mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/nodes/cpu-noise
This commit is contained in:
blessedcoolant
GitHub
commit
75614bbba3
@ -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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@ -133,20 +133,19 @@ class StepParamEasingInvocation(BaseInvocation):
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postlist = list(num_poststeps * [self.post_end_value])
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if log_diagnostics:
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logger = InvokeAILogger.getLogger(name="StepParamEasing")
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logger.debug("start_step: " + str(start_step))
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logger.debug("end_step: " + str(end_step))
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logger.debug("num_easing_steps: " + str(num_easing_steps))
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logger.debug("num_presteps: " + str(num_presteps))
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logger.debug("num_poststeps: " + str(num_poststeps))
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logger.debug("prelist size: " + str(len(prelist)))
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logger.debug("postlist size: " + str(len(postlist)))
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logger.debug("prelist: " + str(prelist))
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logger.debug("postlist: " + str(postlist))
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context.services.logger.debug("start_step: " + str(start_step))
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context.services.logger.debug("end_step: " + str(end_step))
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context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
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context.services.logger.debug("num_presteps: " + str(num_presteps))
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context.services.logger.debug("num_poststeps: " + str(num_poststeps))
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context.services.logger.debug("prelist size: " + str(len(prelist)))
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context.services.logger.debug("postlist size: " + str(len(postlist)))
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context.services.logger.debug("prelist: " + str(prelist))
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context.services.logger.debug("postlist: " + str(postlist))
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easing_class = EASING_FUNCTIONS_MAP[self.easing]
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if log_diagnostics:
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logger.debug("easing class: " + str(easing_class))
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context.services.logger.debug("easing class: " + str(easing_class))
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easing_list = list()
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if self.mirror: # "expected" mirroring
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# if number of steps is even, squeeze duration down to (number_of_steps)/2
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@ -156,7 +155,7 @@ class StepParamEasingInvocation(BaseInvocation):
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# but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
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base_easing_duration = int(np.ceil(num_easing_steps/2.0))
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if log_diagnostics: logger.debug("base easing duration: " + str(base_easing_duration))
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if log_diagnostics: context.services.logger.debug("base easing duration: " + str(base_easing_duration))
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even_num_steps = (num_easing_steps % 2 == 0) # even number of steps
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easing_function = easing_class(start=self.start_value,
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end=self.end_value,
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@ -166,14 +165,14 @@ class StepParamEasingInvocation(BaseInvocation):
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easing_val = easing_function.ease(step_index)
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base_easing_vals.append(easing_val)
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if log_diagnostics:
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logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
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context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
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if even_num_steps:
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mirror_easing_vals = list(reversed(base_easing_vals))
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else:
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mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
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if log_diagnostics:
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logger.debug("base easing vals: " + str(base_easing_vals))
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logger.debug("mirror easing vals: " + str(mirror_easing_vals))
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context.services.logger.debug("base easing vals: " + str(base_easing_vals))
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context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
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easing_list = base_easing_vals + mirror_easing_vals
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# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
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@ -206,12 +205,12 @@ class StepParamEasingInvocation(BaseInvocation):
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step_val = easing_function.ease(step_index)
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easing_list.append(step_val)
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if log_diagnostics:
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logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
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context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
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if log_diagnostics:
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logger.debug("prelist size: " + str(len(prelist)))
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logger.debug("easing_list size: " + str(len(easing_list)))
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logger.debug("postlist size: " + str(len(postlist)))
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context.services.logger.debug("prelist size: " + str(len(prelist)))
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context.services.logger.debug("easing_list size: " + str(len(easing_list)))
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context.services.logger.debug("postlist size: " + str(len(postlist)))
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param_list = prelist + easing_list + postlist
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@ -35,8 +35,8 @@ const ParamDynamicPromptsCollapse = () => {
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withSwitch
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>
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<Flex sx={{ gap: 2, flexDir: 'column' }}>
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<ParamDynamicPromptsMaxPrompts />
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<ParamDynamicPromptsCombinatorial />
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<ParamDynamicPromptsMaxPrompts />
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</Flex>
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</IAICollapse>
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);
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@ -9,17 +9,18 @@ import { stateSelector } from 'app/store/store';
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const selector = createSelector(
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stateSelector,
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(state) => {
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const { maxPrompts } = state.dynamicPrompts;
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const { maxPrompts, combinatorial } = state.dynamicPrompts;
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const { min, sliderMax, inputMax } =
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state.config.sd.dynamicPrompts.maxPrompts;
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return { maxPrompts, min, sliderMax, inputMax };
|
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return { maxPrompts, min, sliderMax, inputMax, combinatorial };
|
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},
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defaultSelectorOptions
|
||||
);
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const ParamDynamicPromptsMaxPrompts = () => {
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const { maxPrompts, min, sliderMax, inputMax } = useAppSelector(selector);
|
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const { maxPrompts, min, sliderMax, inputMax, combinatorial } =
|
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useAppSelector(selector);
|
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const dispatch = useAppDispatch();
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const handleChange = useCallback(
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@ -36,6 +37,7 @@ const ParamDynamicPromptsMaxPrompts = () => {
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return (
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<IAISlider
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||||
label="Max Prompts"
|
||||
isDisabled={!combinatorial}
|
||||
min={min}
|
||||
max={sliderMax}
|
||||
value={maxPrompts}
|
||||
|
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@ -37,7 +37,7 @@ export const addDynamicPromptsToGraph = (
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||||
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;
|
||||
|
||||
|
||||
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