Jitter Image

Documentation

• Class name: JitterImage
• Category: image/filters/jitter
• Output node: False

The JitterImage node applies a jitter effect to images, transforming them by applying a series of affine transformations to simulate camera or object movement. This effect can enhance the robustness of models to variations in input data or be used for data augmentation.

Input types

Required

• images
  • The input images to which the jitter effect will be applied. This parameter is crucial for defining the visual content that will undergo transformation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• jitter_scale
  • Defines the scale of the jitter effect, adjusting the intensity of the transformations applied to the images. This parameter allows for fine-tuning the extent of the jitter effect.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • Comfy dtype: IMAGE
  • The output images after the jitter effect has been applied, showcasing the transformed visual content.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class JitterImage:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE",),
                "jitter_scale": ("FLOAT", {"default": 1.0, "min": 0.1, "step": 0.1}),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "jitter"

    CATEGORY = "image/filters/jitter"

    def jitter(self, images, jitter_scale):
        t = images.detach().clone().movedim(-1,1) # [B x C x H x W]

        theta = jitter_matrix.detach().clone().to(t.device)
        theta[:,0,2] *= jitter_scale * 2 / t.shape[3]
        theta[:,1,2] *= jitter_scale * 2 / t.shape[2]
        affine = torch.nn.functional.affine_grid(theta, torch.Size([9, t.shape[1], t.shape[2], t.shape[3]]))

        batch = []
        for i in range(t.shape[0]):
            jb = t[i].repeat(9,1,1,1)
            jb = torch.nn.functional.grid_sample(jb, affine, mode='bilinear', padding_mode='border', align_corners=None)
            batch.append(jb)

        t = torch.cat(batch, dim=0).movedim(1,-1) # [B x H x W x C]
        return (t,)