SEGSDetailer¶
Documentation¶
- Class name:
SEGSDetailer - Category:
ImpactPack/Detailer - Output node:
False
The SEGSDetailer node is designed to provide detailed analysis or modifications to segmentation data. It focuses on enhancing, refining, or otherwise processing segmentation results to meet specific criteria or to improve their utility for further analysis or visualization.
Input types¶
Required¶
image- The 'image' parameter is essential for operations that require a visual context or background against which segmentation elements are analyzed or modified.
- Comfy dtype:
IMAGE - Python dtype:
numpy.ndarray
segs- The 'segs' parameter represents the collection of segmentation elements to be analyzed or modified, serving as the primary data structure for segmentation operations.
- Comfy dtype:
SEGS - Python dtype:
tuple
guide_size- The 'guide_size' parameter provides dimensional guidance for scaling or adjusting segmentation elements, ensuring compatibility with other data or specific analysis requirements.
- Comfy dtype:
FLOAT - Python dtype:
tuple
guide_size_for- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
max_size- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
seed- unknown
- Comfy dtype:
INT - Python dtype:
unknown
steps- unknown
- Comfy dtype:
INT - Python dtype:
unknown
cfg- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
sampler_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
scheduler- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
denoise- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
noise_mask- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
force_inpaint- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
basic_pipe- unknown
- Comfy dtype:
BASIC_PIPE - Python dtype:
unknown
refiner_ratio- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
batch_size- unknown
- Comfy dtype:
INT - Python dtype:
unknown
cycle- unknown
- Comfy dtype:
INT - Python dtype:
unknown
Optional¶
refiner_basic_pipe_opt- unknown
- Comfy dtype:
BASIC_PIPE - Python dtype:
unknown
inpaint_model- unknown
- Comfy dtype:
BOOLEAN - Python dtype:
unknown
noise_mask_feather- unknown
- Comfy dtype:
INT - Python dtype:
unknown
Output types¶
segs- Comfy dtype:
SEGS - This output type represents the enhanced or modified segmentation data, ready for further analysis or visualization.
- Python dtype:
tuple
- Comfy dtype:
cnet_images- Comfy dtype:
IMAGE - The 'cnet_images' output includes images processed through a control network, showcasing the applied modifications or enhancements.
- Python dtype:
list
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class SEGSDetailer:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE", ),
"segs": ("SEGS", ),
"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
"max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
"noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
"force_inpaint": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"basic_pipe": ("BASIC_PIPE",),
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 100}),
"cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
},
"optional": {
"refiner_basic_pipe_opt": ("BASIC_PIPE",),
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
}
}
RETURN_TYPES = ("SEGS", "IMAGE")
RETURN_NAMES = ("segs", "cnet_images")
OUTPUT_IS_LIST = (False, True)
FUNCTION = "doit"
CATEGORY = "ImpactPack/Detailer"
@staticmethod
def do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1,
refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
model, clip, vae, positive, negative = basic_pipe
if refiner_basic_pipe_opt is None:
refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
else:
refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
segs = core.segs_scale_match(segs, image.shape)
new_segs = []
cnet_pil_list = []
for i in range(batch_size):
seed += 1
for seg in segs[1]:
cropped_image = seg.cropped_image if seg.cropped_image is not None \
else crop_ndarray4(image.numpy(), seg.crop_region)
cropped_image = to_tensor(cropped_image)
is_mask_all_zeros = (seg.cropped_mask == 0).all().item()
if is_mask_all_zeros:
print(f"Detailer: segment skip [empty mask]")
new_segs.append(seg)
continue
if noise_mask:
cropped_mask = seg.cropped_mask
else:
cropped_mask = None
cropped_positive = [
[condition, {
k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
for k, v in details.items()
}]
for condition, details in positive
]
cropped_negative = [
[condition, {
k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
for k, v in details.items()
}]
for condition, details in negative
]
enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size,
seg.bbox, seed, steps, cfg, sampler_name, scheduler,
cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
control_net_wrapper=seg.control_net_wrapper, cycle=cycle,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
if cnet_pils is not None:
cnet_pil_list.extend(cnet_pils)
if enhanced_image is None:
new_cropped_image = cropped_image
else:
new_cropped_image = enhanced_image
new_seg = SEG(to_numpy(new_cropped_image), seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, None)
new_segs.append(new_seg)
return (segs[0], new_segs), cnet_pil_list
def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1,
refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0):
if len(image) > 1:
raise Exception('[Impact Pack] ERROR: SEGSDetailer does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
segs, cnet_pil_list = SEGSDetailer.do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
scheduler, denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio, batch_size, cycle=cycle,
refiner_basic_pipe_opt=refiner_basic_pipe_opt,
inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
# set fallback image
if len(cnet_pil_list) == 0:
cnet_pil_list = [empty_pil_tensor()]
return (segs, cnet_pil_list)