import sys import time import comfy import torch import folder_paths from comfy_extras.chainner_models import model_loading from server import PromptServer from nodes import MAX_RESOLUTION, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS from ..libs.utils import easySave, get_sd_version from ..libs.sampler import easySampler from .. import easyCache, sampler class hiresFix: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos", "bislerp"] crop_methods = ["disabled", "center"] @classmethod def INPUT_TYPES(s): return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"),), "rescale_after_model": ([False, True], {"default": True}), "rescale_method": (s.upscale_methods,), "rescale": (["by percentage", "to Width/Height", 'to longer side - maintain aspect'],), "percent": ("INT", {"default": 50, "min": 0, "max": 1000, "step": 1}), "width": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "longer_side": ("INT", {"default": 1024, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "crop": (s.crop_methods,), "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), "save_prefix": ("STRING", {"default": "ComfyUI"}), }, "optional": { "pipe": ("PIPE_LINE",), "image": ("IMAGE",), "vae": ("VAE",), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", }, } RETURN_TYPES = ("PIPE_LINE", "IMAGE", "LATENT", ) RETURN_NAMES = ('pipe', 'image', "latent", ) FUNCTION = "upscale" CATEGORY = "EasyUse/Fix" OUTPUT_NODE = True def vae_encode_crop_pixels(self, pixels): x = (pixels.shape[1] // 8) * 8 y = (pixels.shape[2] // 8) * 8 if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) // 2 y_offset = (pixels.shape[2] % 8) // 2 pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] return pixels def upscale(self, model_name, rescale_after_model, rescale_method, rescale, percent, width, height, longer_side, crop, image_output, link_id, save_prefix, pipe=None, image=None, vae=None, prompt=None, extra_pnginfo=None, my_unique_id=None): new_pipe = {} if pipe is not None: image = image if image is not None else pipe["images"] vae = vae if vae is not None else pipe.get("vae") elif image is None or vae is None: raise ValueError("pipe or image or vae missing.") # Load Model model_path = folder_paths.get_full_path("upscale_models", model_name) sd = comfy.utils.load_torch_file(model_path, safe_load=True) upscale_model = model_loading.load_state_dict(sd).eval() # Model upscale device = comfy.model_management.get_torch_device() upscale_model.to(device) in_img = image.movedim(-1, -3).to(device) tile = 128 + 64 overlap = 8 steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) pbar = comfy.utils.ProgressBar(steps) s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) upscale_model.cpu() s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0) # Post Model Rescale if rescale_after_model == True: samples = s.movedim(-1, 1) orig_height = samples.shape[2] orig_width = samples.shape[3] if rescale == "by percentage" and percent != 0: height = percent / 100 * orig_height width = percent / 100 * orig_width if (width > MAX_RESOLUTION): width = MAX_RESOLUTION if (height > MAX_RESOLUTION): height = MAX_RESOLUTION width = easySampler.enforce_mul_of_64(width) height = easySampler.enforce_mul_of_64(height) elif rescale == "to longer side - maintain aspect": longer_side = easySampler.enforce_mul_of_64(longer_side) if orig_width > orig_height: width, height = longer_side, easySampler.enforce_mul_of_64(longer_side * orig_height / orig_width) else: width, height = easySampler.enforce_mul_of_64(longer_side * orig_width / orig_height), longer_side s = comfy.utils.common_upscale(samples, width, height, rescale_method, crop) s = s.movedim(1, -1) # vae encode pixels = self.vae_encode_crop_pixels(s) t = vae.encode(pixels[:, :, :, :3]) if pipe is not None: new_pipe = { "model": pipe['model'], "positive": pipe['positive'], "negative": pipe['negative'], "vae": vae, "clip": pipe['clip'], "samples": {"samples": t}, "images": s, "seed": pipe['seed'], "loader_settings": { **pipe["loader_settings"], } } del pipe else: new_pipe = {} results = easySave(s, save_prefix, image_output, prompt, extra_pnginfo) if image_output in ("Sender", "Sender&Save"): PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) if image_output in ("Hide", "Hide&Save"): return (new_pipe, s, {"samples": t},) return {"ui": {"images": results}, "result": (new_pipe, s, {"samples": t},)} # 预细节修复 class preDetailerFix: @classmethod def INPUT_TYPES(s): return {"required": { "pipe": ("PIPE_LINE",), "guide_size": ("FLOAT", {"default": 256, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}), "max_size": ("FLOAT", {"default": 768, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS,), "scheduler": (comfy.samplers.KSampler.SCHEDULERS + ['align_your_steps'],), "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"}), "drop_size": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 1, "default": 10}), "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}), "cycle": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}), }, "optional": { "bbox_segm_pipe": ("PIPE_LINE",), "sam_pipe": ("PIPE_LINE",), "optional_image": ("IMAGE",), }, } RETURN_TYPES = ("PIPE_LINE",) RETURN_NAMES = ("pipe",) OUTPUT_IS_LIST = (False,) FUNCTION = "doit" CATEGORY = "EasyUse/Fix" def doit(self, pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, denoise, feather, noise_mask, force_inpaint, drop_size, wildcard, cycle, bbox_segm_pipe=None, sam_pipe=None, optional_image=None): model = pipe["model"] if "model" in pipe else None if model is None: raise Exception(f"[ERROR] pipe['model'] is missing") clip = pipe["clip"] if"clip" in pipe else None if clip is None: raise Exception(f"[ERROR] pipe['clip'] is missing") vae = pipe["vae"] if "vae" in pipe else None if vae is None: raise Exception(f"[ERROR] pipe['vae'] is missing") if optional_image is not None: images = optional_image else: images = pipe["images"] if "images" in pipe else None if images is None: raise Exception(f"[ERROR] pipe['image'] is missing") positive = pipe["positive"] if "positive" in pipe else None if positive is None: raise Exception(f"[ERROR] pipe['positive'] is missing") negative = pipe["negative"] if "negative" in pipe else None if negative is None: raise Exception(f"[ERROR] pipe['negative'] is missing") bbox_segm_pipe = bbox_segm_pipe or (pipe["bbox_segm_pipe"] if pipe and "bbox_segm_pipe" in pipe else None) if bbox_segm_pipe is None: raise Exception(f"[ERROR] bbox_segm_pipe or pipe['bbox_segm_pipe'] is missing") sam_pipe = sam_pipe or (pipe["sam_pipe"] if pipe and "sam_pipe" in pipe else None) if sam_pipe is None: raise Exception(f"[ERROR] sam_pipe or pipe['sam_pipe'] is missing") loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {} if(scheduler == 'align_your_steps'): model_version = get_sd_version(model) if model_version == 'sdxl': scheduler = 'AYS SDXL' elif model_version == 'svd': scheduler = 'AYS SVD' else: scheduler = 'AYS SD1' new_pipe = { "images": images, "model": model, "clip": clip, "vae": vae, "positive": positive, "negative": negative, "seed": seed, "bbox_segm_pipe": bbox_segm_pipe, "sam_pipe": sam_pipe, "loader_settings": loader_settings, "detail_fix_settings": { "guide_size": guide_size, "guide_size_for": guide_size_for, "max_size": max_size, "seed": seed, "steps": steps, "cfg": cfg, "sampler_name": sampler_name, "scheduler": scheduler, "denoise": denoise, "feather": feather, "noise_mask": noise_mask, "force_inpaint": force_inpaint, "drop_size": drop_size, "wildcard": wildcard, "cycle": cycle } } del bbox_segm_pipe del sam_pipe return (new_pipe,) # 预遮罩细节修复 class preMaskDetailerFix: @classmethod def INPUT_TYPES(s): return {"required": { "pipe": ("PIPE_LINE",), "mask": ("MASK",), "guide_size": ("FLOAT", {"default": 384, "min": 64, "max": 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": 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": (comfy.samplers.KSampler.SCHEDULERS,), "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}), "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}), "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": { # "patch": ("INPAINT_PATCH",), "optional_image": ("IMAGE",), "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}), "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}), }, } RETURN_TYPES = ("PIPE_LINE",) RETURN_NAMES = ("pipe",) OUTPUT_IS_LIST = (False,) FUNCTION = "doit" CATEGORY = "EasyUse/Fix" def doit(self, pipe, mask, 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, optional_image=None, inpaint_model=False, noise_mask_feather=20): model = pipe["model"] if "model" in pipe else None if model is None: raise Exception(f"[ERROR] pipe['model'] is missing") clip = pipe["clip"] if"clip" in pipe else None if clip is None: raise Exception(f"[ERROR] pipe['clip'] is missing") vae = pipe["vae"] if "vae" in pipe else None if vae is None: raise Exception(f"[ERROR] pipe['vae'] is missing") if optional_image is not None: images = optional_image else: images = pipe["images"] if "images" in pipe else None if images is None: raise Exception(f"[ERROR] pipe['image'] is missing") positive = pipe["positive"] if "positive" in pipe else None if positive is None: raise Exception(f"[ERROR] pipe['positive'] is missing") negative = pipe["negative"] if "negative" in pipe else None if negative is None: raise Exception(f"[ERROR] pipe['negative'] is missing") latent = pipe["samples"] if "samples" in pipe else None if latent is None: raise Exception(f"[ERROR] pipe['samples'] is missing") if 'noise_mask' not in latent: if images is None: raise Exception("No Images found") if vae is None: raise Exception("No VAE found") x = (images.shape[1] // 8) * 8 y = (images.shape[2] // 8) * 8 mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(images.shape[1], images.shape[2]), mode="bilinear") pixels = images.clone() if pixels.shape[1] != x or pixels.shape[2] != y: x_offset = (pixels.shape[1] % 8) // 2 y_offset = (pixels.shape[2] % 8) // 2 pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset] mask_erosion = mask m = (1.0 - mask.round()).squeeze(1) for i in range(3): pixels[:, :, :, i] -= 0.5 pixels[:, :, :, i] *= m pixels[:, :, :, i] += 0.5 t = vae.encode(pixels) latent = {"samples": t, "noise_mask": (mask_erosion[:, :, :x, :y].round())} # when patch was linked # if patch is not None: # worker = InpaintWorker(node_name="easy kSamplerInpainting") # model, = worker.patch(model, latent, patch) loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {} new_pipe = { "images": images, "model": model, "clip": clip, "vae": vae, "positive": positive, "negative": negative, "seed": seed, "mask": mask, "loader_settings": loader_settings, "detail_fix_settings": { "guide_size": guide_size, "guide_size_for": guide_size_for, "max_size": max_size, "seed": seed, "steps": steps, "cfg": cfg, "sampler_name": sampler_name, "scheduler": scheduler, "denoise": denoise, "feather": feather, "crop_factor": crop_factor, "drop_size": drop_size, "refiner_ratio": refiner_ratio, "batch_size": batch_size, "cycle": cycle }, "mask_settings": { "mask_mode": mask_mode, "inpaint_model": inpaint_model, "noise_mask_feather": noise_mask_feather } } del pipe return (new_pipe,) # 细节修复 class detailerFix: @classmethod def INPUT_TYPES(s): return {"required": { "pipe": ("PIPE_LINE",), "image_output": (["Hide", "Preview", "Save", "Hide&Save", "Sender", "Sender&Save"],{"default": "Preview"}), "link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}), "save_prefix": ("STRING", {"default": "ComfyUI"}), }, "optional": { "model": ("MODEL",), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID", } } RETURN_TYPES = ("PIPE_LINE", "IMAGE", "IMAGE", "IMAGE") RETURN_NAMES = ("pipe", "image", "cropped_refined", "cropped_enhanced_alpha") OUTPUT_NODE = True OUTPUT_IS_LIST = (False, False, True, True) FUNCTION = "doit" CATEGORY = "EasyUse/Fix" def doit(self, pipe, image_output, link_id, save_prefix, model=None, prompt=None, extra_pnginfo=None, my_unique_id=None): # Clean loaded_objects easyCache.update_loaded_objects(prompt) my_unique_id = int(my_unique_id) model = model or (pipe["model"] if "model" in pipe else None) if model is None: raise Exception(f"[ERROR] model or pipe['model'] is missing") detail_fix_settings = pipe["detail_fix_settings"] if "detail_fix_settings" in pipe else None if detail_fix_settings is None: raise Exception(f"[ERROR] detail_fix_settings or pipe['detail_fix_settings'] is missing") mask = pipe["mask"] if "mask" in pipe else None image = pipe["images"] clip = pipe["clip"] vae = pipe["vae"] seed = pipe["seed"] positive = pipe["positive"] negative = pipe["negative"] loader_settings = pipe["loader_settings"] if "loader_settings" in pipe else {} guide_size = pipe["detail_fix_settings"]["guide_size"] if "guide_size" in pipe["detail_fix_settings"] else 256 guide_size_for = pipe["detail_fix_settings"]["guide_size_for"] if "guide_size_for" in pipe[ "detail_fix_settings"] else True max_size = pipe["detail_fix_settings"]["max_size"] if "max_size" in pipe["detail_fix_settings"] else 768 steps = pipe["detail_fix_settings"]["steps"] if "steps" in pipe["detail_fix_settings"] else 20 cfg = pipe["detail_fix_settings"]["cfg"] if "cfg" in pipe["detail_fix_settings"] else 1.0 sampler_name = pipe["detail_fix_settings"]["sampler_name"] if "sampler_name" in pipe[ "detail_fix_settings"] else None scheduler = pipe["detail_fix_settings"]["scheduler"] if "scheduler" in pipe["detail_fix_settings"] else None denoise = pipe["detail_fix_settings"]["denoise"] if "denoise" in pipe["detail_fix_settings"] else 0.5 feather = pipe["detail_fix_settings"]["feather"] if "feather" in pipe["detail_fix_settings"] else 5 crop_factor = pipe["detail_fix_settings"]["crop_factor"] if "crop_factor" in pipe["detail_fix_settings"] else 3.0 drop_size = pipe["detail_fix_settings"]["drop_size"] if "drop_size" in pipe["detail_fix_settings"] else 10 refiner_ratio = pipe["detail_fix_settings"]["refiner_ratio"] if "refiner_ratio" in pipe else 0.2 batch_size = pipe["detail_fix_settings"]["batch_size"] if "batch_size" in pipe["detail_fix_settings"] else 1 noise_mask = pipe["detail_fix_settings"]["noise_mask"] if "noise_mask" in pipe["detail_fix_settings"] else None force_inpaint = pipe["detail_fix_settings"]["force_inpaint"] if "force_inpaint" in pipe["detail_fix_settings"] else False wildcard = pipe["detail_fix_settings"]["wildcard"] if "wildcard" in pipe["detail_fix_settings"] else "" cycle = pipe["detail_fix_settings"]["cycle"] if "cycle" in pipe["detail_fix_settings"] else 1 bbox_segm_pipe = pipe["bbox_segm_pipe"] if pipe and "bbox_segm_pipe" in pipe else None sam_pipe = pipe["sam_pipe"] if "sam_pipe" in pipe else None # 细节修复初始时间 start_time = int(time.time() * 1000) if "mask_settings" in pipe: mask_mode = pipe['mask_settings']["mask_mode"] if "inpaint_model" in pipe['mask_settings'] else True inpaint_model = pipe['mask_settings']["inpaint_model"] if "inpaint_model" in pipe['mask_settings'] else False noise_mask_feather = pipe['mask_settings']["noise_mask_feather"] if "noise_mask_feather" in pipe['mask_settings'] else 20 cls = ALL_NODE_CLASS_MAPPINGS["MaskDetailerPipe"] if "MaskDetailerPipe" not in ALL_NODE_CLASS_MAPPINGS: raise Exception(f"[ERROR] To use MaskDetailerPipe, you need to install 'Impact Pack'") basic_pipe = (model, clip, vae, positive, negative) result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, basic_pipe, refiner_basic_pipe_opt = cls().doit(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=inpaint_model, noise_mask_feather=noise_mask_feather) result_mask = mask result_cnet_images = () else: if bbox_segm_pipe is None: raise Exception(f"[ERROR] bbox_segm_pipe or pipe['bbox_segm_pipe'] is missing") if sam_pipe is None: raise Exception(f"[ERROR] sam_pipe or pipe['sam_pipe'] is missing") bbox_detector_opt, bbox_threshold, bbox_dilation, bbox_crop_factor, segm_detector_opt = bbox_segm_pipe sam_model_opt, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative = sam_pipe if "FaceDetailer" not in ALL_NODE_CLASS_MAPPINGS: raise Exception(f"[ERROR] To use FaceDetailer, you need to install 'Impact Pack'") cls = ALL_NODE_CLASS_MAPPINGS["FaceDetailer"] result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, pipe, result_cnet_images = cls().doit( 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_opt, wildcard, cycle, sam_model_opt, segm_detector_opt, detailer_hook=None) # 细节修复结束时间 end_time = int(time.time() * 1000) spent_time = 'Fix:' + str((end_time - start_time) / 1000) + '"' results = easySave(result_img, save_prefix, image_output, prompt, extra_pnginfo) sampler.update_value_by_id("results", my_unique_id, results) # Clean loaded_objects easyCache.update_loaded_objects(prompt) new_pipe = { "samples": None, "images": result_img, "model": model, "clip": clip, "vae": vae, "seed": seed, "positive": positive, "negative": negative, "wildcard": wildcard, "bbox_segm_pipe": bbox_segm_pipe, "sam_pipe": sam_pipe, "loader_settings": { **loader_settings, "spent_time": spent_time }, "detail_fix_settings": detail_fix_settings } if "mask_settings" in pipe: new_pipe["mask_settings"] = pipe["mask_settings"] sampler.update_value_by_id("pipe_line", my_unique_id, new_pipe) del bbox_segm_pipe del sam_pipe del pipe if image_output in ("Hide", "Hide&Save"): return (new_pipe, result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, result_cnet_images) if image_output in ("Sender", "Sender&Save"): PromptServer.instance.send_sync("img-send", {"link_id": link_id, "images": results}) return {"ui": {"images": results}, "result": (new_pipe, result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, result_cnet_images )} class ultralyticsDetectorForDetailerFix: @classmethod def INPUT_TYPES(s): bboxs = ["bbox/" + x for x in folder_paths.get_filename_list("ultralytics_bbox")] segms = ["segm/" + x for x in folder_paths.get_filename_list("ultralytics_segm")] return {"required": {"model_name": (bboxs + segms,), "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}), } } RETURN_TYPES = ("PIPE_LINE",) RETURN_NAMES = ("bbox_segm_pipe",) FUNCTION = "doit" CATEGORY = "EasyUse/Fix" def doit(self, model_name, bbox_threshold, bbox_dilation, bbox_crop_factor): if 'UltralyticsDetectorProvider' not in ALL_NODE_CLASS_MAPPINGS: raise Exception(f"[ERROR] To use UltralyticsDetectorProvider, you need to install 'Impact Pack'") cls = ALL_NODE_CLASS_MAPPINGS['UltralyticsDetectorProvider'] bbox_detector, segm_detector = cls().doit(model_name) pipe = (bbox_detector, bbox_threshold, bbox_dilation, bbox_crop_factor, segm_detector) return (pipe,) class samLoaderForDetailerFix: @classmethod def INPUT_TYPES(cls): return { "required": { "model_name": (folder_paths.get_filename_list("sams"),), "device_mode": (["AUTO", "Prefer GPU", "CPU"],{"default": "AUTO"}), "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"],), } } RETURN_TYPES = ("PIPE_LINE",) RETURN_NAMES = ("sam_pipe",) FUNCTION = "doit" CATEGORY = "EasyUse/Fix" def doit(self, model_name, device_mode, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative): if 'SAMLoader' not in ALL_NODE_CLASS_MAPPINGS: raise Exception(f"[ERROR] To use SAMLoader, you need to install 'Impact Pack'") cls = ALL_NODE_CLASS_MAPPINGS['SAMLoader'] (sam_model,) = cls().load_model(model_name, device_mode) pipe = (sam_model, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative) return (pipe,) NODE_CLASS_MAPPINGS = { "easy hiresFix": hiresFix, "easy preDetailerFix": preDetailerFix, "easy preMaskDetailerFix": preMaskDetailerFix, "easy ultralyticsDetectorPipe": ultralyticsDetectorForDetailerFix, "easy samLoaderPipe": samLoaderForDetailerFix, "easy detailerFix": detailerFix } NODE_DISPLAY_NAME_MAPPINGS = { "easy hiresFix": "HiresFix", "easy preDetailerFix": "PreDetailerFix", "easy preMaskDetailerFix": "preMaskDetailerFix", "easy ultralyticsDetectorPipe": "UltralyticsDetector (Pipe)", "easy samLoaderPipe": "SAMLoader (Pipe)", "easy detailerFix": "DetailerFix", }