958 lines
38 KiB
Python
958 lines
38 KiB
Python
import os
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import folder_paths
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import comfy.samplers
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import comfy.sd
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import warnings
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from segment_anything import sam_model_registry
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from impact_utils import *
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import impact_core as core
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from impact_core import SEG, NO_BBOX_DETECTOR, NO_SEGM_DETECTOR
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warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
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model_path = folder_paths.models_dir
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# Nodes
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# folder_paths.supported_pt_extensions
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folder_paths.folder_names_and_paths["mmdets_bbox"] = ([os.path.join(model_path, "mmdets", "bbox")], folder_paths.supported_pt_extensions)
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folder_paths.folder_names_and_paths["mmdets_segm"] = ([os.path.join(model_path, "mmdets", "segm")], folder_paths.supported_pt_extensions)
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folder_paths.folder_names_and_paths["mmdets"] = ([os.path.join(model_path, "mmdets")], folder_paths.supported_pt_extensions)
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folder_paths.folder_names_and_paths["sams"] = ([os.path.join(model_path, "sams")], folder_paths.supported_pt_extensions)
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folder_paths.folder_names_and_paths["onnx"] = ([os.path.join(model_path, "onnx")], {'.onnx'})
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class ONNXDetectorProvider:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model_name": (folder_paths.get_filename_list("onnx"), )}}
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RETURN_TYPES = ("ONNX_DETECTOR", )
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FUNCTION = "load_onnx"
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CATEGORY = "ImpactPack"
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def load_onnx(self, model_name):
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model = folder_paths.get_full_path("onnx", model_name)
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return (core.ONNXDetector(model), )
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class MMDetDetectorProvider:
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@classmethod
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def INPUT_TYPES(s):
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bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("mmdets_bbox")]
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segms = ["segm/"+x for x in folder_paths.get_filename_list("mmdets_segm")]
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return {"required": {"model_name": (bboxs + segms, )}}
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RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
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FUNCTION = "load_mmdet"
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CATEGORY = "ImpactPack"
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def load_mmdet(self, model_name):
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mmdet_path = folder_paths.get_full_path("mmdets", model_name)
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model = core.load_mmdet(mmdet_path)
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if model_name.startswith("bbox"):
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return core.BBoxDetector(model), NO_SEGM_DETECTOR()
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else:
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return NO_BBOX_DETECTOR(), model
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class CLIPSegDetectorProvider:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"text": ("STRING", {"multiline": False}),
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"blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 7}),
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"threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.4}),
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"dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
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}
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}
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RETURN_TYPES = ("BBOX_DETECTOR", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Util"
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def doit(self, text, blur, threshold, dilation_factor):
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try:
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import custom_nodes.clipseg
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return (core.BBoxDetectorBasedOnCLIPSeg(text, blur, threshold, dilation_factor), )
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except Exception as e:
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print("[ERROR] CLIPSegToBboxDetector: CLIPSeg custom node isn't installed. You must install ComfyUI-CLIPSeg extension to use this node.")
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print(f"\t{e}")
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pass
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class SAMLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model_name": (folder_paths.get_filename_list("sams"), )}}
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RETURN_TYPES = ("SAM_MODEL", )
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FUNCTION = "load_model"
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CATEGORY = "ImpactPack"
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def load_model(self, model_name):
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modelname = folder_paths.get_full_path("sams", model_name)
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sam = sam_model_registry["vit_b"](checkpoint=modelname)
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print(f"Loads SAM model: {modelname}")
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return (sam, )
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class ONNXDetectorForEach:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"onnx_detector": ("ONNX_DETECTOR",),
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"image": ("IMAGE",),
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"threshold": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}),
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"dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 10, "step": 0.1}),
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}
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}
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RETURN_TYPES = ("SEGS", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detector"
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OUTPUT_NODE = True
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def doit(self, onnx_detector, image, threshold, dilation, crop_factor):
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segs = onnx_detector.detect(image, threshold, dilation, crop_factor)
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return (segs, )
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class DetailerForEach:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE", ),
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"segs": ("SEGS", ),
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"model": ("MODEL",),
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"vae": ("VAE",),
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"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": (["bbox", "crop_region"],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
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"noise_mask": (["enabled", "disabled"], ),
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"force_inpaint": (["disabled", "enabled"], ),
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},
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}
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RETURN_TYPES = ("IMAGE", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detailer"
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@staticmethod
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def do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, feather, noise_mask, force_inpaint):
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image_pil = tensor2pil(image).convert('RGBA')
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for seg in segs[1]:
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cropped_image = seg.cropped_image if seg.cropped_image is not None \
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else crop_ndarray4(image.numpy(), seg.crop_region)
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mask_pil = feather_mask(seg.cropped_mask, feather)
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if noise_mask == "enabled":
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cropped_mask = seg.cropped_mask
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else:
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cropped_mask = None
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enhanced_pil = core.enhance_detail(cropped_image, model, vae, guide_size, guide_size_for, seg.bbox,
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seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, cropped_mask, force_inpaint)
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if not (enhanced_pil is None):
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# don't latent composite-> converting to latent caused poor quality
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# use image paste
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image_pil.paste(enhanced_pil, (seg.crop_region[0], seg.crop_region[1]), mask_pil)
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image_tensor = pil2tensor(image_pil.convert('RGB'))
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if len(segs[1]) > 0:
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enhanced_tensor = pil2tensor(enhanced_pil) if enhanced_pil is not None else None
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return image_tensor, torch.from_numpy(cropped_image), enhanced_tensor,
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else:
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return image_tensor, None, None,
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def doit(self, image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, feather, noise_mask, force_inpaint):
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enhanced_img, cropped, cropped_enhanced = \
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DetailerForEach.do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg,
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sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
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force_inpaint)
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return (enhanced_img, )
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class DetailerForEachPipe:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE", ),
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"segs": ("SEGS", ),
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"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": (["bbox", "crop_region"],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
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"noise_mask": (["enabled", "disabled"], ),
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"force_inpaint": (["disabled", "enabled"], ),
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"basic_pipe": ("BASIC_PIPE", )
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},
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}
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RETURN_TYPES = ("IMAGE", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Detailer"
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def doit(self, image, segs, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
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denoise, feather, noise_mask, force_inpaint, basic_pipe):
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model, _, vae, positive, negative = basic_pipe
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enhanced_img, cropped, cropped_enhanced = \
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DetailerForEach.do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg,
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sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
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force_inpaint)
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return (enhanced_img, )
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class FaceDetailer:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE", ),
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"model": ("MODEL",),
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"vae": ("VAE",),
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"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": (["bbox", "crop_region"],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
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"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
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"noise_mask": (["enabled", "disabled"], ),
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"force_inpaint": (["disabled", "enabled"], ),
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"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"bbox_dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
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"bbox_crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
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"sam_detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", "mask-point-bbox", "none"],),
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"sam_dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
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"sam_threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
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"sam_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
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"sam_mask_hint_use_negative": (["False", "Small", "Outter"],),
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"bbox_detector": ("BBOX_DETECTOR", ),
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},
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"optional": {
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"sam_model_opt": ("SAM_MODEL", ),
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}}
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RETURN_TYPES = ("IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", )
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RETURN_NAMES = ("image", "cropped_refined", "mask", "detailer_pipe")
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Simple"
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@staticmethod
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def enhance_face(image, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, feather, noise_mask, force_inpaint,
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bbox_threshold, bbox_dilation, bbox_crop_factor,
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sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
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sam_mask_hint_use_negative,
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bbox_detector, sam_model_opt=None):
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# make default prompt as 'face' if empty prompt for CLIPSeg
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bbox_detector.setAux('face')
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segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, bbox_crop_factor)
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bbox_detector.setAux(None)
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# bbox + sam combination
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if sam_model_opt is not None:
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sam_mask = core.make_sam_mask(sam_model_opt, segs, image, sam_detection_hint, sam_dilation,
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sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
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sam_mask_hint_use_negative, )
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segs = core.segs_bitwise_and_mask(segs, sam_mask)
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enhanced_img, _, cropped_enhanced = \
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DetailerForEach.do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg,
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sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
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force_inpaint)
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# Mask Generator
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mask = core.segs_to_combined_mask(segs)
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return enhanced_img, cropped_enhanced, mask
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def doit(self, image, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, feather, noise_mask, force_inpaint,
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bbox_threshold, bbox_dilation, bbox_crop_factor,
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sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative,
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bbox_detector, sam_model_opt=None):
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enhanced_img, cropped_enhanced, mask = FaceDetailer.enhance_face(
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image, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, feather, noise_mask, force_inpaint,
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bbox_threshold, bbox_dilation, bbox_crop_factor,
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sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
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sam_mask_hint_use_negative,
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bbox_detector, sam_model_opt)
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pipe = (model, vae, positive, negative, bbox_detector, sam_model_opt)
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return enhanced_img, cropped_enhanced, mask, pipe
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class LatentPixelScale:
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upscale_methods = ["nearest-exact", "bilinear", "area"]
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"samples": ("LATENT", ),
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"scale_method": (s.upscale_methods,),
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"scale_factor": ("FLOAT", {"default": 1.5, "min": 0.1, "max": 10000, "step": 0.1}),
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"vae": ("VAE", ),
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},
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"optional": {
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"upscale_model_opt": ("UPSCALE_MODEL", ),
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}
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}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Upscale"
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def doit(self, samples, scale_method, scale_factor, vae, upscale_model_opt=None):
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if upscale_model_opt is None:
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latent = core.latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae)
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else:
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latent = core.latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model_opt, scale_factor, vae)
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return (latent,)
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class PixelKSampleUpscalerProvider:
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upscale_methods = ["nearest-exact", "bilinear", "area"]
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"scale_method": (s.upscale_methods,),
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"model": ("MODEL",),
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"vae": ("VAE",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": {
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"upscale_model_opt": ("UPSCALE_MODEL", ),
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}
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}
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RETURN_TYPES = ("UPSCALER",)
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Upscale"
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def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, upscale_model_opt=None):
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upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, upscale_model_opt)
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return (upscaler, )
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class PixelKSampleUpscalerProviderPipe(PixelKSampleUpscalerProvider):
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upscale_methods = ["nearest-exact", "bilinear", "area"]
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"scale_method": (s.upscale_methods,),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"basic_pipe": ("BASIC_PIPE",) },
|
|
"optional": {
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, scale_method, seed, steps, cfg, sampler_name, scheduler, denoise, basic_pipe, upscale_model_opt=None):
|
|
model, _, vae, positive, negative = basic_pipe
|
|
upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, upscale_model_opt)
|
|
return (upscaler, )
|
|
|
|
|
|
class IterativeLatentUpscale:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"samples": ("LATENT", ),
|
|
"upscale_factor": ("FLOAT", {"default": 1.5, "min": 1, "max": 10000, "step": 0.1}),
|
|
"steps": ("INT", {"default": 3, "min": 1, "max": 10000, "step": 1}),
|
|
"upscaler": ("UPSCALER",),
|
|
}}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
RETURN_NAMES = ("latent",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, samples, upscale_factor, steps, upscaler):
|
|
w = samples['samples'].shape[3]*8 # image width
|
|
h = samples['samples'].shape[2]*8 # image height
|
|
|
|
upscale_factor_unit = max(0, (upscale_factor-1.0)/steps)
|
|
current_latent = samples
|
|
scale = 1
|
|
for i in range(steps-1):
|
|
scale += upscale_factor_unit
|
|
new_w = (w*scale//8)*8
|
|
new_h = (h*scale//8)*8
|
|
print(f"IterativeLatentUpscale[{i+1}/{steps}]: {new_w}x{new_h} (scale:{scale:.2f}) ")
|
|
current_latent = upscaler.upscale_shape(current_latent, new_w, new_h)
|
|
|
|
if scale < upscale_factor:
|
|
new_w = (w*upscale_factor//8)*8
|
|
new_h = (h*upscale_factor//8)*8
|
|
print(f"IterativeLatentUpscale[Final]: {new_w}x{new_h} (scale:{upscale_factor:.2f}) ")
|
|
current_latent = upscaler.upscale_shape(current_latent, new_w, new_h)
|
|
|
|
return (current_latent, )
|
|
|
|
|
|
class IterativeImageUpscale:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"pixels": ("IMAGE", ),
|
|
"upscale_factor": ("FLOAT", {"default": 1.5, "min": 1, "max": 10000, "step": 0.1}),
|
|
"steps": ("INT", {"default": 3, "min": 1, "max": 10000, "step": 1}),
|
|
"upscaler": ("UPSCALER",),
|
|
"vae": ("VAE",),
|
|
}}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("image",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, pixels, upscale_factor, steps, upscaler, vae):
|
|
latent = nodes.VAEEncode().encode(vae, pixels)[0]
|
|
refined_latent = IterativeLatentUpscale().doit(latent, upscale_factor, steps, upscaler)
|
|
pixels = nodes.VAEDecode().decode(vae, refined_latent[0])[0]
|
|
return (pixels, )
|
|
|
|
|
|
class FaceDetailerPipe:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE", ),
|
|
"detailer_pipe": ("DETAILER_PIPE",),
|
|
"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
|
"guide_size_for": (["bbox", "crop_region"],),
|
|
"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}),
|
|
"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
|
|
"noise_mask": (["enabled", "disabled"], ),
|
|
"force_inpaint": (["disabled", "enabled"], ),
|
|
|
|
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"bbox_dilation": ("INT", {"default": 10, "min": 0, "max": 255, "step": 1}),
|
|
"bbox_crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
|
|
|
|
"sam_detection_hint": (["center-1", "horizontal-2", "vertical-2", "rect-4", "diamond-4", "mask-area", "mask-points", "mask-point-bbox", "none"],),
|
|
"sam_dilation": ("INT", {"default": 0, "min": 0, "max": 255, "step": 1}),
|
|
"sam_threshold": ("FLOAT", {"default": 0.93, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"sam_bbox_expansion": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
|
|
"sam_mask_hint_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"sam_mask_hint_use_negative": (["False", "Small", "Outter"],),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", )
|
|
RETURN_NAMES = ("image", "cropped_refined", "mask", "detailer_pipe")
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Simple"
|
|
|
|
def doit(self, image, detailer_pipe, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
|
|
denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor,
|
|
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative):
|
|
|
|
model, vae, positive, negative, bbox_detector, sam_model_opt = detailer_pipe
|
|
|
|
enhanced_img, cropped_enhanced, mask = FaceDetailer.enhance_face(
|
|
image, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, feather, noise_mask, force_inpaint,
|
|
bbox_threshold, bbox_dilation, bbox_crop_factor,
|
|
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
|
|
sam_mask_hint_use_negative,
|
|
bbox_detector, sam_model_opt)
|
|
|
|
return enhanced_img, cropped_enhanced, mask, detailer_pipe
|
|
|
|
|
|
class DetailerForEachTest(DetailerForEach):
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", )
|
|
RETURN_NAMES = ("image", "cropped", "cropped_refined")
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, feather, noise_mask, force_inpaint):
|
|
|
|
enhanced_img, cropped, cropped_enhanced = \
|
|
DetailerForEach.do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg,
|
|
sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint)
|
|
|
|
# set fallback image
|
|
if cropped is None:
|
|
cropped = enhanced_img
|
|
|
|
if cropped_enhanced is None:
|
|
cropped_enhanced = enhanced_img
|
|
|
|
return enhanced_img, cropped, cropped_enhanced,
|
|
|
|
|
|
class DetailerForEachTestPipe(DetailerForEachPipe):
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", )
|
|
RETURN_NAMES = ("image", "cropped", "cropped_refined")
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, image, segs, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
|
|
denoise, feather, noise_mask, force_inpaint, basic_pipe):
|
|
|
|
model, _, vae, positive, negative = basic_pipe
|
|
enhanced_img, cropped, cropped_enhanced = \
|
|
DetailerForEach.do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg,
|
|
sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint)
|
|
|
|
# set fallback image
|
|
if cropped is None:
|
|
cropped = enhanced_img
|
|
|
|
if cropped_enhanced is None:
|
|
cropped_enhanced = enhanced_img
|
|
|
|
return enhanced_img, cropped, cropped_enhanced,
|
|
|
|
|
|
class EmptySEGS:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {}
|
|
|
|
RETURN_TYPES = ("SEGS",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
def doit(self):
|
|
shape = 0, 0
|
|
return ((shape, []),)
|
|
|
|
|
|
class SegsToCombinedMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"segs": ("SEGS", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, segs):
|
|
return (core.segs_to_combined_mask(segs), )
|
|
|
|
|
|
class SegsBitwiseAndMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"segs": ("SEGS",),
|
|
"mask": ("MASK",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("SEGS",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, segs, mask):
|
|
return (core.segs_bitwise_and_mask(segs, mask), )
|
|
|
|
|
|
class BitwiseAndMaskForEach:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{
|
|
"base_segs": ("SEGS",),
|
|
"mask_segs": ("SEGS",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("SEGS",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, base_segs, mask_segs):
|
|
|
|
result = []
|
|
|
|
for bseg in base_segs[1]:
|
|
cropped_mask1 = bseg.cropped_mask.copy()
|
|
crop_region1 = bseg.crop_region
|
|
|
|
for mseg in mask_segs[1]:
|
|
cropped_mask2 = mseg.cropped_mask
|
|
crop_region2 = mseg.crop_region
|
|
|
|
# compute the intersection of the two crop regions
|
|
intersect_region = (max(crop_region1[0], crop_region2[0]),
|
|
max(crop_region1[1], crop_region2[1]),
|
|
min(crop_region1[2], crop_region2[2]),
|
|
min(crop_region1[3], crop_region2[3]))
|
|
|
|
overlapped = False
|
|
|
|
# set all pixels in cropped_mask1 to 0 except for those that overlap with cropped_mask2
|
|
for i in range(intersect_region[0], intersect_region[2]):
|
|
for j in range(intersect_region[1], intersect_region[3]):
|
|
if cropped_mask1[j - crop_region1[1], i - crop_region1[0]] == 1 and \
|
|
cropped_mask2[j - crop_region2[1], i - crop_region2[0]] == 1:
|
|
# pixel overlaps with both masks, keep it as 1
|
|
overlapped = True
|
|
pass
|
|
else:
|
|
# pixel does not overlap with both masks, set it to 0
|
|
cropped_mask1[j - crop_region1[1], i - crop_region1[0]] = 0
|
|
|
|
if overlapped:
|
|
item = SEG(bseg.cropped_image, cropped_mask1, bseg.confidence, bseg.crop_region, bseg.bbox, bseg.label)
|
|
result.append(item)
|
|
|
|
return ((base_segs[0], result),)
|
|
|
|
|
|
class SubtractMaskForEach:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"base_segs": ("SEGS",),
|
|
"mask_segs": ("SEGS",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("SEGS",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, base_segs, mask_segs):
|
|
|
|
result = []
|
|
|
|
for bseg in base_segs[1]:
|
|
cropped_mask1 = bseg.cropped_mask.copy()
|
|
crop_region1 = bseg.crop_region
|
|
|
|
for mseg in mask_segs[1]:
|
|
cropped_mask2 = mseg.cropped_mask
|
|
crop_region2 = mseg.crop_region
|
|
|
|
# compute the intersection of the two crop regions
|
|
intersect_region = (max(crop_region1[0], crop_region2[0]),
|
|
max(crop_region1[1], crop_region2[1]),
|
|
min(crop_region1[2], crop_region2[2]),
|
|
min(crop_region1[3], crop_region2[3]))
|
|
|
|
changed = False
|
|
|
|
# subtract operation
|
|
for i in range(intersect_region[0], intersect_region[2]):
|
|
for j in range(intersect_region[1], intersect_region[3]):
|
|
if cropped_mask1[j - crop_region1[1], i - crop_region1[0]] == 1 and \
|
|
cropped_mask2[j - crop_region2[1], i - crop_region2[0]] == 1:
|
|
# pixel overlaps with both masks, set it as 0
|
|
changed = True
|
|
cropped_mask1[j - crop_region1[1], i - crop_region1[0]] = 0
|
|
else:
|
|
# pixel does not overlap with both masks, don't care
|
|
pass
|
|
|
|
if changed:
|
|
item = SEG(bseg.cropped_image, cropped_mask1, bseg.confidence, bseg.crop_region, bseg.bbox, bseg.label)
|
|
result.append(item)
|
|
else:
|
|
result.append(base_segs)
|
|
|
|
return ((base_segs[0], result),)
|
|
|
|
|
|
class MaskToSEGS:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"mask": ("MASK",),
|
|
"combined": (["False", "True"], ),
|
|
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
|
|
"bbox_fill": (["disabled", "enabled"], ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("SEGS",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, mask, combined, crop_factor, bbox_fill):
|
|
result = core.mask_to_segs(mask, combined, crop_factor, bbox_fill == "enabled")
|
|
return (result, )
|
|
|
|
|
|
class ToBinaryMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"mask": ("MASK",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, mask,):
|
|
mask = to_binary_mask(mask)
|
|
return (mask,)
|
|
|
|
|
|
class BitwiseAndMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"mask1": ("MASK",),
|
|
"mask2": ("MASK",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, mask1, mask2):
|
|
mask = bitwise_and_masks(mask1, mask2)
|
|
return (mask,)
|
|
|
|
|
|
class SubtractMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"mask1": ("MASK", ),
|
|
"mask2": ("MASK", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, mask1, mask2):
|
|
mask = subtract_masks(mask1, mask2)
|
|
return (mask,)
|
|
|
|
|
|
|
|
import nodes
|
|
|
|
|
|
class MaskPainter(nodes.PreviewImage):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"images": ("IMAGE", ), },
|
|
"hidden": {
|
|
"prompt": "PROMPT",
|
|
"extra_pnginfo": "EXTRA_PNGINFO",
|
|
},
|
|
"optional": {"mask_image": ("IMAGE_PATH", ), },
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK", )
|
|
|
|
FUNCTION = "save_painted_images"
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
def load_mask(self, imagepath):
|
|
if imagepath['type'] == "temp":
|
|
input_dir = folder_paths.get_temp_directory()
|
|
else:
|
|
input_dir = folder_paths.get_input_directory()
|
|
|
|
image_path = os.path.join(input_dir, imagepath['filename'])
|
|
|
|
if os.path.exists(image_path):
|
|
i = Image.open(image_path)
|
|
|
|
if 'A' in i.getbands():
|
|
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
else:
|
|
mask = torch.zeros((8, 8), dtype=torch.float32, device="cpu")
|
|
else:
|
|
mask = torch.zeros((8, 8), dtype=torch.float32, device="cpu")
|
|
|
|
return (mask, )
|
|
|
|
def save_painted_images(self, images, filename_prefix="impact-mask",
|
|
prompt=None, extra_pnginfo=None, mask_image=None):
|
|
res = self.save_images(images, filename_prefix, prompt, extra_pnginfo)
|
|
|
|
if mask_image is not None:
|
|
res['result'] = self.load_mask(mask_image)
|
|
else:
|
|
mask = torch.zeros((8, 8), dtype=torch.float32, device="cpu")
|
|
res['result'] = (mask, )
|
|
|
|
return res
|
|
|
|
|
|
class DetailerForEach:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE",),
|
|
"segs": ("SEGS",),
|
|
"model": ("MODEL",),
|
|
"vae": ("VAE",),
|
|
"guide_size": ("FLOAT", {"default": 256, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": (["bbox", "crop_region"],),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
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|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
|
|
"positive": ("CONDITIONING",),
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|
"negative": ("CONDITIONING",),
|
|
"denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
|
|
"feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
|
|
"noise_mask": (["enabled", "disabled"],),
|
|
"force_inpaint": (["disabled", "enabled"],),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
@staticmethod
|
|
def do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, feather, noise_mask, force_inpaint):
|
|
|
|
image_pil = tensor2pil(image).convert('RGBA')
|
|
|
|
# shape = segs[0]
|
|
segs = segs[1]
|
|
for seg in segs:
|
|
cropped_image = seg.cropped_image if seg.cropped_image is not None \
|
|
else crop_ndarray4(image.numpy(), seg.crop_region)
|
|
|
|
mask_pil = feather_mask(seg.cropped_mask, feather)
|
|
|
|
if noise_mask == "enabled":
|
|
cropped_mask = seg.cropped_mask
|
|
else:
|
|
cropped_mask = None
|
|
|
|
enhanced_pil = core.enhance_detail(cropped_image, model, vae, guide_size, guide_size_for, seg.bbox,
|
|
seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, cropped_mask, force_inpaint)
|
|
|
|
if not (enhanced_pil is None):
|
|
# don't latent composite-> converting to latent caused poor quality
|
|
# use image paste
|
|
image_pil.paste(enhanced_pil, (seg.crop_region[0], seg.crop_region[1]), mask_pil)
|
|
|
|
image_tensor = pil2tensor(image_pil.convert('RGB'))
|
|
|
|
if len(segs) > 0:
|
|
enhanced_tensor = pil2tensor(enhanced_pil) if enhanced_pil is not None else None
|
|
return image_tensor, torch.from_numpy(cropped_image), enhanced_tensor,
|
|
else:
|
|
return image_tensor, None, None,
|
|
|
|
def doit(self, image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, feather, noise_mask, force_inpaint):
|
|
|
|
enhanced_img, cropped, cropped_enhanced = \
|
|
DetailerForEach.do_detail(image, segs, model, vae, guide_size, guide_size_for, seed, steps, cfg,
|
|
sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint)
|
|
|
|
return (enhanced_img,)
|
|
|