mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
035425ef24
- Address database feedback: - Remove all the extraneous tables. Only an `images` table now: - `image_type` and `image_category` are unrestricted strings. When creating images, the provided values are checked to ensure they are a valid type and category. - Add `updated_at` and `deleted_at` columns. `deleted_at` is currently unused. - Use SQLite's built-in timestamp features to populate these. Add a trigger to update `updated_at` when the row is updated. Currently no way to update a row. - Rename the `id` column in `images` to `image_name` - Rename `ImageCategory.IMAGE` to `ImageCategory.GENERAL` - Move all exceptions outside their base classes to make them more portable. - Add `width` and `height` columns to the database. These store the actual dimensions of the image file, whereas the metadata's `width` and `height` refer to the respective generation parameters and are nullable. - Make `deserialize_image_record` take a `dict` instead of `sqlite3.Row` - Improve comments throughout - Tidy up unused code/files and some minor organisation
313 lines
12 KiB
Python
313 lines
12 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from functools import partial
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from typing import Literal, Optional, Union, get_args
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import numpy as np
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from torch import Tensor
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from pydantic import BaseModel, Field
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from invokeai.app.models.image import ColorField, ImageField, ImageType
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from invokeai.app.invocations.util.choose_model import choose_model
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from invokeai.app.models.image import ImageCategory, ImageType
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from invokeai.app.util.misc import SEED_MAX, get_random_seed
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from invokeai.backend.generator.inpaint import infill_methods
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from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
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from .image import ImageOutput, build_image_output
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from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
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from ...backend.stable_diffusion import PipelineIntermediateState
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from ..util.step_callback import stable_diffusion_step_callback
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SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
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INFILL_METHODS = Literal[tuple(infill_methods())]
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DEFAULT_INFILL_METHOD = 'patchmatch' if 'patchmatch' in get_args(INFILL_METHODS) else 'tile'
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class SDImageInvocation(BaseModel):
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"""Helper class to provide all Stable Diffusion raster image invocations with additional config"""
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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": ["stable-diffusion", "image"],
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"type_hints": {
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"model": "model",
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},
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},
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}
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# Text to image
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class TextToImageInvocation(BaseInvocation, SDImageInvocation):
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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(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
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steps: int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
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width: int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
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height: int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
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cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
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scheduler: SAMPLER_NAME_VALUES = Field(default="lms", description="The scheduler to use" )
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model: str = Field(default="", description="The model to use (currently ignored)")
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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,
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context: InvocationContext,
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source_node_id: str,
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intermediate_state: PipelineIntermediateState,
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) -> None:
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stable_diffusion_step_callback(
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context=context,
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intermediate_state=intermediate_state,
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node=self.dict(),
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source_node_id=source_node_id,
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)
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def invoke(self, context: InvocationContext) -> ImageOutput:
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# Handle invalid model parameter
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model = choose_model(context.services.model_manager, self.model)
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# Get the source node id (we are invoking the prepared node)
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graph_execution_state = context.services.graph_execution_manager.get(
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context.graph_execution_state_id
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)
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source_node_id = graph_execution_state.prepared_source_mapping[self.id]
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outputs = Txt2Img(model).generate(
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prompt=self.prompt,
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step_callback=partial(self.dispatch_progress, context, source_node_id),
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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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# Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
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# each time it is called. We only need the first one.
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generate_output = next(outputs)
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image_dto = context.services.images_new.create(
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image=generate_output.image,
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image_type=ImageType.RESULT,
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image_category=ImageCategory.GENERAL,
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session_id=context.graph_execution_state_id,
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node_id=self.id,
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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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# metadata = context.services.metadata.build_metadata(
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# session_id=context.graph_execution_state_id, node=self
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# )
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# context.services.images.save(
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# image_type, image_name, generate_output.image, metadata
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# )
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# context.services.images_db.set(
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# id=image_name,
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# image_type=ImageType.RESULT,
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# image_category=ImageCategory.GENERAL,
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# session_id=context.graph_execution_state_id,
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# node_id=self.id,
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# metadata=GeneratedImageOrLatentsMetadata(),
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# )
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return build_image_output(
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image_type=image_dto.image_type,
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image_name=image_dto.image_name,
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image=generate_output.image,
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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 dispatch_progress(
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self,
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context: InvocationContext,
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source_node_id: str,
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intermediate_state: PipelineIntermediateState,
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) -> None:
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stable_diffusion_step_callback(
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context=context,
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intermediate_state=intermediate_state,
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node=self.dict(),
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source_node_id=source_node_id,
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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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if self.fit:
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image = image.resize((self.width, self.height))
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# Handle invalid model parameter
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model = choose_model(context.services.model_manager, self.model)
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# Get the source node id (we are invoking the prepared node)
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graph_execution_state = context.services.graph_execution_manager.get(
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context.graph_execution_state_id
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)
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source_node_id = graph_execution_state.prepared_source_mapping[self.id]
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outputs = Img2Img(model).generate(
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prompt=self.prompt,
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init_image=image,
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step_callback=partial(self.dispatch_progress, context, source_node_id),
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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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# Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
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# each time it is called. We only need the first one.
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generator_output = next(outputs)
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result_image = generator_output.image
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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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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(image_type, image_name, result_image, metadata)
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return build_image_output(
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image_type=image_type,
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image_name=image_name,
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image=result_image,
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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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seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
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seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
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seam_strength: float = Field(
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default=0.75, gt=0, le=1, description="The seam inpaint strength"
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)
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seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
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tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
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infill_method: INFILL_METHODS = Field(default=DEFAULT_INFILL_METHOD, description="The method used to infill empty regions (px)")
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inpaint_width: Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The width of the inpaint region (px)")
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inpaint_height: Optional[int] = Field(default=None, multiple_of=8, gt=0, description="The height of the inpaint region (px)")
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inpaint_fill: Optional[ColorField] = Field(default=ColorField(r=127, g=127, b=127, a=255), description="The solid infill method color")
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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 dispatch_progress(
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self,
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context: InvocationContext,
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source_node_id: str,
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intermediate_state: PipelineIntermediateState,
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) -> None:
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stable_diffusion_step_callback(
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context=context,
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intermediate_state=intermediate_state,
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node=self.dict(),
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source_node_id=source_node_id,
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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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# Handle invalid model parameter
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model = choose_model(context.services.model_manager, self.model)
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# Get the source node id (we are invoking the prepared node)
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graph_execution_state = context.services.graph_execution_manager.get(
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context.graph_execution_state_id
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)
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source_node_id = graph_execution_state.prepared_source_mapping[self.id]
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outputs = Inpaint(model).generate(
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prompt=self.prompt,
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init_image=image,
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mask_image=mask,
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step_callback=partial(self.dispatch_progress, context, source_node_id),
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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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# Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
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# each time it is called. We only need the first one.
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generator_output = next(outputs)
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result_image = generator_output.image
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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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metadata = context.services.metadata.build_metadata(
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session_id=context.graph_execution_state_id, node=self
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)
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context.services.images.save(image_type, image_name, result_image, metadata)
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return build_image_output(
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image_type=image_type,
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image_name=image_name,
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image=result_image,
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)
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