FIXED: ConcatConditioningsWithMultiplier - version compatibility patch

https://github.com/ltdrdata/ComfyUI-Inspire-Pack/issues/160#issuecomment-2416008408

README.md

@@ -13,12 +13,16 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
 ## Nodes
 ### Lora Block Weight - This is a node that provides functionality related to Lora block weight.
   * This provides similar functionality to [sd-webui-lora-block-weight](https://github.com/hako-mikan/sd-webui-lora-block-weight)
-  * `Lora Loader (Block Weight)`: When loading Lora, the block weight vector is applied.
+  * `LoRA Loader (Block Weight)`: When loading Lora, the block weight vector is applied.
       * In the block vector, you can use numbers, R, A, a, B, and b.
       * R is determined sequentially based on a random seed, while A and B represent the values of the A and B parameters, respectively. a and b are half of the values of A and B, respectively.
-  * `XY Input: Lora Block Weight`: This is a node in the [Efficiency Nodes](https://github.com/LucianoCirino/efficiency-nodes-comfyui)' XY Plot that allows you to use Lora block weight.
+  * `XY Input: LoRA Block Weight`: This is a node in the [Efficiency Nodes](https://github.com/LucianoCirino/efficiency-nodes-comfyui)' XY Plot that allows you to use Lora block weight.
       * You must ensure that X and Y connections are made, and dependencies should be connected to the XY Plot.
       * Note: To use this feature, update `Efficient Nodes` to a version released after September 3rd.
+  * Make LoRA Block Weight: Instead of directly applying the LoRA Block Weight to the MODEL, it is generated in a separate LBW_MODEL form
+  * Apply LoRA Block Weight: Apply LBW_MODEL to MODEL and CLIP
+  * Save LoRA Block Weight: Save LBW_MODEL as a .lbw.safetensors file
+  * Load LoRA Block Weight: Load LBW_MODEL from .lbw.safetensors file
 
 ### SEGS Supports nodes - This is a node that supports ApplyControlNet (SEGS) from the Impact Pack.
   * `OpenPose Preprocessor Provider (SEGS)`: OpenPose preprocessor is applied for the purpose of using OpenPose ControlNet in SEGS.

__init__.py

@@ -7,7 +7,7 @@
 
 import importlib
 
-version_code = [0, 82, 6]
+version_code = [1, 5, 1]
 version_str = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 print(f"### Loading: ComfyUI-Inspire-Pack ({version_str})")
 

inspire/conditioning_nodes.py

@@ -8,19 +8,26 @@ from nodes import MAX_RESOLUTION
 class ConcatConditioningsWithMultiplier:
     @classmethod
     def INPUT_TYPES(s):
-        flex_inputs = {}
-
         stack = inspect.stack()
-        if stack[1].function == 'get_input_data':
+        if stack[1].function == 'get_input_info':
             # bypass validation
-            for x in range(0, 100):
-                flex_inputs[f"multiplier{x}"] = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
-        else:
-            flex_inputs["multiplier1"] = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
+            class AllContainer:
+                def __contains__(self, item):
+                    return True
+
+                def __getitem__(self, key):
+                    # Return a default value appropriate for your use case
+                    # Adjust the return value as needed
+                    return "FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}
+
+            return {
+                "required": {"conditioning1": ("CONDITIONING",), },
+                "optional": AllContainer()
+            }
 
         return {
             "required": {"conditioning1": ("CONDITIONING",), },
-            "optional": flex_inputs
+            "optional": {"multiplier1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), },
         }
 
     RETURN_TYPES = ("CONDITIONING",)

inspire/image_util.py

@@ -18,7 +18,7 @@ class LoadImagesFromDirBatch:
             },
             "optional": {
                 "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
-                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "start_index": ("INT", {"default": 0, "min": -1, "step": 1}),
                 "load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
             }
         }
@@ -94,10 +94,10 @@ class LoadImagesFromDirBatch:
                     image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
                 image1 = torch.cat((image1, image2), dim=0)
 
-            for mask2 in masks[1:]:
+            for mask2 in masks:
                 if has_non_empty_mask:
                     if image1.shape[1:3] != mask2.shape:
-                        mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[2], image1.shape[1]), mode='bilinear', align_corners=False)
+                        mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[1], image1.shape[2]), mode='bilinear', align_corners=False)
                         mask2 = mask2.squeeze(0)
                     else:
                         mask2 = mask2.unsqueeze(0)
@@ -126,8 +126,9 @@ class LoadImagesFromDirList:
             }
         }
 
-    RETURN_TYPES = ("IMAGE", "MASK")
-    OUTPUT_IS_LIST = (True, True)
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING")
+    RETURN_NAMES = ("IMAGE", "MASK", "FILE PATH")
+    OUTPUT_IS_LIST = (True, True, True)
 
     FUNCTION = "load_images"
 
@@ -159,6 +160,7 @@ class LoadImagesFromDirList:
 
         images = []
         masks = []
+        file_paths = []
 
         limit_images = False
         if image_load_cap > 0:
@@ -184,9 +186,10 @@ class LoadImagesFromDirList:
 
             images.append(image)
             masks.append(mask)
+            file_paths.append(str(image_path))
             image_count += 1
 
-        return images, masks
+        return (images, masks, file_paths)
 
 
 class LoadImageInspire:

inspire/libs/utils.py

@@ -5,6 +5,7 @@ import torch
 from PIL import Image, ImageDraw
 import math
 import cv2
+import folder_paths
 
 
 def apply_variation_noise(latent_image, noise_device, variation_seed, variation_strength, mask=None, variation_method='linear'):
@@ -56,6 +57,8 @@ def slerp(val, low, high):
 
 
 def mix_noise(from_noise, to_noise, strength, variation_method):
+    to_noise = to_noise.to(from_noise.device)
+
     if variation_method == 'slerp':
         mixed_noise = slerp(strength, from_noise, to_noise)
     else:
@@ -334,3 +337,14 @@ def flatten_non_zero_override(masks: torch.Tensor):
         final_mask[non_zero_mask] = masks[i][non_zero_mask]
 
     return final_mask
+
+
+def add_folder_path_and_extensions(folder_name, full_folder_paths, extensions):
+    for full_folder_path in full_folder_paths:
+        folder_paths.add_model_folder_path(folder_name, full_folder_path)
+    if folder_name in folder_paths.folder_names_and_paths:
+        current_paths, current_extensions = folder_paths.folder_names_and_paths[folder_name]
+        updated_extensions = current_extensions | extensions
+        folder_paths.folder_names_and_paths[folder_name] = (current_paths, updated_extensions)
+    else:
+        folder_paths.folder_names_and_paths[folder_name] = (full_folder_paths, extensions)

inspire/list_nodes.py

@@ -18,9 +18,14 @@ class FloatRange:
     CATEGORY = "InspirePack/Util"
 
     def doit(self, start, stop, step, limit, ensure_end):
-        if start >= stop or step == 0:
+        if start == stop or step == 0:
             return ([start], )
 
+        reverse = False
+        if start > stop:
+            reverse = True
+            start, stop = stop, start
+
         res = []
         x = start
         last = x
@@ -36,6 +41,9 @@ class FloatRange:
 
             res.append(stop)
 
+        if reverse:
+            res.reverse()
+
         return (res, )
 
 

inspire/lora_block_weight.py

@@ -6,11 +6,20 @@ import torch
 import numpy as np
 import nodes
 import re
+import json
+from comfy.cli_args import args
+from safetensors.torch import safe_open
+import ast
+
 
 from server import PromptServer
 from .libs import utils
 
 
+model_path = folder_paths.models_dir
+utils.add_folder_path_and_extensions("lbw_models", [os.path.join(model_path, "lbw_models")], {'.safetensors'})
+
+
 def is_numeric_string(input_str):
     return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None
 
@@ -34,6 +43,76 @@ def load_lbw_preset(filename):
         return []
 
 
+def parse_unet_num(s):
+    if s[1] == '.':
+        return int(s[0])
+    else:
+        return int(s)
+
+
+class MakeLBW:
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        preset = ["Preset"]  # 20
+        preset += load_lbw_preset("lbw-preset.txt")
+        preset += load_lbw_preset("lbw-preset.custom.txt")
+        preset = [name for name in preset if not name.startswith('@')]
+
+        lora_names = folder_paths.get_filename_list("loras")
+        lora_dirs = [os.path.dirname(name) for name in lora_names]
+        lora_dirs = ["All"] + list(set(lora_dirs))
+
+        return {"required": {"model": ("MODEL",),
+                             "clip": ("CLIP", ),
+                             "category_filter": (lora_dirs,),
+                             "lora_name": (lora_names, ),
+                             "inverse": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False", "tooltip": "Apply the following weights for each block:\nTrue: 1 - weight\nFalse: weight"}),
+                             "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": ""}),
+                             "A": ("FLOAT", {"default": 4.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                             "B": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                             "preset": (preset,),
+                             "block_vector": ("STRING", {"multiline": True, "placeholder": "block weight vectors", "default": "1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1", "pysssss.autocomplete": False}),
+                             "bypass": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
+                             }
+                }
+
+    RETURN_TYPES = ("LBW_MODEL", "STRING")
+    RETURN_NAMES = ("lbw_model", "populated_vector")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/LoraBlockWeight"
+
+    DESCRIPTION = "Instead of directly applying the LoRA Block Weight to the MODEL, it is generated in a separate LBW_MODEL form."
+
+    def __init__(self):
+        self.loaded_lora = None
+
+    def doit(self, model, clip, lora_name, inverse, seed, A, B, preset, block_vector, bypass=False, category_filter=None):
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                temp = self.loaded_lora
+                self.loaded_lora = None
+                del temp
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        block_weights, muted_weights, populated_vector = LoraLoaderBlockWeight.load_lbw(model, clip, lora, inverse, seed, A, B, block_vector)
+        lbw_model = {
+                        'blocks': block_weights,
+                        'muted': muted_weights
+                    }
+        return lbw_model, populated_vector
+
+
 class LoraLoaderBlockWeight:
     def __init__(self):
         self.loaded_lora = None
@@ -55,8 +134,8 @@ class LoraLoaderBlockWeight:
                              "lora_name": (lora_names, ),
                              "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                             "inverse": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
-                             "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                             "inverse": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False", "tooltip": "Apply the following weights for each block:\nTrue: 1 - weight\nFalse: weight"}),
+                             "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": ""}),
                              "A": ("FLOAT", {"default": 4.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                              "B": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                              "preset": (preset,),
@@ -130,11 +209,150 @@ class LoraLoaderBlockWeight:
             return value
 
     @staticmethod
-    def load_lora_for_models(model, clip, lora, strength_model, strength_clip, inverse, seed, A, B, block_vector):
+    def block_spec_parser(loaded, spec):
+        if not spec.startswith("%"):
+            return spec
+        else:
+            items = [x.strip() for x in spec[1:].split(',')]
+
+            input_blocks_set = set()
+            middle_blocks_set= set()
+            output_blocks_set = set()
+            double_blocks_set = set()
+            single_blocks_set = set()
+
+            for key, v in loaded.items():
+                if isinstance(key, tuple):
+                    k = key[0]
+                else:
+                    k = key
+
+                k_unet = k[len("diffusion_model."):]
+
+                if k_unet.startswith("input_blocks."):
+                    k_unet_num = k_unet[len("input_blocks."):len("input_blocks.")+2]
+                    k_unet_int = parse_unet_num(k_unet_num)
+                    input_blocks_set.add(k_unet_int)
+                elif k_unet.startswith("middle_block."):
+                    k_unet_num = k_unet[len("middle_block."):len("middle_block.")+2]
+                    k_unet_int = parse_unet_num(k_unet_num)
+                    middle_blocks_set.add(k_unet_int)
+                elif k_unet.startswith("output_blocks."):
+                    k_unet_num = k_unet[len("output_blocks."):len("output_blocks.")+2]
+                    k_unet_int = parse_unet_num(k_unet_num)
+                    output_blocks_set.add(k_unet_int)
+                elif k_unet.startswith("double_blocks."):
+                    k_unet_num = k_unet[len("double_blocks."):len("double_blocks.") + 2]
+                    k_unet_int = parse_unet_num(k_unet_num)
+                    double_blocks_set.add(k_unet_int)
+                elif k_unet.startswith("single_blocks."):
+                    k_unet_num = k_unet[len("single_blocks."):len("single_blocks.") + 2]
+                    k_unet_int = parse_unet_num(k_unet_num)
+                    single_blocks_set.add(k_unet_int)
+
+            pat1 = re.compile(r"(default|base)=([0-9.]+)")
+            pat2 = re.compile(r"(in|out|mid|double|single)([0-9]+)-([0-9]+)=([0-9.]+)")
+            pat3 = re.compile(r"(in|out|mid|double|single)([0-9]+)=([0-9.]+)")
+            pat4 = re.compile(r"(in|out|mid|double|single)=([0-9.]+)")
+
+            base_spec = None
+            default_spec = 1.0
+
+            for item in items:
+                match = pat1.match(item)
+                if match:
+                    if match[1] == 'base':
+                        base_spec = match[2]
+                        continue
+
+                    if match[1] == 'default':
+                        default_spec = match[2]
+                        continue
+
+            if base_spec is None:
+                base_spec = default_spec
+
+            input_blocks = [default_spec] * len(input_blocks_set)
+            middle_blocks = [default_spec] * len(middle_blocks_set)
+            output_blocks = [default_spec] * len(output_blocks_set)
+            double_blocks = [default_spec] * len(double_blocks_set)
+            single_blocks = [default_spec] * len(single_blocks_set)
+
+            for item in items:
+                match = pat2.match(item)
+                if match:
+                    for x in range(int(match[2])-1, int(match[3])):
+                        value = float(match[4])
+
+                        if x < 0:
+                            continue
+
+                        if match[1] == 'in' and len(input_blocks) > x:
+                            input_blocks[x] = value
+                        elif match[1] == 'out' and len(output_blocks) > x:
+                            output_blocks[x] = value
+                        elif match[1] == 'mid' and len(middle_blocks) > x:
+                            middle_blocks[x] = value
+                        elif match[1] == 'double' and len(double_blocks) > x:
+                            double_blocks[x] = value
+                        elif match[1] == 'single' and len(single_blocks) > x:
+                            single_blocks[x] = value
+
+                    continue
+
+                match = pat3.match(item)
+                if match:
+                    value = float(match[3])
+                    x = int(match[2]) - 1
+
+                    if x < 0:
+                        continue
+
+                    if match[1] == 'in' and len(input_blocks) > x:
+                        input_blocks[x] = value
+                    elif match[1] == 'out' and len(output_blocks) > x:
+                        output_blocks[x] = value
+                    elif match[1] == 'mid' and len(middle_blocks) > x:
+                        middle_blocks[x] = value
+                    elif match[1] == 'double' and len(double_blocks) > x:
+                        double_blocks[x] = value
+                    elif match[1] == 'single' and len(single_blocks) > x:
+                        single_blocks[x] = value
+
+                    continue
+
+                match = pat4.match(item)
+                if match:
+                    value = float(match[2])
+
+                    if match[1] == 'in':
+                        input_blocks = [value] * len(input_blocks)
+                    elif match[1] == 'out':
+                        output_blocks = [value] * len(output_blocks)
+                    elif match[1] == 'mid':
+                        middle_blocks = [value] * len(middle_blocks)
+                    elif match[1] == 'double':
+                        double_blocks = [value] * len(double_blocks)
+                    elif match[1] == 'single':
+                        single_blocks = [value] * len(single_blocks)
+
+                    continue
+
+            # concat specs
+            res = [str(base_spec)]
+            for x in (input_blocks + middle_blocks + output_blocks + double_blocks + single_blocks):
+                res.append(str(x))
+
+            return ",".join(res)
+
+    @staticmethod
+    def load_lbw(model, clip, lora, inverse, seed, A, B, block_vector):
         key_map = comfy.lora.model_lora_keys_unet(model.model)
         key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
         loaded = comfy.lora.load_lora(lora, key_map)
 
+        block_vector = LoraLoaderBlockWeight.block_spec_parser(loaded, block_vector)
+
         block_vector = block_vector.split(":")
         if len(block_vector) > 1:
             block_vector = block_vector[1]
@@ -142,7 +360,6 @@ class LoraLoaderBlockWeight:
             block_vector = block_vector[0]
 
         vector = block_vector.split(",")
-        vector_i = 1
 
         if not LoraLoaderBlockWeight.validate(vector):
             preset_dict = load_preset_dict()
@@ -151,22 +368,19 @@ class LoraLoaderBlockWeight:
             else:
                 raise ValueError(f"[LoraLoaderBlockWeight] invalid block_vector '{block_vector}'")
 
-        last_k_unet_num = None
-        new_modelpatcher = model.clone()
-        populated_ratio = strength_model
-
-        def parse_unet_num(s):
-            if s[1] == '.':
-                return int(s[0])
-            else:
-                return int(s)
-
         # sort: input, middle, output, others
         input_blocks = []
         middle_blocks = []
         output_blocks = []
+        double_blocks = []
+        single_blocks = []
         others = []
-        for k, v in loaded.items():
+        for key, v in loaded.items():
+            if isinstance(key, tuple):
+                k = key[0]
+            else:
+                k = key
+
             k_unet = k[len("diffusion_model."):]
 
             if k_unet.startswith("input_blocks."):
@@ -178,18 +392,34 @@ class LoraLoaderBlockWeight:
             elif k_unet.startswith("output_blocks."):
                 k_unet_num = k_unet[len("output_blocks."):len("output_blocks.")+2]
                 output_blocks.append((k, v, parse_unet_num(k_unet_num), k_unet))
+            elif k_unet.startswith("double_blocks."):
+                k_unet_num = k_unet[len("double_blocks."):len("double_blocks.")+2]
+                double_blocks.append((key, v, parse_unet_num(k_unet_num), k_unet))
+            elif k_unet.startswith("single_blocks."):
+                k_unet_num = k_unet[len("single_blocks."):len("single_blocks.")+2]
+                single_blocks.append((key, v, parse_unet_num(k_unet_num), k_unet))
             else:
                 others.append((k, v, k_unet))
 
         input_blocks = sorted(input_blocks, key=lambda x: x[2])
         middle_blocks = sorted(middle_blocks, key=lambda x: x[2])
         output_blocks = sorted(output_blocks, key=lambda x: x[2])
+        double_blocks = sorted(double_blocks, key=lambda x: x[2])
+        single_blocks = sorted(single_blocks, key=lambda x: x[2])
 
         # prepare patch
         np.random.seed(seed % (2**31))
         populated_vector_list = []
         ratios = []
-        for k, v, k_unet_num, k_unet in (input_blocks + middle_blocks + output_blocks):
+        ratio = 1.0
+        vector_i = 1
+
+        last_k_unet_num = None
+
+        block_weights = {}
+        muted_weights = []
+
+        for k, v, k_unet_num, k_unet in (input_blocks + middle_blocks + output_blocks + double_blocks + single_blocks):
             if last_k_unet_num != k_unet_num and len(vector) > vector_i:
                 ratios = LoraLoaderBlockWeight.convert_vector_value(A, B, vector[vector_i].strip())
                 ratio = ratios.pop(0)
@@ -205,6 +435,8 @@ class LoraLoaderBlockWeight:
             else:
                 if len(ratios) > 0:
                     ratio = ratios.pop(0)
+                else:
+                    pass # use last used ratio if no more user specified ratio is given
 
                 if inverse:
                     populated_ratio = 1 - ratio
@@ -213,11 +445,10 @@ class LoraLoaderBlockWeight:
 
             last_k_unet_num = k_unet_num
 
-            new_modelpatcher.add_patches({k: v}, strength_model * populated_ratio)
-            # if inverse:
-            #     print(f"\t{k_unet} -> inv({ratio}) ")
-            # else:
-            #     print(f"\t{k_unet} -> ({ratio}) ")
+            if populated_ratio != 0:
+                block_weights[k] = v, populated_ratio
+            else:
+                muted_weights.append(k)
 
         # prepare base patch
         ratios = LoraLoaderBlockWeight.convert_vector_value(A, B, vector[0].strip())
@@ -226,25 +457,43 @@ class LoraLoaderBlockWeight:
         if inverse:
             populated_ratio = 1 - ratio
         else:
-            populated_ratio = 1
+            populated_ratio = ratio
 
         populated_vector_list.insert(0, LoraLoaderBlockWeight.norm_value(populated_ratio))
 
         for k, v, k_unet in others:
-            new_modelpatcher.add_patches({k: v}, strength_model * populated_ratio)
-            # if inverse:
-            #     print(f"\t{k_unet} -> inv({ratio}) ")
-            # else:
-            #     print(f"\t{k_unet} -> ({ratio}) ")
+            if populated_ratio != 0:
+                block_weights[k] = v, populated_ratio
+            else:
+                muted_weights.append(k)
 
-        new_clip = clip.clone()
-        new_clip.add_patches(loaded, strength_clip)
         populated_vector = ','.join(map(str, populated_vector_list))
-        return (new_modelpatcher, new_clip, populated_vector)
+        return block_weights, muted_weights, populated_vector
+
+    @staticmethod
+    def load_lora_for_models(model, clip, lora, strength_model, strength_clip, inverse, seed, A, B, block_vector):
+        block_weights, muted_weights, populated_vector = LoraLoaderBlockWeight.load_lbw(model, clip, lora, inverse, seed, A, B, block_vector)
+
+        new_modelpatcher = model.clone()
+        new_clip = clip.clone()
+
+        muted_weights = set(muted_weights)
+
+        for k, v in block_weights.items():
+            weights, ratio = v
+
+            if k in muted_weights:
+                pass
+            elif 'text' in k:
+                new_clip.add_patches({k: weights}, strength_clip * ratio)
+            else:
+                new_modelpatcher.add_patches({k: weights}, strength_model * ratio)
+
+        return new_modelpatcher, new_clip, populated_vector
 
     def doit(self, model, clip, lora_name, strength_model, strength_clip, inverse, seed, A, B, preset, block_vector, bypass=False, category_filter=None):
         if strength_model == 0 and strength_clip == 0 or bypass:
-            return (model, clip, "")
+            return model, clip, ""
 
         lora_path = folder_paths.get_full_path("loras", lora_name)
         lora = None
@@ -261,7 +510,48 @@ class LoraLoaderBlockWeight:
             self.loaded_lora = (lora_path, lora)
 
         model_lora, clip_lora, populated_vector = LoraLoaderBlockWeight.load_lora_for_models(model, clip, lora, strength_model, strength_clip, inverse, seed, A, B, block_vector)
-        return (model_lora, clip_lora, populated_vector)
+        return model_lora, clip_lora, populated_vector
+
+
+class ApplyLBW:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "model": ("MODEL", ),
+                    "clip": ("CLIP", ),
+                    "strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                    "strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                    "lbw_model": ("LBW_MODEL",),
+                }}
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/LoraBlockWeight"
+
+    DESCRIPTION = "Apply LBW_MODEL to MODEL and CLIP"
+
+    @staticmethod
+    def doit(model, clip, strength_model, strength_clip, lbw_model):
+        block_weights = lbw_model['blocks']
+        muted_weights = lbw_model['muted']
+
+        new_modelpatcher = model.clone()
+        new_clip = clip.clone()
+
+        muted_weights = set(muted_weights)
+
+        for k, v in block_weights.items():
+            weights, ratio = v
+
+            if k in muted_weights:
+                pass
+            elif 'text' in k:
+                new_clip.add_patches({k: weights}, strength_clip * ratio)
+            else:
+                new_modelpatcher.add_patches({k: weights}, strength_model * ratio)
+
+        return new_modelpatcher, new_clip
 
 
 class XY_Capsule_LoraBlockWeight:
@@ -328,6 +618,7 @@ class XY_Capsule_LoraBlockWeight:
             else:
                 image = torch.abs(weighted_image - reference_image)
                 self.storage[(self.another_capsule.x, self.y)] = image
+
         elif self.y == 3:
             import matplotlib.cm as cm
             # heatmap
@@ -336,7 +627,7 @@ class XY_Capsule_LoraBlockWeight:
             if image == "fail":
                 image = utils.empty_pil_tensor(8,8)
                 latent = utils.empty_latent()
-                return (image, latent)
+                return image, latent
             else:
                 image = image.clone()
 
@@ -368,7 +659,7 @@ class XY_Capsule_LoraBlockWeight:
                 image = heatmap_alpha * heatmap + (1 - heatmap_alpha) * image
 
         latent = nodes.VAEEncode().encode(vae, image)[0]
-        return (image, latent)
+        return image, latent
 
     def getLabel(self):
         return self.label
@@ -485,7 +776,7 @@ class XYInput_LoraBlockWeight:
                         XY_Capsule_LoraBlockWeight(0, 2, '', 'diff', storage, common_params),
                         XY_Capsule_LoraBlockWeight(0, 3, '', 'heatmap', storage, common_params)]
 
-        return ((xy_type, x_values), (xy_type, y_values), )
+        return (xy_type, x_values), (xy_type, y_values),
 
 
 class LoraBlockInfo:
@@ -535,8 +826,21 @@ class LoraBlockInfo:
         text_blocks = []
         text_blocks_map = {}
 
+        double_block_count = set()
+        double_blocks = []
+        double_blocks_map = {}
+
+        single_block_count = set()
+        single_blocks = []
+        single_blocks_map = {}
+
         others = []
-        for k, v in loaded.items():
+        for key, v in loaded.items():
+            if isinstance(key, tuple):
+                k = key[0]
+            else:
+                k = key
+
             k_unet = k[len("diffusion_model."):]
 
             if k_unet.startswith("input_blocks."):
@@ -572,6 +876,28 @@ class LoraBlockInfo:
                 else:
                     output_blocks_map[k_unet_int] = [k_unet]
 
+            elif k_unet.startswith("double_blocks."):
+                k_unet_num = k_unet[len("double_blocks."):len("double_blocks.") + 2]
+                k_unet_int = parse_unet_num(k_unet_num)
+
+                double_block_count.add(k_unet_int)
+                double_blocks.append(k_unet)
+                if k_unet_int in double_blocks_map:
+                    double_blocks_map[k_unet_int].append(k_unet)
+                else:
+                    double_blocks_map[k_unet_int] = [k_unet]
+
+            elif k_unet.startswith("single_blocks."):
+                k_unet_num = k_unet[len("single_blocks."):len("single_blocks.") + 2]
+                k_unet_int = parse_unet_num(k_unet_num)
+
+                single_block_count.add(k_unet_int)
+                single_blocks.append(k_unet)
+                if k_unet_int in single_blocks_map:
+                    single_blocks_map[k_unet_int].append(k_unet)
+                else:
+                    single_blocks_map[k_unet_int] = [k_unet]
+
             elif k_unet.startswith("er.text_model.encoder.layers."):
                 k_unet_num = k_unet[len("er.text_model.encoder.layers."):len("er.text_model.encoder.layers.")+2]
                 k_unet_int = parse_unet_num(k_unet_num)
@@ -591,22 +917,39 @@ class LoraBlockInfo:
         input_blocks = sorted(input_blocks)
         middle_blocks = sorted(middle_blocks)
         output_blocks = sorted(output_blocks)
+        double_blocks = sorted(double_blocks)
+        single_blocks = sorted(single_blocks)
         others = sorted(others)
 
-        text += f"\n-------[Input blocks] ({len(input_block_count)}, Subs={len(input_blocks)})-------\n"
-        input_keys = sorted(input_blocks_map.keys())
-        for x in input_keys:
-            text += f" IN{x}: {len(input_blocks_map[x])}\n"
+        if len(input_block_count) > 0:
+            text += f"\n-------[Input blocks] ({len(input_block_count)}, Subs={len(input_blocks)})-------\n"
+            input_keys = sorted(input_blocks_map.keys())
+            for x in input_keys:
+                text += f" IN{x}: {len(input_blocks_map[x])}\n"
 
-        text += f"\n-------[Middle blocks] ({len(middle_block_count)}, Subs={len(middle_blocks)})-------\n"
-        middle_keys = sorted(middle_blocks_map.keys())
-        for x in middle_keys:
-            text += f" MID{x}: {len(middle_blocks_map[x])}\n"
+        if len(middle_block_count) > 0:
+            text += f"\n-------[Middle blocks] ({len(middle_block_count)}, Subs={len(middle_blocks)})-------\n"
+            middle_keys = sorted(middle_blocks_map.keys())
+            for x in middle_keys:
+                text += f" MID{x}: {len(middle_blocks_map[x])}\n"
 
-        text += f"\n-------[Output blocks] ({len(output_block_count)}, Subs={len(output_blocks)})-------\n"
-        output_keys = sorted(output_blocks_map.keys())
-        for x in output_keys:
-            text += f" OUT{x}: {len(output_blocks_map[x])}\n"
+        if len(output_block_count) > 0:
+            text += f"\n-------[Output blocks] ({len(output_block_count)}, Subs={len(output_blocks)})-------\n"
+            output_keys = sorted(output_blocks_map.keys())
+            for x in output_keys:
+                text += f" OUT{x}: {len(output_blocks_map[x])}\n"
+
+        if len(double_block_count) > 0:
+            text += f"\n-------[Double blocks(MMDiT)] ({len(double_block_count)}, Subs={len(double_blocks)})-------\n"
+            double_keys = sorted(double_blocks_map.keys())
+            for x in double_keys:
+                text += f" DOUBLE{x}: {len(double_blocks_map[x])}\n"
+
+        if len(single_block_count) > 0:
+            text += f"\n-------[Single blocks(DiT)] ({len(single_block_count)}, Subs={len(single_blocks)})-------\n"
+            single_keys = sorted(single_blocks_map.keys())
+            for x in single_keys:
+                text += f" SINGLE{x}: {len(single_blocks_map[x])}\n"
 
         text += f"\n-------[Base blocks] ({len(text_block_count) + len(others)}, Subs={len(text_blocks) + len(others)})-------\n"
         text_keys = sorted(text_blocks_map.keys())
@@ -628,13 +971,162 @@ class LoraBlockInfo:
         return {}
 
 
+class LoadLBW:
+    @classmethod
+    def INPUT_TYPES(s):
+        files = folder_paths.get_filename_list('lbw_models')
+        return {"required": {
+            "lbw_model": [sorted(files), ]},
+        }
+
+    RETURN_TYPES = ("LBW_MODEL",)
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/LoraBlockWeight"
+
+    DESCRIPTION = "Load LBW_MODEL from .lbw.safetensors file"
+
+    @staticmethod
+    def decode_dict(encoded_dict, tensor_dict):
+        original_dict = {}
+
+        def decode_value(value):
+            if isinstance(value, str) and value.startswith('t') and value[1:].isdigit():
+                return tensor_dict[value]
+            return value
+
+        for k, tuple_value in encoded_dict.items():
+            decoded_tuple = tuple(decode_value(v) for v in tuple_value[0][1])
+            key = ast.literal_eval(k) if isinstance(k, str) and (k.startswith('(') or k.startswith('[')) else k
+            original_dict[key] = ((tuple_value[0][0], decoded_tuple), tuple_value[1])
+
+        return original_dict
+
+    @staticmethod
+    def load(file):
+        tensor_dict = comfy.utils.load_torch_file(file)
+
+        with safe_open(file, framework="pt") as f:
+            metadata = f.metadata()
+
+        encoded_dict = json.loads(metadata.get('blocks', '{}'))
+        muted_blocks = ast.literal_eval(metadata.get('muted_blocks', '[]'))
+
+        decoded_dict = LoadLBW.decode_dict(encoded_dict, tensor_dict)
+
+        lbw_model = {
+            'blocks': decoded_dict,
+            'muted': muted_blocks
+        }
+
+        return lbw_model, metadata
+
+    def doit(self, lbw_model):
+        lbw_path = folder_paths.get_full_path("lbw_models", lbw_model)
+        lbw_model, _ = LoadLBW.load(lbw_path)
+        return (lbw_model,)
+
+
+class SaveLBW:
+    def __init__(self):
+        self.output_dir = folder_paths.get_folder_paths('lbw_models')[-1]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "lbw_model": ("LBW_MODEL", ),
+                              "filename_prefix": ("STRING", {"default": "ComfyUI"}) },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+    RETURN_TYPES = ()
+    FUNCTION = "doit"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "InspirePack/LoraBlockWeight"
+
+    DESCRIPTION = "Save LBW_MODEL as a .lbw.safetensors file"
+
+    @staticmethod
+    def encode_dict(original_dict):
+        tensor_dict = {}
+        encoded_dict = {}
+        counter = 0
+
+        def generate_unique_id():
+            nonlocal counter
+            counter += 1
+            return f"t{counter}"
+
+        def encode_value(value):
+            if isinstance(value, torch.Tensor):
+                unique_id = generate_unique_id()
+                tensor_dict[unique_id] = value
+                return unique_id
+            return value
+
+        for k, tuple_value in original_dict.items():
+            encoded_tuple = tuple(encode_value(v) for v in tuple_value[0][1])
+            encoded_dict[str(k)] = (tuple_value[0][0], encoded_tuple), tuple_value[1]
+
+        return encoded_dict, tensor_dict
+
+    @staticmethod
+    def save(lbw_model, file, metadata):
+        metadata['format'] = 'Inspire LBW 1.0'
+        weighted_blocks = lbw_model['blocks']
+        metadata['muted_blocks'] = str(lbw_model['muted'])
+        encoded_dict, tensor_dict = SaveLBW.encode_dict(weighted_blocks)
+        metadata['blocks'] = json.dumps(encoded_dict)
+
+        comfy.utils.save_torch_file(tensor_dict, file, metadata=metadata)
+
+    def doit(self, lbw_model, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        # support save metadata for lbw sharing
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = {}
+        if not args.disable_metadata:
+            metadata = {"prompt": prompt_info}
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
+        file = f"{filename}_{counter:05}_.lbw.safetensors"
+
+        results = list()
+        results.append({
+            "filename": file,
+            "subfolder": subfolder,
+            "type": "output"
+        })
+
+        file = os.path.join(full_output_folder, file)
+
+        SaveLBW.save(lbw_model, file, metadata)
+
+        return {}
+
+
 NODE_CLASS_MAPPINGS = {
     "XY Input: Lora Block Weight //Inspire": XYInput_LoraBlockWeight,
     "LoraLoaderBlockWeight //Inspire": LoraLoaderBlockWeight,
     "LoraBlockInfo //Inspire": LoraBlockInfo,
+    "MakeLBW //Inspire": MakeLBW,
+    "ApplyLBW //Inspire": ApplyLBW,
+    "SaveLBW //Inspire": SaveLBW,
+    "LoadLBW //Inspire": LoadLBW,
 }
+
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "XY Input: Lora Block Weight //Inspire": "XY Input: Lora Block Weight",
-    "LoraLoaderBlockWeight //Inspire": "Lora Loader (Block Weight)",
-    "LoraBlockInfo //Inspire": "Lora Block Info",
+    "XY Input: Lora Block Weight //Inspire": "XY Input: LoRA Block Weight",
+    "LoraLoaderBlockWeight //Inspire": "LoRA Loader (Block Weight)",
+    "LoraBlockInfo //Inspire": "LoRA Block Info",
+    "MakeLBW //Inspire": "Make LoRA Block Weight",
+    "ApplyLBW //Inspire": "Apply LoRA Block Weight",
+    "SaveLBW //Inspire": "Save LoRA Block Weight",
+    "LoadLBW //Inspire": "Load LoRA Block Weight",
 }

inspire/model_nodes.py

@@ -19,6 +19,7 @@ model_preset = {
     "SDXL ViT-H":           ("ip-adapter_sdxl_vit-h",           "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
     "SDXL Plus ViT-H":      ("ip-adapter-plus_sdxl_vit-h",      "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
     "SDXL Plus Face ViT-H": ("ip-adapter-plus-face_sdxl_vit-h", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
+    "Kolors Plus":          ("Kolors-IP-Adapter-Plus",          "clip-vit-large-patch14-336", None, False),
 
     # faceid
     "SD1.5 FaceID":                ("ip-adapter-faceid_sd15",                 "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid_sd15_lora", True),
@@ -29,6 +30,7 @@ model_preset = {
     "SDXL FaceID":                 ("ip-adapter-faceid_sdxl",                 "CLIP-ViT-H-14-laion2B-s32B-b79K", "ip-adapter-faceid_sdxl_lora", True),
     "SDXL FaceID Portrait":        ("ip-adapter-faceid-portrait_sdxl",        "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True),
     "SDXL FaceID Portrait unnorm": ("ip-adapter-faceid-portrait_sdxl_unnorm", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, True),
+    "Kolors FaceID Plus":          ("Kolors-IP-Adapter-FaceID-Plus",          "clip-vit-large-patch14-336", None, True),
 
     # composition
     "SD1.5 Plus Composition":      ("ip-adapter_sd15", "CLIP-ViT-H-14-laion2B-s32B-b79K", None, False),
@@ -56,13 +58,16 @@ class IPAdapterModelHelper:
         return {
             "required": {
                 "model": ("MODEL",),
-                "clip": ("CLIP",),
                 "preset": (list(model_preset.keys()),),
                 "lora_strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                 "lora_strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                 "insightface_provider": (["CPU", "CUDA", "ROCM"], ),
                 "cache_mode": (["insightface only", "clip_vision only", "all", "none"], {"default": "insightface only"}),
             },
+            "optional": {
+                "clip": ("CLIP",),
+                "insightface_model_name": (['buffalo_l', 'antelopev2'],),
+            },
             "hidden": {"unique_id": "UNIQUE_ID"}
         }
 
@@ -72,14 +77,17 @@ class IPAdapterModelHelper:
 
     CATEGORY = "InspirePack/models"
 
-    def doit(self, model, clip, preset, lora_strength_model, lora_strength_clip, insightface_provider, cache_mode="none", unique_id=None):
+    def doit(self, model, preset, lora_strength_model, lora_strength_clip, insightface_provider, clip=None, cache_mode="none", unique_id=None, insightface_model_name='buffalo_l'):
         if 'IPAdapter' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/cubiq/ComfyUI_IPAdapter_plus',
                                           "To use 'IPAdapterModelHelper' node, 'ComfyUI IPAdapter Plus' extension is required.")
             raise Exception(f"[ERROR] To use IPAdapterModelHelper, you need to install 'ComfyUI IPAdapter Plus'")
 
         is_sdxl_preset = 'SDXL' in preset
-        is_sdxl_model = isinstance(clip.tokenizer, sdxl_clip.SDXLTokenizer)
+        if clip is not None:
+            is_sdxl_model = isinstance(clip.tokenizer, sdxl_clip.SDXLTokenizer)
+        else:
+            is_sdxl_model = False
 
         if is_sdxl_preset != is_sdxl_model:
             server.PromptServer.instance.send_sync("inspire-node-output-label", {"node_id": unique_id, "output_idx": 1, "label": "IPADAPTER (fail)"})
@@ -157,7 +165,7 @@ class IPAdapterModelHelper:
             if cache_mode in ["insightface only", "all"]:
                 icache_key = 'insightface-' + insightface_provider
                 if icache_key not in backend_support.cache:
-                    backend_support.update_cache(icache_key, "insightface", (False, insight_face_loader(insightface_provider)[0]))
+                    backend_support.update_cache(icache_key, "insightface", (False, insight_face_loader(provider=insightface_provider, model_name=insightface_model_name)[0]))
                 _, (_, insightface) = backend_support.cache[icache_key]
             else:
                 insightface = insight_face_loader(insightface_provider)[0]

inspire/prompt_support.py

@@ -12,6 +12,7 @@ import folder_paths
 import comfy
 import traceback
 import random
+import hashlib
 
 from server import PromptServer
 from .libs import utils, common
@@ -37,7 +38,7 @@ try:
     with open(pb_yaml_path, 'r', encoding="utf-8") as f:
         prompt_builder_preset = yaml.load(f, Loader=yaml.FullLoader)
 except Exception as e:
-    print(f"[Inspire Pack] Failed to load 'prompt-builder.yaml'")
+    print(f"[Inspire Pack] Failed to load 'prompt-builder.yaml'\nNOTE: Only files with UTF-8 encoding are supported.")
 
 
 class LoadPromptsFromDir:
@@ -49,7 +50,13 @@ class LoadPromptsFromDir:
         except Exception:
             prompt_dirs = []
 
-        return {"required": {"prompt_dir": (prompt_dirs,)}}
+        return {"required": {
+                    "prompt_dir": (prompt_dirs,)
+                    },
+                "optional": {
+                    "reload": ("BOOLEAN", { "default": False, "label_on": "if file changed", "label_off": "if value changed"}),
+                    }
+                }
 
     RETURN_TYPES = ("ZIPPED_PROMPT",)
     OUTPUT_IS_LIST = (True,)
@@ -59,7 +66,30 @@ class LoadPromptsFromDir:
     CATEGORY = "InspirePack/Prompt"
 
     @staticmethod
-    def doit(prompt_dir):
+    def IS_CHANGED(prompt_dir, reload=False):
+        if not reload:
+            return prompt_dir
+        else:
+            global prompts_path
+            prompt_dir = os.path.join(prompts_path, prompt_dir)
+            files = [f for f in os.listdir(prompt_dir) if f.endswith(".txt")]
+
+            md5 = hashlib.md5()
+            files.sort()
+
+            for file in files:
+                md5.update(file.encode('utf-8'))
+                with open(os.path.join(prompt_dir, file), 'rb') as f:
+                    while True:
+                        chunk = f.read(4096)
+                        if not chunk:
+                            break
+                        md5.update(chunk)
+
+            return md5.hexdigest()
+
+    @staticmethod
+    def doit(prompt_dir, reload=False):
         global prompts_path
         prompt_dir = os.path.join(prompts_path, prompt_dir)
         files = [f for f in os.listdir(prompt_dir) if f.endswith(".txt")]
@@ -85,7 +115,7 @@ class LoadPromptsFromDir:
                         else:
                             print(f"[WARN] LoadPromptsFromDir: invalid prompt format in '{file_name}'")
             except Exception as e:
-                print(f"[ERROR] LoadPromptsFromDir: an error occurred while processing '{file_name}': {str(e)}")
+                print(f"[ERROR] LoadPromptsFromDir: an error occurred while processing '{file_name}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
 
         return (prompts, )
 
@@ -105,8 +135,14 @@ class LoadPromptsFromFile:
         except Exception:
             prompt_files = []
 
-        return {"required": {"prompt_file": (prompt_files,)},
-                "optional": {"text_data_opt": ("STRING", {"defaultInput": True})}}
+        return {"required": {
+                        "prompt_file": (prompt_files,)
+                        },
+                "optional": {
+                        "text_data_opt": ("STRING", {"defaultInput": True}),
+                        "reload": ("BOOLEAN", {"default": False, "label_on": "if file changed", "label_off": "if value changed"}),
+                        }
+                }
 
     RETURN_TYPES = ("ZIPPED_PROMPT",)
     OUTPUT_IS_LIST = (True,)
@@ -116,7 +152,28 @@ class LoadPromptsFromFile:
     CATEGORY = "InspirePack/Prompt"
 
     @staticmethod
-    def doit(prompt_file, text_data_opt=None):
+    def IS_CHANGED(prompt_file, text_data_opt=None, reload=False):
+        md5 = hashlib.md5()
+
+        if text_data_opt is not None:
+            md5.update(text_data_opt)
+            return md5.hexdigest()
+        elif not reload:
+            return prompt_file
+        else:
+            prompt_path = os.path.join(prompts_path, prompt_file)
+
+            with open(prompt_path, 'rb') as f:
+                while True:
+                    chunk = f.read(4096)
+                    if not chunk:
+                        break
+                    md5.update(chunk)
+
+            return md5.hexdigest()
+
+    @staticmethod
+    def doit(prompt_file, text_data_opt=None, reload=False):
         prompt_path = os.path.join(prompts_path, prompt_file)
 
         prompts = []
@@ -142,7 +199,7 @@ class LoadPromptsFromFile:
                 else:
                     print(f"[WARN] LoadPromptsFromFile: invalid prompt format in '{prompt_file}'")
         except Exception as e:
-            print(f"[ERROR] LoadPromptsFromFile: an error occurred while processing '{prompt_file}': {str(e)}")
+            print(f"[ERROR] LoadPromptsFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
 
         return (prompts, )
 
@@ -205,7 +262,7 @@ class LoadSinglePromptFromFile:
             else:
                 print(f"[WARN] LoadSinglePromptFromFile: invalid prompt format in '{prompt_file}'")
         except Exception as e:
-            print(f"[ERROR] LoadSinglePromptFromFile: an error occurred while processing '{prompt_file}': {str(e)}")
+            print(f"[ERROR] LoadSinglePromptFromFile: an error occurred while processing '{prompt_file}': {str(e)}\nNOTE: Only files with UTF-8 encoding are supported.")
 
         return (prompts, )
 

inspire/regional_nodes.py

@@ -4,6 +4,7 @@ import comfy
 import nodes
 import torch
 import re
+import webcolors
 
 from . import prompt_support
 from .libs import utils, common
@@ -81,8 +82,9 @@ class RegionalPromptSimple:
 
 def color_to_mask(color_mask, mask_color):
     try:
-        if mask_color.startswith("#"):
-            selected = int(mask_color[1:], 16)
+        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:

inspire/sampler_nodes.py

@@ -24,6 +24,7 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
                     "noise_mode": (["GPU(=A1111)", "CPU"],),
                     "interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
                     "omit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
+                    "omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
                     },
                 "optional": {
                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
@@ -36,27 +37,29 @@ class KSampler_progress(a1111_compat.KSampler_inspire):
     RETURN_NAMES = ("latent", "progress_latent")
 
     @staticmethod
-    def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, interval, omit_start_latent, scheduler_func_opt=None):
+    def doit(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode,
+             interval, omit_start_latent, omit_final_latent, scheduler_func_opt=None):
         adv_steps = int(steps / denoise)
 
         if omit_start_latent:
             result = []
         else:
-            result = [latent_image['samples']]
-
-        result = []
+            result = [comfy.sample.fix_empty_latent_channels(model, latent_image['samples']).cpu()]
 
         def progress_callback(step, x0, x, total_steps):
             if (total_steps-1) != step and step % interval != 0:
                 return
 
             x = model.model.process_latent_out(x)
-            x = x.to(model_management.intermediate_device())
+            x = x.cpu()
             result.append(x)
 
         latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, True, seed, adv_steps, cfg, sampler_name, scheduler, positive, negative, latent_image, (adv_steps-steps),
                                                                            adv_steps, noise_mode, False, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
 
+        if not omit_final_latent:
+            result.append(latent_image['samples'].cpu())
+
         if len(result) > 0:
             result = torch.cat(result)
             result = {'samples': result}
@@ -86,6 +89,7 @@ class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
                     "return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
                     "interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
                     "omit_start_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
+                    "omit_final_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
                     },
                 "optional": {
                     "prev_progress_latent_opt": ("LATENT",),
@@ -100,25 +104,28 @@ class KSamplerAdvanced_progress(a1111_compat.KSamplerAdvanced_inspire):
     RETURN_TYPES = ("LATENT", "LATENT")
     RETURN_NAMES = ("latent", "progress_latent")
 
-    def doit(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step,
-             noise_mode, return_with_leftover_noise, interval, omit_start_latent, prev_progress_latent_opt=None, scheduler_func_opt=None):
+    def doit(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
+             start_at_step, end_at_step, noise_mode, return_with_leftover_noise, interval, omit_start_latent, omit_final_latent,
+             prev_progress_latent_opt=None, scheduler_func_opt=None):
+
         if omit_start_latent:
             result = []
         else:
             result = [latent_image['samples']]
 
-        result = []
-
         def progress_callback(step, x0, x, total_steps):
             if (total_steps-1) != step and step % interval != 0:
                 return
 
             x = model.model.process_latent_out(x)
-            x = x.to(model_management.intermediate_device())
+            x = x.cpu()
             result.append(x)
 
         latent_image, noise = a1111_compat.KSamplerAdvanced_inspire.sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step,
-                                                                           noise_mode, False, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
+                                                                           noise_mode, return_with_leftover_noise, callback=progress_callback, scheduler_func_opt=scheduler_func_opt)
+
+        if not omit_final_latent:
+            result.append(latent_image['samples'].cpu())
 
         if len(result) > 0:
             result = torch.cat(result)
@@ -165,6 +172,15 @@ def logarithmic_interpolation(from_cfg, to_cfg, i, steps):
     return from_cfg + (to_cfg - from_cfg) * t
 
 
+def cosine_interpolation(from_cfg, to_cfg, i, steps):
+    if (i == 0) or (i == steps-1):
+        return from_cfg
+
+    t = (1.0 + math.cos(math.pi*2*(i/steps))) / 2
+
+    return from_cfg + (to_cfg - from_cfg) * t
+
+
 class Guider_scheduled(CFGGuider):
     def __init__(self, model_patcher, sigmas, from_cfg, to_cfg, schedule):
         super().__init__(model_patcher)
@@ -194,6 +210,8 @@ class Guider_scheduled(CFGGuider):
                 self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             elif self.schedule == 'log':
                 self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i
+            elif self.schedule == 'cos':
+                self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             else:
                 self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i
 
@@ -245,6 +263,8 @@ class Guider_PerpNeg_scheduled(Guider_PerpNeg):
                 self.cfg_sigmas[k] = exponential_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             elif self.schedule == 'log':
                 self.cfg_sigmas[k] = logarithmic_interpolation(self.from_cfg, self.to_cfg, i, steps), i
+            elif self.schedule == 'cos':
+                self.cfg_sigmas[k] = cosine_interpolation(self.from_cfg, self.to_cfg, i, steps), i
             else:
                 self.cfg_sigmas[k] = self.from_cfg + delta * i / steps, i
 
@@ -276,7 +296,7 @@ class ScheduledCFGGuider:
                     "sigmas": ("SIGMAS", ),
                     "from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
                     "to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-                    "schedule": (["linear", "log", "exp"], {'default': 'log'})
+                    "schedule": (["linear", "log", "exp", "cos"], {'default': 'log'})
                     }
                 }
 
@@ -303,7 +323,7 @@ class ScheduledPerpNegCFGGuider:
                     "sigmas": ("SIGMAS", ),
                     "from_cfg": ("FLOAT", {"default": 6.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
                     "to_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
-                    "schedule": (["linear", "log", "exp"], {'default': 'log'})
+                    "schedule": (["linear", "log", "exp", "cos"], {'default': 'log'})
                     }
                 }
 

inspire/segs_support.py

@@ -110,6 +110,9 @@ class Color_Preprocessor_wrapper:
 
 
 class InpaintPreprocessor_wrapper:
+    def __init__(self, black_pixel_for_xinsir_cn):
+        self.black_pixel_for_xinsir_cn = black_pixel_for_xinsir_cn
+    
     def apply(self, image, mask=None):
         if 'InpaintPreprocessor' not in nodes.NODE_CLASS_MAPPINGS:
             utils.try_install_custom_node('https://github.com/Fannovel16/comfyui_controlnet_aux',
@@ -120,7 +123,16 @@ class InpaintPreprocessor_wrapper:
         if mask is None:
             mask = torch.ones((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu").unsqueeze(0)
 
-        return obj.preprocess(image, mask)[0]
+        try:
+            res = obj.preprocess(image, mask, black_pixel_for_xinsir_cn=self.black_pixel_for_xinsir_cn)[0]
+        except Exception as e:
+            if self.black_pixel_for_xinsir_cn:
+                raise e
+            else:
+                res = obj.preprocess(image, mask)[0]
+                print(f"[Inspire Pack] Installed 'ComfyUI's ControlNet Auxiliary Preprocessors.' is outdated.")
+
+        return res
 
 
 class TilePreprocessor_wrapper:
@@ -547,14 +559,19 @@ class Color_Preprocessor_Provider_for_SEGS:
 class InpaintPreprocessor_Provider_for_SEGS:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {}}
+        return {
+            "required": {},
+            "optional": {
+                "black_pixel_for_xinsir_cn": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
+            }
+        }
     RETURN_TYPES = ("SEGS_PREPROCESSOR",)
     FUNCTION = "doit"
 
     CATEGORY = "InspirePack/SEGS/ControlNet"
 
-    def doit(self):
-        obj = InpaintPreprocessor_wrapper()
+    def doit(self, black_pixel_for_xinsir_cn=False):
+        obj = InpaintPreprocessor_wrapper(black_pixel_for_xinsir_cn)
         return (obj, )
 
 

js/lora_block_weight.js

@@ -4,7 +4,7 @@ app.registerExtension({
 	name: "Comfy.Inspire.LBW",
 
 	nodeCreated(node, app) {
-		if(node.comfyClass == "LoraLoaderBlockWeight //Inspire") {
+		if(node.comfyClass == "LoraLoaderBlockWeight //Inspire" || node.comfyClass == "MakeLBW //Inspire") {
 		    // category filter
 			const lora_names_widget = node.widgets[node.widgets.findIndex(obj => obj.name === 'lora_name')];
 			var full_lora_list = lora_names_widget.options.values;
@@ -26,6 +26,12 @@ app.registerExtension({
 		    // vector selector
 		    let preset_i = 9;
 		    let vector_i = 10;
+
+		    if(node.comfyClass == "MakeLBW //Inspire") {
+		        preset_i = 7;
+		        vector_i = 8;
+		    }
+
 			node._value = "Preset";
 
 			Object.defineProperty(node.widgets[preset_i], "value", {

pyproject.toml

@@ -1,8 +1,8 @@
 [project]
 name = "comfyui-inspire-pack"
 description = "This extension provides various nodes to support Lora Block Weight and the Impact Pack. Provides many easily applicable regional features and applications for Variation Seed."
-version = "0.82.6"
-license = "LICENSE"
+version = "1.5.1"
+license = { file = "LICENSE" }
 dependencies = ["matplotlib", "cachetools"]
 
 [project.urls]

requirements.txt

@@ -1,3 +1,5 @@
 matplotlib
 cachetools
-numpy<2
+numpy<2
+webcolors
+opencv-python

resources/lbw-preset.txt

@@ -29,6 +29,16 @@ SDXL-LyC-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 SDXL-LyC-INALL:1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
 SDXL-LyC-MIDALL:1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0
 SDXL-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
+FLUX-DBL-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+FLUX-DBL-FRONT7:1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0
+FLUX-DBL-MID6:1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0
+FLUX-DBL-TAIL6:1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1
+FLUX-SINGLE-ALL:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+FLUX-SINGLE-1to10:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+FLUX-SINGLE-11to20:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+FLUX-SINGLE-21to30:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0
+FLUX-SINGLE-31to37:1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1
+FLUX-ALL:1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
 @SD-FULL-TEST:17
 @SD-BLOCK1-TEST:17,12,1
 @SD-BLOCK2-TEST:17,12,2
@@ -49,4 +59,64 @@ SDXL-LyC-OUTALL:1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1
 @SD-BLOCK17-TEST:17,12,17
 @SD-LyC-FULL-TEST:27
 @SDXL-FULL-TEST:12
-@SDXL-LyC-FULL-TEST:21
+@SDXL-LyC-FULL-TEST:21
+@FLUX-DBL-FULL:19
+@FLUX-DBL-SGL-FULL:58
+@FLUX-DBL0-TEST:19,14,2
+@FLUX-DBL1-TEST:19,14,3
+@FLUX-DBL2-TEST:19,14,4
+@FLUX-DBL3-TEST:19,14,5
+@FLUX-DBL4-TEST:19,14,6
+@FLUX-DBL5-TEST:19,14,7
+@FLUX-DBL6-TEST:19,14,8
+@FLUX-DBL7-TEST:19,14,9
+@FLUX-DBL8-TEST:19,14,10
+@FLUX-DBL9-TEST:19,14,11
+@FLUX-DBL10-TEST:19,14,12
+@FLUX-DBL11-TEST:19,14,13
+@FLUX-DBL12-TEST:19,14,14
+@FLUX-DBL13-TEST:19,14,15
+@FLUX-DBL14-TEST:19,14,16
+@FLUX-DBL15-TEST:19,14,17
+@FLUX-DBL16-TEST:19,14,18
+@FLUX-DBL17-TEST:19,14,19
+@FLUX-DBL18-TEST:19,14,20
+@FLUX-SGL0-TEST:58,6,21
+@FLUX-SGL1-TEST:58,6,22
+@FLUX-SGL2-TEST:58,6,23
+@FLUX-SGL3-TEST:58,6,24
+@FLUX-SGL4-TEST:58,6,25
+@FLUX-SGL5-TEST:58,6,26
+@FLUX-SGL6-TEST:58,6,27
+@FLUX-SGL7-TEST:58,6,28
+@FLUX-SGL8-TEST:58,6,29
+@FLUX-SGL9-TEST:58,6,30
+@FLUX-SGL10-TEST:58,6,31
+@FLUX-SGL11-TEST:58,6,32
+@FLUX-SGL12-TEST:58,6,33
+@FLUX-SGL13-TEST:58,6,34
+@FLUX-SGL14-TEST:58,6,35
+@FLUX-SGL15-TEST:58,6,36
+@FLUX-SGL16-TEST:58,6,37
+@FLUX-SGL17-TEST:58,6,38
+@FLUX-SGL18-TEST:58,6,39
+@FLUX-SGL19-TEST:58,6,40
+@FLUX-SGL20-TEST:58,6,41
+@FLUX-SGL21-TEST:58,6,42
+@FLUX-SGL22-TEST:58,6,43
+@FLUX-SGL23-TEST:58,6,44
+@FLUX-SGL24-TEST:58,6,45
+@FLUX-SGL25-TEST:58,6,46
+@FLUX-SGL26-TEST:58,6,47
+@FLUX-SGL27-TEST:58,6,48
+@FLUX-SGL28-TEST:58,6,49
+@FLUX-SGL29-TEST:58,6,50
+@FLUX-SGL30-TEST:58,6,51
+@FLUX-SGL31-TEST:58,6,52
+@FLUX-SGL32-TEST:58,6,53
+@FLUX-SGL33-TEST:58,6,54
+@FLUX-SGL34-TEST:58,6,55
+@FLUX-SGL35-TEST:58,6,56
+@FLUX-SGL36-TEST:58,6,57
+@FLUX-SGL37-TEST:58,6,58
+@FLUX-SGL38-TEST:58,6,59