Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-09 00:55:26 +00:00
parent 2b70ab9ad0
commit f09734b0ee
2274 changed files with 748556 additions and 3 deletions
--- a/custom_nodes/comfyui-inspire-pack/inspire/regional_nodes.py
+++ b/custom_nodes/comfyui-inspire-pack/inspire/regional_nodes.py
@@ -0,0 +1,699 @@
+import traceback
+
+import comfy
+import nodes
+import torch
+import re
+import webcolors
+
+from . import prompt_support
+from .libs import utils, common
+
+import logging
+
+class RegionalPromptSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "basic_pipe": ("BASIC_PIPE",),
+                "mask": ("MASK",),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                "scheduler": (common.get_schedulers(),),
+                "wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}),
+                "controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}),
+                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+            },
+            "optional": {
+                "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "variation_method": (["linear", "slerp"],),
+                "scheduler_func_opt": ("SCHEDULER_FUNC",),
+            }
+        }
+
+    RETURN_TYPES = ("REGIONAL_PROMPTS", )
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt,
+             controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method='linear', scheduler_func_opt=None):
+        if 'RegionalPrompt' not in nodes.NODE_CLASS_MAPPINGS:
+            utils.try_install_custom_node('https://github.com/ltdrdata/ComfyUI-Impact-Pack',
+                                          "To use 'RegionalPromptSimple' node, 'Impact Pack' extension is required.")
+            raise Exception("[ERROR] To use RegionalPromptSimple, you need to install 'ComfyUI-Impact-Pack'")
+
+        model, clip, vae, positive, negative = basic_pipe
+
+        iwe = nodes.NODE_CLASS_MAPPINGS['ImpactWildcardEncode']()
+        kap = nodes.NODE_CLASS_MAPPINGS['KSamplerAdvancedProvider']()
+        rp = nodes.NODE_CLASS_MAPPINGS['RegionalPrompt']()
+
+        if wildcard_prompt != "":
+            model, clip, new_positive, _ = iwe.doit(model=model, clip=clip, populated_text=wildcard_prompt, seed=None)
+
+            if controlnet_in_pipe:
+                prev_cnet = None
+                for t in positive:
+                    if 'control' in t[1] and 'control_apply_to_uncond' in t[1]:
+                        prev_cnet = t[1]['control'], t[1]['control_apply_to_uncond']
+                        break
+
+                if prev_cnet is not None:
+                    for t in new_positive:
+                        t[1]['control'] = prev_cnet[0]
+                        t[1]['control_apply_to_uncond'] = prev_cnet[1]
+
+        else:
+            new_positive = positive
+
+        basic_pipe = model, clip, vae, new_positive, negative
+
+        sampler = kap.doit(cfg, sampler_name, scheduler, basic_pipe, sigma_factor=sigma_factor, scheduler_func_opt=scheduler_func_opt)[0]
+        try:
+            regional_prompts = rp.doit(mask, sampler, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method)[0]
+        except:
+            raise Exception("[Inspire-Pack] ERROR: Impact Pack is outdated. Update Impact Pack to latest version to use this.")
+
+        return (regional_prompts, )
+
+
+def color_to_mask(color_mask, mask_color):
+    try:
+        if mask_color.startswith("#") or mask_color.isalpha():
+            hex = mask_color[1:] if mask_color.startswith("#") else webcolors.name_to_hex(mask_color)[1:]
+            selected = int(hex, 16)
+        else:
+            selected = int(mask_color, 10)
+    except Exception:
+        raise Exception("[ERROR] Invalid mask_color value. mask_color should be a color value for RGB")
+
+    temp = (torch.clamp(color_mask, 0, 1.0) * 255.0).round().to(torch.int)
+    temp = torch.bitwise_left_shift(temp[:, :, :, 0], 16) + torch.bitwise_left_shift(temp[:, :, :, 1], 8) + temp[:, :, :, 2]
+    mask = torch.where(temp == selected, 1.0, 0.0)
+    return mask
+
+
+class RegionalPromptColorMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "basic_pipe": ("BASIC_PIPE",),
+                "color_mask": ("IMAGE",),
+                "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                "scheduler": (common.get_schedulers(),),
+                "wildcard_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "wildcard prompt"}),
+                "controlnet_in_pipe": ("BOOLEAN", {"default": False, "label_on": "Keep", "label_off": "Override"}),
+                "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+            },
+            "optional": {
+                "variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "variation_method": (["linear", "slerp"],),
+                "scheduler_func_opt": ("SCHEDULER_FUNC",),
+            }
+        }
+
+    RETURN_TYPES = ("REGIONAL_PROMPTS", "MASK")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(basic_pipe, color_mask, mask_color, cfg, sampler_name, scheduler, wildcard_prompt,
+             controlnet_in_pipe=False, sigma_factor=1.0, variation_seed=0, variation_strength=0.0, variation_method="linear", scheduler_func_opt=None):
+        mask = color_to_mask(color_mask, mask_color)
+        rp = RegionalPromptSimple().doit(basic_pipe, mask, cfg, sampler_name, scheduler, wildcard_prompt, controlnet_in_pipe,
+                                         sigma_factor=sigma_factor, variation_seed=variation_seed, variation_strength=variation_strength, variation_method=variation_method, scheduler_func_opt=scheduler_func_opt)[0]
+        return rp, mask
+
+
+class RegionalConditioningSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP", ),
+                "mask": ("MASK",),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+                "prompt": ("STRING", {"multiline": True, "placeholder": "prompt"}),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING", )
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(clip, mask, strength, set_cond_area, prompt):
+        conditioning = nodes.CLIPTextEncode().encode(clip, prompt)[0]
+        conditioning = nodes.ConditioningSetMask().append(conditioning, mask, set_cond_area, strength)[0]
+        return (conditioning, )
+
+
+class RegionalConditioningColorMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP", ),
+                "color_mask": ("IMAGE",),
+                "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "set_cond_area": (["default", "mask bounds"],),
+                "prompt": ("STRING", {"multiline": True, "placeholder": "prompt"}),
+            },
+            "optional": {
+                "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING", "MASK")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(clip, color_mask, mask_color, strength, set_cond_area, prompt, dilation=0):
+        mask = color_to_mask(color_mask, mask_color)
+
+        if dilation != 0:
+            mask = utils.dilate_mask(mask, dilation)
+
+        conditioning = nodes.CLIPTextEncode().encode(clip, prompt)[0]
+        conditioning = nodes.ConditioningSetMask().append(conditioning, mask, set_cond_area, strength)[0]
+        return conditioning, mask
+
+
+class ToIPAdapterPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ipadapter": ("IPADAPTER", ),
+                "model": ("MODEL",),
+            },
+            "optional": {
+                "clip_vision": ("CLIP_VISION",),
+                "insightface": ("INSIGHTFACE",),
+            }
+        }
+
+    RETURN_TYPES = ("IPADAPTER_PIPE",)
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Util"
+
+    @staticmethod
+    def doit(ipadapter, model, clip_vision, insightface=None):
+        pipe = ipadapter, model, clip_vision, insightface, lambda x: x
+
+        return (pipe,)
+
+
+class FromIPAdapterPipe:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ipadapter_pipe": ("IPADAPTER_PIPE", ),
+            }
+        }
+
+    RETURN_TYPES = ("IPADAPTER", "MODEL", "CLIP_VISION", "INSIGHTFACE")
+    RETURN_NAMES = ("ipadapter", "model", "clip_vision", "insight_face")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Util"
+
+    def doit(self, ipadapter_pipe):
+        ipadapter, model, clip_vision, insightface, _ = ipadapter_pipe
+        return ipadapter, model, clip_vision, insightface
+
+
+class IPAdapterConditioning:
+    def __init__(self, mask, weight, weight_type, noise=None, image=None, neg_image=None, embeds=None, start_at=0.0, end_at=1.0, combine_embeds='concat', unfold_batch=False, weight_v2=False, neg_embeds=None):
+        self.mask = mask
+        self.image = image
+        self.neg_image = neg_image
+        self.embeds = embeds
+        self.neg_embeds = neg_embeds
+        self.weight = weight
+        self.noise = noise
+        self.weight_type = weight_type
+        self.start_at = start_at
+        self.end_at = end_at
+        self.unfold_batch = unfold_batch
+        self.weight_v2 = weight_v2
+        self.combine_embeds = combine_embeds
+
+    def doit(self, ipadapter_pipe):
+        ipadapter, model, clip_vision, insightface, _ = ipadapter_pipe
+
+        if 'IPAdapterAdvanced' not in nodes.NODE_CLASS_MAPPINGS:
+            utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
+                                          "To use 'Regional IPAdapter' node, 'ComfyUI IPAdapter Plus' extension is required.")
+            raise Exception("[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
+
+        if self.embeds is None:
+            obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']
+            model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, weight=self.weight, weight_type=self.weight_type,
+                                          start_at=self.start_at, end_at=self.end_at, combine_embeds=self.combine_embeds,
+                                          clip_vision=clip_vision, image=self.image, image_negative=self.neg_image, attn_mask=self.mask,
+                                          insightface=insightface, weight_faceidv2=self.weight_v2)[0]
+        else:
+            obj = nodes.NODE_CLASS_MAPPINGS['IPAdapterEmbeds']
+            model = obj().apply_ipadapter(model=model, ipadapter=ipadapter, pos_embed=self.embeds, weight=self.weight, weight_type=self.weight_type,
+                                          start_at=self.start_at, end_at=self.end_at, neg_embed=self.neg_embeds,
+                                          attn_mask=self.mask, clip_vision=clip_vision)[0]
+
+        return model
+
+
+IPADAPTER_WEIGHT_TYPES_CACHE = None
+
+
+def IPADAPTER_WEIGHT_TYPES():
+    global IPADAPTER_WEIGHT_TYPES_CACHE
+
+    if IPADAPTER_WEIGHT_TYPES_CACHE is None:
+        try:
+            IPADAPTER_WEIGHT_TYPES_CACHE = nodes.NODE_CLASS_MAPPINGS['IPAdapterAdvanced']().INPUT_TYPES()['required']['weight_type'][0]
+        except Exception:
+            logging.error("[Inspire Pack] IPAdapterPlus is not installed.")
+            IPADAPTER_WEIGHT_TYPES_CACHE = ["IPAdapterPlus is not installed"]
+
+    return IPADAPTER_WEIGHT_TYPES_CACHE
+
+
+class RegionalIPAdapterMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
+                "noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "unfold_batch": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "faceid_v2": ("BOOLEAN", {"default": False}),
+                "weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "neg_image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("REGIONAL_IPADAPTER", )
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(mask, image, weight, noise, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, faceid_v2=False, weight_v2=False, combine_embeds="concat", neg_image=None):
+        cond = IPAdapterConditioning(mask, weight, weight_type, noise=noise, image=image, neg_image=neg_image, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, weight_v2=weight_v2, combine_embeds=combine_embeds)
+        return (cond, )
+
+
+class RegionalIPAdapterColorMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "color_mask": ("IMAGE",),
+                "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
+                "image": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
+                "noise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "unfold_batch": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "faceid_v2": ("BOOLEAN", {"default": False }),
+                "weight_v2": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
+                "combine_embeds": (["concat", "add", "subtract", "average", "norm average"],),
+                "neg_image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("REGIONAL_IPADAPTER", "MASK")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(color_mask, mask_color, image, weight, noise, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, faceid_v2=False, weight_v2=False, combine_embeds="concat", neg_image=None):
+        mask = color_to_mask(color_mask, mask_color)
+        cond = IPAdapterConditioning(mask, weight, weight_type, noise=noise, image=image, neg_image=neg_image, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, weight_v2=weight_v2, combine_embeds=combine_embeds)
+        return (cond, mask)
+
+
+class RegionalIPAdapterEncodedMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+
+                "embeds": ("EMBEDS",),
+                "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
+                "weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "unfold_batch": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "neg_embeds": ("EMBEDS",),
+            }
+        }
+
+    RETURN_TYPES = ("REGIONAL_IPADAPTER", )
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(mask, embeds, weight, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, neg_embeds=None):
+        cond = IPAdapterConditioning(mask, weight, weight_type, embeds=embeds, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, neg_embeds=neg_embeds)
+        return (cond, )
+
+
+class RegionalIPAdapterEncodedColorMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "color_mask": ("IMAGE",),
+                "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
+
+                "embeds": ("EMBEDS",),
+                "weight": ("FLOAT", {"default": 0.7, "min": -1, "max": 3, "step": 0.05}),
+                "weight_type": (IPADAPTER_WEIGHT_TYPES(), ),
+                "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "unfold_batch": ("BOOLEAN", {"default": False}),
+            },
+            "optional": {
+                "neg_embeds": ("EMBEDS",),
+            }
+        }
+
+    RETURN_TYPES = ("REGIONAL_IPADAPTER", "MASK")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(color_mask, mask_color, embeds, weight, weight_type, start_at=0.0, end_at=1.0, unfold_batch=False, neg_embeds=None):
+        mask = color_to_mask(color_mask, mask_color)
+        cond = IPAdapterConditioning(mask, weight, weight_type, embeds=embeds, start_at=start_at, end_at=end_at, unfold_batch=unfold_batch, neg_embeds=neg_embeds)
+        return (cond, mask)
+
+
+class ApplyRegionalIPAdapters:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "ipadapter_pipe": ("IPADAPTER_PIPE",),
+                    "regional_ipadapter1": ("REGIONAL_IPADAPTER", ),
+                    },
+                }
+
+    RETURN_TYPES = ("MODEL", )
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(**kwargs):
+        ipadapter_pipe = kwargs['ipadapter_pipe']
+        ipadapter, model, clip_vision, insightface, lora_loader = ipadapter_pipe
+
+        del kwargs['ipadapter_pipe']
+
+        for k, v in kwargs.items():
+            ipadapter_pipe = ipadapter, model, clip_vision, insightface, lora_loader
+            model = v.doit(ipadapter_pipe)
+
+        return (model, )
+
+
+class RegionalSeedExplorerMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK",),
+
+                "noise": ("NOISE_IMAGE",),
+                "seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}),
+                "enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}),
+                "additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "noise_mode": (["GPU(=A1111)", "CPU"],),
+            },
+            "optional":
+                {"variation_method": (["linear", "slerp"],), }
+        }
+
+    RETURN_TYPES = ("NOISE_IMAGE",)
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(mask, noise, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, variation_method='linear'):
+        device = comfy.model_management.get_torch_device()
+        noise_device = "cpu" if noise_mode == "CPU" else device
+
+        noise = noise.to(device)
+        mask = mask.to(device)
+
+        if len(mask.shape) == 2:
+            mask = mask.unsqueeze(0)
+
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noise.shape[2], noise.shape[3]), mode="bilinear").squeeze(0)
+
+        try:
+            seed_prompt = seed_prompt.replace("\n", "")
+            items = seed_prompt.strip().split(",")
+
+            if items == ['']:
+                items = []
+
+            if enable_additional:
+                items.append((additional_seed, additional_strength))
+
+            noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method)
+        except Exception:
+            logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.")
+            traceback.print_exc()
+
+        noise = noise.cpu()
+        mask.cpu()
+        return (noise,)
+
+
+class RegionalSeedExplorerColorMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "color_mask": ("IMAGE",),
+                "mask_color": ("STRING", {"multiline": False, "default": "#FFFFFF"}),
+
+                "noise": ("NOISE_IMAGE",),
+                "seed_prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": False}),
+                "enable_additional": ("BOOLEAN", {"default": True, "label_on": "true", "label_off": "false"}),
+                "additional_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "additional_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "noise_mode": (["GPU(=A1111)", "CPU"],),
+            },
+            "optional":
+                {"variation_method": (["linear", "slerp"],), }
+        }
+
+    RETURN_TYPES = ("NOISE_IMAGE", "MASK")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(color_mask, mask_color, noise, seed_prompt, enable_additional, additional_seed, additional_strength, noise_mode, variation_method='linear'):
+        device = comfy.model_management.get_torch_device()
+        noise_device = "cpu" if noise_mode == "CPU" else device
+
+        color_mask = color_mask.to(device)
+        noise = noise.to(device)
+
+        mask = color_to_mask(color_mask, mask_color)
+        original_mask = mask
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noise.shape[2], noise.shape[3]), mode="bilinear").squeeze(0)
+
+        mask = mask.to(device)
+
+        try:
+            seed_prompt = seed_prompt.replace("\n", "")
+            items = seed_prompt.strip().split(",")
+
+            if items == ['']:
+                items = []
+
+            if enable_additional:
+                items.append((additional_seed, additional_strength))
+
+            noise = prompt_support.SeedExplorer.apply_variation(noise, items, noise_device, mask, variation_method=variation_method)
+        except Exception:
+            logging.error("[Inspire Pack] IGNORED: RegionalSeedExplorerColorMask is failed.")
+            traceback.print_exc()
+
+        color_mask.cpu()
+        noise = noise.cpu()
+        original_mask = original_mask.cpu()
+        return (noise, original_mask)
+
+
+class ColorMaskToDepthMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "color_mask": ("IMAGE",),
+                "spec": ("STRING", {"multiline": True, "default": "#FF0000:1.0\n#000000:1.0"}),
+                "base_value": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}),
+                "dilation": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
+                "flatten_method": (["override", "sum", "max"],),
+            },
+        }
+
+    RETURN_TYPES = ("MASK", )
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    def doit(self, color_mask, spec, base_value, dilation, flatten_method):
+        specs = spec.split('\n')
+        pat = re.compile("(?P<color_code>#[A-F0-9]+):(?P<cfg>[0-9]+(.[0-9]*)?)")
+
+        masks = [torch.ones((1, color_mask.shape[1], color_mask.shape[2])) * base_value]
+        for x in specs:
+            match = pat.match(x)
+            if match:
+                mask = color_to_mask(color_mask=color_mask, mask_color=match['color_code']) * float(match['cfg'])
+                mask = utils.dilate_mask(mask, dilation)
+                masks.append(mask)
+
+        if masks:
+            masks = torch.cat(masks, dim=0)
+            if flatten_method == 'override':
+                masks = utils.flatten_non_zero_override(masks)
+            elif flatten_method == 'max':
+                masks = torch.max(masks, dim=0)[0]
+            else:  # flatten_method == 'sum':
+                masks = torch.sum(masks, dim=0)
+
+            masks = torch.clamp(masks, min=0.0, max=1.0)
+            masks = masks.unsqueeze(0)
+        else:
+            masks = torch.tensor([])
+
+        return (masks, )
+
+
+class RegionalCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "mask": ("MASK",),
+                             }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Regional"
+
+    @staticmethod
+    def doit(model, mask):
+        if len(mask.shape) == 2:
+            mask = mask.unsqueeze(0).unsqueeze(0)
+        elif len(mask.shape) == 3:
+            mask = mask.unsqueeze(0)
+
+        size = None
+
+        def regional_cfg(args):
+            nonlocal mask
+            nonlocal size
+
+            x = args['input']
+
+            if mask.device != x.device:
+                mask = mask.to(x.device)
+
+            if size != (x.shape[2], x.shape[3]):
+                size = (x.shape[2], x.shape[3])
+                mask = torch.nn.functional.interpolate(mask, size=size, mode='bilinear', align_corners=False)
+
+            cond_pred = args["cond_denoised"]
+            uncond_pred = args["uncond_denoised"]
+            cond_scale = args["cond_scale"]
+
+            cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale * mask
+
+            return x - cfg_result
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(regional_cfg)
+        return (m,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "RegionalPromptSimple //Inspire": RegionalPromptSimple,
+    "RegionalPromptColorMask //Inspire": RegionalPromptColorMask,
+    "RegionalConditioningSimple //Inspire": RegionalConditioningSimple,
+    "RegionalConditioningColorMask //Inspire": RegionalConditioningColorMask,
+    "RegionalIPAdapterMask //Inspire": RegionalIPAdapterMask,
+    "RegionalIPAdapterColorMask //Inspire": RegionalIPAdapterColorMask,
+    "RegionalIPAdapterEncodedMask //Inspire": RegionalIPAdapterEncodedMask,
+    "RegionalIPAdapterEncodedColorMask //Inspire": RegionalIPAdapterEncodedColorMask,
+    "RegionalSeedExplorerMask //Inspire": RegionalSeedExplorerMask,
+    "RegionalSeedExplorerColorMask //Inspire": RegionalSeedExplorerColorMask,
+    "ToIPAdapterPipe //Inspire": ToIPAdapterPipe,
+    "FromIPAdapterPipe //Inspire": FromIPAdapterPipe,
+    "ApplyRegionalIPAdapters //Inspire": ApplyRegionalIPAdapters,
+    "RegionalCFG //Inspire": RegionalCFG,
+    "ColorMaskToDepthMask //Inspire": ColorMaskToDepthMask,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RegionalPromptSimple //Inspire": "Regional Prompt Simple (Inspire)",
+    "RegionalPromptColorMask //Inspire": "Regional Prompt By Color Mask (Inspire)",
+    "RegionalConditioningSimple //Inspire": "Regional Conditioning Simple (Inspire)",
+    "RegionalConditioningColorMask //Inspire": "Regional Conditioning By Color Mask (Inspire)",
+    "RegionalIPAdapterMask //Inspire": "Regional IPAdapter Mask (Inspire)",
+    "RegionalIPAdapterColorMask //Inspire": "Regional IPAdapter By Color Mask (Inspire)",
+    "RegionalIPAdapterEncodedMask //Inspire": "Regional IPAdapter Encoded Mask (Inspire)",
+    "RegionalIPAdapterEncodedColorMask //Inspire": "Regional IPAdapter Encoded By Color Mask (Inspire)",
+    "RegionalSeedExplorerMask //Inspire": "Regional Seed Explorer By Mask (Inspire)",
+    "RegionalSeedExplorerColorMask //Inspire": "Regional Seed Explorer By Color Mask (Inspire)",
+    "ToIPAdapterPipe //Inspire": "ToIPAdapterPipe (Inspire)",
+    "FromIPAdapterPipe //Inspire": "FromIPAdapterPipe (Inspire)",
+    "ApplyRegionalIPAdapters //Inspire": "Apply Regional IPAdapters (Inspire)",
+    "RegionalCFG //Inspire": "Regional CFG (Inspire)",
+    "ColorMaskToDepthMask //Inspire": "Color Mask To Depth Mask (Inspire)",
+}