ControlNetApplySD3

Documentation

• Class name: ControlNetApplySD3
• Category: _for_testing/sd3
• Output node: False

The ControlNetApplySD3 node is designed for applying a control network to modify the generation of images based on positive and negative conditioning inputs. It leverages a control network, a VAE, and specific strength parameters to influence the image generation process, allowing for fine-tuned control over the output based on the provided conditioning.

Input types

Required

• positive
  • Positive conditioning inputs that guide the image generation towards desired attributes or features.
  • Comfy dtype: CONDITIONING
  • Python dtype: tuple
• negative
  • Negative conditioning inputs that guide the image generation away from certain attributes or features.
  • Comfy dtype: CONDITIONING
  • Python dtype: tuple
• control_net
  • The control network used to apply specific modifications or influences on the image generation process.
  • Comfy dtype: CONTROL_NET
  • Python dtype: ControlNetType
• vae
  • A variational autoencoder used for encoding and decoding images, contributing to the control network's ability to modify the image generation.
  • Comfy dtype: VAE
  • Python dtype: VAEType
• image
  • The input image to be modified by the control network based on the conditioning.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• strength
  • A parameter controlling the intensity of the control network's influence on the image generation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• start_percent
  • The starting percentage of the control effect applied to the image generation process.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_percent
  • The ending percentage of the control effect applied to the image generation process.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• positive
  • Comfy dtype: CONDITIONING
  • The modified positive conditioning after applying the control network.
  • Python dtype: tuple
• negative
  • Comfy dtype: CONDITIONING
  • The modified negative conditioning after applying the control network.
  • Python dtype: tuple

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"positive": ("CONDITIONING", ),
                             "negative": ("CONDITIONING", ),
                             "control_net": ("CONTROL_NET", ),
                             "vae": ("VAE", ),
                             "image": ("IMAGE", ),
                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
                             }}
    CATEGORY = "_for_testing/sd3"