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