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