mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 01:04:32 +02:00
refactor api
This commit is contained in:
Vladimir Mandic
+136
-134
@@ -91,14 +91,81 @@ class PydanticModelGenerator:
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DynamicModel.__config__.allow_mutation = True
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return DynamicModel
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### item classes
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class IPAdapterItem(BaseModel):
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class ItemSampler(BaseModel):
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name: str = Field(title="Name")
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aliases: List[str] = Field(title="Aliases")
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options: Dict[str, str] = Field(title="Options")
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class ItemVae(BaseModel):
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model_name: str = Field(title="Model Name")
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filename: str = Field(title="Filename")
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class ItemUpscaler(BaseModel):
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name: str = Field(title="Name")
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model_name: Optional[str] = Field(title="Model Name")
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model_path: Optional[str] = Field(title="Path")
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model_url: Optional[str] = Field(title="URL")
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scale: Optional[float] = Field(title="Scale")
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class ItemModel(BaseModel):
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title: str = Field(title="Title")
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model_name: str = Field(title="Model Name")
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filename: str = Field(title="Filename")
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type: str = Field(title="Model type")
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sha256: Optional[str] = Field(title="SHA256 hash")
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hash: Optional[str] = Field(title="Short hash")
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config: Optional[str] = Field(title="Config file")
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class ItemHypernetwork(BaseModel):
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name: str = Field(title="Name")
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path: Optional[str] = Field(title="Path")
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class ItemFaceRestorer(BaseModel):
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name: str = Field(title="Name")
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cmd_dir: Optional[str] = Field(title="Path")
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class ItemGAN(BaseModel):
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name: str = Field(title="Name")
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path: Optional[str] = Field(title="Path")
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scale: Optional[int] = Field(title="Scale")
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class ItemStyle(BaseModel):
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name: str = Field(title="Name")
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prompt: Optional[str] = Field(title="Prompt")
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negative_prompt: Optional[str] = Field(title="Negative Prompt")
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extra: Optional[str] = Field(title="Extra")
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filename: Optional[str] = Field(title="Filename")
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preview: Optional[str] = Field(title="Preview")
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class ItemExtraNetwork(BaseModel):
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name: str = Field(title="Name")
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type: str = Field(title="Type")
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title: Optional[str] = Field(title="Title")
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fullname: Optional[str] = Field(title="Fullname")
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filename: Optional[str] = Field(title="Filename")
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hash: Optional[str] = Field(title="Hash")
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preview: Optional[str] = Field(title="Preview image URL")
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class ItemArtist(BaseModel):
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name: str = Field(title="Name")
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score: float = Field(title="Score")
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category: str = Field(title="Category")
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class ItemEmbedding(BaseModel):
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step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
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sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
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sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
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shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
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vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
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class ItemIPAdapter(BaseModel):
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adapter: str = Field(title="Adapter", default="Base", description="Adapter to use")
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image: str = Field(title="Image", default="", description="Adapter image, must be a base64 string containing the image's data.")
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scale: float = Field(title="Scale", default=0.5, gt=0, le=1, description="Scale of the adapter image, must be between 0 and 1.")
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class FaceIDItem(BaseModel):
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class ItemFaceID(BaseModel):
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mode: list[str] = Field(title="Mode", default=["FaceID"], description="The mode to use (available values: FaceID, FaceSwap).")
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model: str = Field(title="Model", default="FaceID Base", description="The FaceID model to use.")
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image: str = Field(title="Image", default="", description="Source face image, must be a base64 string containing the image's data.")
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@@ -109,8 +176,32 @@ class FaceIDItem(BaseModel):
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tokens: int = Field(title="Tokens", default=4, ge=1, le=16, description="Amount of tokens to use, must be between 1 and 16.")
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cache_model: bool = Field(title="Cache", default=True, description="Should the model be cached?")
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class ScriptArg(BaseModel):
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label: str = Field(default=None, title="Label", description="Name of the argument in UI")
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value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
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minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
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maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
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step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
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choices: Optional[Any] = Field(default=None, title="Choices", description="Possible values for the argument")
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StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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class ItemScript(BaseModel):
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name: str = Field(default=None, title="Name", description="Script name")
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is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
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is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
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args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
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class ItemExtension(BaseModel):
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name: str = Field(title="Name", description="Extension name")
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remote: str = Field(title="Remote", description="Extension Repository URL")
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branch: str = Field(title="Branch", description="Extension Repository Branch")
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commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
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version: str = Field(title="Version", description="Extension Version")
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commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
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enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")
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### request/response classes
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ReqTxt2Img = PydanticModelGenerator(
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"StableDiffusionProcessingTxt2Img",
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StableDiffusionProcessingTxt2Img,
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[
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@@ -120,12 +211,17 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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{"key": "send_images", "type": bool, "default": True},
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{"key": "save_images", "type": bool, "default": False},
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{"key": "alwayson_scripts", "type": dict, "default": {}},
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{"key": "ip_adapter", "type": Optional[IPAdapterItem], "default": None, "exclude": True},
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{"key": "face_id", "type": Optional[FaceIDItem], "default": None, "exclude": True},
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{"key": "ip_adapter", "type": Optional[ItemIPAdapter], "default": None, "exclude": True},
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{"key": "face_id", "type": Optional[ItemFaceID], "default": None, "exclude": True},
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]
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).generate_model()
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StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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class ResTxt2Img(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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ReqImg2Img = PydanticModelGenerator(
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"StableDiffusionProcessingImg2Img",
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StableDiffusionProcessingImg2Img,
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[
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@@ -139,22 +235,21 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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{"key": "send_images", "type": bool, "default": True},
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{"key": "save_images", "type": bool, "default": False},
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{"key": "alwayson_scripts", "type": dict, "default": {}},
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{"key": "ip_adapter", "type": Optional[IPAdapterItem], "default": None, "exclude": True},
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{"key": "face_id", "type": Optional[FaceIDItem], "default": None, "exclude": True},
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{"key": "ip_adapter", "type": Optional[ItemIPAdapter], "default": None, "exclude": True},
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{"key": "face_id", "type": Optional[ItemFaceID], "default": None, "exclude": True},
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]
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).generate_model()
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class TextToImageResponse(BaseModel):
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class ResImg2Img(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ImageToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class FileData(BaseModel):
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data: str = Field(title="File data", description="Base64 representation of the file")
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name: str = Field(title="File name")
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class ExtrasBaseRequest(BaseModel):
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class ReqProcess(BaseModel):
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resize_mode: float = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
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show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
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gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
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@@ -169,61 +264,62 @@ class ExtrasBaseRequest(BaseModel):
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extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
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upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
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class ExtraBaseResponse(BaseModel):
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class ResProcess(BaseModel):
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html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
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class ExtrasSingleImageRequest(ExtrasBaseRequest):
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class ReqProcessImage(ReqProcess):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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class ExtrasSingleImageResponse(ExtraBaseResponse):
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class ResProcessImage(ResProcess):
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image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
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class FileData(BaseModel):
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data: str = Field(title="File data", description="Base64 representation of the file")
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name: str = Field(title="File name")
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class ExtrasBatchImagesRequest(ExtrasBaseRequest):
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class ReqProcessBatch(ReqProcess):
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imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
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class ExtrasBatchImagesResponse(ExtraBaseResponse):
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class ResProcessBatch(ResProcess):
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images: List[str] = Field(title="Images", description="The generated images in base64 format.")
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class PNGInfoRequest(BaseModel):
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class ReqImageInfo(BaseModel):
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image: str = Field(title="Image", description="The base64 encoded PNG image")
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class PNGInfoResponse(BaseModel):
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class ResImageInfo(BaseModel):
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info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
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items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
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parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")
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class LogRequest(BaseModel):
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class ReqLog(BaseModel):
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lines: int = Field(default=100, title="Lines", description="How many lines to return")
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clear: bool = Field(default=False, title="Clear", description="Should the log be cleared after returning the lines?")
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class ProgressRequest(BaseModel):
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class ReqProgress(BaseModel):
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skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
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class ProgressResponse(BaseModel):
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class ResProgress(BaseModel):
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progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
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eta_relative: float = Field(title="ETA in secs")
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state: dict = Field(title="State", description="The current state snapshot")
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current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
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textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
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class InterrogateRequest(BaseModel):
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class ReqInterrogate(BaseModel):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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model: str = Field(default="clip", title="Model", description="The interrogate model used.")
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class InterrogateResponse(BaseModel):
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caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
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class ResInterrogate(BaseModel):
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caption: Optional[str] = Field(default=None, title="Caption", description="The generated caption for the image.")
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medium: Optional[str] = Field(default=None, title="Medium", description="Image medium.")
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artist: Optional[str] = Field(default=None, title="Medium", description="Image artist.")
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movement: Optional[str] = Field(default=None, title="Medium", description="Image movement.")
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trending: Optional[str] = Field(default=None, title="Medium", description="Image trending.")
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flavor: Optional[str] = Field(default=None, title="Medium", description="Image flavor.")
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class TrainResponse(BaseModel):
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class ResTrain(BaseModel):
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info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
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class CreateResponse(BaseModel):
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class ResCreate(BaseModel):
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info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
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class PreprocessResponse(BaseModel):
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class ResPreprocess(BaseModel):
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info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
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fields = {}
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@@ -251,109 +347,15 @@ for key in _options:
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FlagsModel = create_model("Flags", **flags)
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class SamplerItem(BaseModel):
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name: str = Field(title="Name")
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aliases: List[str] = Field(title="Aliases")
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options: Dict[str, str] = Field(title="Options")
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class ResEmbeddings(BaseModel):
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loaded: Dict[str, ItemEmbedding] = Field(title="Loaded", description="Embeddings loaded for the current model")
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skipped: Dict[str, ItemEmbedding] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
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class SDVaeItem(BaseModel):
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model_name: str = Field(title="Model Name")
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filename: str = Field(title="Filename")
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class UpscalerItem(BaseModel):
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name: str = Field(title="Name")
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model_name: Optional[str] = Field(title="Model Name")
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model_path: Optional[str] = Field(title="Path")
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model_url: Optional[str] = Field(title="URL")
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scale: Optional[float] = Field(title="Scale")
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class SDModelItem(BaseModel):
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title: str = Field(title="Title")
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model_name: str = Field(title="Model Name")
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filename: str = Field(title="Filename")
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type: str = Field(title="Model type")
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sha256: Optional[str] = Field(title="SHA256 hash")
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hash: Optional[str] = Field(title="Short hash")
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config: Optional[str] = Field(title="Config file")
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class HypernetworkItem(BaseModel):
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name: str = Field(title="Name")
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path: Optional[str] = Field(title="Path")
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class FaceRestorerItem(BaseModel):
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name: str = Field(title="Name")
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cmd_dir: Optional[str] = Field(title="Path")
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class RealesrganItem(BaseModel):
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name: str = Field(title="Name")
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path: Optional[str] = Field(title="Path")
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scale: Optional[int] = Field(title="Scale")
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class StyleItem(BaseModel):
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name: str = Field(title="Name")
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prompt: Optional[str] = Field(title="Prompt")
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negative_prompt: Optional[str] = Field(title="Negative Prompt")
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extra: Optional[str] = Field(title="Extra")
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filename: Optional[str] = Field(title="Filename")
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preview: Optional[str] = Field(title="Preview")
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class ExtraNetworkItem(BaseModel):
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name: str = Field(title="Name")
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type: str = Field(title="Type")
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title: Optional[str] = Field(title="Title")
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fullname: Optional[str] = Field(title="Fullname")
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filename: Optional[str] = Field(title="Filename")
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hash: Optional[str] = Field(title="Hash")
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preview: Optional[str] = Field(title="Preview image URL")
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# description: Optional[str] = Field(title="Description")
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# info: Optional[str] = Field(title="Information")
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# metadata: Optional[Any] = Field(title="Metadata")
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# local: Optional[str] = Field(title="Local")
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class ArtistItem(BaseModel):
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name: str = Field(title="Name")
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score: float = Field(title="Score")
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category: str = Field(title="Category")
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class EmbeddingItem(BaseModel):
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step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
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sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
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sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
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shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
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vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
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class EmbeddingsResponse(BaseModel):
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loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
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skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
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class MemoryResponse(BaseModel):
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class ResMemory(BaseModel):
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ram: dict = Field(title="RAM", description="System memory stats")
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cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
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class ScriptsList(BaseModel):
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class ResScripts(BaseModel):
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txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
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img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
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control: list = Field(default=None, title="Control", description="Titles of scripts (control)")
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class ScriptArg(BaseModel):
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label: str = Field(default=None, title="Label", description="Name of the argument in UI")
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value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
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minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
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maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
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step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
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choices: Optional[Any] = Field(default=None, title="Choices", description="Possible values for the argument")
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class ScriptInfo(BaseModel):
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name: str = Field(default=None, title="Name", description="Script name")
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is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
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is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
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args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
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class ExtensionItem(BaseModel):
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name: str = Field(title="Name", description="Extension name")
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remote: str = Field(title="Remote", description="Extension Repository URL")
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branch: str = Field(title="Branch", description="Extension Repository Branch")
|
||||
commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
|
||||
version: str = Field(title="Version", description="Extension Version")
|
||||
commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
|
||||
enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")
|
||||
|
||||
Reference in New Issue
Block a user