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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.

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.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

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​