mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
9c89d3452c
feat(nodes): add ResultsServiceABC & SqliteResultsService **Doesn't actually work bc of circular imports. Can't even test it.** - add a base class for ResultsService and SQLite implementation - use `graph_execution_manager` `on_changed` callback to keep `results` table in sync fix(nodes): fix results service bugs chore(ui): regen api fix(ui): fix type guards feat(nodes): add `result_type` to results table, fix types fix(nodes): do not shadow `list` builtin feat(nodes): add results router It doesn't work due to circular imports still fix(nodes): Result class should use outputs classes, not fields feat(ui): crude results router fix(ui): send to canvas in currentimagebuttons not working feat(nodes): add core metadata builder feat(nodes): add design doc feat(nodes): wip latents db stuff feat(nodes): images_db_service and resources router feat(nodes): wip images db & router feat(nodes): update image related names feat(nodes): update urlservice feat(nodes): add high-level images service
71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
from typing import Optional
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from pydantic import BaseModel, Field, StrictFloat, StrictInt, StrictStr
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class GeneratedImageOrLatentsMetadata(BaseModel):
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"""Core generation metadata for an image/tensor generated in InvokeAI.
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Generated by traversing the execution graph, collecting the parameters of the nearest ancestors of a given node.
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Full metadata may be accessed by querying for the session in the `graph_executions` table.
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"""
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positive_conditioning: Optional[StrictStr] = Field(
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default=None, description="The positive conditioning."
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)
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negative_conditioning: Optional[StrictStr] = Field(
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default=None, description="The negative conditioning."
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)
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width: Optional[StrictInt] = Field(
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default=None, description="Width of the image/tensor in pixels."
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)
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height: Optional[StrictInt] = Field(
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default=None, description="Height of the image/tensor in pixels."
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)
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seed: Optional[StrictInt] = Field(
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default=None, description="The seed used for noise generation."
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)
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cfg_scale: Optional[StrictFloat] = Field(
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default=None, description="The classifier-free guidance scale."
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)
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steps: Optional[StrictInt] = Field(
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default=None, description="The number of steps used for inference."
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)
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scheduler: Optional[StrictStr] = Field(
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default=None, description="The scheduler used for inference."
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)
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model: Optional[StrictStr] = Field(
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default=None, description="The model used for inference."
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)
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strength: Optional[StrictFloat] = Field(
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default=None,
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description="The strength used for image-to-image/tensor-to-tensor.",
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)
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image: Optional[StrictStr] = Field(
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default=None, description="The ID of the initial image."
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)
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tensor: Optional[StrictStr] = Field(
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default=None, description="The ID of the initial tensor."
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)
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# Pending model refactor:
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# vae: Optional[str] = Field(default=None,description="The VAE used for decoding.")
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# unet: Optional[str] = Field(default=None,description="The UNet used dor inference.")
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# clip: Optional[str] = Field(default=None,description="The CLIP Encoder used for conditioning.")
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class UploadedImageOrLatentsMetadata(BaseModel):
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"""Limited metadata for an uploaded image/tensor."""
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width: Optional[StrictInt] = Field(
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default=None, description="Width of the image/tensor in pixels."
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)
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height: Optional[StrictInt] = Field(
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default=None, description="Height of the image/tensor in pixels."
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)
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# The extra field will be the contents of the PNG file's tEXt chunk. It may have come
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# from another SD application or InvokeAI, so it needs to be flexible.
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# If the upload is a not an image or `image_latents` tensor, this will be omitted.
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extra: Optional[StrictStr] = Field(
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default=None, description="Extra metadata, extracted from the PNG tEXt chunk."
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)
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