# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) from datetime import datetime, timezone from typing import Any, Literal, Optional, Union import numpy as np from pydantic import Field from PIL import Image from skimage.exposure.histogram_matching import match_histograms from .image import ImageField, ImageOutput from .baseinvocation import BaseInvocation, InvocationContext from ..services.image_storage import ImageType from ..services.invocation_services import InvocationServices SAMPLER_NAME_VALUES = Literal["ddim","plms","k_lms","k_dpm_2","k_dpm_2_a","k_euler","k_euler_a","k_heun"] # Text to image class TextToImageInvocation(BaseInvocation): """Generates an image using text2img.""" type: Literal['txt2img'] = 'txt2img' # Inputs # TODO: consider making prompt optional to enable providing prompt through a link prompt: Optional[str] = Field(description="The prompt to generate an image from") seed: int = Field(default=-1, ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)") steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image") width: int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image") height: int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image") cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt") sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler to use") seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams") model: str = Field(default='', description="The model to use (currently ignored)") progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation") # TODO: pass this an emitter method or something? or a session for dispatching? def dispatch_progress(self, context: InvocationContext, sample: Any = None, step: int = 0) -> None: context.services.events.emit_generator_progress( context.graph_execution_state_id, self.id, step, float(step) / float(self.steps) ) def invoke(self, context: InvocationContext) -> ImageOutput: def step_callback(sample, step = 0): self.dispatch_progress(context, sample, step) # Handle invalid model parameter # TODO: figure out if this can be done via a validator that uses the model_cache # TODO: How to get the default model name now? if self.model is None or self.model == '': self.model = context.services.generate.model_name # Set the model (if already cached, this does nothing) context.services.generate.set_model(self.model) results = context.services.generate.prompt2image( prompt = self.prompt, step_callback = step_callback, **self.dict(exclude = {'prompt'}) # Shorthand for passing all of the parameters above manually ) # Results are image and seed, unwrap for now and ignore the seed # TODO: pre-seed? # TODO: can this return multiple results? Should it? image_type = ImageType.RESULT image_name = context.services.images.create_name(context.graph_execution_state_id, self.id) context.services.images.save(image_type, image_name, results[0][0]) return ImageOutput( image = ImageField(image_type = image_type, image_name = image_name) ) class ImageToImageInvocation(TextToImageInvocation): """Generates an image using img2img.""" type: Literal['img2img'] = 'img2img' # Inputs image: Union[ImageField,None] = Field(description="The input image") strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the original image") fit: bool = Field(default=True, description="Whether or not the result should be fit to the aspect ratio of the input image") def invoke(self, context: InvocationContext) -> ImageOutput: image = None if self.image is None else context.services.images.get(self.image.image_type, self.image.image_name) mask = None def step_callback(sample, step = 0): self.dispatch_progress(context, sample, step) # Handle invalid model parameter # TODO: figure out if this can be done via a validator that uses the model_cache # TODO: How to get the default model name now? if self.model is None or self.model == '': self.model = context.services.generate.model_name # Set the model (if already cached, this does nothing) context.services.generate.set_model(self.model) results = context.services.generate.prompt2image( prompt = self.prompt, init_img = image, init_mask = mask, step_callback = step_callback, **self.dict(exclude = {'prompt','image','mask'}) # Shorthand for passing all of the parameters above manually ) result_image = results[0][0] # Results are image and seed, unwrap for now and ignore the seed # TODO: pre-seed? # TODO: can this return multiple results? Should it? image_type = ImageType.RESULT image_name = context.services.images.create_name(context.graph_execution_state_id, self.id) context.services.images.save(image_type, image_name, result_image) return ImageOutput( image = ImageField(image_type = image_type, image_name = image_name) ) class InpaintInvocation(ImageToImageInvocation): """Generates an image using inpaint.""" type: Literal['inpaint'] = 'inpaint' # Inputs mask: Union[ImageField,None] = Field(description="The mask") inpaint_replace: float = Field(default=0.0, ge=0.0, le=1.0, description="The amount by which to replace masked areas with latent noise") def invoke(self, context: InvocationContext) -> ImageOutput: image = None if self.image is None else context.services.images.get(self.image.image_type, self.image.image_name) mask = None if self.mask is None else context.services.images.get(self.mask.image_type, self.mask.image_name) def step_callback(sample, step = 0): self.dispatch_progress(context, sample, step) # Handle invalid model parameter # TODO: figure out if this can be done via a validator that uses the model_cache # TODO: How to get the default model name now? if self.model is None or self.model == '': self.model = context.services.generate.model_name # Set the model (if already cached, this does nothing) context.services.generate.set_model(self.model) results = context.services.generate.prompt2image( prompt = self.prompt, init_img = image, init_mask = mask, step_callback = step_callback, **self.dict(exclude = {'prompt','image','mask'}) # Shorthand for passing all of the parameters above manually ) result_image = results[0][0] # Results are image and seed, unwrap for now and ignore the seed # TODO: pre-seed? # TODO: can this return multiple results? Should it? image_type = ImageType.RESULT image_name = context.services.images.create_name(context.graph_execution_state_id, self.id) context.services.images.save(image_type, image_name, result_image) return ImageOutput( image = ImageField(image_type = image_type, image_name = image_name) )