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StableDiffusionXLV1ControlNet

GenerativeModel
DashAI.back.models.hugging_face.StableDiffusionXLV1ControlNet

A wrapper implementation of ControlNet with depth preprocessing and stable diffusion xl 1.0 as pipeline.

Parameters​

num_inference_steps : integer, default=15
Number of denoising steps to run. More steps refine the image but increase generation time. Typical range: 20-30 for fast results, 40-50 for higher quality. Values above 100 rarely improve output.
controlnet_conditioning_scale : number, default=1.0
Weight of the ControlNet depth conditioning relative to the base diffusion pipeline. Valid range is 0.0-2.0. At 0.0 the depth map has no effect; at 1.0 (default) the output closely follows the input image structure; above 1.5 the depth constraint dominates and may produce overly rigid results.
device : string, default=CPU
Hardware device for inference. Select a GPU option for hardware acceleration, which is strongly recommended for diffusion models. Select 'CPU' on systems without a compatible GPU, but expect significantly longer generation times.

Methods​

generate(self, input: Tuple[Any, str]) -> List[Any]

Defined on StableDiffusionXLV1ControlNet

Generate output from a generative model.

Parameters

input : Tuple[Any, str]
Input data to be generated

Returns

List[Any]
Generated output data in a list

component_dir(cls) -> pathlib.Path

Defined on DownloadableMixin

Return this component's own storage directory.

Returns

pathlib.Path
<COMPONENT_PATH>/<ClassName>.

delete(cls) -> None

Defined on DownloadableMixin

Remove the component's downloaded artifacts.

download(cls, report: Optional[Callable[[Optional[float], Optional[str]], NoneType]] = None) -> None

Defined on HFDownloadableMixin

Download all repos listed in hf_repos() into component_dir().

Parameters

report : ProgressReporter, optional
Callback invoked before each repo download with report(None, "Downloading <repo_id>"). None means no progress reporting.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseGenerativeModel

Get metadata values for the current generative model.

Returns

Dict[str, Any]
Dictionary indicating whether the model requires a download before use and the expected download size in bytes.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

hf_repos(cls) -> List[Union[Tuple[str, str], Tuple[str, str, List[str]]]]

Defined on HFDownloadableMixin

Return the repo entries this component needs.

Returns

list of tuple
Each entry is either (repo_id, repo_type) or (repo_id, repo_type, allow_patterns).

is_downloaded(cls) -> bool

Defined on HFDownloadableMixin

Return whether all repo directories exist and are non-empty.

Returns

bool
True when every repo listed in hf_repos() has a non-empty local directory; False otherwise (including when the list is empty).

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.

Compatible with​