mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
from typing import Optional
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from pydantic import BaseModel, Field, StrictFloat, StrictInt, StrictStr
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class ImageMetadata(BaseModel):
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"""
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Core generation metadata for an image/tensor generated in InvokeAI.
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Also includes any metadata from the image's PNG tEXt chunks.
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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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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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