Wiro - Controlnet Inpainting Sdxl icon

Wiro - Controlnet Inpainting Sdxl

SDXL ControlNet is a neural network structure to control diffusion models by adding extra condi

Wiro - Controlnet Inpainting Sdxl

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Overview

This node integrates with the Wiro API to perform ControlNet Inpainting using the SDXL model. It allows users to input an image and a mask, specify various parameters such as model selection, prompts, inference steps, guidance scale, and output dimensions, and then processes the image inpainting task via the API. The node is useful for scenarios where controlled image inpainting is needed, such as enhancing or modifying images with specific prompts and control models.

Use Case Examples

  1. A user wants to inpaint parts of an image by providing an input image URL and a mask URL, along with a descriptive prompt to guide the inpainting process.
  2. A user needs to generate multiple inpainted outputs with specific inference steps and guidance scale to fine-tune the results.
  3. A user wants to use ControlNet models with preprocessing enabled to control the diffusion model's behavior during inpainting.

Properties

Name Meaning
Select Model The primary model to be used for the inpainting task.
Select Model Private A private model option for inpainting, if applicable.
Input Image URL URL of the input image to be inpainted.
Input Image Mask URL URL of the mask image that defines the inpainting area.
Select Controlnet Model One or more ControlNet models to guide the inpainting process.
Controlnet Preprocess Boolean flag to enable or disable preprocessing for ControlNet.
Prompt Text prompt describing the desired inpainting output.
Negativeprompt Text prompt describing what should be avoided in the output.
Numberofoutputs Number of output images to generate.
Inferencesteps Number of inference steps for the diffusion process.
Guidancescale Guidance scale to control the strength of the prompt influence.
Strength Strength parameter for the inpainting process.
Seed Seed value for random number generation to ensure reproducibility.
Scheduler Scheduler algorithm used for the diffusion process.
Width Width of the output image.
Height Height of the output image.

Output

JSON

  • taskid - The unique identifier of the inpainting task.
  • url - The URL of the completed inpainted image.
  • status - The status of the task, indicating if it is completed or failed.

Dependencies

  • Wiro API with API key and secret for authentication

Troubleshooting

  • If the API response does not contain a valid task ID or socket access token, the node throws an error indicating an invalid response from the Wiro API. Check API credentials and request parameters.
  • If the task status returned from polling is '-1', '-2', '-3', or '-4', the task is marked as failed. This could be due to invalid input parameters, network issues, or server-side errors. Verify inputs and retry.
  • Ensure that the input image URL and mask URL are accessible and correctly formatted to avoid request failures.

Discussion