||||
|
||||
cf_class = CodeFormer
|
||||
|
||||
cf = cf_class(
|
||||
dim_embd=512,
|
||||
codebook_size=1024,
|
||||
n_head=8,
|
||||
n_layers=9,
|
||||
connect_list=["32", "64", "128", "256"],
|
||||
).to(device)
|
||||
|
||||
# note that this file should already be downloaded and cached at
|
||||
# this point
|
||||
checkpoint_path = load_file_from_url(
|
||||
url=pretrained_model_url,
|
||||
model_dir=os.path.abspath(os.path.dirname(self.model_path)),
|
||||
progress=True,
|
||||
)
|
||||
checkpoint = torch.load(checkpoint_path)["params_ema"]
|
||||
cf.load_state_dict(checkpoint)
|
||||
cf.eval()
|
||||
|
||||
image = image.convert("RGB")
|
||||
# Codeformer expects a BGR np array; make array and flip channels
|
||||
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
||||
|
||||
face_helper = FaceRestoreHelper(
|
||||
upscale_factor=1,
|
||||
use_parse=True,
|
||||
device=device,
|
||||
model_rootpath = self.globals.model_path / 'core/face_restoration/gfpgan/weights'
|
||||
)
|
||||
face_helper.clean_all()
|
||||
face_helper.read_image(bgr_image_array)
|
||||
face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
|
||||
face_helper.align_warp_face()
|
||||
|
||||
for idx, cropped_face in enumerate(face_helper.cropped_faces):
|
||||
cropped_face_t = img2tensor(
|
||||
cropped_face / 255.0, bgr2rgb=True, float32=True
|
||||
)
|
||||
normalize(
|
||||
cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True
|
||||
)
|
||||
cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
|
||||
|
||||
try:
|
||||
with torch.no_grad():
|
||||
output = cf(cropped_face_t, w=fidelity, adain=True)[0]
|
||||
restored_face = tensor2img(
|
||||
output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)
|
||||
)
|
||||
del output
|
||||
torch.cuda.empty_cache()
|
||||
except RuntimeError as error:
|
||||
logger.error(f"Failed inference for CodeFormer: {error}.")
|
||||
restored_face = cropped_face
|
||||
|
||||
restored_face = restored_face.astype("uint8")
|
||||
face_helper.add_restored_face(restored_face)
|
||||
|
||||
face_helper.get_inverse_affine(None)
|
||||
|
||||
restored_img = face_helper.paste_faces_to_input_image()
|
||||
|
||||
# Flip the channels back to RGB
|
||||
res = Image.fromarray(restored_img[..., ::-1])
|
||||
|
||||
if strength < 1.0:
|
||||
# Resize the image to the new image if the sizes have changed
|
||||
if restored_img.size != image.size:
|
||||
image = image.resize(res.size)
|
||||
res = Image.blend(image, res, strength)
|
||||
|
||||
cf = None
|
||||
|
||||
return res
|
||||
@ -1,325 +0,0 @@
|
||||
import math
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from basicsr.utils import get_root_logger
|
||||
from basicsr.utils.registry import ARCH_REGISTRY
|
||||
from torch import Tensor, nn
|
||||
|
||||
from .vqgan_arch import *
|
||||
|
||||
|
||||
def calc_mean_std(feat, eps=1e-5):
|
||||
"""Calculate mean and std for adaptive_instance_normalization.
|
||||
|
||||
Args:
|
||||
feat (Tensor): 4D tensor.
|
||||
eps (float): A small value added to the variance to avoid
|
||||
divide-by-zero. Default: 1e-5.
|
||||
"""
|
||||
size = feat.size()
|
||||
assert len(size) == 4, "The input feature should be 4D tensor."
|
||||
b, c = size[:2]
|
||||
feat_var = feat.view(b, c, -1).var(dim=2) + eps
|
||||
feat_std = feat_var.sqrt().view(b, c, 1, 1)
|
||||
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
|
||||
return feat_mean, feat_std
|
||||
|
||||
|
||||
def adaptive_instance_normalization(content_feat, style_feat):
|
||||
"""Adaptive instance normalization.
|
||||
|
||||
Adjust the reference features to have the similar color and illuminations
|
||||
as those in the degradate features.
|
||||
|
||||
Args:
|
||||
content_feat (Tensor): The reference feature.
|
||||
style_feat (Tensor): The degradate features.
|
||||
"""
|
||||
size = content_feat.size()
|
||||
style_mean, style_std = calc_mean_std(style_feat)
|
||||
content_mean, content_std = calc_mean_std(content_feat)
|
||||
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(
|
||||
size
|
||||
)
|
||||
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
|
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module):
|
||||
"""
|
||||
This is a more standard version of the position embedding, very similar to the one
|
||||
used by the Attention is all you need paper, generalized to work on images.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
|
||||
):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperature = temperature
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
if mask is None:
|
||||
mask = torch.zeros(
|
||||
(x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool
|
||||
)
|
||||
not_mask = ~mask
|
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
||||
|
||||
pos_x = x_embed[:, :, :, None] / dim_t
|
||||
pos_y = y_embed[:, :, :, None] / dim_t
|
||||
pos_x = torch.stack(
|
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos_y = torch.stack(
|
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
||||
return pos
|
||||
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
|
||||
class TransformerSALayer(nn.Module):
|
||||
def __init__(
|
||||
self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model - MLP
|
||||
self.linear1 = nn.Linear(embed_dim, dim_mlp)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_mlp, embed_dim)
|
||||
|
||||
self.norm1 = nn.LayerNorm(embed_dim)
|
||||
self.norm2 = nn.LayerNorm(embed_dim)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None,
|
||||
):
|
||||
# self attention
|
||||
tgt2 = self.norm1(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(
|
||||
q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
|
||||
)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
|
||||
# ffn
|
||||
tgt2 = self.norm2(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
return tgt
|
||||
|
||||
|
||||
class Fuse_sft_block(nn.Module):
|
||||
def __init__(self, in_ch, out_ch):
|
||||
super().__init__()
|
||||
self.encode_enc = ResBlock(2 * in_ch, out_ch)
|
||||
|
||||
self.scale = nn.Sequential(
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
|
||||
)
|
||||
|
||||
self.shift = nn.Sequential(
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, enc_feat, dec_feat, w=1):
|
||||
enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
|
||||
scale = self.scale(enc_feat)
|
||||
shift = self.shift(enc_feat)
|
||||
residual = w * (dec_feat * scale + shift)
|
||||
out = dec_feat + residual
|
||||
return out
|
||||
|
||||
|
||||
@ARCH_REGISTRY.register()
|
||||
class CodeFormer(VQAutoEncoder):
|
||||
def __init__(
|
||||
self,
|
||||
dim_embd=512,
|
||||
n_head=8,
|
||||
n_layers=9,
|
||||
codebook_size=1024,
|
||||
latent_size=256,
|
||||
connect_list=["32", "64", "128", "256"],
|
||||
fix_modules=["quantize", "generator"],
|
||||
):
|
||||
super(CodeFormer, self).__init__(
|
||||
512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
|
||||
)
|
||||
|
||||
if fix_modules is not None:
|
||||
for module in fix_modules:
|
||||
for param in getattr(self, module).parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
self.connect_list = connect_list
|
||||
self.n_layers = n_layers
|
||||
self.dim_embd = dim_embd
|
||||
self.dim_mlp = dim_embd * 2
|
||||
|
||||
self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
|
||||
self.feat_emb = nn.Linear(256, self.dim_embd)
|
||||
|
||||
# transformer
|
||||
self.ft_layers = nn.Sequential(
|
||||
*[
|
||||
TransformerSALayer(
|
||||
embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0
|
||||
)
|
||||
for _ in range(self.n_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# logits_predict head
|
||||
self.idx_pred_layer = nn.Sequential(
|
||||
nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)
|
||||
)
|
||||
|
||||
self.channels = {
|
||||
"16": 512,
|
||||
"32": 256,
|
||||
"64": 256,
|
||||
"128": 128,
|
||||
"256": 128,
|
||||
"512": 64,
|
||||
}
|
||||
|
||||
# after second residual block for > 16, before attn layer for ==16
|
||||
self.fuse_encoder_block = {
|
||||
"512": 2,
|
||||
"256": 5,
|
||||
"128": 8,
|
||||
"64": 11,
|
||||
"32": 14,
|
||||
"16": 18,
|
||||
}
|
||||
# after first residual block for > 16, before attn layer for ==16
|
||||
self.fuse_generator_block = {
|
||||
"16": 6,
|
||||
"32": 9,
|
||||
"64": 12,
|
||||
"128": 15,
|
||||
"256": 18,
|
||||
"512": 21,
|
||||
}
|
||||
|
||||
# fuse_convs_dict
|
||||
self.fuse_convs_dict = nn.ModuleDict()
|
||||
for f_size in self.connect_list:
|
||||
in_ch = self.channels[f_size]
|
||||
self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
|
||||
|
||||
def _init_weights(self, module):
|
||||
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||||
module.weight.data.normal_(mean=0.0, std=0.02)
|
||||
if isinstance(module, nn.Linear) and module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
|
||||
# ################### Encoder #####################
|
||||
enc_feat_dict = {}
|
||||
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
|
||||
for i, block in enumerate(self.encoder.blocks):
|
||||
x = block(x)
|
||||
if i in out_list:
|
||||
enc_feat_dict[str(x.shape[-1])] = x.clone()
|
||||
|
||||
lq_feat = x
|
||||
# ################# Transformer ###################
|
||||
# quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
|
||||
pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)
|
||||
# BCHW -> BC(HW) -> (HW)BC
|
||||
feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))
|
||||
query_emb = feat_emb
|
||||
# Transformer encoder
|
||||
for layer in self.ft_layers:
|
||||
query_emb = layer(query_emb, query_pos=pos_emb)
|
||||
|
||||
# output logits
|
||||
logits = self.idx_pred_layer(query_emb) # (hw)bn
|
||||
logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n
|
||||
|
||||
if code_only: # for training stage II
|
||||
# logits doesn't need softmax before cross_entropy loss
|
||||
return logits, lq_feat
|
||||
|
||||
# ################# Quantization ###################
|
||||
# if self.training:
|
||||
# quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
|
||||
# # b(hw)c -> bc(hw) -> bchw
|
||||
# quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
|
||||
# ------------
|
||||
soft_one_hot = F.softmax(logits, dim=2)
|
||||
_, top_idx = torch.topk(soft_one_hot, 1, dim=2)
|
||||
quant_feat = self.quantize.get_codebook_feat(
|
||||
top_idx, shape=[x.shape[0], 16, 16, 256]
|
||||
)
|
||||
# preserve gradients
|
||||
# quant_feat = lq_feat + (quant_feat - lq_feat).detach()
|
||||
|
||||
if detach_16:
|
||||
quant_feat = quant_feat.detach() # for training stage III
|
||||
if adain:
|
||||
quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
|
||||
|
||||
# ################## Generator ####################
|
||||
x = quant_feat
|
||||
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
|
||||
|
||||
for i, block in enumerate(self.generator.blocks):
|
||||
x = block(x)
|
||||
if i in fuse_list: # fuse after i-th block
|
||||
f_size = str(x.shape[-1])
|
||||
if w > 0:
|
||||
x = self.fuse_convs_dict[f_size](
|
||||
enc_feat_dict[f_size].detach(), x, w
|
||||
)
|
||||
out = x
|
||||
# logits doesn't need softmax before cross_entropy loss
|
||||
return out, logits, lq_feat
|
||||
@ -1,84 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
class GFPGAN:
|
||||
def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
if not os.path.isabs(gfpgan_model_path):
|
||||
gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
|
||||
self.model_path = gfpgan_model_path
|
||||
self.gfpgan_model_exists = os.path.isfile(self.model_path)
|
||||
|
||||
if not self.gfpgan_model_exists:
|
||||
logger.error(f"NOT FOUND: GFPGAN model not found at {self.model_path}")
|
||||
return None
|
||||
|
||||
def model_exists(self):
|
||||
return os.path.isfile(self.model_path)
|
||||
|
||||
def process(self, image, strength: float, seed: str = None):
|
||||
if seed is not None:
|
||||
logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}")
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
cwd = os.getcwd()
|
||||
os.chdir(self.globals.root_dir / 'models')
|
||||
try:
|
||||
from gfpgan import GFPGANer
|
||||
|
||||
self.gfpgan = GFPGANer(
|
||||
model_path=self.model_path,
|
||||
upscale=1,
|
||||
arch="clean",
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=None,
|
||||
)
|
||||
except Exception:
|
||||
import traceback
|
||||
|
||||
logger.error("Error loading GFPGAN:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
os.chdir(cwd)
|
||||
|
||||
if self.gfpgan is None:
|
||||
logger.warning("WARNING: GFPGAN not initialized.")
|
||||
logger.warning(
|
||||
f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
|
||||
)
|
||||
|
||||
image = image.convert("RGB")
|
||||
|
||||
# GFPGAN expects a BGR np array; make array and flip channels
|
||||
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
||||
|
||||
_, _, restored_img = self.gfpgan.enhance(
|
||||
bgr_image_array,
|
||||
has_aligned=False,
|
||||
only_center_face=False,
|
||||
paste_back=True,
|
||||
)
|
||||
|
||||
# Flip the channels back to RGB
|
||||
res = Image.fromarray(restored_img[..., ::-1])
|
||||
|
||||
if strength < 1.0:
|
||||
# Resize the image to the new image if the sizes have changed
|
||||
if restored_img.size != image.size:
|
||||
image = image.resize(res.size)
|
||||
res = Image.blend(image, res, strength)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
self.gfpgan = None
|
||||
|
||||
return res
|
||||
@ -1,118 +0,0 @@
|
||||
import math
|
||||
|
||||
from PIL import Image
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
class Outcrop(object):
|
||||
def __init__(
|
||||
self,
|
||||
image,
|
||||
generate, # current generate object
|
||||
):
|
||||
self.image = image
|
||||
self.generate = generate
|
||||
|
||||
def process(
|
||||
self,
|
||||
extents: dict,
|
||||
opt, # current options
|
||||
orig_opt, # ones originally used to generate the image
|
||||
image_callback=None,
|
||||
prefix=None,
|
||||
):
|
||||
# grow and mask the image
|
||||
extended_image = self._extend_all(extents)
|
||||
|
||||
# switch samplers temporarily
|
||||
curr_sampler = self.generate.sampler
|
||||
self.generate.sampler_name = opt.sampler_name
|
||||
self.generate._set_scheduler()
|
||||
|
||||
def wrapped_callback(img, seed, **kwargs):
|
||||
preferred_seed = (
|
||||
orig_opt.seed
|
||||
if orig_opt.seed is not None and orig_opt.seed >= 0
|
||||
else seed
|
||||
)
|
||||
image_callback(img, preferred_seed, use_prefix=prefix, **kwargs)
|
||||
|
||||
result = self.generate.prompt2image(
|
||||
opt.prompt,
|
||||
seed=opt.seed or orig_opt.seed,
|
||||
sampler=self.generate.sampler,
|
||||
steps=opt.steps,
|
||||
cfg_scale=opt.cfg_scale,
|
||||
ddim_eta=self.generate.ddim_eta,
|
||||
width=extended_image.width,
|
||||
height=extended_image.height,
|
||||
init_img=extended_image,
|
||||
strength=0.90,
|
||||
image_callback=wrapped_callback if image_callback else None,
|
||||
seam_size=opt.seam_size or 96,
|
||||
seam_blur=opt.seam_blur or 16,
|
||||
seam_strength=opt.seam_strength or 0.7,
|
||||
seam_steps=20,
|
||||
tile_size=32,
|
||||
color_match=True,
|
||||
force_outpaint=True, # this just stops the warning about erased regions
|
||||
)
|
||||
|
||||
# swap sampler back
|
||||
self.generate.sampler = curr_sampler
|
||||
return result
|
||||
|
||||
def _extend_all(
|
||||
self,
|
||||
extents: dict,
|
||||
) -> Image:
|
||||
"""
|
||||
Extend the image in direction ('top','bottom','left','right') by
|
||||
the indicated value. The image canvas is extended, and the empty
|
||||
rectangular section will be filled with a blurred copy of the
|
||||
adjacent image.
|
||||
"""
|
||||
image = self.image
|
||||
for direction in extents:
|
||||
assert direction in [
|
||||
"top",
|
||||
"left",
|
||||
"bottom",
|
||||
"right",
|
||||
], 'Direction must be one of "top", "left", "bottom", "right"'
|
||||
pixels = extents[direction]
|
||||
# round pixels up to the nearest 64
|
||||
pixels = math.ceil(pixels / 64) * 64
|
||||
logger.info(f"extending image {direction}ward by {pixels} pixels")
|
||||
image = self._rotate(image, direction)
|
||||
image = self._extend(image, pixels)
|
||||
image = self._rotate(image, direction, reverse=True)
|
||||
return image
|
||||
|
||||
def _rotate(self, image: Image, direction: str, reverse=False) -> Image:
|
||||
"""
|
||||
Rotates image so that the area to extend is always at the top top.
|
||||
Simplifies logic later. The reverse argument, if true, will undo the
|
||||
previous transpose.
|
||||
"""
|
||||
transposes = {
|
||||
"right": ["ROTATE_90", "ROTATE_270"],
|
||||
"bottom": ["ROTATE_180", "ROTATE_180"],
|
||||
"left": ["ROTATE_270", "ROTATE_90"],
|
||||
}
|
||||
if direction not in transposes:
|
||||
return image
|
||||
transpose = transposes[direction][1 if reverse else 0]
|
||||
return image.transpose(Image.Transpose.__dict__[transpose])
|
||||
|
||||
def _extend(self, image: Image, pixels: int) -> Image:
|
||||
extended_img = Image.new("RGBA", (image.width, image.height + pixels))
|
||||
|
||||
extended_img.paste((0, 0, 0), [0, 0, image.width, image.height + pixels])
|
||||
extended_img.paste(image, box=(0, pixels))
|
||||
|
||||
# now make the top part transparent to use as a mask
|
||||
alpha = extended_img.getchannel("A")
|
||||
alpha.paste(0, (0, 0, extended_img.width, pixels))
|
||||
extended_img.putalpha(alpha)
|
||||
|
||||
return extended_img
|
||||
@ -1,102 +0,0 @@
|
||||
import math
|
||||
import warnings
|
||||
|
||||
from PIL import Image, ImageFilter
|
||||
|
||||
|
||||
class Outpaint(object):
|
||||
def __init__(self, image, generate):
|
||||
self.image = image
|
||||
self.generate = generate
|
||||
|
||||
def process(self, opt, old_opt, image_callback=None, prefix=None):
|
||||
image = self._create_outpaint_image(self.image, opt.out_direction)
|
||||
|
||||
seed = old_opt.seed
|
||||
prompt = old_opt.prompt
|
||||
|
||||
def wrapped_callback(img, seed, **kwargs):
|
||||
image_callback(img, seed, use_prefix=prefix, **kwargs)
|
||||
|
||||
return self.generate.prompt2image(
|
||||
prompt,
|
||||
seed=seed,
|
||||
sampler=self.generate.sampler,
|
||||
steps=opt.steps,
|
||||
cfg_scale=opt.cfg_scale,
|
||||
ddim_eta=self.generate.ddim_eta,
|
||||
width=opt.width,
|
||||
height=opt.height,
|
||||
init_img=image,
|
||||
strength=0.83,
|
||||
image_callback=wrapped_callback,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
def _create_outpaint_image(self, image, direction_args):
|
||||
assert len(direction_args) in [
|
||||
1,
|
||||
2,
|
||||
], "Direction (-D) must have exactly one or two arguments."
|
||||
|
||||
if len(direction_args) == 1:
|
||||
direction = direction_args[0]
|
||||
pixels = None
|
||||
elif len(direction_args) == 2:
|
||||
direction = direction_args[0]
|
||||
pixels = int(direction_args[1])
|
||||
|
||||
assert direction in [
|
||||
"top",
|
||||
"left",
|
||||
"bottom",
|
||||
"right",
|
||||
], 'Direction (-D) must be one of "top", "left", "bottom", "right"'
|
||||
|
||||
image = image.convert("RGBA")
|
||||
# we always extend top, but rotate to extend along the requested side
|
||||
if direction == "left":
|
||||
image = image.transpose(Image.Transpose.ROTATE_270)
|
||||
elif direction == "bottom":
|
||||
image = image.transpose(Image.Transpose.ROTATE_180)
|
||||
elif direction == "right":
|
||||
image = image.transpose(Image.Transpose.ROTATE_90)
|
||||
|
||||
pixels = image.height // 2 if pixels is None else int(pixels)
|
||||
assert (
|
||||
0 < pixels < image.height
|
||||
), "Direction (-D) pixels length must be in the range 0 - image.size"
|
||||
|
||||
# the top part of the image is taken from the source image mirrored
|
||||
# coordinates (0,0) are the upper left corner of an image
|
||||
top = image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).convert("RGBA")
|
||||
top = top.crop((0, top.height - pixels, top.width, top.height))
|
||||
|
||||
# setting all alpha of the top part to 0
|
||||
alpha = top.getchannel("A")
|
||||
alpha.paste(0, (0, 0, top.width, top.height))
|
||||
top.putalpha(alpha)
|
||||
|
||||
# taking the bottom from the original image
|
||||
bottom = image.crop((0, 0, image.width, image.height - pixels))
|
||||
|
||||
new_img = image.copy()
|
||||
new_img.paste(top, (0, 0))
|
||||
new_img.paste(bottom, (0, pixels))
|
||||
|
||||
# create a 10% dither in the middle
|
||||
dither = min(image.height // 10, pixels)
|
||||
for x in range(0, image.width, 2):
|
||||
for y in range(pixels - dither, pixels + dither):
|
||||
(r, g, b, a) = new_img.getpixel((x, y))
|
||||
new_img.putpixel((x, y), (r, g, b, 0))
|
||||
|
||||
# let's rotate back again
|
||||
if direction == "left":
|
||||
new_img = new_img.transpose(Image.Transpose.ROTATE_90)
|
||||
elif direction == "bottom":
|
||||
new_img = new_img.transpose(Image.Transpose.ROTATE_180)
|
||||
elif direction == "right":
|
||||
new_img = new_img.transpose(Image.Transpose.ROTATE_270)
|
||||
|
||||
return new_img
|
||||
@ -1,104 +0,0 @@
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from PIL.Image import Image as ImageType
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class ESRGAN:
|
||||
def __init__(self, bg_tile_size=400) -> None:
|
||||
self.bg_tile_size = bg_tile_size
|
||||
|
||||
def load_esrgan_bg_upsampler(self, denoise_str):
|
||||
if not torch.cuda.is_available(): # CPU or MPS on M1
|
||||
use_half_precision = False
|
||||
else:
|
||||
use_half_precision = True
|
||||
|
||||
from realesrgan import RealESRGANer
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
|
||||
|
||||
model = SRVGGNetCompact(
|
||||
num_in_ch=3,
|
||||
num_out_ch=3,
|
||||
num_feat=64,
|
||||
num_conv=32,
|
||||
upscale=4,
|
||||
act_type="prelu",
|
||||
)
|
||||
model_path = config.models_path / "core/upscaling/realesrgan/realesr-general-x4v3.pth"
|
||||
wdn_model_path = config.models_path / "core/upscaling/realesrgan/realesr-general-wdn-x4v3.pth"
|
||||
scale = 4
|
||||
|
||||
bg_upsampler = RealESRGANer(
|
||||
scale=scale,
|
||||
model_path=[model_path, wdn_model_path],
|
||||
model=model,
|
||||
tile=self.bg_tile_size,
|
||||
dni_weight=[denoise_str, 1 - denoise_str],
|
||||
tile_pad=10,
|
||||
pre_pad=0,
|
||||
half=use_half_precision,
|
||||
)
|
||||
|
||||
return bg_upsampler
|
||||
|
||||
def process(
|
||||
self,
|
||||
image: ImageType,
|
||||
strength: float,
|
||||
seed: str = None,
|
||||
upsampler_scale: int = 2,
|
||||
denoise_str: float = 0.75,
|
||||
):
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
try:
|
||||
upsampler = self.load_esrgan_bg_upsampler(denoise_str)
|
||||
except Exception:
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
logger.error("Error loading Real-ESRGAN:")
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
if upsampler_scale == 0:
|
||||
logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.")
|
||||
return image
|
||||
|
||||
if seed is not None:
|
||||
logger.info(
|
||||
f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
|
||||
)
|
||||
# ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
|
||||
image = image.convert("RGB")
|
||||
|
||||
# REALSRGAN expects a BGR np array; make array and flip channels
|
||||
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
||||
|
||||
output, _ = upsampler.enhance(
|
||||
bgr_image_array,
|
||||
outscale=upsampler_scale,
|
||||
alpha_upsampler="realesrgan",
|
||||
)
|
||||
|
||||
# Flip the channels back to RGB
|
||||
res = Image.fromarray(output[..., ::-1])
|
||||
|
||||
if strength < 1.0:
|
||||
# Resize the image to the new image if the sizes have changed
|
||||
if output.size != image.size:
|
||||
image = image.resize(res.size)
|
||||
res = Image.blend(image, res, strength)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
upsampler = None
|
||||
|
||||
return res
|
||||
@ -1,514 +0,0 @@
|
||||
"""
|
||||
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
|
||||
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
|
||||
|
||||
"""
|
||||
import copy
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from basicsr.utils import get_root_logger
|
||||
from basicsr.utils.registry import ARCH_REGISTRY
|
||||
|
||||
|
||||
def normalize(in_channels):
|
||||
return torch.nn.GroupNorm(
|
||||
num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
|
||||
)
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def swish(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
# Define VQVAE classes
|
||||
class VectorQuantizer(nn.Module):
|
||||
def __init__(self, codebook_size, emb_dim, beta):
|
||||
super(VectorQuantizer, self).__init__()
|
||||
self.codebook_size = codebook_size # number of embeddings
|
||||
self.emb_dim = emb_dim # dimension of embedding
|
||||
self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
|
||||
self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
|
||||
self.embedding.weight.data.uniform_(
|
||||
-1.0 / self.codebook_size, 1.0 / self.codebook_size
|
||||
)
|
||||
|
||||
def forward(self, z):
|
||||
# reshape z -> (batch, height, width, channel) and flatten
|
||||
z = z.permute(0, 2, 3, 1).contiguous()
|
||||
z_flattened = z.view(-1, self.emb_dim)
|
||||
|
||||
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
||||
d = (
|
||||
(z_flattened**2).sum(dim=1, keepdim=True)
|
||||
+ (self.embedding.weight**2).sum(1)
|
||||
- 2 * torch.matmul(z_flattened, self.embedding.weight.t())
|
||||
)
|
||||
|
||||
mean_distance = torch.mean(d)
|
||||
# find closest encodings
|
||||
# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
|
||||
min_encoding_scores, min_encoding_indices = torch.topk(
|
||||
d, 1, dim=1, largest=False
|
||||
)
|
||||
# [0-1], higher score, higher confidence
|
||||
min_encoding_scores = torch.exp(-min_encoding_scores / 10)
|
||||
|
||||
min_encodings = torch.zeros(
|
||||
min_encoding_indices.shape[0], self.codebook_size
|
||||
).to(z)
|
||||
min_encodings.scatter_(1, min_encoding_indices, 1)
|
||||
|
||||
# get quantized latent vectors
|
||||
z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
|
||||
# compute loss for embedding
|
||||
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
|
||||
(z_q - z.detach()) ** 2
|
||||
)
|
||||
# preserve gradients
|
||||
z_q = z + (z_q - z).detach()
|
||||
|
||||
# perplexity
|
||||
e_mean = torch.mean(min_encodings, dim=0)
|
||||
perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
|
||||
# reshape back to match original input shape
|
||||
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return (
|
||||
z_q,
|
||||
loss,
|
||||
{
|
||||
"perplexity": perplexity,
|
||||
"min_encodings": min_encodings,
|
||||
"min_encoding_indices": min_encoding_indices,
|
||||
"min_encoding_scores": min_encoding_scores,
|
||||
"mean_distance": mean_distance,
|
||||
},
|
||||
)
|
||||
|
||||
def get_codebook_feat(self, indices, shape):
|
||||
# input indices: batch*token_num -> (batch*token_num)*1
|
||||
# shape: batch, height, width, channel
|
||||
indices = indices.view(-1, 1)
|
||||
min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
|
||||
min_encodings.scatter_(1, indices, 1)
|
||||
# get quantized latent vectors
|
||||
z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
|
||||
|
||||
if shape is not None: # reshape back to match original input shape
|
||||
z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return z_q
|
||||
|
||||
|
||||
class GumbelQuantizer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
codebook_size,
|
||||
emb_dim,
|
||||
num_hiddens,
|
||||
straight_through=False,
|
||||
kl_weight=5e-4,
|
||||
temp_init=1.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.codebook_size = codebook_size # number of embeddings
|
||||
self.emb_dim = emb_dim # dimension of embedding
|
||||
self.straight_through = straight_through
|
||||
self.temperature = temp_init
|
||||
self.kl_weight = kl_weight
|
||||
self.proj = nn.Conv2d(
|
||||
num_hiddens, codebook_size, 1
|
||||
) # projects last encoder layer to quantized logits
|
||||
self.embed = nn.Embedding(codebook_size, emb_dim)
|
||||
|
||||
def forward(self, z):
|
||||
hard = self.straight_through if self.training else True
|
||||
|
||||
logits = self.proj(z)
|
||||
|
||||
soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
|
||||
|
||||
z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
|
||||
|
||||
# + kl divergence to the prior loss
|
||||
qy = F.softmax(logits, dim=1)
|
||||
diff = (
|
||||
self.kl_weight
|
||||
* torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
|
||||
)
|
||||
min_encoding_indices = soft_one_hot.argmax(dim=1)
|
||||
|
||||
return z_q, diff, {"min_encoding_indices": min_encoding_indices}
|
||||
|
||||
|
||||
class Downsample(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=3, stride=2, padding=0
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
pad = (0, 1, 0, 1)
|
||||
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
|
||||
x = self.conv(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
def __init__(self, in_channels, out_channels=None):
|
||||
super(ResBlock, self).__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = in_channels if out_channels is None else out_channels
|
||||
self.norm1 = normalize(in_channels)
|
||||
self.conv1 = nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
self.norm2 = normalize(out_channels)
|
||||
self.conv2 = nn.Conv2d(
|
||||
out_channels, out_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
if self.in_channels != self.out_channels:
|
||||
self.conv_out = nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
|
||||
def forward(self, x_in):
|
||||
x = x_in
|
||||
x = self.norm1(x)
|
||||
x = swish(x)
|
||||
x = self.conv1(x)
|
||||
x = self.norm2(x)
|
||||
x = swish(x)
|
||||
x = self.conv2(x)
|
||||
if self.in_channels != self.out_channels:
|
||||
x_in = self.conv_out(x_in)
|
||||
|
||||
return x + x_in
|
||||
|
||||
|
||||
class AttnBlock(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
self.k = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
self.v = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
self.proj_out = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b, c, h, w = q.shape
|
||||
q = q.reshape(b, c, h * w)
|
||||
q = q.permute(0, 2, 1)
|
||||
k = k.reshape(b, c, h * w)
|
||||
w_ = torch.bmm(q, k)
|
||||
w_ = w_ * (int(c) ** (-0.5))
|
||||
w_ = F.softmax(w_, dim=2)
|
||||
|
||||
# attend to values
|
||||
v = v.reshape(b, c, h * w)
|
||||
w_ = w_.permute(0, 2, 1)
|
||||
h_ = torch.bmm(v, w_)
|
||||
h_ = h_.reshape(b, c, h, w)
|
||||
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x + h_
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
nf,
|
||||
emb_dim,
|
||||
ch_mult,
|
||||
num_res_blocks,
|
||||
resolution,
|
||||
attn_resolutions,
|
||||
):
|
||||
super().__init__()
|
||||
self.nf = nf
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.attn_resolutions = attn_resolutions
|
||||
|
||||
curr_res = self.resolution
|
||||
in_ch_mult = (1,) + tuple(ch_mult)
|
||||
|
||||
blocks = []
|
||||
# initial convultion
|
||||
blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
|
||||
|
||||
# residual and downsampling blocks, with attention on smaller res (16x16)
|
||||
for i in range(self.num_resolutions):
|
||||
block_in_ch = nf * in_ch_mult[i]
|
||||
block_out_ch = nf * ch_mult[i]
|
||||
for _ in range(self.num_res_blocks):
|
||||
blocks.append(ResBlock(block_in_ch, block_out_ch))
|
||||
block_in_ch = block_out_ch
|
||||
if curr_res in attn_resolutions:
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
|
||||
if i != self.num_resolutions - 1:
|
||||
blocks.append(Downsample(block_in_ch))
|
||||
curr_res = curr_res // 2
|
||||
|
||||
# non-local attention block
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
|
||||
# normalise and convert to latent size
|
||||
blocks.append(normalize(block_in_ch))
|
||||
blocks.append(
|
||||
nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)
|
||||
)
|
||||
self.blocks = nn.ModuleList(blocks)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Generator(nn.Module):
|
||||
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
|
||||
super().__init__()
|
||||
self.nf = nf
|
||||
self.ch_mult = ch_mult
|
||||
self.num_resolutions = len(self.ch_mult)
|
||||
self.num_res_blocks = res_blocks
|
||||
self.resolution = img_size
|
||||
self.attn_resolutions = attn_resolutions
|
||||
self.in_channels = emb_dim
|
||||
self.out_channels = 3
|
||||
block_in_ch = self.nf * self.ch_mult[-1]
|
||||
curr_res = self.resolution // 2 ** (self.num_resolutions - 1)
|
||||
|
||||
blocks = []
|
||||
# initial conv
|
||||
blocks.append(
|
||||
nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)
|
||||
)
|
||||
|
||||
# non-local attention block
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
|
||||
for i in reversed(range(self.num_resolutions)):
|
||||
block_out_ch = self.nf * self.ch_mult[i]
|
||||
|
||||
for _ in range(self.num_res_blocks):
|
||||
blocks.append(ResBlock(block_in_ch, block_out_ch))
|
||||
block_in_ch = block_out_ch
|
||||
|
||||
if curr_res in self.attn_resolutions:
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
|
||||
if i != 0:
|
||||
blocks.append(Upsample(block_in_ch))
|
||||
curr_res = curr_res * 2
|
||||
|
||||
blocks.append(normalize(block_in_ch))
|
||||
blocks.append(
|
||||
nn.Conv2d(
|
||||
block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
)
|
||||
|
||||
self.blocks = nn.ModuleList(blocks)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
@ARCH_REGISTRY.register()
|
||||
class VQAutoEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
img_size,
|
||||
nf,
|
||||
ch_mult,
|
||||
quantizer="nearest",
|
||||
res_blocks=2,
|
||||
attn_resolutions=[16],
|
||||
codebook_size=1024,
|
||||
emb_dim=256,
|
||||
beta=0.25,
|
||||
gumbel_straight_through=False,
|
||||
gumbel_kl_weight=1e-8,
|
||||
model_path=None,
|
||||
):
|
||||
super().__init__()
|
||||
logger = get_root_logger()
|
||||
self.in_channels = 3
|
||||
self.nf = nf
|
||||
self.n_blocks = res_blocks
|
||||
self.codebook_size = codebook_size
|
||||
self.embed_dim = emb_dim
|
||||
self.ch_mult = ch_mult
|
||||
self.resolution = img_size
|
||||
self.attn_resolutions = attn_resolutions
|
||||
self.quantizer_type = quantizer
|
||||
self.encoder = Encoder(
|
||||
self.in_channels,
|
||||
self.nf,
|
||||
self.embed_dim,
|
||||
self.ch_mult,
|
||||
self.n_blocks,
|
||||
self.resolution,
|
||||
self.attn_resolutions,
|
||||
)
|
||||
if self.quantizer_type == "nearest":
|
||||
self.beta = beta # 0.25
|
||||
self.quantize = VectorQuantizer(
|
||||
self.codebook_size, self.embed_dim, self.beta
|
||||
)
|
||||
elif self.quantizer_type == "gumbel":
|
||||
self.gumbel_num_hiddens = emb_dim
|
||||
self.straight_through = gumbel_straight_through
|
||||
self.kl_weight = gumbel_kl_weight
|
||||
self.quantize = GumbelQuantizer(
|
||||
self.codebook_size,
|
||||
self.embed_dim,
|
||||
self.gumbel_num_hiddens,
|
||||
self.straight_through,
|
||||
self.kl_weight,
|
||||
)
|
||||
self.generator = Generator(
|
||||
self.nf,
|
||||
self.embed_dim,
|
||||
self.ch_mult,
|
||||
self.n_blocks,
|
||||
self.resolution,
|
||||
self.attn_resolutions,
|
||||
)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location="cpu")
|
||||
if "params_ema" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params_ema"]
|
||||
)
|
||||
logger.info(f"vqgan is loaded from: {model_path} [params_ema]")
|
||||
elif "params" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params"]
|
||||
)
|
||||
logger.info(f"vqgan is loaded from: {model_path} [params]")
|
||||
else:
|
||||
raise ValueError(f"Wrong params!")
|
||||
|
||||
def forward(self, x):
|
||||
x = self.encoder(x)
|
||||
quant, codebook_loss, quant_stats = self.quantize(x)
|
||||
x = self.generator(quant)
|
||||
return x, codebook_loss, quant_stats
|
||||
|
||||
|
||||
# patch based discriminator
|
||||
@ARCH_REGISTRY.register()
|
||||
class VQGANDiscriminator(nn.Module):
|
||||
def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
|
||||
super().__init__()
|
||||
|
||||
layers = [
|
||||
nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
]
|
||||
ndf_mult = 1
|
||||
ndf_mult_prev = 1
|
||||
for n in range(1, n_layers): # gradually increase the number of filters
|
||||
ndf_mult_prev = ndf_mult
|
||||
ndf_mult = min(2**n, 8)
|
||||
layers += [
|
||||
nn.Conv2d(
|
||||
ndf * ndf_mult_prev,
|
||||
ndf * ndf_mult,
|
||||
kernel_size=4,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(ndf * ndf_mult),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
]
|
||||
|
||||
ndf_mult_prev = ndf_mult
|
||||
ndf_mult = min(2**n_layers, 8)
|
||||
|
||||
layers += [
|
||||
nn.Conv2d(
|
||||
ndf * ndf_mult_prev,
|
||||
ndf * ndf_mult,
|
||||
kernel_size=4,
|
||||
stride=1,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(ndf * ndf_mult),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
]
|
||||
|
||||
layers += [
|
||||
nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)
|
||||
] # output 1 channel prediction map
|
||||
self.main = nn.Sequential(*layers)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location="cpu")
|
||||
if "params_d" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params_d"]
|
||||
)
|
||||
elif "params" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params"]
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Wrong params!")
|
||||
|
||||
def forward(self, x):
|
||||
return self.main(x)
|
||||
@ -58,22 +58,29 @@ sd-1/main/waifu-diffusion:
|
||||
recommended: False
|
||||
sd-1/controlnet/canny:
|
||||
repo_id: lllyasviel/control_v11p_sd15_canny
|
||||
recommended: True
|
||||
sd-1/controlnet/inpaint:
|
||||
repo_id: lllyasviel/control_v11p_sd15_inpaint
|
||||
sd-1/controlnet/mlsd:
|
||||
repo_id: lllyasviel/control_v11p_sd15_mlsd
|
||||
sd-1/controlnet/depth:
|
||||
repo_id: lllyasviel/control_v11f1p_sd15_depth
|
||||
recommended: True
|
||||
sd-1/controlnet/normal_bae:
|
||||
repo_id: lllyasviel/control_v11p_sd15_normalbae
|
||||
sd-1/controlnet/seg:
|
||||
repo_id: lllyasviel/control_v11p_sd15_seg
|
||||
sd-1/controlnet/lineart:
|
||||
repo_id: lllyasviel/control_v11p_sd15_lineart
|
||||
recommended: True
|
||||
sd-1/controlnet/lineart_anime:
|
||||
repo_id: lllyasviel/control_v11p_sd15s2_lineart_anime
|
||||
sd-1/controlnet/openpose:
|
||||
repo_id: lllyasviel/control_v11p_sd15_openpose
|
||||
recommended: True
|
||||
sd-1/controlnet/scribble:
|
||||
repo_id: lllyasviel/control_v11p_sd15_scribble
|
||||
recommended: False
|
||||
sd-1/controlnet/softedge:
|
||||
repo_id: lllyasviel/control_v11p_sd15_softedge
|
||||
sd-1/controlnet/shuffle:
|
||||
@ -84,6 +91,7 @@ sd-1/controlnet/ip2p:
|
||||
repo_id: lllyasviel/control_v11e_sd15_ip2p
|
||||
sd-1/embedding/EasyNegative:
|
||||
path: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
|
||||
recommended: True
|
||||
sd-1/embedding/ahx-beta-453407d:
|
||||
repo_id: sd-concepts-library/ahx-beta-453407d
|
||||
sd-1/lora/LowRA:
|
||||
|
||||
@ -256,6 +256,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
widgets = dict()
|
||||
model_list = [x for x in self.all_models if self.all_models[x].model_type==model_type and not x in exclude]
|
||||
model_labels = [self.model_labels[x] for x in model_list]
|
||||
|
||||
show_recommended = len(self.installed_models)==0
|
||||
if len(model_list) > 0:
|
||||
max_width = max([len(x) for x in model_labels])
|
||||
columns = window_width // (max_width+8) # 8 characters for "[x] " and padding
|
||||
@ -280,7 +282,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
value=[
|
||||
model_list.index(x)
|
||||
for x in model_list
|
||||
if self.all_models[x].installed
|
||||
if (show_recommended and self.all_models[x].recommended) \
|
||||
or self.all_models[x].installed
|
||||
],
|
||||
max_height=len(model_list)//columns + 1,
|
||||
relx=4,
|
||||
@ -672,7 +675,9 @@ def select_and_download_models(opt: Namespace):
|
||||
# pass
|
||||
|
||||
installer = ModelInstall(config, prediction_type_helper=helper)
|
||||
if opt.add or opt.delete:
|
||||
if opt.list_models:
|
||||
installer.list_models(opt.list_models)
|
||||
elif opt.add or opt.delete:
|
||||
selections = InstallSelections(
|
||||
install_models = opt.add or [],
|
||||
remove_models = opt.delete or []
|
||||
@ -745,7 +750,7 @@ def main():
|
||||
)
|
||||
parser.add_argument(
|
||||
"--list-models",
|
||||
choices=["diffusers","loras","controlnets","tis"],
|
||||
choices=[x.value for x in ModelType],
|
||||
help="list installed models",
|
||||
)
|
||||
parser.add_argument(
|
||||
@ -773,7 +778,7 @@ def main():
|
||||
config.parse_args(invoke_args)
|
||||
logger = InvokeAILogger().getLogger(config=config)
|
||||
|
||||
if not (config.conf_path / 'models.yaml').exists():
|
||||
if not config.model_conf_path.exists():
|
||||
logger.info(
|
||||
"Your InvokeAI root directory is not set up. Calling invokeai-configure."
|
||||
)
|
||||
|
||||
355
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
355
invokeai/frontend/web/src/services/api/schema.d.ts
vendored
@ -75,11 +75,6 @@ export type paths = {
|
||||
* @description Gets a list of models
|
||||
*/
|
||||
get: operations["list_models"];
|
||||
/**
|
||||
* Import Model
|
||||
* @description Add a model using its local path, repo_id, or remote URL
|
||||
*/
|
||||
post: operations["import_model"];
|
||||
};
|
||||
"/api/v1/models/{base_model}/{model_type}/{model_name}": {
|
||||
/**
|
||||
@ -93,13 +88,53 @@ export type paths = {
|
||||
*/
|
||||
patch: operations["update_model"];
|
||||
};
|
||||
"/api/v1/models/import": {
|
||||
/**
|
||||
* Import Model
|
||||
* @description Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically
|
||||
*/
|
||||
post: operations["import_model"];
|
||||
};
|
||||
"/api/v1/models/add": {
|
||||
/**
|
||||
* Add Model
|
||||
* @description Add a model using the configuration information appropriate for its type. Only local models can be added by path
|
||||
*/
|
||||
post: operations["add_model"];
|
||||
};
|
||||
"/api/v1/models/rename/{base_model}/{model_type}/{model_name}": {
|
||||
/**
|
||||
* Rename Model
|
||||
* @description Rename a model
|
||||
*/
|
||||
post: operations["rename_model"];
|
||||
};
|
||||
"/api/v1/models/convert/{base_model}/{model_type}/{model_name}": {
|
||||
/**
|
||||
* Convert Model
|
||||
* @description Convert a checkpoint model into a diffusers model
|
||||
* @description Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none.
|
||||
*/
|
||||
put: operations["convert_model"];
|
||||
};
|
||||
"/api/v1/models/search": {
|
||||
/** Search For Models */
|
||||
get: operations["search_for_models"];
|
||||
};
|
||||
"/api/v1/models/ckpt_confs": {
|
||||
/**
|
||||
* List Ckpt Configs
|
||||
* @description Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT.
|
||||
*/
|
||||
get: operations["list_ckpt_configs"];
|
||||
};
|
||||
"/api/v1/models/sync": {
|
||||
/**
|
||||
* Sync To Config
|
||||
* @description Call after making changes to models.yaml, autoimport directories or models directory to synchronize
|
||||
* in-memory data structures with disk data structures.
|
||||
*/
|
||||
get: operations["sync_to_config"];
|
||||
};
|
||||
"/api/v1/models/merge/{base_model}": {
|
||||
/**
|
||||
* Merge Models
|
||||
@ -397,6 +432,11 @@ export type components = {
|
||||
* @default false
|
||||
*/
|
||||
force?: boolean;
|
||||
/**
|
||||
* Merge Dest Directory
|
||||
* @description Save the merged model to the designated directory (with 'merged_model_name' appended)
|
||||
*/
|
||||
merge_dest_directory?: string;
|
||||
};
|
||||
/** Body_remove_board_image */
|
||||
Body_remove_board_image: {
|
||||
@ -1186,7 +1226,7 @@ export type components = {
|
||||
* @description The nodes in this graph
|
||||
*/
|
||||
nodes?: {
|
||||
[key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
|
||||
[key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
|
||||
};
|
||||
/**
|
||||
* Edges
|
||||
@ -3302,7 +3342,7 @@ export type components = {
|
||||
/** ModelsList */
|
||||
ModelsList: {
|
||||
/** Models */
|
||||
models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"])[];
|
||||
models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"])[];
|
||||
};
|
||||
/**
|
||||
* MultiplyInvocation
|
||||
@ -3893,6 +3933,41 @@ export type components = {
|
||||
*/
|
||||
step?: number;
|
||||
};
|
||||
/**
|
||||
* RealESRGANInvocation
|
||||
* @description Upscales an image using RealESRGAN.
|
||||
*/
|
||||
RealESRGANInvocation: {
|
||||
/**
|
||||
* Id
|
||||
* @description The id of this node. Must be unique among all nodes.
|
||||
*/
|
||||
id: string;
|
||||
/**
|
||||
* Is Intermediate
|
||||
* @description Whether or not this node is an intermediate node.
|
||||
* @default false
|
||||
*/
|
||||
is_intermediate?: boolean;
|
||||
/**
|
||||
* Type
|
||||
* @default realesrgan
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "realesrgan";
|
||||
/**
|
||||
* Image
|
||||
* @description The input image
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Model Name
|
||||
* @description The Real-ESRGAN model to use
|
||||
* @default RealESRGAN_x4plus.pth
|
||||
* @enum {string}
|
||||
*/
|
||||
model_name?: "RealESRGAN_x4plus.pth" | "RealESRGAN_x4plus_anime_6B.pth" | "ESRGAN_SRx4_DF2KOST_official-ff704c30.pth";
|
||||
};
|
||||
/**
|
||||
* ResizeLatentsInvocation
|
||||
* @description Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.
|
||||
@ -4452,47 +4527,6 @@ export type components = {
|
||||
*/
|
||||
loras: (components["schemas"]["LoraInfo"])[];
|
||||
};
|
||||
/**
|
||||
* UpscaleInvocation
|
||||
* @description Upscales an image.
|
||||
*/
|
||||
UpscaleInvocation: {
|
||||
/**
|
||||
* Id
|
||||
* @description The id of this node. Must be unique among all nodes.
|
||||
*/
|
||||
id: string;
|
||||
/**
|
||||
* Is Intermediate
|
||||
* @description Whether or not this node is an intermediate node.
|
||||
* @default false
|
||||
*/
|
||||
is_intermediate?: boolean;
|
||||
/**
|
||||
* Type
|
||||
* @default upscale
|
||||
* @enum {string}
|
||||
*/
|
||||
type?: "upscale";
|
||||
/**
|
||||
* Image
|
||||
* @description The input image
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
/**
|
||||
* Strength
|
||||
* @description The strength
|
||||
* @default 0.75
|
||||
*/
|
||||
strength?: number;
|
||||
/**
|
||||
* Level
|
||||
* @description The upscale level
|
||||
* @default 2
|
||||
* @enum {integer}
|
||||
*/
|
||||
level?: 2 | 4;
|
||||
};
|
||||
/**
|
||||
* VAEModelField
|
||||
* @description Vae model field
|
||||
@ -4619,18 +4653,18 @@ export type components = {
|
||||
*/
|
||||
image?: components["schemas"]["ImageField"];
|
||||
};
|
||||
/**
|
||||
* StableDiffusion1ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusion2ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
|
||||
/**
|
||||
* StableDiffusion1ModelFormat
|
||||
* @description An enumeration.
|
||||
* @enum {string}
|
||||
*/
|
||||
StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
|
||||
};
|
||||
responses: never;
|
||||
parameters: never;
|
||||
@ -4741,7 +4775,7 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
"application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
@ -4778,7 +4812,7 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
"application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RealESRGANInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
@ -4997,37 +5031,6 @@ export type operations = {
|
||||
};
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Import Model
|
||||
* @description Add a model using its local path, repo_id, or remote URL
|
||||
*/
|
||||
import_model: {
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["Body_import_model"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description The model imported successfully */
|
||||
201: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
|
||||
};
|
||||
};
|
||||
/** @description The model could not be found */
|
||||
404: never;
|
||||
/** @description There is already a model corresponding to this path or repo_id */
|
||||
409: never;
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
/** @description The model appeared to import successfully, but could not be found in the model manager */
|
||||
424: never;
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Delete Model
|
||||
* @description Delete Model
|
||||
@ -5044,12 +5047,6 @@ export type operations = {
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description Successful Response */
|
||||
200: {
|
||||
content: {
|
||||
"application/json": unknown;
|
||||
};
|
||||
};
|
||||
/** @description Model deleted successfully */
|
||||
204: never;
|
||||
/** @description Model not found */
|
||||
@ -5079,14 +5076,14 @@ export type operations = {
|
||||
};
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description The model was updated successfully */
|
||||
200: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
/** @description Bad request */
|
||||
@ -5101,12 +5098,118 @@ export type operations = {
|
||||
};
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Import Model
|
||||
* @description Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically
|
||||
*/
|
||||
import_model: {
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["Body_import_model"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description The model imported successfully */
|
||||
201: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
/** @description The model could not be found */
|
||||
404: never;
|
||||
/** @description There is already a model corresponding to this path or repo_id */
|
||||
409: never;
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
/** @description The model appeared to import successfully, but could not be found in the model manager */
|
||||
424: never;
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Add Model
|
||||
* @description Add a model using the configuration information appropriate for its type. Only local models can be added by path
|
||||
*/
|
||||
add_model: {
|
||||
requestBody: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description The model added successfully */
|
||||
201: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
/** @description The model could not be found */
|
||||
404: never;
|
||||
/** @description There is already a model corresponding to this path or repo_id */
|
||||
409: never;
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
/** @description The model appeared to add successfully, but could not be found in the model manager */
|
||||
424: never;
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Rename Model
|
||||
* @description Rename a model
|
||||
*/
|
||||
rename_model: {
|
||||
parameters: {
|
||||
query?: {
|
||||
/** @description new model name */
|
||||
new_name?: string;
|
||||
/** @description new model base */
|
||||
new_base?: components["schemas"]["BaseModelType"];
|
||||
};
|
||||
path: {
|
||||
/** @description Base model */
|
||||
base_model: components["schemas"]["BaseModelType"];
|
||||
/** @description The type of model */
|
||||
model_type: components["schemas"]["ModelType"];
|
||||
/** @description current model name */
|
||||
model_name: string;
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description The model was renamed successfully */
|
||||
201: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
/** @description The model could not be found */
|
||||
404: never;
|
||||
/** @description There is already a model corresponding to the new name */
|
||||
409: never;
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Convert Model
|
||||
* @description Convert a checkpoint model into a diffusers model
|
||||
* @description Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none.
|
||||
*/
|
||||
convert_model: {
|
||||
parameters: {
|
||||
query?: {
|
||||
/** @description Save the converted model to the designated directory */
|
||||
convert_dest_directory?: string;
|
||||
};
|
||||
path: {
|
||||
/** @description Base model */
|
||||
base_model: components["schemas"]["BaseModelType"];
|
||||
@ -5120,7 +5223,7 @@ export type operations = {
|
||||
/** @description Model converted successfully */
|
||||
200: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
/** @description Bad request */
|
||||
@ -5135,6 +5238,60 @@ export type operations = {
|
||||
};
|
||||
};
|
||||
};
|
||||
/** Search For Models */
|
||||
search_for_models: {
|
||||
parameters: {
|
||||
query: {
|
||||
/** @description Directory path to search for models */
|
||||
search_path: string;
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
/** @description Directory searched successfully */
|
||||
200: {
|
||||
content: {
|
||||
"application/json": (string)[];
|
||||
};
|
||||
};
|
||||
/** @description Invalid directory path */
|
||||
404: never;
|
||||
/** @description Validation Error */
|
||||
422: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["HTTPValidationError"];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
/**
|
||||
* List Ckpt Configs
|
||||
* @description Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT.
|
||||
*/
|
||||
list_ckpt_configs: {
|
||||
responses: {
|
||||
/** @description paths retrieved successfully */
|
||||
200: {
|
||||
content: {
|
||||
"application/json": (string)[];
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Sync To Config
|
||||
* @description Call after making changes to models.yaml, autoimport directories or models directory to synchronize
|
||||
* in-memory data structures with disk data structures.
|
||||
*/
|
||||
sync_to_config: {
|
||||
responses: {
|
||||
/** @description synchronization successful */
|
||||
201: {
|
||||
content: {
|
||||
"application/json": unknown;
|
||||
};
|
||||
};
|
||||
};
|
||||
};
|
||||
/**
|
||||
* Merge Models
|
||||
* @description Convert a checkpoint model into a diffusers model
|
||||
@ -5155,7 +5312,7 @@ export type operations = {
|
||||
/** @description Model converted successfully */
|
||||
200: {
|
||||
content: {
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
|
||||
"application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"];
|
||||
};
|
||||
};
|
||||
/** @description Incompatible models */
|
||||
|
||||
@ -1 +1 @@
|
||||
__version__ = "3.0.0+b5"
|
||||
__version__ = "3.0.0+b6"
|
||||
|
||||
@ -55,7 +55,6 @@ def mock_services() -> InvocationServices:
|
||||
),
|
||||
graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
|
||||
processor = DefaultInvocationProcessor(),
|
||||
restoration = None, # type: ignore
|
||||
configuration = None, # type: ignore
|
||||
)
|
||||
|
||||
|
||||
@ -48,7 +48,6 @@ def mock_services() -> InvocationServices:
|
||||
),
|
||||
graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = sqlite_memory, table_name = 'graph_executions'),
|
||||
processor = DefaultInvocationProcessor(),
|
||||
restoration = None, # type: ignore
|
||||
configuration = None, # type: ignore
|
||||
)
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
from .test_nodes import ImageToImageTestInvocation, TextToImageTestInvocation, ListPassThroughInvocation, PromptTestInvocation
|
||||
from invokeai.app.services.graph import Edge, Graph, GraphInvocation, InvalidEdgeError, NodeAlreadyInGraphError, NodeNotFoundError, are_connections_compatible, EdgeConnection, CollectInvocation, IterateInvocation
|
||||
from invokeai.app.invocations.upscale import UpscaleInvocation
|
||||
from invokeai.app.invocations.upscale import RealESRGANInvocation
|
||||
from invokeai.app.invocations.image import *
|
||||
from invokeai.app.invocations.math import AddInvocation, SubtractInvocation
|
||||
from invokeai.app.invocations.params import ParamIntInvocation
|
||||
@ -19,7 +19,7 @@ def create_edge(from_id: str, from_field: str, to_id: str, to_field: str) -> Edg
|
||||
def test_connections_are_compatible():
|
||||
from_node = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
from_field = "image"
|
||||
to_node = UpscaleInvocation(id = "2")
|
||||
to_node = RealESRGANInvocation(id = "2")
|
||||
to_field = "image"
|
||||
|
||||
result = are_connections_compatible(from_node, from_field, to_node, to_field)
|
||||
@ -29,7 +29,7 @@ def test_connections_are_compatible():
|
||||
def test_connections_are_incompatible():
|
||||
from_node = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
from_field = "image"
|
||||
to_node = UpscaleInvocation(id = "2")
|
||||
to_node = RealESRGANInvocation(id = "2")
|
||||
to_field = "strength"
|
||||
|
||||
result = are_connections_compatible(from_node, from_field, to_node, to_field)
|
||||
@ -39,7 +39,7 @@ def test_connections_are_incompatible():
|
||||
def test_connections_incompatible_with_invalid_fields():
|
||||
from_node = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
from_field = "invalid_field"
|
||||
to_node = UpscaleInvocation(id = "2")
|
||||
to_node = RealESRGANInvocation(id = "2")
|
||||
to_field = "image"
|
||||
|
||||
# From field is invalid
|
||||
@ -86,10 +86,10 @@ def test_graph_fails_to_update_node_if_type_changes():
|
||||
g = Graph()
|
||||
n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
g.add_node(n)
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n2)
|
||||
|
||||
nu = UpscaleInvocation(id = "1")
|
||||
nu = RealESRGANInvocation(id = "1")
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
g.update_node("1", nu)
|
||||
@ -98,7 +98,7 @@ def test_graph_allows_non_conflicting_id_change():
|
||||
g = Graph()
|
||||
n = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
g.add_node(n)
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n2)
|
||||
e1 = create_edge(n.id,"image",n2.id,"image")
|
||||
g.add_edge(e1)
|
||||
@ -128,7 +128,7 @@ def test_graph_fails_to_update_node_id_if_conflict():
|
||||
def test_graph_adds_edge():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e = create_edge(n1.id,"image",n2.id,"image")
|
||||
@ -139,7 +139,7 @@ def test_graph_adds_edge():
|
||||
|
||||
def test_graph_fails_to_add_edge_with_cycle():
|
||||
g = Graph()
|
||||
n1 = UpscaleInvocation(id = "1")
|
||||
n1 = RealESRGANInvocation(id = "1")
|
||||
g.add_node(n1)
|
||||
e = create_edge(n1.id,"image",n1.id,"image")
|
||||
with pytest.raises(InvalidEdgeError):
|
||||
@ -148,8 +148,8 @@ def test_graph_fails_to_add_edge_with_cycle():
|
||||
def test_graph_fails_to_add_edge_with_long_cycle():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n3 = UpscaleInvocation(id = "3")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
n3 = RealESRGANInvocation(id = "3")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
g.add_node(n3)
|
||||
@ -164,7 +164,7 @@ def test_graph_fails_to_add_edge_with_long_cycle():
|
||||
def test_graph_fails_to_add_edge_with_missing_node_id():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e1 = create_edge("1","image","3","image")
|
||||
@ -177,8 +177,8 @@ def test_graph_fails_to_add_edge_with_missing_node_id():
|
||||
def test_graph_fails_to_add_edge_when_destination_exists():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n3 = UpscaleInvocation(id = "3")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
n3 = RealESRGANInvocation(id = "3")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
g.add_node(n3)
|
||||
@ -194,7 +194,7 @@ def test_graph_fails_to_add_edge_when_destination_exists():
|
||||
def test_graph_fails_to_add_edge_with_mismatched_types():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e1 = create_edge("1","image","2","strength")
|
||||
@ -344,7 +344,7 @@ def test_graph_iterator_invalid_if_output_and_input_types_different():
|
||||
def test_graph_validates():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e1 = create_edge("1","image","2","image")
|
||||
@ -377,8 +377,8 @@ def test_graph_invalid_if_subgraph_invalid():
|
||||
|
||||
def test_graph_invalid_if_has_cycle():
|
||||
g = Graph()
|
||||
n1 = UpscaleInvocation(id = "1")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n1 = RealESRGANInvocation(id = "1")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.nodes[n1.id] = n1
|
||||
g.nodes[n2.id] = n2
|
||||
e1 = create_edge("1","image","2","image")
|
||||
@ -391,7 +391,7 @@ def test_graph_invalid_if_has_cycle():
|
||||
def test_graph_invalid_with_invalid_connection():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.nodes[n1.id] = n1
|
||||
g.nodes[n2.id] = n2
|
||||
e1 = create_edge("1","image","2","strength")
|
||||
@ -503,7 +503,7 @@ def test_graph_fails_to_enumerate_non_subgraph_node():
|
||||
|
||||
g.add_node(n1)
|
||||
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n2)
|
||||
|
||||
with pytest.raises(NodeNotFoundError):
|
||||
@ -512,7 +512,7 @@ def test_graph_fails_to_enumerate_non_subgraph_node():
|
||||
def test_graph_gets_networkx_graph():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e = create_edge(n1.id,"image",n2.id,"image")
|
||||
@ -529,7 +529,7 @@ def test_graph_gets_networkx_graph():
|
||||
def test_graph_can_serialize():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e = create_edge(n1.id,"image",n2.id,"image")
|
||||
@ -541,7 +541,7 @@ def test_graph_can_serialize():
|
||||
def test_graph_can_deserialize():
|
||||
g = Graph()
|
||||
n1 = TextToImageTestInvocation(id = "1", prompt = "Banana sushi")
|
||||
n2 = UpscaleInvocation(id = "2")
|
||||
n2 = RealESRGANInvocation(id = "2")
|
||||
g.add_node(n1)
|
||||
g.add_node(n2)
|
||||
e = create_edge(n1.id,"image",n2.id,"image")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user