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SD15OpenPoseControlNetModel

GenerativeModel
DashAI.back.models.hugging_face.SD15OpenPoseControlNetModel

OpenPose-conditioned ControlNet pipeline built on Stable Diffusion 1.5.

Takes an input image and a text prompt. Human body keypoints and skeleton structures are detected from the image using OpenposeDetector from lllyasviel/Annotators, then fed as spatial conditioning into the lllyasviel/sd-controlnet-openpose ControlNet backbone together with the runwayml/stable-diffusion-v1-5 diffusion pipeline. This model is ideal for generating images with specific human poses or body positions while retaining full control over appearance via the text prompt.

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 pose conditioning (range 0.0-2.0). At 1.0 the output closely follows the input pose. Lower values allow more creative freedom while preserving the general pose.
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 for diffusion models. CPU inference is possible but very slow.
gpu_memory_mode : string, default=auto
How much of the model is kept on the GPU. 'auto' measures free VRAM when the model loads and picks the fastest option that fits. 'full' keeps everything on the GPU (fastest, most VRAM). 'model' moves one component (text encoder, denoiser, VAE) to the GPU at a time. 'sequential' streams the weights layer by layer: slowest, but runs large models on small GPUs. Ignored on CPU.

Methods​

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

Defined on SD15OpenPoseControlNetModel

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_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str
Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential
An instance of the requested credential component.

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​