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import os
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import sys
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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 io import BytesIO
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import piexif
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import zipfile
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import re
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import impact.wildcards
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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
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from impact.config import latent_letter_path
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from nodes import MAX_RESOLUTION
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from PIL import Image, ImageOps
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import numpy as np
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import hashlib
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import json
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import safetensors.torch
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from PIL.PngImagePlugin import PngInfo
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import comfy.model_management
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import base64
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import impact.wildcards as wildcards
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from . import hooks
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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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add_folder_path_and_extensions("mmdets_bbox", [os.path.join(model_path, "mmdets", "bbox")], folder_paths.supported_pt_extensions)
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add_folder_path_and_extensions("mmdets_segm", [os.path.join(model_path, "mmdets", "segm")], folder_paths.supported_pt_extensions)
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add_folder_path_and_extensions("mmdets", [os.path.join(model_path, "mmdets")], folder_paths.supported_pt_extensions)
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add_folder_path_and_extensions("sams", [os.path.join(model_path, "sams")], folder_paths.supported_pt_extensions)
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add_folder_path_and_extensions("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 = ("BBOX_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 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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if "CLIPSeg" in nodes.NODE_CLASS_MAPPINGS:
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return (core.BBoxDetectorBasedOnCLIPSeg(text, blur, threshold, dilation_factor), )
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else:
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print("[ERROR] CLIPSegToBboxDetector: CLIPSeg custom node isn't installed. You must install biegert/ComfyUI-CLIPSeg extension to use this node.")
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class SAMLoader:
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@classmethod
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def INPUT_TYPES(cls):
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models = [x for x in folder_paths.get_filename_list("sams") if 'hq' not in x]
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return {
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"required": {
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"model_name": (models + ['ESAM'], ),
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"device_mode": (["AUTO", "Prefer GPU", "CPU"],),
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}
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}
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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, device_mode="auto"):
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if model_name == 'ESAM':
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if 'ESAM_ModelLoader_Zho' not in nodes.NODE_CLASS_MAPPINGS:
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try_install_custom_node('https://github.com/ZHO-ZHO-ZHO/ComfyUI-YoloWorld-EfficientSAM',
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"To use 'ESAM' model, 'ComfyUI-YoloWorld-EfficientSAM' extension is required.")
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raise Exception("'ComfyUI-YoloWorld-EfficientSAM' node isn't installed.")
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esam_loader = nodes.NODE_CLASS_MAPPINGS['ESAM_ModelLoader_Zho']()
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if device_mode == 'CPU':
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esam = esam_loader.load_esam_model('CPU')[0]
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else:
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device_mode = 'CUDA'
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esam = esam_loader.load_esam_model('CUDA')[0]
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sam_obj = core.ESAMWrapper(esam, device_mode)
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esam.sam_wrapper = sam_obj
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print(f"Loads EfficientSAM model: (device:{device_mode})")
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return (esam, )
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modelname = folder_paths.get_full_path("sams", model_name)
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if 'vit_h' in model_name:
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model_kind = 'vit_h'
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elif 'vit_l' in model_name:
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model_kind = 'vit_l'
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else:
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model_kind = 'vit_b'
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sam = sam_model_registry[model_kind](checkpoint=modelname)
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size = os.path.getsize(modelname)
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safe_to = core.SafeToGPU(size)
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device = comfy.model_management.get_torch_device() if device_mode == "Prefer GPU" else "CPU"
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if device_mode == "Prefer GPU":
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safe_to.to_device(sam, device)
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is_auto_mode = device_mode == "AUTO"
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sam_obj = core.SAMWrapper(sam, is_auto_mode=is_auto_mode, safe_to_gpu=safe_to)
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sam.sam_wrapper = sam_obj
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print(f"Loads SAM model: {modelname} (device:{device_mode})")
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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": -512, "max": 512, "step": 1}),
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"crop_factor": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 100, "step": 0.1}),
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"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
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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, drop_size):
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segs = onnx_detector.detect(image, threshold, dilation, crop_factor, drop_size)
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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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"clip": ("CLIP",),
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"vae": ("VAE",),
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"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
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"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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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": (core.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": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
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"force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
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"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
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"cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
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},
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"optional": {
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"detailer_hook": ("DETAILER_HOOK",),
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"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
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"scheduler_func_opt": ("SCHEDULER_FUNC",),
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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, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler,
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positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None,
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refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None,
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cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
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if len(image) > 1:
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raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
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image = image.clone()
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enhanced_alpha_list = []
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enhanced_list = []
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cropped_list = []
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cnet_pil_list = []
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segs = core.segs_scale_match(segs, image.shape)
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new_segs = []
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wildcard_concat_mode = None
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if wildcard_opt is not None:
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if wildcard_opt.startswith('[CONCAT]'):
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wildcard_concat_mode = 'concat'
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wildcard_opt = wildcard_opt[8:]
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wmode, wildcard_chooser = wildcards.process_wildcard_for_segs(wildcard_opt)
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else:
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wmode, wildcard_chooser = None, None
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if wmode in ['ASC', 'DSC']:
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if wmode == 'ASC':
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ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]))
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else:
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ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]), reverse=True)
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else:
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ordered_segs = segs[1]
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for i, seg in enumerate(ordered_segs):
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cropped_image = crop_ndarray4(image.cpu().numpy(), seg.crop_region)
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cropped_image = to_tensor(cropped_image)
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mask = to_tensor(seg.cropped_mask)
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mask = tensor_gaussian_blur_mask(mask, feather)
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is_mask_all_zeros = (seg.cropped_mask == 0).all().item()
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if is_mask_all_zeros:
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print(f"Detailer: segment skip [empty mask]")
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continue
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if noise_mask:
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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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if wildcard_chooser is not None and wmode != "LAB":
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seg_seed, wildcard_item = wildcard_chooser.get(seg)
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elif wildcard_chooser is not None and wmode == "LAB":
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seg_seed, wildcard_item = None, wildcard_chooser.get(seg)
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else:
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seg_seed, wildcard_item = None, None
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seg_seed = seed + i if seg_seed is None else seg_seed
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cropped_positive = [
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[condition, {
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k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
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for k, v in details.items()
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}]
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for condition, details in positive
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]
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if not isinstance(negative, str):
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cropped_negative = [
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[condition, {
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k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
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for k, v in details.items()
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}]
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for condition, details in negative
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]
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else:
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cropped_negative = negative
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enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size,
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seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler,
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cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
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wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode,
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detailer_hook=detailer_hook,
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refiner_ratio=refiner_ratio, refiner_model=refiner_model,
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refiner_clip=refiner_clip, refiner_positive=refiner_positive,
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refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
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cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
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scheduler_func=scheduler_func_opt)
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if cnet_pils is not None:
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cnet_pil_list.extend(cnet_pils)
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if not (enhanced_image is None):
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image = image.cpu()
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enhanced_image = enhanced_image.cpu()
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tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)
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enhanced_list.append(enhanced_image)
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if detailer_hook is not None:
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image = detailer_hook.post_paste(image)
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if not (enhanced_image is None):
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enhanced_image_alpha = tensor_convert_rgba(enhanced_image)
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new_seg_image = enhanced_image.numpy()
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mask = tensor_resize(mask, *tensor_get_size(enhanced_image))
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tensor_putalpha(enhanced_image_alpha, mask)
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enhanced_alpha_list.append(enhanced_image_alpha)
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else:
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new_seg_image = None
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cropped_list.append(cropped_image)
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new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
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new_segs.append(new_seg)
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image_tensor = tensor_convert_rgb(image)
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cropped_list.sort(key=lambda x: x.shape, reverse=True)
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enhanced_list.sort(key=lambda x: x.shape, reverse=True)
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enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True)
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return image_tensor, cropped_list, enhanced_list, enhanced_alpha_list, cnet_pil_list, (segs[0], new_segs)
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def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
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scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1,
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detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
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enhanced_img, *_ = \
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DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
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cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
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force_inpaint, wildcard, detailer_hook,
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cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
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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": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
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"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
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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": (core.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": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
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"force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
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"basic_pipe": ("BASIC_PIPE", ),
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"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
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"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
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"cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
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},
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"optional": {
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"detailer_hook": ("DETAILER_HOOK",),
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"refiner_basic_pipe_opt": ("BASIC_PIPE",),
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"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
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"scheduler_func_opt": ("SCHEDULER_FUNC",),
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}
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}
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RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE")
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RETURN_NAMES = ("image", "segs", "basic_pipe", "cnet_images")
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OUTPUT_IS_LIST = (False, False, False, True)
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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, max_size, seed, steps, cfg, sampler_name, scheduler,
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denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard,
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refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
|
|
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
|
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|
|
if len(image) > 1:
|
|
raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
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|
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model, clip, vae, positive, negative = basic_pipe
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|
|
if refiner_basic_pipe_opt is None:
|
|
refiner_model, refiner_clip, refiner_positive, refiner_negative = None, None, None, None
|
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else:
|
|
refiner_model, refiner_clip, _, refiner_positive, refiner_negative = refiner_basic_pipe_opt
|
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|
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enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \
|
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DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg,
|
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sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint, wildcard, detailer_hook,
|
|
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
|
|
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
|
|
|
|
if len(cnet_pil_list) == 0:
|
|
cnet_pil_list = [empty_pil_tensor()]
|
|
|
|
return enhanced_img, new_segs, basic_pipe, cnet_pil_list
|
|
|
|
|
|
class FaceDetailer:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE", ),
|
|
"model": ("MODEL",),
|
|
"clip": ("CLIP",),
|
|
"vae": ("VAE",),
|
|
"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
|
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
|
|
"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.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": (core.SCHEDULERS,),
|
|
"positive": ("CONDITIONING",),
|
|
"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": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
|
"force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
|
|
|
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"bbox_dilation": ("INT", {"default": 10, "min": -512, "max": 512, "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": -512, "max": 512, "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"],),
|
|
|
|
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
|
|
|
|
"bbox_detector": ("BBOX_DETECTOR", ),
|
|
"wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
|
|
|
"cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"sam_model_opt": ("SAM_MODEL", ),
|
|
"segm_detector_opt": ("SEGM_DETECTOR", ),
|
|
"detailer_hook": ("DETAILER_HOOK",),
|
|
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
|
|
"scheduler_func_opt": ("SCHEDULER_FUNC",),
|
|
}}
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
|
|
RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "mask", "detailer_pipe", "cnet_images")
|
|
OUTPUT_IS_LIST = (False, True, True, False, False, True)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Simple"
|
|
|
|
@staticmethod
|
|
def enhance_face(image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, 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, drop_size,
|
|
bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None,
|
|
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1,
|
|
inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
|
|
|
|
|
|
bbox_detector.setAux('face')
|
|
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size, detailer_hook=detailer_hook)
|
|
bbox_detector.setAux(None)
|
|
|
|
|
|
if sam_model_opt is not None:
|
|
sam_mask = core.make_sam_mask(sam_model_opt, segs, image, sam_detection_hint, sam_dilation,
|
|
sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
|
|
sam_mask_hint_use_negative, )
|
|
segs = core.segs_bitwise_and_mask(segs, sam_mask)
|
|
|
|
elif segm_detector is not None:
|
|
segm_segs = segm_detector.detect(image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size)
|
|
|
|
if (hasattr(segm_detector, 'override_bbox_by_segm') and segm_detector.override_bbox_by_segm and
|
|
not (detailer_hook is not None and not hasattr(detailer_hook, 'override_bbox_by_segm'))):
|
|
segs = segm_segs
|
|
else:
|
|
segm_mask = core.segs_to_combined_mask(segm_segs)
|
|
segs = core.segs_bitwise_and_mask(segs, segm_mask)
|
|
|
|
if len(segs[1]) > 0:
|
|
enhanced_img, _, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \
|
|
DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg,
|
|
sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint, wildcard_opt, detailer_hook,
|
|
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
|
|
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
|
|
refiner_negative=refiner_negative,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
else:
|
|
enhanced_img = image
|
|
cropped_enhanced = []
|
|
cropped_enhanced_alpha = []
|
|
cnet_pil_list = []
|
|
|
|
|
|
mask = core.segs_to_combined_mask(segs)
|
|
|
|
if len(cropped_enhanced) == 0:
|
|
cropped_enhanced = [empty_pil_tensor()]
|
|
|
|
if len(cropped_enhanced_alpha) == 0:
|
|
cropped_enhanced_alpha = [empty_pil_tensor()]
|
|
|
|
if len(cnet_pil_list) == 0:
|
|
cnet_pil_list = [empty_pil_tensor()]
|
|
|
|
return enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list
|
|
|
|
def doit(self, image, model, clip, vae, guide_size, guide_size_for, max_size, 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, drop_size, bbox_detector, wildcard, cycle=1,
|
|
sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
|
|
|
|
result_img = None
|
|
result_mask = None
|
|
result_cropped_enhanced = []
|
|
result_cropped_enhanced_alpha = []
|
|
result_cnet_images = []
|
|
|
|
if len(image) > 1:
|
|
print(f"[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.")
|
|
|
|
for i, single_image in enumerate(image):
|
|
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(
|
|
single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, 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, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
|
|
result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
|
|
result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
|
|
result_cropped_enhanced.extend(cropped_enhanced)
|
|
result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha)
|
|
result_cnet_images.extend(cnet_pil_list)
|
|
|
|
pipe = (model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None)
|
|
return result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, pipe, result_cnet_images
|
|
|
|
|
|
class LatentPixelScale:
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"samples": ("LATENT", ),
|
|
"scale_method": (s.upscale_methods,),
|
|
"scale_factor": ("FLOAT", {"default": 1.5, "min": 0.1, "max": 10000, "step": 0.1}),
|
|
"vae": ("VAE", ),
|
|
"use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
},
|
|
"optional": {
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "IMAGE")
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, samples, scale_method, scale_factor, vae, use_tiled_vae, upscale_model_opt=None):
|
|
if upscale_model_opt is None:
|
|
latimg = core.latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=use_tiled_vae)
|
|
else:
|
|
latimg = core.latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model_opt, scale_factor, vae, use_tile=use_tiled_vae)
|
|
return latimg
|
|
|
|
|
|
class NoiseInjectionDetailerHookProvider:
|
|
schedules = ["skip_start", "from_start"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"schedule_for_cycle": (s.schedules,),
|
|
"source": (["CPU", "GPU"],),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"start_strength": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 200.0, "step": 0.01}),
|
|
"end_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, schedule_for_cycle, source, seed, start_strength, end_strength):
|
|
try:
|
|
hook = hooks.InjectNoiseHookForDetailer(source, seed, start_strength, end_strength,
|
|
from_start=('from_start' in schedule_for_cycle))
|
|
return (hook, )
|
|
except Exception as e:
|
|
print("[ERROR] NoiseInjectionDetailerHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.")
|
|
print(f"\t{e}")
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class VariationNoiseDetailerHookProvider:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01})}
|
|
}
|
|
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, seed, strength):
|
|
hook = hooks.VariationNoiseDetailerHookProvider(seed, strength)
|
|
return (hook, )
|
|
|
|
|
|
class UnsamplerDetailerHookProvider:
|
|
schedules = ["skip_start", "from_start"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"model": ("MODEL",),
|
|
"steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
|
|
"start_end_at_step": ("INT", {"default": 21, "min": 0, "max": 10000}),
|
|
"end_end_at_step": ("INT", {"default": 24, "min": 0, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
|
|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
|
"normalize": (["disable", "enable"], ),
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"schedule_for_cycle": (s.schedules,),
|
|
}}
|
|
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
|
|
scheduler, normalize, positive, negative, schedule_for_cycle):
|
|
try:
|
|
hook = hooks.UnsamplerDetailerHook(model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
|
|
scheduler, normalize, positive, negative,
|
|
from_start=('from_start' in schedule_for_cycle))
|
|
|
|
return (hook, )
|
|
except Exception as e:
|
|
print("[ERROR] UnsamplerDetailerHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.")
|
|
print(f"\t{e}")
|
|
pass
|
|
|
|
|
|
class DenoiseSchedulerDetailerHookProvider:
|
|
schedules = ["simple"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"schedule_for_cycle": (s.schedules,),
|
|
"target_denoise": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, schedule_for_cycle, target_denoise):
|
|
hook = hooks.SimpleDetailerDenoiseSchedulerHook(target_denoise)
|
|
return (hook, )
|
|
|
|
|
|
class CoreMLDetailerHookProvider:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"mode": (["512x512", "768x768", "512x768", "768x512"], )}, }
|
|
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, mode):
|
|
hook = hooks.CoreMLHook(mode)
|
|
return (hook, )
|
|
|
|
|
|
class CfgScheduleHookProvider:
|
|
schedules = ["simple"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"schedule_for_iteration": (s.schedules,),
|
|
"target_cfg": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PK_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, schedule_for_iteration, target_cfg):
|
|
hook = None
|
|
if schedule_for_iteration == "simple":
|
|
hook = hooks.SimpleCfgScheduleHook(target_cfg)
|
|
|
|
return (hook, )
|
|
|
|
|
|
class UnsamplerHookProvider:
|
|
schedules = ["simple"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{"model": ("MODEL",),
|
|
"steps": ("INT", {"default": 25, "min": 1, "max": 10000}),
|
|
"start_end_at_step": ("INT", {"default": 21, "min": 0, "max": 10000}),
|
|
"end_end_at_step": ("INT", {"default": 24, "min": 0, "max": 10000}),
|
|
"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
|
|
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
|
|
"normalize": (["disable", "enable"], ),
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"schedule_for_iteration": (s.schedules,),
|
|
}}
|
|
|
|
RETURN_TYPES = ("PK_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
|
|
scheduler, normalize, positive, negative, schedule_for_iteration):
|
|
try:
|
|
hook = None
|
|
if schedule_for_iteration == "simple":
|
|
hook = hooks.UnsamplerHook(model, steps, start_end_at_step, end_end_at_step, cfg, sampler_name,
|
|
scheduler, normalize, positive, negative)
|
|
|
|
return (hook, )
|
|
except Exception as e:
|
|
print("[ERROR] UnsamplerHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.")
|
|
print(f"\t{e}")
|
|
pass
|
|
|
|
|
|
class NoiseInjectionHookProvider:
|
|
schedules = ["simple"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"schedule_for_iteration": (s.schedules,),
|
|
"source": (["CPU", "GPU"],),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"start_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}),
|
|
"end_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PK_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, schedule_for_iteration, source, seed, start_strength, end_strength):
|
|
try:
|
|
hook = None
|
|
if schedule_for_iteration == "simple":
|
|
hook = hooks.InjectNoiseHook(source, seed, start_strength, end_strength)
|
|
|
|
return (hook, )
|
|
except Exception as e:
|
|
print("[ERROR] NoiseInjectionHookProvider: 'ComfyUI Noise' custom node isn't installed. You must install 'BlenderNeko/ComfyUI Noise' extension to use this node.")
|
|
print(f"\t{e}")
|
|
pass
|
|
|
|
|
|
class DenoiseScheduleHookProvider:
|
|
schedules = ["simple"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"schedule_for_iteration": (s.schedules,),
|
|
"target_denoise": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PK_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, schedule_for_iteration, target_denoise):
|
|
hook = None
|
|
if schedule_for_iteration == "simple":
|
|
hook = hooks.SimpleDenoiseScheduleHook(target_denoise)
|
|
|
|
return (hook, )
|
|
|
|
|
|
class StepsScheduleHookProvider:
|
|
schedules = ["simple"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"schedule_for_iteration": (s.schedules,),
|
|
"target_steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PK_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, schedule_for_iteration, target_steps):
|
|
hook = None
|
|
if schedule_for_iteration == "simple":
|
|
hook = hooks.SimpleStepsScheduleHook(target_steps)
|
|
|
|
return (hook, )
|
|
|
|
|
|
class DetailerHookCombine:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"hook1": ("DETAILER_HOOK",),
|
|
"hook2": ("DETAILER_HOOK",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, hook1, hook2):
|
|
hook = hooks.DetailerHookCombine(hook1, hook2)
|
|
return (hook, )
|
|
|
|
|
|
class PixelKSampleHookCombine:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"hook1": ("PK_HOOK",),
|
|
"hook2": ("PK_HOOK",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PK_HOOK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, hook1, hook2):
|
|
hook = hooks.PixelKSampleHookCombine(hook1, hook2)
|
|
return (hook, )
|
|
|
|
|
|
class PixelTiledKSampleUpscalerProvider:
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scale_method": (s.upscale_methods,),
|
|
"model": ("MODEL",),
|
|
"vae": ("VAE",),
|
|
"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, ),
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
|
|
"tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
|
|
"tiling_strategy": (["random", "padded", 'simple'], ),
|
|
},
|
|
"optional": {
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
"pk_hook_opt": ("PK_HOOK", ),
|
|
"tile_cnet_opt": ("CONTROL_NET", ),
|
|
"tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy, upscale_model_opt=None,
|
|
pk_hook_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0):
|
|
if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
|
|
upscaler = core.PixelTiledKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
|
|
tile_width, tile_height, tiling_strategy, upscale_model_opt, pk_hook_opt, tile_cnet_opt,
|
|
tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength)
|
|
return (upscaler, )
|
|
else:
|
|
print("[ERROR] PixelTiledKSampleUpscalerProvider: ComfyUI_TiledKSampler custom node isn't installed. You must install BlenderNeko/ComfyUI_TiledKSampler extension to use this node.")
|
|
|
|
|
|
class PixelTiledKSampleUpscalerProviderPipe:
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scale_method": (s.upscale_methods,),
|
|
"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": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"tile_width": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
|
|
"tile_height": ("INT", {"default": 512, "min": 320, "max": MAX_RESOLUTION, "step": 64}),
|
|
"tiling_strategy": (["random", "padded", 'simple'], ),
|
|
"basic_pipe": ("BASIC_PIPE",)
|
|
},
|
|
"optional": {
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
"pk_hook_opt": ("PK_HOOK", ),
|
|
"tile_cnet_opt": ("CONTROL_NET", ),
|
|
"tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, scale_method, seed, steps, cfg, sampler_name, scheduler, denoise, tile_width, tile_height, tiling_strategy, basic_pipe, upscale_model_opt=None, pk_hook_opt=None,
|
|
tile_cnet_opt=None, tile_cnet_strength=1.0):
|
|
if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
|
|
model, _, vae, positive, negative = basic_pipe
|
|
upscaler = core.PixelTiledKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
|
|
tile_width, tile_height, tiling_strategy, upscale_model_opt, pk_hook_opt, tile_cnet_opt,
|
|
tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength)
|
|
return (upscaler, )
|
|
else:
|
|
print("[ERROR] PixelTiledKSampleUpscalerProviderPipe: ComfyUI_TiledKSampler custom node isn't installed. You must install BlenderNeko/ComfyUI_TiledKSampler extension to use this node.")
|
|
|
|
|
|
class PixelKSampleUpscalerProvider:
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scale_method": (s.upscale_methods,),
|
|
"model": ("MODEL",),
|
|
"vae": ("VAE",),
|
|
"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": (core.SCHEDULERS, ),
|
|
"positive": ("CONDITIONING", ),
|
|
"negative": ("CONDITIONING", ),
|
|
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
|
|
},
|
|
"optional": {
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
"pk_hook_opt": ("PK_HOOK", ),
|
|
"scheduler_func_opt": ("SCHEDULER_FUNC",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
|
|
use_tiled_vae, upscale_model_opt=None, pk_hook_opt=None, tile_size=512, scheduler_func_opt=None):
|
|
upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, use_tiled_vae, upscale_model_opt, pk_hook_opt,
|
|
tile_size=tile_size, scheduler_func=scheduler_func_opt)
|
|
return (upscaler, )
|
|
|
|
|
|
class PixelKSampleUpscalerProviderPipe(PixelKSampleUpscalerProvider):
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scale_method": (s.upscale_methods,),
|
|
"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": (core.SCHEDULERS, ),
|
|
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"basic_pipe": ("BASIC_PIPE",),
|
|
"tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
|
|
},
|
|
"optional": {
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
"pk_hook_opt": ("PK_HOOK", ),
|
|
"scheduler_func_opt": ("SCHEDULER_FUNC",),
|
|
"tile_cnet_opt": ("CONTROL_NET", ),
|
|
"tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER",)
|
|
FUNCTION = "doit_pipe"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit_pipe(self, scale_method, seed, steps, cfg, sampler_name, scheduler, denoise,
|
|
use_tiled_vae, basic_pipe, upscale_model_opt=None, pk_hook_opt=None,
|
|
tile_size=512, scheduler_func_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0):
|
|
model, _, vae, positive, negative = basic_pipe
|
|
upscaler = core.PixelKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler,
|
|
positive, negative, denoise, use_tiled_vae, upscale_model_opt, pk_hook_opt,
|
|
tile_size=tile_size, scheduler_func=scheduler_func_opt,
|
|
tile_cnet_opt=tile_cnet_opt, tile_cnet_strength=tile_cnet_strength)
|
|
return (upscaler, )
|
|
|
|
|
|
class TwoSamplersForMaskUpscalerProvider:
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scale_method": (s.upscale_methods,),
|
|
"full_sample_schedule": (
|
|
["none", "interleave1", "interleave2", "interleave3",
|
|
"last1", "last2",
|
|
"interleave1+last1", "interleave2+last1", "interleave3+last1",
|
|
],),
|
|
"use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"base_sampler": ("KSAMPLER", ),
|
|
"mask_sampler": ("KSAMPLER", ),
|
|
"mask": ("MASK", ),
|
|
"vae": ("VAE",),
|
|
"tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
|
|
},
|
|
"optional": {
|
|
"full_sampler_opt": ("KSAMPLER",),
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
"pk_hook_base_opt": ("PK_HOOK", ),
|
|
"pk_hook_mask_opt": ("PK_HOOK", ),
|
|
"pk_hook_full_opt": ("PK_HOOK", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER", )
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, scale_method, full_sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, vae,
|
|
full_sampler_opt=None, upscale_model_opt=None,
|
|
pk_hook_base_opt=None, pk_hook_mask_opt=None, pk_hook_full_opt=None, tile_size=512):
|
|
upscaler = core.TwoSamplersForMaskUpscaler(scale_method, full_sample_schedule, use_tiled_vae,
|
|
base_sampler, mask_sampler, mask, vae, full_sampler_opt, upscale_model_opt,
|
|
pk_hook_base_opt, pk_hook_mask_opt, pk_hook_full_opt, tile_size=tile_size)
|
|
return (upscaler, )
|
|
|
|
|
|
class TwoSamplersForMaskUpscalerProviderPipe:
|
|
upscale_methods = ["nearest-exact", "bilinear", "lanczos", "area"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scale_method": (s.upscale_methods,),
|
|
"full_sample_schedule": (
|
|
["none", "interleave1", "interleave2", "interleave3",
|
|
"last1", "last2",
|
|
"interleave1+last1", "interleave2+last1", "interleave3+last1",
|
|
],),
|
|
"use_tiled_vae": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"base_sampler": ("KSAMPLER", ),
|
|
"mask_sampler": ("KSAMPLER", ),
|
|
"mask": ("MASK", ),
|
|
"basic_pipe": ("BASIC_PIPE",),
|
|
"tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
|
|
},
|
|
"optional": {
|
|
"full_sampler_opt": ("KSAMPLER",),
|
|
"upscale_model_opt": ("UPSCALE_MODEL", ),
|
|
"pk_hook_base_opt": ("PK_HOOK", ),
|
|
"pk_hook_mask_opt": ("PK_HOOK", ),
|
|
"pk_hook_full_opt": ("PK_HOOK", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("UPSCALER", )
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, scale_method, full_sample_schedule, use_tiled_vae, base_sampler, mask_sampler, mask, basic_pipe,
|
|
full_sampler_opt=None, upscale_model_opt=None,
|
|
pk_hook_base_opt=None, pk_hook_mask_opt=None, pk_hook_full_opt=None, tile_size=512):
|
|
|
|
mask = make_2d_mask(mask)
|
|
|
|
_, _, vae, _, _ = basic_pipe
|
|
upscaler = core.TwoSamplersForMaskUpscaler(scale_method, full_sample_schedule, use_tiled_vae,
|
|
base_sampler, mask_sampler, mask, vae, full_sampler_opt, upscale_model_opt,
|
|
pk_hook_base_opt, pk_hook_mask_opt, pk_hook_full_opt, tile_size=tile_size)
|
|
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}),
|
|
"temp_prefix": ("STRING", {"default": ""}),
|
|
"upscaler": ("UPSCALER",),
|
|
"step_mode": (["simple", "geometric"], {"default": "simple"})
|
|
},
|
|
"hidden": {"unique_id": "UNIQUE_ID"},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "VAE")
|
|
RETURN_NAMES = ("latent", "vae")
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, samples, upscale_factor, steps, temp_prefix, upscaler, step_mode="simple", unique_id=None):
|
|
w = samples['samples'].shape[3]*8
|
|
h = samples['samples'].shape[2]*8
|
|
|
|
if temp_prefix == "":
|
|
temp_prefix = None
|
|
|
|
if step_mode == "geometric":
|
|
upscale_factor_unit = pow(upscale_factor, 1.0/steps)
|
|
else:
|
|
upscale_factor_unit = max(0, (upscale_factor - 1.0) / steps)
|
|
|
|
current_latent = samples
|
|
scale = 1
|
|
|
|
for i in range(steps-1):
|
|
if step_mode == "geometric":
|
|
scale *= upscale_factor_unit
|
|
else:
|
|
scale += upscale_factor_unit
|
|
|
|
new_w = w*scale
|
|
new_h = h*scale
|
|
core.update_node_status(unique_id, f"{i+1}/{steps} steps | x{scale:.2f}", (i+1)/steps)
|
|
print(f"IterativeLatentUpscale[{i+1}/{steps}]: {new_w:.1f}x{new_h:.1f} (scale:{scale:.2f}) ")
|
|
step_info = i, steps
|
|
current_latent = upscaler.upscale_shape(step_info, current_latent, new_w, new_h, temp_prefix)
|
|
|
|
if scale < upscale_factor:
|
|
new_w = w*upscale_factor
|
|
new_h = h*upscale_factor
|
|
core.update_node_status(unique_id, f"Final step | x{upscale_factor:.2f}", 1.0)
|
|
print(f"IterativeLatentUpscale[Final]: {new_w:.1f}x{new_h:.1f} (scale:{upscale_factor:.2f}) ")
|
|
step_info = steps-1, steps
|
|
current_latent = upscaler.upscale_shape(step_info, current_latent, new_w, new_h, temp_prefix)
|
|
|
|
core.update_node_status(unique_id, "", None)
|
|
|
|
return (current_latent, upscaler.vae)
|
|
|
|
|
|
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}),
|
|
"temp_prefix": ("STRING", {"default": ""}),
|
|
"upscaler": ("UPSCALER",),
|
|
"vae": ("VAE",),
|
|
"step_mode": (["simple", "geometric"], {"default": "simple"})
|
|
},
|
|
"hidden": {"unique_id": "UNIQUE_ID"}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("image",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Upscale"
|
|
|
|
def doit(self, pixels, upscale_factor, steps, temp_prefix, upscaler, vae, step_mode="simple", unique_id=None):
|
|
if temp_prefix == "":
|
|
temp_prefix = None
|
|
|
|
core.update_node_status(unique_id, "VAEEncode (first)", 0)
|
|
if upscaler.is_tiled:
|
|
latent = nodes.VAEEncodeTiled().encode(vae, pixels, upscaler.tile_size)[0]
|
|
else:
|
|
latent = nodes.VAEEncode().encode(vae, pixels)[0]
|
|
|
|
refined_latent = IterativeLatentUpscale().doit(latent, upscale_factor, steps, temp_prefix, upscaler, step_mode, unique_id)
|
|
|
|
core.update_node_status(unique_id, "VAEDecode (final)", 1.0)
|
|
if upscaler.is_tiled:
|
|
pixels = nodes.VAEDecodeTiled().decode(vae, refined_latent[0], upscaler.tile_size)[0]
|
|
else:
|
|
pixels = nodes.VAEDecode().decode(vae, refined_latent[0])[0]
|
|
|
|
core.update_node_status(unique_id, "", None)
|
|
|
|
return (pixels, )
|
|
|
|
|
|
class FaceDetailerPipe:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE", ),
|
|
"detailer_pipe": ("DETAILER_PIPE",),
|
|
"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
|
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
|
|
"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.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": (core.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": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
|
"force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
|
|
|
"bbox_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"bbox_dilation": ("INT", {"default": 10, "min": -512, "max": 512, "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": -512, "max": 512, "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"],),
|
|
|
|
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
|
|
"refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
|
|
|
|
"cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
|
|
},
|
|
"optional": {
|
|
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
|
|
"scheduler_func_opt": ("SCHEDULER_FUNC",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
|
|
RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "mask", "detailer_pipe", "cnet_images")
|
|
OUTPUT_IS_LIST = (False, True, True, False, False, True)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Simple"
|
|
|
|
def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, 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, drop_size, refiner_ratio=None,
|
|
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
|
|
|
|
result_img = None
|
|
result_mask = None
|
|
result_cropped_enhanced = []
|
|
result_cropped_enhanced_alpha = []
|
|
result_cnet_images = []
|
|
|
|
if len(image) > 1:
|
|
print(f"[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.")
|
|
|
|
model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector, sam_model_opt, detailer_hook, \
|
|
refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
|
|
|
|
for i, single_image in enumerate(image):
|
|
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(
|
|
single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, 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, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook,
|
|
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
|
|
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
|
|
result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
|
|
result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
|
|
result_cropped_enhanced.extend(cropped_enhanced)
|
|
result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha)
|
|
result_cnet_images.extend(cnet_pil_list)
|
|
|
|
if len(result_cropped_enhanced) == 0:
|
|
result_cropped_enhanced = [empty_pil_tensor()]
|
|
|
|
if len(result_cropped_enhanced_alpha) == 0:
|
|
result_cropped_enhanced_alpha = [empty_pil_tensor()]
|
|
|
|
if len(result_cnet_images) == 0:
|
|
result_cnet_images = [empty_pil_tensor()]
|
|
|
|
return result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, detailer_pipe, result_cnet_images
|
|
|
|
|
|
class MaskDetailerPipe:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE", ),
|
|
"mask": ("MASK", ),
|
|
"basic_pipe": ("BASIC_PIPE",),
|
|
|
|
"guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
|
"guide_size_for": ("BOOLEAN", {"default": True, "label_on": "mask bbox", "label_off": "crop region"}),
|
|
"max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
|
"mask_mode": ("BOOLEAN", {"default": True, "label_on": "masked only", "label_off": "whole"}),
|
|
|
|
"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": (core.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}),
|
|
"crop_factor": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10, "step": 0.1}),
|
|
"drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}),
|
|
"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", ),
|
|
"detailer_hook": ("DETAILER_HOOK",),
|
|
"inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
|
|
"bbox_fill": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
|
|
"contour_fill": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
|
|
"scheduler_func_opt": ("SCHEDULER_FUNC",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "BASIC_PIPE", "BASIC_PIPE")
|
|
RETURN_NAMES = ("image", "cropped_refined", "cropped_enhanced_alpha", "basic_pipe", "refiner_basic_pipe_opt")
|
|
OUTPUT_IS_LIST = (False, True, True, False, False)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
|
|
seed, steps, cfg, sampler_name, scheduler, denoise,
|
|
feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1,
|
|
refiner_basic_pipe_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0,
|
|
bbox_fill=False, contour_fill=True, scheduler_func_opt=None):
|
|
|
|
if len(image) > 1:
|
|
raise Exception('[Impact Pack] ERROR: MaskDetailer 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.')
|
|
|
|
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
|
|
|
|
|
|
if mask is not None:
|
|
mask = make_2d_mask(mask)
|
|
segs = core.mask_to_segs(mask, False, crop_factor, bbox_fill, drop_size, is_contour=contour_fill)
|
|
else:
|
|
segs = ((image.shape[1], image.shape[2]), [])
|
|
|
|
enhanced_img_batch = None
|
|
cropped_enhanced_list = []
|
|
cropped_enhanced_alpha_list = []
|
|
|
|
for i in range(batch_size):
|
|
if mask is not None:
|
|
enhanced_img, _, cropped_enhanced, cropped_enhanced_alpha, _, _ = \
|
|
DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed+i, steps,
|
|
cfg, sampler_name, scheduler, positive, negative, denoise, feather, mask_mode,
|
|
force_inpaint=True, wildcard_opt=None, detailer_hook=detailer_hook,
|
|
refiner_ratio=refiner_ratio, refiner_model=refiner_model, refiner_clip=refiner_clip,
|
|
refiner_positive=refiner_positive, refiner_negative=refiner_negative,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
else:
|
|
enhanced_img, cropped_enhanced, cropped_enhanced_alpha = image, [], []
|
|
|
|
if enhanced_img_batch is None:
|
|
enhanced_img_batch = enhanced_img
|
|
else:
|
|
enhanced_img_batch = torch.cat((enhanced_img_batch, enhanced_img), dim=0)
|
|
|
|
cropped_enhanced_list += cropped_enhanced
|
|
cropped_enhanced_alpha_list += cropped_enhanced_alpha
|
|
|
|
|
|
if len(cropped_enhanced_list) == 0:
|
|
cropped_enhanced_list = [empty_pil_tensor()]
|
|
|
|
if len(cropped_enhanced_alpha_list) == 0:
|
|
cropped_enhanced_alpha_list = [empty_pil_tensor()]
|
|
|
|
return enhanced_img_batch, cropped_enhanced_list, cropped_enhanced_alpha_list, basic_pipe, refiner_basic_pipe_opt
|
|
|
|
|
|
class DetailerForEachTest(DetailerForEach):
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE")
|
|
RETURN_NAMES = ("image", "cropped", "cropped_refined", "cropped_refined_alpha", "cnet_images")
|
|
OUTPUT_IS_LIST = (False, True, True, True, True)
|
|
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
|
|
scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, detailer_hook=None,
|
|
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
|
|
|
|
if len(image) > 1:
|
|
raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
|
|
|
|
enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \
|
|
DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
|
|
cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint, wildcard, detailer_hook,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
|
|
|
|
if len(cropped) == 0:
|
|
cropped = [empty_pil_tensor()]
|
|
|
|
if len(cropped_enhanced) == 0:
|
|
cropped_enhanced = [empty_pil_tensor()]
|
|
|
|
if len(cropped_enhanced_alpha) == 0:
|
|
cropped_enhanced_alpha = [empty_pil_tensor()]
|
|
|
|
if len(cnet_pil_list) == 0:
|
|
cnet_pil_list = [empty_pil_tensor()]
|
|
|
|
return enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list
|
|
|
|
|
|
class DetailerForEachTestPipe(DetailerForEachPipe):
|
|
RETURN_TYPES = ("IMAGE", "SEGS", "BASIC_PIPE", "IMAGE", "IMAGE", "IMAGE", "IMAGE", )
|
|
RETURN_NAMES = ("image", "segs", "basic_pipe", "cropped", "cropped_refined", "cropped_refined_alpha", 'cnet_images')
|
|
OUTPUT_IS_LIST = (False, False, False, True, True, True, True)
|
|
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Detailer"
|
|
|
|
def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
|
|
denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard, cycle=1,
|
|
refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
|
|
|
|
if len(image) > 1:
|
|
raise Exception('[Impact Pack] ERROR: DetailerForEach 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.')
|
|
|
|
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
|
|
|
|
enhanced_img, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list, new_segs = \
|
|
DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg,
|
|
sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
|
|
force_inpaint, wildcard, detailer_hook,
|
|
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
|
|
refiner_clip=refiner_clip, refiner_positive=refiner_positive,
|
|
refiner_negative=refiner_negative,
|
|
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
|
|
|
|
|
|
if len(cropped) == 0:
|
|
cropped = [empty_pil_tensor()]
|
|
|
|
if len(cropped_enhanced) == 0:
|
|
cropped_enhanced = [empty_pil_tensor()]
|
|
|
|
if len(cropped_enhanced_alpha) == 0:
|
|
cropped_enhanced_alpha = [empty_pil_tensor()]
|
|
|
|
if len(cnet_pil_list) == 0:
|
|
cnet_pil_list = [empty_pil_tensor()]
|
|
|
|
return enhanced_img, new_segs, basic_pipe, cropped, cropped_enhanced, cropped_enhanced_alpha, cnet_pil_list
|
|
|
|
|
|
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 SegsBitwiseAndMaskForEach:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"segs": ("SEGS",),
|
|
"masks": ("MASK",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("SEGS",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, segs, masks):
|
|
return (core.apply_mask_to_each_seg(segs, masks), )
|
|
|
|
|
|
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):
|
|
mask = core.segs_to_combined_mask(mask_segs)
|
|
mask = make_3d_mask(mask)
|
|
|
|
return SegsBitwiseAndMask().doit(base_segs, mask)
|
|
|
|
|
|
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):
|
|
mask = core.segs_to_combined_mask(mask_segs)
|
|
mask = make_3d_mask(mask)
|
|
return (core.segs_bitwise_subtract_mask(base_segs, mask), )
|
|
|
|
|
|
class ToBinaryMask:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"mask": ("MASK",),
|
|
"threshold": ("INT", {"default": 20, "min": 1, "max": 255}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MASK",)
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Operation"
|
|
|
|
def doit(self, mask, threshold):
|
|
mask = to_binary_mask(mask, threshold/255.0)
|
|
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,)
|
|
|
|
|
|
class AddMask:
|
|
@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 = add_masks(mask1, mask2)
|
|
return (mask,)
|
|
|
|
|
|
import nodes
|
|
|
|
|
|
def get_image_hash(arr):
|
|
split_index1 = arr.shape[0] // 2
|
|
split_index2 = arr.shape[1] // 2
|
|
part1 = arr[:split_index1, :split_index2]
|
|
part2 = arr[:split_index1, split_index2:]
|
|
part3 = arr[split_index1:, :split_index2]
|
|
part4 = arr[split_index1:, split_index2:]
|
|
|
|
|
|
sum1 = np.sum(part1)
|
|
sum2 = np.sum(part2)
|
|
sum3 = np.sum(part3)
|
|
sum4 = np.sum(part4)
|
|
|
|
return hash((sum1, sum2, sum3, sum4))
|
|
|
|
|
|
def get_file_item(base_type, path):
|
|
path_type = base_type
|
|
|
|
if path == "[output]":
|
|
path_type = "output"
|
|
path = path[:-9]
|
|
elif path == "[input]":
|
|
path_type = "input"
|
|
path = path[:-8]
|
|
elif path == "[temp]":
|
|
path_type = "temp"
|
|
path = path[:-7]
|
|
|
|
subfolder = os.path.dirname(path)
|
|
filename = os.path.basename(path)
|
|
|
|
return {
|
|
"filename": filename,
|
|
"subfolder": subfolder,
|
|
"type": path_type
|
|
}
|
|
|
|
|
|
class ImageReceiver:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
input_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
|
return {"required": {
|
|
"image": (sorted(files), ),
|
|
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
|
|
"save_to_workflow": ("BOOLEAN", {"default": False}),
|
|
"image_data": ("STRING", {"multiline": False}),
|
|
"trigger_always": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
|
|
},
|
|
}
|
|
|
|
FUNCTION = "doit"
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK")
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
def doit(self, image, link_id, save_to_workflow, image_data, trigger_always):
|
|
if save_to_workflow:
|
|
try:
|
|
image_data = base64.b64decode(image_data.split(",")[1])
|
|
i = Image.open(BytesIO(image_data))
|
|
i = ImageOps.exif_transpose(i)
|
|
image = i.convert("RGB")
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
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((64, 64), dtype=torch.float32, device="cpu")
|
|
return (image, mask.unsqueeze(0))
|
|
except Exception as e:
|
|
print(f"[WARN] ComfyUI-Impact-Pack: ImageReceiver - invalid 'image_data'")
|
|
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
|
|
return (empty_pil_tensor(64, 64), mask, )
|
|
else:
|
|
return nodes.LoadImage().load_image(image)
|
|
|
|
@classmethod
|
|
def VALIDATE_INPUTS(s, image, link_id, save_to_workflow, image_data, trigger_always):
|
|
if image != '#DATA' and not folder_paths.exists_annotated_filepath(image) or image.startswith("/") or ".." in image:
|
|
return "Invalid image file: {}".format(image)
|
|
|
|
return True
|
|
|
|
@classmethod
|
|
def IS_CHANGED(s, image, link_id, save_to_workflow, image_data, trigger_always):
|
|
if trigger_always:
|
|
return float("NaN")
|
|
else:
|
|
if save_to_workflow:
|
|
return hash(image_data)
|
|
else:
|
|
return hash(image)
|
|
|
|
|
|
from server import PromptServer
|
|
|
|
class ImageSender(nodes.PreviewImage):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"images": ("IMAGE", ),
|
|
"filename_prefix": ("STRING", {"default": "ImgSender"}),
|
|
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), },
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
OUTPUT_NODE = True
|
|
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
def doit(self, images, filename_prefix="ImgSender", link_id=0, prompt=None, extra_pnginfo=None):
|
|
result = nodes.PreviewImage().save_images(images, filename_prefix, prompt, extra_pnginfo)
|
|
PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": result['ui']['images']})
|
|
return result
|
|
|
|
|
|
class LatentReceiver:
|
|
def __init__(self):
|
|
self.input_dir = folder_paths.get_input_directory()
|
|
self.type = "input"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
def check_file_extension(x):
|
|
return x.endswith(".latent") or x.endswith(".latent.png")
|
|
|
|
input_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and check_file_extension(f)]
|
|
return {"required": {
|
|
"latent": (sorted(files), ),
|
|
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
|
|
"trigger_always": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
|
|
},
|
|
}
|
|
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
|
|
@staticmethod
|
|
def load_preview_latent(image_path):
|
|
if not os.path.exists(image_path):
|
|
return None
|
|
|
|
image = Image.open(image_path)
|
|
exif_data = piexif.load(image.info["exif"])
|
|
|
|
if piexif.ExifIFD.UserComment in exif_data["Exif"]:
|
|
compressed_data = exif_data["Exif"][piexif.ExifIFD.UserComment]
|
|
compressed_data_io = BytesIO(compressed_data)
|
|
with zipfile.ZipFile(compressed_data_io, mode='r') as archive:
|
|
tensor_bytes = archive.read("latent")
|
|
tensor = safetensors.torch.load(tensor_bytes)
|
|
return {"samples": tensor['latent_tensor']}
|
|
return None
|
|
|
|
def parse_filename(self, filename):
|
|
pattern = r"^(.*)/(.*?)\[(.*)\]\s*$"
|
|
match = re.match(pattern, filename)
|
|
if match:
|
|
subfolder = match.group(1)
|
|
filename = match.group(2).rstrip()
|
|
file_type = match.group(3)
|
|
else:
|
|
subfolder = ''
|
|
file_type = self.type
|
|
|
|
return {'filename': filename, 'subfolder': subfolder, 'type': file_type}
|
|
|
|
def doit(self, **kwargs):
|
|
if 'latent' not in kwargs:
|
|
return (torch.zeros([1, 4, 8, 8]), )
|
|
|
|
latent = kwargs['latent']
|
|
|
|
latent_name = latent
|
|
latent_path = folder_paths.get_annotated_filepath(latent_name)
|
|
|
|
if latent.endswith(".latent"):
|
|
latent = safetensors.torch.load_file(latent_path, device="cpu")
|
|
multiplier = 1.0
|
|
if "latent_format_version_0" not in latent:
|
|
multiplier = 1.0 / 0.18215
|
|
samples = {"samples": latent["latent_tensor"].float() * multiplier}
|
|
else:
|
|
samples = LatentReceiver.load_preview_latent(latent_path)
|
|
|
|
if samples is None:
|
|
samples = {'samples': torch.zeros([1, 4, 8, 8])}
|
|
|
|
preview = self.parse_filename(latent_name)
|
|
|
|
return {
|
|
'ui': {"images": [preview]},
|
|
'result': (samples, )
|
|
}
|
|
|
|
@classmethod
|
|
def IS_CHANGED(s, latent, link_id, trigger_always):
|
|
if trigger_always:
|
|
return float("NaN")
|
|
else:
|
|
image_path = folder_paths.get_annotated_filepath(latent)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
@classmethod
|
|
def VALIDATE_INPUTS(s, latent, link_id, trigger_always):
|
|
if not folder_paths.exists_annotated_filepath(latent) or latent.startswith("/") or ".." in latent:
|
|
return "Invalid latent file: {}".format(latent)
|
|
return True
|
|
|
|
|
|
class LatentSender(nodes.SaveLatent):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.output_dir = folder_paths.get_temp_directory()
|
|
self.type = "temp"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"samples": ("LATENT", ),
|
|
"filename_prefix": ("STRING", {"default": "latents/LatentSender"}),
|
|
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
|
|
"preview_method": (["Latent2RGB-SDXL", "Latent2RGB-SD15", "TAESDXL", "TAESD15"],)
|
|
},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
|
}
|
|
|
|
OUTPUT_NODE = True
|
|
|
|
RETURN_TYPES = ()
|
|
|
|
FUNCTION = "doit"
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
@staticmethod
|
|
def save_to_file(tensor_bytes, prompt, extra_pnginfo, image, image_path):
|
|
compressed_data = BytesIO()
|
|
with zipfile.ZipFile(compressed_data, mode='w') as archive:
|
|
archive.writestr("latent", tensor_bytes)
|
|
image = image.copy()
|
|
exif_data = {"Exif": {piexif.ExifIFD.UserComment: compressed_data.getvalue()}}
|
|
|
|
metadata = PngInfo()
|
|
if prompt is not None:
|
|
metadata.add_text("prompt", json.dumps(prompt))
|
|
if extra_pnginfo is not None:
|
|
for x in extra_pnginfo:
|
|
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
|
|
|
exif_bytes = piexif.dump(exif_data)
|
|
image.save(image_path, format='png', exif=exif_bytes, pnginfo=metadata, optimize=True)
|
|
|
|
@staticmethod
|
|
def prepare_preview(latent_tensor, preview_method):
|
|
from comfy.cli_args import LatentPreviewMethod
|
|
import comfy.latent_formats as latent_formats
|
|
|
|
lower_bound = 128
|
|
upper_bound = 256
|
|
|
|
if preview_method == "Latent2RGB-SD15":
|
|
latent_format = latent_formats.SD15()
|
|
method = LatentPreviewMethod.Latent2RGB
|
|
elif preview_method == "TAESD15":
|
|
latent_format = latent_formats.SD15()
|
|
method = LatentPreviewMethod.TAESD
|
|
elif preview_method == "TAESDXL":
|
|
latent_format = latent_formats.SDXL()
|
|
method = LatentPreviewMethod.TAESD
|
|
else:
|
|
latent_format = latent_formats.SDXL()
|
|
method = LatentPreviewMethod.Latent2RGB
|
|
|
|
previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
|
|
|
|
image = previewer.decode_latent_to_preview(latent_tensor)
|
|
min_size = min(image.size[0], image.size[1])
|
|
max_size = max(image.size[0], image.size[1])
|
|
|
|
scale_factor = 1
|
|
if max_size > upper_bound:
|
|
scale_factor = upper_bound/max_size
|
|
|
|
|
|
if min_size*scale_factor < lower_bound:
|
|
scale_factor = lower_bound/min_size
|
|
|
|
w = int(image.size[0] * scale_factor)
|
|
h = int(image.size[1] * scale_factor)
|
|
|
|
image = image.resize((w, h), resample=Image.NEAREST)
|
|
|
|
return LatentSender.attach_format_text(image)
|
|
|
|
@staticmethod
|
|
def attach_format_text(image):
|
|
width_a, height_a = image.size
|
|
|
|
letter_image = Image.open(latent_letter_path)
|
|
width_b, height_b = letter_image.size
|
|
|
|
new_width = max(width_a, width_b)
|
|
new_height = height_a + height_b
|
|
|
|
new_image = Image.new('RGB', (new_width, new_height), (0, 0, 0))
|
|
|
|
offset_x = (new_width - width_b) // 2
|
|
offset_y = (height_a + (new_height - height_a - height_b) // 2)
|
|
new_image.paste(letter_image, (offset_x, offset_y))
|
|
|
|
new_image.paste(image, (0, 0))
|
|
|
|
return new_image
|
|
|
|
def doit(self, samples, filename_prefix="latents/LatentSender", link_id=0, preview_method="Latent2RGB-SDXL", prompt=None, extra_pnginfo=None):
|
|
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
|
|
|
|
|
|
preview = LatentSender.prepare_preview(samples['samples'], preview_method)
|
|
|
|
|
|
file = f"{filename}_{counter:05}_.latent.png"
|
|
fullpath = os.path.join(full_output_folder, file)
|
|
|
|
output = {"latent_tensor": samples["samples"]}
|
|
|
|
tensor_bytes = safetensors.torch.save(output)
|
|
LatentSender.save_to_file(tensor_bytes, prompt, extra_pnginfo, preview, fullpath)
|
|
|
|
latent_path = {
|
|
'filename': file,
|
|
'subfolder': subfolder,
|
|
'type': self.type
|
|
}
|
|
|
|
PromptServer.instance.send_sync("latent-send", {"link_id": link_id, "images": [latent_path]})
|
|
|
|
return {'ui': {'images': [latent_path]}}
|
|
|
|
|
|
class ImpactWildcardProcessor:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
|
"populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
|
"mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
|
|
},
|
|
}
|
|
|
|
CATEGORY = "ImpactPack/Prompt"
|
|
|
|
RETURN_TYPES = ("STRING", )
|
|
FUNCTION = "doit"
|
|
|
|
@staticmethod
|
|
def process(**kwargs):
|
|
return impact.wildcards.process(**kwargs)
|
|
|
|
def doit(self, *args, **kwargs):
|
|
populated_text = ImpactWildcardProcessor.process(text=kwargs['populated_text'], seed=kwargs['seed'])
|
|
return (populated_text, )
|
|
|
|
|
|
class ImpactWildcardEncode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"model": ("MODEL",),
|
|
"clip": ("CLIP",),
|
|
"wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
|
"populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
|
"mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
|
|
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ),
|
|
"Select to add Wildcard": (["Select the Wildcard to add to the text"], ),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
}
|
|
|
|
CATEGORY = "ImpactPack/Prompt"
|
|
|
|
RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING")
|
|
RETURN_NAMES = ("model", "clip", "conditioning", "populated_text")
|
|
FUNCTION = "doit"
|
|
|
|
@staticmethod
|
|
def process_with_loras(**kwargs):
|
|
return impact.wildcards.process_with_loras(**kwargs)
|
|
|
|
@staticmethod
|
|
def get_wildcard_list():
|
|
return impact.wildcards.get_wildcard_list()
|
|
|
|
def doit(self, *args, **kwargs):
|
|
populated = kwargs['populated_text']
|
|
processed = []
|
|
model, clip, conditioning = impact.wildcards.process_with_loras(wildcard_opt=populated, model=kwargs['model'], clip=kwargs['clip'], seed=kwargs['seed'], processed=processed)
|
|
return model, clip, conditioning, processed[0]
|
|
|
|
|
|
class ImpactSchedulerAdapter:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"defaultInput": True, }),
|
|
"extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]'],),
|
|
}}
|
|
|
|
CATEGORY = "ImpactPack/Util"
|
|
|
|
RETURN_TYPES = (core.SCHEDULERS,)
|
|
RETURN_NAMES = ("scheduler",)
|
|
|
|
FUNCTION = "doit"
|
|
|
|
def doit(self, scheduler, extra_scheduler):
|
|
if extra_scheduler != 'None':
|
|
return (extra_scheduler,)
|
|
|
|
return (scheduler,)
|
|
|
|
|
