mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into lstein/installer-for-new-model-layout
This commit is contained in:
Lincoln Stein
GitHub
commit
72209d0cc3
@ -1,10 +1,11 @@
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# InvokeAI nodes for ControlNet image preprocessors
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# Invocations for ControlNet image preprocessors
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# initial implementation by Gregg Helt, 2023
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# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
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from builtins import float, bool
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import cv2
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import numpy as np
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from typing import Literal, Optional, Union, List
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from typing import Literal, Optional, Union, List, Dict
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from PIL import Image, ImageFilter, ImageOps
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from pydantic import BaseModel, Field, validator
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@ -29,8 +30,13 @@ from controlnet_aux import (
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ContentShuffleDetector,
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ZoeDetector,
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MediapipeFaceDetector,
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SamDetector,
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LeresDetector,
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)
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from controlnet_aux.util import HWC3, ade_palette
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from .image import ImageOutput, PILInvocationConfig
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CONTROLNET_DEFAULT_MODELS = [
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@ -95,6 +101,9 @@ CONTROLNET_DEFAULT_MODELS = [
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CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
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CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
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# crop and fill options not ready yet
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# CONTROLNET_RESIZE_VALUES = Literal[tuple(["just_resize", "crop_resize", "fill_resize"])]
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class ControlField(BaseModel):
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image: ImageField = Field(default=None, description="The control image")
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@ -105,7 +114,8 @@ class ControlField(BaseModel):
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description="When the ControlNet is first applied (% of total steps)")
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end_step_percent: float = Field(default=1, ge=0, le=1,
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description="When the ControlNet is last applied (% of total steps)")
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control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The contorl mode to use")
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control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
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# resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
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@validator("control_weight")
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def abs_le_one(cls, v):
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@ -180,7 +190,7 @@ class ControlNetInvocation(BaseInvocation):
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),
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)
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# TODO: move image processors to separate file (image_analysis.py
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class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
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"""Base class for invocations that preprocess images for ControlNet"""
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@ -452,6 +462,104 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
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# fmt: on
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def run_processor(self, image):
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# MediaPipeFaceDetector throws an error if image has alpha channel
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# so convert to RGB if needed
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if image.mode == 'RGBA':
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image = image.convert('RGB')
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mediapipe_face_processor = MediapipeFaceDetector()
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processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
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return processed_image
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class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies leres processing to image"""
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# fmt: off
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type: Literal["leres_image_processor"] = "leres_image_processor"
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# Inputs
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thr_a: float = Field(default=0, description="Leres parameter `thr_a`")
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thr_b: float = Field(default=0, description="Leres parameter `thr_b`")
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boost: bool = Field(default=False, description="Whether to use boost mode")
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detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
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image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
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# fmt: on
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def run_processor(self, image):
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leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
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processed_image = leres_processor(image,
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thr_a=self.thr_a,
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thr_b=self.thr_b,
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boost=self.boost,
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detect_resolution=self.detect_resolution,
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image_resolution=self.image_resolution)
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return processed_image
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class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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# fmt: off
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type: Literal["tile_image_processor"] = "tile_image_processor"
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# Inputs
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#res: int = Field(default=512, ge=0, le=1024, description="The pixel resolution for each tile")
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down_sampling_rate: float = Field(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
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# fmt: on
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# tile_resample copied from sd-webui-controlnet/scripts/processor.py
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def tile_resample(self,
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np_img: np.ndarray,
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res=512, # never used?
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down_sampling_rate=1.0,
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):
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np_img = HWC3(np_img)
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if down_sampling_rate < 1.1:
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return np_img
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H, W, C = np_img.shape
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H = int(float(H) / float(down_sampling_rate))
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W = int(float(W) / float(down_sampling_rate))
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np_img = cv2.resize(np_img, (W, H), interpolation=cv2.INTER_AREA)
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return np_img
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def run_processor(self, img):
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np_img = np.array(img, dtype=np.uint8)
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processed_np_image = self.tile_resample(np_img,
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#res=self.tile_size,
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down_sampling_rate=self.down_sampling_rate
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)
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processed_image = Image.fromarray(processed_np_image)
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return processed_image
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class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
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"""Applies segment anything processing to image"""
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# fmt: off
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type: Literal["segment_anything_processor"] = "segment_anything_processor"
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# fmt: on
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def run_processor(self, image):
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# segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
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segment_anything_processor = SamDetectorReproducibleColors.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
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np_img = np.array(image, dtype=np.uint8)
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processed_image = segment_anything_processor(np_img)
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return processed_image
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class SamDetectorReproducibleColors(SamDetector):
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# overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
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# base class show_anns() method randomizes colors,
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# which seems to also lead to non-reproducible image generation
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# so using ADE20k color palette instead
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def show_anns(self, anns: List[Dict]):
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if len(anns) == 0:
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return
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sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
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h, w = anns[0]['segmentation'].shape
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final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
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palette = ade_palette()
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for i, ann in enumerate(sorted_anns):
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m = ann['segmentation']
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img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
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# doing modulo just in case number of annotated regions exceeds number of colors in palette
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ann_color = palette[i % len(palette)]
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img[:, :] = ann_color
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final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
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return np.array(final_img, dtype=np.uint8)
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@ -23,7 +23,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
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from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
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PostprocessingSettings
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from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
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from ...backend.util.devices import choose_torch_device, torch_dtype
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from ...backend.util.devices import torch_dtype
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from ...backend.model_management.lora import ModelPatcher
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from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
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InvocationConfig, InvocationContext)
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@ -59,31 +59,12 @@ def build_latents_output(latents_name: str, latents: torch.Tensor):
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height=latents.size()[2] * 8,
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)
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class NoiseOutput(BaseInvocationOutput):
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"""Invocation noise output"""
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#fmt: off
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type: Literal["noise_output"] = "noise_output"
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# Inputs
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noise: LatentsField = Field(default=None, description="The output noise")
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width: int = Field(description="The width of the noise in pixels")
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height: int = Field(description="The height of the noise in pixels")
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#fmt: on
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def build_noise_output(latents_name: str, latents: torch.Tensor):
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return NoiseOutput(
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noise=LatentsField(latents_name=latents_name),
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width=latents.size()[3] * 8,
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height=latents.size()[2] * 8,
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)
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SAMPLER_NAME_VALUES = Literal[
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tuple(list(SCHEDULER_MAP.keys()))
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]
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def get_scheduler(
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context: InvocationContext,
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scheduler_info: ModelInfo,
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@ -105,62 +86,6 @@ def get_scheduler(
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return scheduler
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def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_channels:int=4, use_mps_noise:bool=False, downsampling_factor:int = 8):
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# limit noise to only the diffusion image channels, not the mask channels
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input_channels = min(latent_channels, 4)
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use_device = "cpu" if (use_mps_noise or device.type == "mps") else device
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generator = torch.Generator(device=use_device).manual_seed(seed)
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x = torch.randn(
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[
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1,
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input_channels,
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height // downsampling_factor,
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width // downsampling_factor,
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],
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dtype=torch_dtype(device),
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device=use_device,
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generator=generator,
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).to(device)
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# if self.perlin > 0.0:
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# perlin_noise = self.get_perlin_noise(
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# width // self.downsampling_factor, height // self.downsampling_factor
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# )
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# x = (1 - self.perlin) * x + self.perlin * perlin_noise
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return x
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class NoiseInvocation(BaseInvocation):
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"""Generates latent noise."""
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type: Literal["noise"] = "noise"
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# Inputs
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seed: int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
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width: int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
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height: int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
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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": ["latents", "noise"],
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},
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}
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@validator("seed", pre=True)
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def modulo_seed(cls, v):
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"""Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
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return v % SEED_MAX
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def invoke(self, context: InvocationContext) -> NoiseOutput:
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device = torch.device(choose_torch_device())
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noise = get_noise(self.width, self.height, device, self.seed)
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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.save(name, noise)
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return build_noise_output(latents_name=name, latents=noise)
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# Text to image
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class TextToLatentsInvocation(BaseInvocation):
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"""Generates latents from conditionings."""
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134
invokeai/app/invocations/noise.py
Normal file
134
invokeai/app/invocations/noise.py
Normal file
@ -0,0 +1,134 @@
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# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
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import math
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from typing import Literal
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from pydantic import Field, validator
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import torch
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from invokeai.app.invocations.latent import LatentsField
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from invokeai.app.util.misc import SEED_MAX, get_random_seed
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from ...backend.util.devices import choose_torch_device, torch_dtype
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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InvocationConfig,
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InvocationContext,
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)
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"""
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Utilities
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"""
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def get_noise(
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width: int,
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height: int,
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device: torch.device,
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seed: int = 0,
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latent_channels: int = 4,
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downsampling_factor: int = 8,
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use_cpu: bool = True,
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perlin: float = 0.0,
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):
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"""Generate noise for a given image size."""
|
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noise_device_type = "cpu" if (use_cpu or device.type == "mps") else device.type
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# limit noise to only the diffusion image channels, not the mask channels
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input_channels = min(latent_channels, 4)
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generator = torch.Generator(device=noise_device_type).manual_seed(seed)
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||||
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noise_tensor = torch.randn(
|
||||
[
|
||||
1,
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||||
input_channels,
|
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height // downsampling_factor,
|
||||
width // downsampling_factor,
|
||||
],
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dtype=torch_dtype(device),
|
||||
device=noise_device_type,
|
||||
generator=generator,
|
||||
).to(device)
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return noise_tensor
|
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|
||||
"""
|
||||
Nodes
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"""
|
||||
|
||||
|
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class NoiseOutput(BaseInvocationOutput):
|
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"""Invocation noise output"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["noise_output"] = "noise_output"
|
||||
|
||||
# Inputs
|
||||
noise: LatentsField = Field(default=None, description="The output noise")
|
||||
width: int = Field(description="The width of the noise in pixels")
|
||||
height: int = Field(description="The height of the noise in pixels")
|
||||
# fmt: on
|
||||
|
||||
|
||||
def build_noise_output(latents_name: str, latents: torch.Tensor):
|
||||
return NoiseOutput(
|
||||
noise=LatentsField(latents_name=latents_name),
|
||||
width=latents.size()[3] * 8,
|
||||
height=latents.size()[2] * 8,
|
||||
)
|
||||
|
||||
|
||||
class NoiseInvocation(BaseInvocation):
|
||||
"""Generates latent noise."""
|
||||
|
||||
type: Literal["noise"] = "noise"
|
||||
|
||||
# Inputs
|
||||
seed: int = Field(
|
||||
ge=0,
|
||||
le=SEED_MAX,
|
||||
description="The seed to use",
|
||||
default_factory=get_random_seed,
|
||||
)
|
||||
width: int = Field(
|
||||
default=512,
|
||||
multiple_of=8,
|
||||
gt=0,
|
||||
description="The width of the resulting noise",
|
||||
)
|
||||
height: int = Field(
|
||||
default=512,
|
||||
multiple_of=8,
|
||||
gt=0,
|
||||
description="The height of the resulting noise",
|
||||
)
|
||||
use_cpu: bool = Field(
|
||||
default=True,
|
||||
description="Use CPU for noise generation (for reproducible results across platforms)",
|
||||
)
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["latents", "noise"],
|
||||
},
|
||||
}
|
||||
|
||||
@validator("seed", pre=True)
|
||||
def modulo_seed(cls, v):
|
||||
"""Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
|
||||
return v % SEED_MAX
|
||||
|
||||
def invoke(self, context: InvocationContext) -> NoiseOutput:
|
||||
noise = get_noise(
|
||||
width=self.width,
|
||||
height=self.height,
|
||||
device=choose_torch_device(),
|
||||
seed=self.seed,
|
||||
use_cpu=self.use_cpu,
|
||||
)
|
||||
name = f"{context.graph_execution_state_id}__{self.id}"
|
||||
context.services.latents.save(name, noise)
|
||||
return build_noise_output(latents_name=name, latents=noise)
|
||||
@ -133,20 +133,19 @@ class StepParamEasingInvocation(BaseInvocation):
|
||||
postlist = list(num_poststeps * [self.post_end_value])
|
||||
|
||||
if log_diagnostics:
|
||||
logger = InvokeAILogger.getLogger(name="StepParamEasing")
|
||||
logger.debug("start_step: " + str(start_step))
|
||||
logger.debug("end_step: " + str(end_step))
|
||||
logger.debug("num_easing_steps: " + str(num_easing_steps))
|
||||
logger.debug("num_presteps: " + str(num_presteps))
|
||||
logger.debug("num_poststeps: " + str(num_poststeps))
|
||||
logger.debug("prelist size: " + str(len(prelist)))
|
||||
logger.debug("postlist size: " + str(len(postlist)))
|
||||
logger.debug("prelist: " + str(prelist))
|
||||
logger.debug("postlist: " + str(postlist))
|
||||
context.services.logger.debug("start_step: " + str(start_step))
|
||||
context.services.logger.debug("end_step: " + str(end_step))
|
||||
context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
|
||||
context.services.logger.debug("num_presteps: " + str(num_presteps))
|
||||
context.services.logger.debug("num_poststeps: " + str(num_poststeps))
|
||||
context.services.logger.debug("prelist size: " + str(len(prelist)))
|
||||
context.services.logger.debug("postlist size: " + str(len(postlist)))
|
||||
context.services.logger.debug("prelist: " + str(prelist))
|
||||
context.services.logger.debug("postlist: " + str(postlist))
|
||||
|
||||
easing_class = EASING_FUNCTIONS_MAP[self.easing]
|
||||
if log_diagnostics:
|
||||
logger.debug("easing class: " + str(easing_class))
|
||||
context.services.logger.debug("easing class: " + str(easing_class))
|
||||
easing_list = list()
|
||||
if self.mirror: # "expected" mirroring
|
||||
# if number of steps is even, squeeze duration down to (number_of_steps)/2
|
||||
@ -156,7 +155,7 @@ class StepParamEasingInvocation(BaseInvocation):
|
||||
# but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
|
||||
|
||||
base_easing_duration = int(np.ceil(num_easing_steps/2.0))
|
||||
if log_diagnostics: logger.debug("base easing duration: " + str(base_easing_duration))
|
||||
if log_diagnostics: context.services.logger.debug("base easing duration: " + str(base_easing_duration))
|
||||
even_num_steps = (num_easing_steps % 2 == 0) # even number of steps
|
||||
easing_function = easing_class(start=self.start_value,
|
||||
end=self.end_value,
|
||||
@ -166,14 +165,14 @@ class StepParamEasingInvocation(BaseInvocation):
|
||||
easing_val = easing_function.ease(step_index)
|
||||
base_easing_vals.append(easing_val)
|
||||
if log_diagnostics:
|
||||
logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
|
||||
context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
|
||||
if even_num_steps:
|
||||
mirror_easing_vals = list(reversed(base_easing_vals))
|
||||
else:
|
||||
mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
|
||||
if log_diagnostics:
|
||||
logger.debug("base easing vals: " + str(base_easing_vals))
|
||||
logger.debug("mirror easing vals: " + str(mirror_easing_vals))
|
||||
context.services.logger.debug("base easing vals: " + str(base_easing_vals))
|
||||
context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
|
||||
easing_list = base_easing_vals + mirror_easing_vals
|
||||
|
||||
# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
|
||||
@ -206,12 +205,12 @@ class StepParamEasingInvocation(BaseInvocation):
|
||||
step_val = easing_function.ease(step_index)
|
||||
easing_list.append(step_val)
|
||||
if log_diagnostics:
|
||||
logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
|
||||
context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
|
||||
|
||||
if log_diagnostics:
|
||||
logger.debug("prelist size: " + str(len(prelist)))
|
||||
logger.debug("easing_list size: " + str(len(easing_list)))
|
||||
logger.debug("postlist size: " + str(len(postlist)))
|
||||
context.services.logger.debug("prelist size: " + str(len(prelist)))
|
||||
context.services.logger.debug("easing_list size: " + str(len(easing_list)))
|
||||
context.services.logger.debug("postlist size: " + str(len(postlist)))
|
||||
|
||||
param_list = prelist + easing_list + postlist
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
|
||||
from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation
|
||||
from ..invocations.noise import NoiseInvocation
|
||||
from ..invocations.compel import CompelInvocation
|
||||
from ..invocations.params import ParamIntInvocation
|
||||
from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
|
||||
|
||||
@ -35,8 +35,8 @@ const ParamDynamicPromptsCollapse = () => {
|
||||
withSwitch
|
||||
>
|
||||
<Flex sx={{ gap: 2, flexDir: 'column' }}>
|
||||
<ParamDynamicPromptsMaxPrompts />
|
||||
<ParamDynamicPromptsCombinatorial />
|
||||
<ParamDynamicPromptsMaxPrompts />
|
||||
</Flex>
|
||||
</IAICollapse>
|
||||
);
|
||||
|
||||
@ -9,17 +9,18 @@ import { stateSelector } from 'app/store/store';
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
(state) => {
|
||||
const { maxPrompts } = state.dynamicPrompts;
|
||||
const { maxPrompts, combinatorial } = state.dynamicPrompts;
|
||||
const { min, sliderMax, inputMax } =
|
||||
state.config.sd.dynamicPrompts.maxPrompts;
|
||||
|
||||
return { maxPrompts, min, sliderMax, inputMax };
|
||||
return { maxPrompts, min, sliderMax, inputMax, combinatorial };
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const ParamDynamicPromptsMaxPrompts = () => {
|
||||
const { maxPrompts, min, sliderMax, inputMax } = useAppSelector(selector);
|
||||
const { maxPrompts, min, sliderMax, inputMax, combinatorial } =
|
||||
useAppSelector(selector);
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
const handleChange = useCallback(
|
||||
@ -36,6 +37,7 @@ const ParamDynamicPromptsMaxPrompts = () => {
|
||||
return (
|
||||
<IAISlider
|
||||
label="Max Prompts"
|
||||
isDisabled={!combinatorial}
|
||||
min={min}
|
||||
max={sliderMax}
|
||||
value={maxPrompts}
|
||||
|
||||
@ -37,7 +37,7 @@ export const addDynamicPromptsToGraph = (
|
||||
const dynamicPromptNode: DynamicPromptInvocation = {
|
||||
id: DYNAMIC_PROMPT,
|
||||
type: 'dynamic_prompt',
|
||||
max_prompts: maxPrompts,
|
||||
max_prompts: combinatorial ? maxPrompts : iterations,
|
||||
combinatorial,
|
||||
prompt: positivePrompt,
|
||||
};
|
||||
|
||||
@ -16,7 +16,8 @@ const selector = createSelector([stateSelector], (state) => {
|
||||
state.config.sd.iterations;
|
||||
const { iterations } = state.generation;
|
||||
const { shouldUseSliders } = state.ui;
|
||||
const isDisabled = state.dynamicPrompts.isEnabled;
|
||||
const isDisabled =
|
||||
state.dynamicPrompts.isEnabled && state.dynamicPrompts.combinatorial;
|
||||
|
||||
const step = state.hotkeys.shift ? fineStep : coarseStep;
|
||||
|
||||
|
||||
@ -1030,7 +1030,7 @@ 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"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["NoiseInvocation"] | 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"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | 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"]["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"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | 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"]["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"]["LatentsToLatentsInvocation"]) | undefined;
|
||||
};
|
||||
/**
|
||||
* Edges
|
||||
@ -1073,7 +1073,7 @@ export type components = {
|
||||
* @description The results of node executions
|
||||
*/
|
||||
results: {
|
||||
[key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
|
||||
[key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
|
||||
};
|
||||
/**
|
||||
* Errors
|
||||
@ -2917,7 +2917,7 @@ export type components = {
|
||||
/** ModelsList */
|
||||
ModelsList: {
|
||||
/** Models */
|
||||
models: (components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | 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
|
||||
@ -2993,6 +2993,18 @@ export type components = {
|
||||
* @default 512
|
||||
*/
|
||||
height?: number;
|
||||
/**
|
||||
* Perlin
|
||||
* @description The amount of perlin noise to add to the noise
|
||||
* @default 0
|
||||
*/
|
||||
perlin?: number;
|
||||
/**
|
||||
* Use Cpu
|
||||
* @description Use CPU for noise generation (for reproducible results across platforms)
|
||||
* @default true
|
||||
*/
|
||||
use_cpu?: boolean;
|
||||
};
|
||||
/**
|
||||
* NoiseOutput
|
||||
@ -4177,18 +4189,18 @@ 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;
|
||||
@ -4299,7 +4311,7 @@ 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"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["NoiseInvocation"] | 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"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | 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"]["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"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | 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"]["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"]["LatentsToLatentsInvocation"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
@ -4336,7 +4348,7 @@ 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"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["NoiseInvocation"] | 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"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | 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"]["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"]["PipelineModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | 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"]["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"]["LatentsToLatentsInvocation"];
|
||||
};
|
||||
};
|
||||
responses: {
|
||||
|
||||
122
invokeai/frontend/web/src/services/api/types.d.ts
vendored
122
invokeai/frontend/web/src/services/api/types.d.ts
vendored
@ -4,91 +4,89 @@ import { components } from './schema';
|
||||
type schemas = components['schemas'];
|
||||
|
||||
/**
|
||||
* Helper type to extract the invocation type from the schema.
|
||||
* Also flags the `type` property as required.
|
||||
* Extracts the schema type from the schema.
|
||||
*/
|
||||
type Invocation<T extends keyof schemas> = O.Required<schemas[T], 'type'>;
|
||||
type S<T extends keyof components['schemas']> = components['schemas'][T];
|
||||
|
||||
/**
|
||||
* Types from the API, re-exported from the types generated by `openapi-typescript`.
|
||||
* Extracts the node type from the schema.
|
||||
* Also flags the `type` property as required.
|
||||
*/
|
||||
type N<T extends keyof components['schemas']> = O.Required<
|
||||
components['schemas'][T],
|
||||
'type'
|
||||
>;
|
||||
|
||||
// Images
|
||||
export type ImageDTO = schemas['ImageDTO'];
|
||||
export type BoardDTO = schemas['BoardDTO'];
|
||||
export type BoardChanges = schemas['BoardChanges'];
|
||||
export type ImageChanges = schemas['ImageRecordChanges'];
|
||||
export type ImageCategory = schemas['ImageCategory'];
|
||||
export type ResourceOrigin = schemas['ResourceOrigin'];
|
||||
export type ImageField = schemas['ImageField'];
|
||||
export type ImageDTO = S<'ImageDTO'>;
|
||||
export type BoardDTO = S<'BoardDTO'>;
|
||||
export type BoardChanges = S<'BoardChanges'>;
|
||||
export type ImageChanges = S<'ImageRecordChanges'>;
|
||||
export type ImageCategory = S<'ImageCategory'>;
|
||||
export type ResourceOrigin = S<'ResourceOrigin'>;
|
||||
export type ImageField = S<'ImageField'>;
|
||||
export type OffsetPaginatedResults_BoardDTO_ =
|
||||
schemas['OffsetPaginatedResults_BoardDTO_'];
|
||||
S<'OffsetPaginatedResults_BoardDTO_'>;
|
||||
export type OffsetPaginatedResults_ImageDTO_ =
|
||||
schemas['OffsetPaginatedResults_ImageDTO_'];
|
||||
S<'OffsetPaginatedResults_ImageDTO_'>;
|
||||
|
||||
// Models
|
||||
export type ModelType = schemas['ModelType'];
|
||||
export type BaseModelType = schemas['BaseModelType'];
|
||||
export type PipelineModelField = schemas['PipelineModelField'];
|
||||
export type ModelsList = schemas['ModelsList'];
|
||||
export type ModelType = S<'ModelType'>;
|
||||
export type BaseModelType = S<'BaseModelType'>;
|
||||
export type PipelineModelField = S<'PipelineModelField'>;
|
||||
export type ModelsList = S<'ModelsList'>;
|
||||
|
||||
// Graphs
|
||||
export type Graph = schemas['Graph'];
|
||||
export type Edge = schemas['Edge'];
|
||||
export type GraphExecutionState = schemas['GraphExecutionState'];
|
||||
export type Graph = S<'Graph'>;
|
||||
export type Edge = S<'Edge'>;
|
||||
export type GraphExecutionState = S<'GraphExecutionState'>;
|
||||
|
||||
// General nodes
|
||||
export type CollectInvocation = Invocation<'CollectInvocation'>;
|
||||
export type IterateInvocation = Invocation<'IterateInvocation'>;
|
||||
export type RangeInvocation = Invocation<'RangeInvocation'>;
|
||||
export type RandomRangeInvocation = Invocation<'RandomRangeInvocation'>;
|
||||
export type RangeOfSizeInvocation = Invocation<'RangeOfSizeInvocation'>;
|
||||
export type InpaintInvocation = Invocation<'InpaintInvocation'>;
|
||||
export type ImageResizeInvocation = Invocation<'ImageResizeInvocation'>;
|
||||
export type RandomIntInvocation = Invocation<'RandomIntInvocation'>;
|
||||
export type CompelInvocation = Invocation<'CompelInvocation'>;
|
||||
export type DynamicPromptInvocation = Invocation<'DynamicPromptInvocation'>;
|
||||
export type NoiseInvocation = Invocation<'NoiseInvocation'>;
|
||||
export type TextToLatentsInvocation = Invocation<'TextToLatentsInvocation'>;
|
||||
export type LatentsToLatentsInvocation =
|
||||
Invocation<'LatentsToLatentsInvocation'>;
|
||||
export type ImageToLatentsInvocation = Invocation<'ImageToLatentsInvocation'>;
|
||||
export type LatentsToImageInvocation = Invocation<'LatentsToImageInvocation'>;
|
||||
export type PipelineModelLoaderInvocation =
|
||||
Invocation<'PipelineModelLoaderInvocation'>;
|
||||
export type CollectInvocation = N<'CollectInvocation'>;
|
||||
export type IterateInvocation = N<'IterateInvocation'>;
|
||||
export type RangeInvocation = N<'RangeInvocation'>;
|
||||
export type RandomRangeInvocation = N<'RandomRangeInvocation'>;
|
||||
export type RangeOfSizeInvocation = N<'RangeOfSizeInvocation'>;
|
||||
export type InpaintInvocation = N<'InpaintInvocation'>;
|
||||
export type ImageResizeInvocation = N<'ImageResizeInvocation'>;
|
||||
export type RandomIntInvocation = N<'RandomIntInvocation'>;
|
||||
export type CompelInvocation = N<'CompelInvocation'>;
|
||||
export type DynamicPromptInvocation = N<'DynamicPromptInvocation'>;
|
||||
export type NoiseInvocation = N<'NoiseInvocation'>;
|
||||
export type TextToLatentsInvocation = N<'TextToLatentsInvocation'>;
|
||||
export type LatentsToLatentsInvocation = N<'LatentsToLatentsInvocation'>;
|
||||
export type ImageToLatentsInvocation = N<'ImageToLatentsInvocation'>;
|
||||
export type LatentsToImageInvocation = N<'LatentsToImageInvocation'>;
|
||||
export type PipelineModelLoaderInvocation = N<'PipelineModelLoaderInvocation'>;
|
||||
|
||||
// ControlNet Nodes
|
||||
export type ControlNetInvocation = Invocation<'ControlNetInvocation'>;
|
||||
export type CannyImageProcessorInvocation =
|
||||
Invocation<'CannyImageProcessorInvocation'>;
|
||||
export type ControlNetInvocation = N<'ControlNetInvocation'>;
|
||||
export type CannyImageProcessorInvocation = N<'CannyImageProcessorInvocation'>;
|
||||
export type ContentShuffleImageProcessorInvocation =
|
||||
Invocation<'ContentShuffleImageProcessorInvocation'>;
|
||||
export type HedImageProcessorInvocation =
|
||||
Invocation<'HedImageProcessorInvocation'>;
|
||||
N<'ContentShuffleImageProcessorInvocation'>;
|
||||
export type HedImageProcessorInvocation = N<'HedImageProcessorInvocation'>;
|
||||
export type LineartAnimeImageProcessorInvocation =
|
||||
Invocation<'LineartAnimeImageProcessorInvocation'>;
|
||||
N<'LineartAnimeImageProcessorInvocation'>;
|
||||
export type LineartImageProcessorInvocation =
|
||||
Invocation<'LineartImageProcessorInvocation'>;
|
||||
N<'LineartImageProcessorInvocation'>;
|
||||
export type MediapipeFaceProcessorInvocation =
|
||||
Invocation<'MediapipeFaceProcessorInvocation'>;
|
||||
N<'MediapipeFaceProcessorInvocation'>;
|
||||
export type MidasDepthImageProcessorInvocation =
|
||||
Invocation<'MidasDepthImageProcessorInvocation'>;
|
||||
export type MlsdImageProcessorInvocation =
|
||||
Invocation<'MlsdImageProcessorInvocation'>;
|
||||
N<'MidasDepthImageProcessorInvocation'>;
|
||||
export type MlsdImageProcessorInvocation = N<'MlsdImageProcessorInvocation'>;
|
||||
export type NormalbaeImageProcessorInvocation =
|
||||
Invocation<'NormalbaeImageProcessorInvocation'>;
|
||||
N<'NormalbaeImageProcessorInvocation'>;
|
||||
export type OpenposeImageProcessorInvocation =
|
||||
Invocation<'OpenposeImageProcessorInvocation'>;
|
||||
export type PidiImageProcessorInvocation =
|
||||
Invocation<'PidiImageProcessorInvocation'>;
|
||||
N<'OpenposeImageProcessorInvocation'>;
|
||||
export type PidiImageProcessorInvocation = N<'PidiImageProcessorInvocation'>;
|
||||
export type ZoeDepthImageProcessorInvocation =
|
||||
Invocation<'ZoeDepthImageProcessorInvocation'>;
|
||||
N<'ZoeDepthImageProcessorInvocation'>;
|
||||
|
||||
// Node Outputs
|
||||
export type ImageOutput = schemas['ImageOutput'];
|
||||
export type MaskOutput = schemas['MaskOutput'];
|
||||
export type PromptOutput = schemas['PromptOutput'];
|
||||
export type IterateInvocationOutput = schemas['IterateInvocationOutput'];
|
||||
export type CollectInvocationOutput = schemas['CollectInvocationOutput'];
|
||||
export type LatentsOutput = schemas['LatentsOutput'];
|
||||
export type GraphInvocationOutput = schemas['GraphInvocationOutput'];
|
||||
export type ImageOutput = S<'ImageOutput'>;
|
||||
export type MaskOutput = S<'MaskOutput'>;
|
||||
export type PromptOutput = S<'PromptOutput'>;
|
||||
export type IterateInvocationOutput = S<'IterateInvocationOutput'>;
|
||||
export type CollectInvocationOutput = S<'CollectInvocationOutput'>;
|
||||
export type LatentsOutput = S<'LatentsOutput'>;
|
||||
export type GraphInvocationOutput = S<'GraphInvocationOutput'>;
|
||||
|
||||
@ -39,7 +39,7 @@ dependencies = [
|
||||
"click",
|
||||
"clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
|
||||
"compel>=1.2.1",
|
||||
"controlnet-aux>=0.0.4",
|
||||
"controlnet-aux>=0.0.6",
|
||||
"timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26
|
||||
"datasets",
|
||||
"diffusers[torch]~=0.17.1",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user