SEGSPaste¶
Documentation¶
- Class name:
SEGSPaste - Category:
ImpactPack/Detailer - Output node:
False
This node specializes in blending and manipulating image segments, leveraging latent space representations to achieve seamless integration of visual elements. It focuses on the precise control over the placement and blending of image segments within a given scene, enhancing the overall composition through advanced conditioning techniques.
Input types¶
Required¶
image- A string path or identifier for an image that is to be blended or manipulated within the scene. It serves as a secondary input for visual reference or direct integration.
- Comfy dtype:
IMAGE - Python dtype:
str
segs- Represents the segments to be manipulated or blended, providing the primary elements for visual composition within the node.
- Comfy dtype:
SEGS - Python dtype:
torch.Tensor
feather- Defines the feathering amount for the edges of the segments, allowing for smoother integration into the target scene.
- Comfy dtype:
INT - Python dtype:
int
alpha- Specifies the opacity level of the segments, enabling fine-tuned control over their visibility and blending with the background.
- Comfy dtype:
INT - Python dtype:
float
Optional¶
ref_image_opt- Optional. A reference image that can be used for additional context or alignment purposes during the manipulation process.
- Comfy dtype:
IMAGE - Python dtype:
Optional[str]
Output types¶
image- Comfy dtype:
IMAGE - Outputs the modified image after the blending and manipulation processes, incorporating the segments seamlessly into the scene.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes:
Source code¶
class SEGSPaste:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE", ),
"segs": ("SEGS", ),
"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
"alpha": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
},
"optional": {"ref_image_opt": ("IMAGE", ), }
}
RETURN_TYPES = ("IMAGE", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detailer"
@staticmethod
def doit(image, segs, feather, alpha=255, ref_image_opt=None):
segs = core.segs_scale_match(segs, image.shape)
result = None
for i, single_image in enumerate(image):
image_i = single_image.unsqueeze(0).clone()
for seg in segs[1]:
ref_image = None
if ref_image_opt is None and seg.cropped_image is not None:
cropped_image = seg.cropped_image
if isinstance(cropped_image, np.ndarray):
cropped_image = torch.from_numpy(cropped_image)
ref_image = cropped_image[i].unsqueeze(0)
elif ref_image_opt is not None:
ref_tensor = ref_image_opt[i].unsqueeze(0)
ref_image = crop_image(ref_tensor, seg.crop_region)
if ref_image is not None:
if seg.cropped_mask.ndim == 3 and len(seg.cropped_mask) == len(image):
mask = seg.cropped_mask[i]
elif seg.cropped_mask.ndim == 3 and len(seg.cropped_mask) > 1:
print(f"[Impact Pack] WARN: SEGSPaste - The number of the mask batch({len(seg.cropped_mask)}) and the image batch({len(image)}) are different. Combine the mask frames and apply.")
combined_mask = (seg.cropped_mask[0] * 255).to(torch.uint8)
for frame_mask in seg.cropped_mask[1:]:
combined_mask |= (frame_mask * 255).to(torch.uint8)
combined_mask = (combined_mask/255.0).to(torch.float32)
mask = utils.to_binary_mask(combined_mask, 0.1)
else: # ndim == 2
mask = seg.cropped_mask
mask = tensor_gaussian_blur_mask(mask, feather) * (alpha/255)
x, y, *_ = seg.crop_region
# ensure same device
mask = mask.to(image_i.device)
ref_image = ref_image.to(image_i.device)
tensor_paste(image_i, ref_image, (x, y), mask)
if result is None:
result = image_i
else:
result = torch.concat((result, image_i), dim=0)
if not args.highvram and not args.gpu_only:
result = result.cpu()
return (result, )