feat: ForeachListBegin/End, WorklistToItemList

https://github.com/ltdrdata/ComfyUI-Inspire-Pack/issues/185

README.md

@@ -24,6 +24,7 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
   * 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.
       * You need to install [ControlNet Auxiliary Preprocessors](https://github.com/Fannovel16/comfyui_controlnet_aux) to use this.
@@ -34,6 +35,7 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
     `Color Preprocessor Provider (SEGS)`, `Inpaint Preprocessor Provider (SEGS)`, `Tile Preprocessor Provider (SEGS)`, `MeshGraphormer Depth Map Preprocessor Provider (SEGS)`  
   * `MediaPipeFaceMeshDetectorProvider`: This node provides `BBOX_DETECTOR` and `SEGM_DETECTOR` that can be used in Impact Pack's Detector using the `MediaPipe-FaceMesh Preprocessor` of ControlNet Auxiliary Preprocessors.
 
+
 ### A1111 Compatibility support - These nodes assists in replicating the creation of A1111 in ComfyUI exactly.
   * `KSampler (Inspire)`: ComfyUI uses the CPU for generating random noise, while A1111 uses the GPU. One of the three factors that significantly impact reproducing A1111's results in ComfyUI can be addressed using `KSampler (Inspire)`.
       * Other point #1 : Please make sure you haven't forgotten to include 'embedding:' in the embedding used in the prompt, like 'embedding:easynegative.'
@@ -48,6 +50,7 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
     * `variation_seed` and `variation_strength` - Initial noise generated by the seed is transformed to the shape of `variation_seed` by `variation_strength`. If `variation_strength` is 0, it only relies on the influence of the seed, and if `variation_strength` is 1.0, it is solely influenced by `variation_seed`.
       * These parameters are used when you want to maintain the composition of an image generated by the seed but wish to introduce slight changes.
 
+
 ### Sampler nodes
   * `KSampler Progress (Inspire)` - In KSampler, the sampling process generates latent batches. By using `Video Combine` node from [ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite), you can create a video from the progress.
   * `Scheduled CFGGuider (Inspire)` - This is a CFGGuider that adjusts the schedule from from_cfg to to_cfg using linear, log, and exp methods.
@@ -61,7 +64,9 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
     * e.g. `prompts/example`
   * `Load Prompts From File (Inspire)`: It sequentially reads prompts from the specified file. The output it returns is ZIPPED_PROMPT.
     * Specify the file located under `ComfyUI-Inspire-Pack/prompts/`
-    * e.g. `prompts/example/prompt2.txt` 
+    * e.g. `prompts/example/prompt2.txt`
+  * `Load Single Prompt From File (Inspire)`: Loads a single prompt from a file containing multiple prompts by using an index.
+  * The prompts file directory can be specified as `inspire_prompts` in `extra_model_paths.yaml`
   * `Unzip Prompt (Inspire)`: Separate ZIPPED_PROMPT into `positive`, `negative`, and name components. 
     * `positive` and `negative` represent text prompts, while `name` represents the name of the prompt. When loaded from a file using `Load Prompts From File (Inspire)`, the name corresponds to the file name.
   * `Zip Prompt (Inspire)`: Create ZIPPED_PROMPT from positive, negative, and name_opt.
@@ -152,8 +157,13 @@ This repository offers various extension nodes for ComfyUI. Nodes here have diff
   * `IPAdapter Model Helper (Inspire)`: This provides presets that allow for easy loading of the IPAdapter related models. However, it is essential for the model's name to be accurate.
     * You can download the appropriate model through ComfyUI-Manager.
 
-### Util - Utilities
+### List - Nodes for List processing
   * `Float Range (Inspire)`: Create a float list that increases the value by `step` from `start` to `stop`. A list as large as the maximum limit is created, and when `ensure_end` is enabled, the last value of the list becomes the stop value.
+  * `Worklist To Item List (Inspire)`: The list in ComfyUI allows for repeated execution of a sub-workflow. This groups these repetitions (a.k.a. list) into a single ITEM_LIST output. ITEM_LIST can then be used in ForeachList.
+  * `▶Foreach List (Inspire)`: A starting node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nGenerate a new intermediate_output using item and intermediate_output as inputs, then connect it to ForeachListEnd.\nNOTE:If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list.
+  * `Foreach List◀ (Inspire)`: A end node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nNOTE:Directly connect the outputs of ForeachListBegin to 'flow_control' and 'remained_list'.
+
+### Util - Utilities
   * `ToIPAdapterPipe (Inspire)`, `FromIPAdapterPipe (Inspire)`: These nodes assists in conveniently using the bundled ipadapter_model, clip_vision, and model required for applying IPAdapter.
   * `List Counter (Inspire)`: When each item in the list traverses through this node, it increments a counter by one, generating an integer value.
   * `RGB Hex To HSV (Inspire)`: Convert an RGB hex string like `#FFD500` to HSV:
@@ -177,3 +187,5 @@ cubiq/[ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus)
 Davemane42/[ComfyUI_Dave_CustomNode](https://github.com/Davemane42/ComfyUI_Dave_CustomNode) - Original author of ConditioningStretch, ConditioningUpscale
 
 BlenderNeko/[ComfyUI_Noise](https://github.com/BlenderNeko/ComfyUI_Noise) - slerp code for noise variation
+
+BadCafeCode/[execution-inversion-demo-comfyui](https://github.com/BadCafeCode/execution-inversion-demo-comfyui) - reference loop implementation for ComfyUI

__init__.py

@@ -2,12 +2,12 @@
 @author: Dr.Lt.Data
 @title: Inspire Pack
 @nickname: Inspire Pack
-@description: This extension provides various nodes to support Lora Block Weight and the Impact Pack.
+@description: This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils and the Impact Pack.
 """
 
 import importlib
 
-version_code = [1, 2, 1]
+version_code = [1, 7]
 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 +8,24 @@ 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 "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/inspire_server.py

@@ -332,7 +332,7 @@ def populate_wildcards(json_data):
 
         if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
             extra_pnginfo = json_data['extra_data']['extra_pnginfo']
-            if 'workflow' in extra_pnginfo and 'nodes' in extra_pnginfo['workflow']:
+            if 'workflow' in extra_pnginfo and extra_pnginfo['workflow'] is not None and 'nodes' in extra_pnginfo['workflow']:
                 for node in extra_pnginfo['workflow']['nodes']:
                     key = str(node['id'])
                     if key in updated_widget_values:

inspire/list_nodes.py

@@ -1,3 +1,6 @@
+from comfy_execution.graph_utils import GraphBuilder, is_link
+from .libs.utils import any_typ
+
 class FloatRange:
     @classmethod
     def INPUT_TYPES(s):
@@ -15,7 +18,7 @@ class FloatRange:
 
     FUNCTION = "doit"
 
-    CATEGORY = "InspirePack/Util"
+    CATEGORY = "InspirePack/List"
 
     def doit(self, start, stop, step, limit, ensure_end):
         if start == stop or step == 0:
@@ -47,10 +50,170 @@ class FloatRange:
         return (res, )
 
 
+class WorklistToItemList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "item": (any_typ, ),
+                     }
+                }
+
+    INPUT_IS_LIST = True
+
+    RETURN_TYPES = ("ITEM_LIST",)
+    RETURN_NAMES = ("item_list",)
+
+    FUNCTION = "doit"
+
+    DESCRIPTION = "The list in ComfyUI allows for repeated execution of a sub-workflow.\nThis groups these repetitions (a.k.a. list) into a single ITEM_LIST output.\nITEM_LIST can then be used in ForeachList."
+
+    CATEGORY = "InspirePack/List"
+
+    def doit(self, item):
+        return (item, )
+
+
+# Loop nodes are implemented based on BadCafeCode's reference loop implementation
+# https://github.com/BadCafeCode/execution-inversion-demo-comfyui/blob/main/flow_control.py
+
+class ForeachListBegin:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "item_list": ("ITEM_LIST", {"tooltip": "ITEM_LIST containing items to be processed iteratively."}),
+                     },
+                "optional": {
+                        "initial_input": (any_typ, {"tooltip": "If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list."}),
+                    }
+                }
+
+    RETURN_TYPES = ("FOREACH_LIST_CONTROL", "ITEM_LIST", any_typ, any_typ)
+    RETURN_NAMES = ("flow_control", "remained_list", "item", "intermediate_output")
+    OUTPUT_TOOLTIPS = (
+        "Pass ForeachListEnd as is to indicate the end of the iteration.",
+        "Output the ITEM_LIST containing the remaining items during the iteration, passing ForeachListEnd as is to indicate the end of the iteration.",
+        "Output the current item during the iteration.",
+        "Output the intermediate results during the iteration.")
+
+    FUNCTION = "doit"
+
+    DESCRIPTION = "A starting node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nGenerate a new intermediate_output using item and intermediate_output as inputs, then connect it to ForeachListEnd.\nNOTE:If initial_input is omitted, the first item in item_list is used as the initial value, and the processing starts from the second item in item_list."
+
+    CATEGORY = "InspirePack/List"
+
+    def doit(self, item_list, initial_input=None):
+        if initial_input is None:
+            initial_input = item_list[0]
+            item_list = item_list[1:]
+            
+        if len(item_list) > 0:
+            return ("stub", item_list[1:], item_list[0], initial_input)
+
+        return ("stub", [], None, initial_input)
+
+
+class ForeachListEnd:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "flow_control": ("FOREACH_LIST_CONTROL", {"rawLink": True, "tooltip": "Directly connect the output of ForeachListBegin, the starting node of the iteration."}),
+                        "remained_list": ("ITEM_LIST", {"tooltip":"Directly connect the output of ForeachListBegin, the starting node of the iteration."}),
+                        "intermediate_output": (any_typ, {"tooltip":"Connect the intermediate outputs processed within the iteration here."}),
+                     },
+                "hidden": {
+                    "dynprompt": "DYNPROMPT",
+                    "unique_id": "UNIQUE_ID",
+                    }
+                }
+
+    RETURN_TYPES = (any_typ,)
+    RETURN_NAMES = ("result",)
+    OUTPUT_TOOLTIPS = ("This is the final output value.",)
+
+    FUNCTION = "doit"
+
+    DESCRIPTION = "A end node for performing iterative tasks by retrieving items one by one from the ITEM_LIST.\nNOTE:Directly connect the outputs of ForeachListBegin to 'flow_control' and 'remained_list'."
+
+    CATEGORY = "InspirePack/List"
+
+    def explore_dependencies(self, node_id, dynprompt, upstream):
+        node_info = dynprompt.get_node(node_id)
+        if "inputs" not in node_info:
+            return
+        for k, v in node_info["inputs"].items():
+            if is_link(v):
+                parent_id = v[0]
+                if parent_id not in upstream:
+                    upstream[parent_id] = []
+                    self.explore_dependencies(parent_id, dynprompt, upstream)
+                upstream[parent_id].append(node_id)
+
+    def collect_contained(self, node_id, upstream, contained):
+        if node_id not in upstream:
+            return
+        for child_id in upstream[node_id]:
+            if child_id not in contained:
+                contained[child_id] = True
+                self.collect_contained(child_id, upstream, contained)
+
+    def doit(self, flow_control, remained_list, intermediate_output, dynprompt, unique_id):
+        if len(remained_list) == 0:
+            return (intermediate_output,)
+
+        # We want to loop
+        this_node = dynprompt.get_node(unique_id)
+        upstream = {}
+
+        # Get the list of all nodes between the open and close nodes
+        self.explore_dependencies(unique_id, dynprompt, upstream)
+
+        contained = {}
+        open_node = flow_control[0]
+        self.collect_contained(open_node, upstream, contained)
+        contained[unique_id] = True
+        contained[open_node] = True
+
+        # We'll use the default prefix, but to avoid having node names grow exponentially in size,
+        # we'll use "Recurse" for the name of the recursively-generated copy of this node.
+        graph = GraphBuilder()
+        for node_id in contained:
+            original_node = dynprompt.get_node(node_id)
+            node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id)
+            node.set_override_display_id(node_id)
+
+        for node_id in contained:
+            original_node = dynprompt.get_node(node_id)
+            node = graph.lookup_node("Recurse" if node_id == unique_id else node_id)
+            for k, v in original_node["inputs"].items():
+                if is_link(v) and v[0] in contained:
+                    parent = graph.lookup_node(v[0])
+                    node.set_input(k, parent.out(v[1]))
+                else:
+                    node.set_input(k, v)
+
+        new_open = graph.lookup_node(open_node)
+        new_open.set_input("item_list", remained_list)
+        new_open.set_input("initial_input", intermediate_output)
+
+        my_clone = graph.lookup_node("Recurse" )
+        result = (my_clone.out(0),)
+
+        return {
+            "result": result,
+            "expand": graph.finalize(),
+        }
+
+
 NODE_CLASS_MAPPINGS = {
     "FloatRange //Inspire": FloatRange,
+    "WorklistToItemList //Inspire": WorklistToItemList,
+    "ForeachListBegin //Inspire": ForeachListBegin,
+    "ForeachListEnd //Inspire": ForeachListEnd,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "FloatRange //Inspire": "Float Range (Inspire)"
+    "FloatRange //Inspire": "Float Range (Inspire)",
+    "WorklistToItemList //Inspire": "Worklist To Item List (Inspire)",
+    "ForeachListBegin //Inspire": "▶Foreach List (Inspire)",
+    "ForeachListEnd //Inspire": "Foreach List◀ (Inspire)",
 }

inspire/lora_block_weight.py

@@ -484,7 +484,7 @@ class LoraLoaderBlockWeight:
 
             if k in muted_weights:
                 pass
-            elif 'text' in k:
+            elif 'text' in k or 'encoder' in k:
                 new_clip.add_patches({k: weights}, strength_clip * ratio)
             else:
                 new_modelpatcher.add_patches({k: weights}, strength_model * ratio)
@@ -546,7 +546,7 @@ class ApplyLBW:
 
             if k in muted_weights:
                 pass
-            elif 'text' in k:
+            elif 'text' in k or 'encoder' in k:
                 new_clip.add_patches({k: weights}, strength_clip * ratio)
             else:
                 new_modelpatcher.add_patches({k: weights}, strength_model * ratio)
@@ -822,9 +822,13 @@ class LoraBlockInfo:
         output_blocks = []
         output_blocks_map = {}
 
-        text_block_count = set()
-        text_blocks = []
-        text_blocks_map = {}
+        text_block_count1 = set()
+        text_blocks1 = []
+        text_blocks_map1 = {}
+
+        text_block_count2 = set()
+        text_blocks2 = []
+        text_blocks_map2 = {}
 
         double_block_count = set()
         double_blocks = []
@@ -902,12 +906,23 @@ class LoraBlockInfo:
                 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)
 
-                text_block_count.add(k_unet_int)
-                text_blocks.append(k_unet)
-                if k_unet_int in text_blocks_map:
-                    text_blocks_map[k_unet_int].append(k_unet)
+                text_block_count1.add(k_unet_int)
+                text_blocks1.append(k_unet)
+                if k_unet_int in text_blocks_map1:
+                    text_blocks_map1[k_unet_int].append(k_unet)
                 else:
-                    text_blocks_map[k_unet_int] = [k_unet]
+                    text_blocks_map1[k_unet_int] = [k_unet]
+
+            elif k_unet.startswith("r.encoder.block."):
+                k_unet_num = k_unet[len("r.encoder.block."):len("r.encoder.block.")+2]
+                k_unet_int = parse_unet_num(k_unet_num)
+
+                text_block_count2.add(k_unet_int)
+                text_blocks2.append(k_unet)
+                if k_unet_int in text_blocks_map2:
+                    text_blocks_map2[k_unet_int].append(k_unet)
+                else:
+                    text_blocks_map2[k_unet_int] = [k_unet]
 
             else:
                 others.append(k_unet)
@@ -951,10 +966,14 @@ class LoraBlockInfo:
             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())
-        for x in text_keys:
-            text += f" TXT_ENC{x}: {len(text_blocks_map[x])}\n"
+        text += f"\n-------[Base blocks] ({len(text_block_count1) + len(text_block_count2) + len(others)}, Subs={len(text_blocks1) + len(text_blocks2) + len(others)})-------\n"
+        text_keys1 = sorted(text_blocks_map1.keys())
+        for x in text_keys1:
+            text += f" TXT_ENC{x}: {len(text_blocks_map1[x])}\n"
+
+        text_keys2 = sorted(text_blocks_map2.keys())
+        for x in text_keys2:
+            text += f" TXT_ENC{x} [B]: {len(text_blocks_map2[x])}\n"
 
         for x in others:
             text += f" {x}\n"

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),
@@ -56,7 +58,6 @@ 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}),
@@ -64,6 +65,7 @@ class IPAdapterModelHelper:
                 "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"}
@@ -75,14 +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, insightface_model_name='buffalo_l'):
+    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)"})

inspire/prompt_support.py

@@ -18,15 +18,17 @@ from server import PromptServer
 from .libs import utils, common
 from .backend_support import CheckpointLoaderSimpleShared
 
+
+model_path = folder_paths.models_dir
+utils.add_folder_path_and_extensions("inspire_prompts", [os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "prompts"))], {'.txt'})
+
+
 prompt_builder_preset = {}
 
 
 resource_path = os.path.join(os.path.dirname(__file__), "..", "resources")
 resource_path = os.path.abspath(resource_path)
 
-prompts_path = os.path.join(os.path.dirname(__file__), "..", "prompts")
-prompts_path = os.path.abspath(prompts_path)
-
 
 try:
     pb_yaml_path = os.path.join(resource_path, 'prompt-builder.yaml')
@@ -38,15 +40,18 @@ 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:
     @classmethod
     def INPUT_TYPES(cls):
-        global prompts_path
         try:
-            prompt_dirs = [d for d in os.listdir(prompts_path) if os.path.isdir(os.path.join(prompts_path, d))]
+            prompt_dirs = []
+            for x in folder_paths.get_folder_paths('inspire_prompts'):
+                for d in os.listdir(x):
+                    if os.path.isdir(os.path.join(x, d)):
+                        prompt_dirs.append(d)
         except Exception:
             prompt_dirs = []
 
@@ -70,16 +75,24 @@ class LoadPromptsFromDir:
         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")]
+            candidates = []
+            for d in folder_paths.get_folder_paths('inspire_prompts'):
+                candidates.append(os.path.join(d, prompt_dir))
+
+            prompt_files = []
+            for x in candidates:
+                for root, dirs, files in os.walk(x):
+                    for file in files:
+                        if file.endswith(".txt"):
+                            prompt_files.append(os.path.join(root, file))
+
+            prompt_files.sort()
 
             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:
+            for file_name in prompt_files:
+                md5.update(file_name.encode('utf-8'))
+                with open(folder_paths.get_full_path('inspire_prompts', file_name), 'rb') as f:
                     while True:
                         chunk = f.read(4096)
                         if not chunk:
@@ -90,16 +103,24 @@ class LoadPromptsFromDir:
 
     @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")]
-        files.sort()
+        candidates = []
+        for d in folder_paths.get_folder_paths('inspire_prompts'):
+            candidates.append(os.path.join(d, prompt_dir))
+
+        prompt_files = []
+        for x in candidates:
+            for root, dirs, files in os.walk(x):
+                for file in files:
+                    if file.endswith(".txt"):
+                        prompt_files.append(os.path.join(root, file))
+
+        prompt_files.sort()
 
         prompts = []
-        for file_name in files:
+        for file_name in prompt_files:
             print(f"file_name: {file_name}")
             try:
-                with open(os.path.join(prompt_dir, file_name), "r", encoding="utf-8") as file:
+                with open(file_name, "r", encoding="utf-8") as file:
                     prompt_data = file.read()
                     prompt_list = re.split(r'\n\s*-+\s*\n', prompt_data)
 
@@ -115,7 +136,7 @@ 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, )
 
@@ -123,15 +144,16 @@ class LoadPromptsFromDir:
 class LoadPromptsFromFile:
     @classmethod
     def INPUT_TYPES(cls):
-        global prompts_path
+        prompt_files = []
         try:
-            prompt_files = []
-            for root, dirs, files in os.walk(prompts_path):
-                for file in files:
-                    if file.endswith(".txt"):
-                        file_path = os.path.join(root, file)
-                        rel_path = os.path.relpath(file_path, prompts_path)
-                        prompt_files.append(rel_path)
+            prompts_paths = folder_paths.get_folder_paths('inspire_prompts')
+            for prompts_path in prompts_paths:
+                for root, dirs, files in os.walk(prompts_path):
+                    for file in files:
+                        if file.endswith(".txt"):
+                            file_path = os.path.join(root, file)
+                            rel_path = os.path.relpath(file_path, prompts_path)
+                            prompt_files.append(rel_path)
         except Exception:
             prompt_files = []
 
@@ -161,9 +183,18 @@ class LoadPromptsFromFile:
         elif not reload:
             return prompt_file
         else:
-            prompt_path = os.path.join(prompts_path, prompt_file)
+            matched_path = None
+            for x in folder_paths.get_folder_paths('inspire_prompts'):
+                matched_path = os.path.join(x, prompt_file)
+                if not os.path.exists(matched_path):
+                    matched_path = None
+                else:
+                    break
 
-            with open(prompt_path, 'rb') as f:
+            if matched_path is None:
+                return float('NaN')
+
+            with open(matched_path, 'rb') as f:
                 while True:
                     chunk = f.read(4096)
                     if not chunk:
@@ -174,12 +205,21 @@ class LoadPromptsFromFile:
 
     @staticmethod
     def doit(prompt_file, text_data_opt=None, reload=False):
-        prompt_path = os.path.join(prompts_path, prompt_file)
+        matched_path = None
+        for d in folder_paths.get_folder_paths('inspire_prompts'):
+            matched_path = os.path.join(d, prompt_file)
+            if not os.path.exists(matched_path):
+                matched_path = None
+            else:
+                break
+
+        if matched_path:
+            print(f"[WARN] LoadPromptsFromFile: file not found '{prompt_file}'")
 
         prompts = []
         try:
             if not text_data_opt:
-                with open(prompt_path, "r", encoding="utf-8") as file:
+                with open(matched_path, "r", encoding="utf-8") as file:
                     prompt_data = file.read()
             else:
                 prompt_data = text_data_opt
@@ -188,8 +228,8 @@ class LoadPromptsFromFile:
 
             pattern = r"positive:(.*?)(?:\n*|$)negative:(.*)"
 
-            for prompt in prompt_list:
-                matches = re.search(pattern, prompt, re.DOTALL)
+            for p in prompt_list:
+                matches = re.search(pattern, p, re.DOTALL)
 
                 if matches:
                     positive_text = matches.group(1).strip()
@@ -199,7 +239,7 @@ 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, )
 
@@ -207,15 +247,16 @@ class LoadPromptsFromFile:
 class LoadSinglePromptFromFile:
     @classmethod
     def INPUT_TYPES(cls):
-        global prompts_path
+        prompt_files = []
         try:
-            prompt_files = []
-            for root, dirs, files in os.walk(prompts_path):
-                for file in files:
-                    if file.endswith(".txt"):
-                        file_path = os.path.join(root, file)
-                        rel_path = os.path.relpath(file_path, prompts_path)
-                        prompt_files.append(rel_path)
+            prompts_paths = folder_paths.get_folder_paths('inspire_prompts')
+            for prompts_path in prompts_paths:
+                for root, dirs, files in os.walk(prompts_path):
+                    for file in files:
+                        if file.endswith(".txt"):
+                            file_path = os.path.join(root, file)
+                            rel_path = os.path.relpath(file_path, prompts_path)
+                            prompt_files.append(rel_path)
         except Exception:
             prompt_files = []
 
@@ -235,7 +276,17 @@ class LoadSinglePromptFromFile:
 
     @staticmethod
     def doit(prompt_file, index, text_data_opt=None):
-        prompt_path = os.path.join(prompts_path, prompt_file)
+        prompt_path = None
+        prompts_paths = folder_paths.get_folder_paths('inspire_prompts')
+        for d in prompts_paths:
+            prompt_path = os.path.join(d, prompt_file)
+            if os.path.exists(prompt_path):
+                break
+            else:
+                prompt_path = None
+
+        if prompt_path:
+            print(f"[WARN] LoadSinglePromptFromFile: file not found '{prompt_file}'")
 
         prompts = []
         try:
@@ -262,7 +313,7 @@ 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, )
 
@@ -623,7 +674,7 @@ class SeedExplorer:
             "optional":
                 {
                     "variation_method": (["linear", "slerp"],),
-                    "model": ("model",),
+                    "model": ("MODEL",),
                 }
         }
 

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/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, )
 
 

pyproject.toml

@@ -1,7 +1,7 @@
 [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 = "1.2.1"
+description = "This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils, Noise(Seed) Utils, ... and the Impact Pack."
+version = "1.7"
 license = { file = "LICENSE" }
 dependencies = ["matplotlib", "cachetools"]
 

requirements.txt

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