Saltar al contenido principal

SD15HEDControlNetModel

GenerativeModel
DashAI.back.models.hugging_face.SD15HEDControlNetModel

HED soft-edge-conditioned ControlNet pipeline built on Stable Diffusion 1.5.

Takes an input image and a text prompt. Soft edge maps are extracted from the image using the Holistically-nested Edge Detection (HED) detector from lllyasviel/Annotators, then fed as spatial conditioning into the lllyasviel/sd-controlnet-hed ControlNet backbone together with the runwayml/stable-diffusion-v1-5 diffusion pipeline. HED produces sketch-like contours that preserve structural outlines while allowing more creative variation than hard-edge Canny maps, making this model well-suited for artistic reinterpretation of existing images.

Requires the controlnet_aux package (pip install controlnet_aux).

References​

Parameters​

num_inference_steps : integer, default=20
Number of denoising steps. Typical range: 20-30 for fast results, 40-50 for higher quality.
controlnet_conditioning_scale : number, default=1.0
Weight of the ControlNet soft-edge conditioning (range 0.0-2.0). HED produces soft, sketch-like edge maps that are less strict than Canny. At 1.0 the output closely follows the input edges. Lower values give more creative freedom.
guidance_scale : number, default=7.5
Classifier-Free Guidance (CFG) scale. Controls prompt adherence. Values 7-9 are typical for SD 1.5.
device : string, default=CPU
Hardware device for inference. GPU is strongly recommended. CPU inference is possible but very slow.

Methods​

generate(self, input: Tuple[ForwardRef('Image.Image'), str]) -> List[Any]

Defined on SD15HEDControlNetModel

Generate output from a generative model.

Parameters

input : Tuple[Image.Image, str]
Input image and text prompt.

Returns

List[Any]
Generated output images 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​