add ImpactWildCards

refactoring directory structure

.gitignore

@@ -1,2 +1,3 @@
 __pycache__
-*.ini
+*.ini
+wildcards/**

README.md

@@ -64,7 +64,10 @@ This takes latent as input and outputs latent as the result.
 * ImageSender, ImageReceiver - The images generated in ImageSender are automatically sent to the ImageReceiver with the same link_id.
   * NOTE: requires this [patch](https://github.com/comfyanonymous/ComfyUI/pull/746)
 * Switch (image,mask), Switch (latent) - Among multiple inputs, it selects the input designated by the selector and outputs it. The first input must be provided, while the others are optional. However, if the input specified by the selector is not connected, an error may occur.
- 
+* ImpactWildcardProcessor - The text is generated by processing the wildcard in the Text. If the mode is set to "populate", a dynamic prompt is generated with each execution and the input is filled in the second textbox. If the mode is set to "fixed", the content of the second textbox remains unchanged.
+  * When an image is generated with the "fixed" mode, the prompt used for that particular generation is stored in the metadata.
+  * Known Issue: The presetText.js script from **pythongosssss's [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts)** is causing a conflict, preventing it from being used together.
+
 # Feature
 * Interactive SAM Detector (Clipspace) - When you right-click on a node that has 'MASK' and 'IMAGE' outputs, a context menu will open. From this menu, you can either open a dialog to create a SAM Mask using 'Open in SAM Detector', or copy the content (likely mask data) using 'Copy (Clipspace)' and generate a mask using 'Impact SAM Detector' from the clipspace menu, and then paste it using 'Paste (Clipspace)'.
 

__init__.py

@@ -2,18 +2,27 @@ import shutil
 import folder_paths
 import os
 import sys
+import importlib
 
 comfy_path = os.path.dirname(folder_paths.__file__)
-impact_path = os.path.dirname(__file__)
+impact_path = os.path.join(os.path.dirname(__file__))
+modules_path = os.path.join(os.path.dirname(__file__), "modules")
+wildcards_path = os.path.join(os.path.dirname(__file__), "wildcards")
 
-sys.path.append(impact_path)
+sys.path.append(modules_path)
 
-import impact_config
-print(f"### Loading: ComfyUI-Impact-Pack ({impact_config.version})")
+import impact.config
+print(f"### Loading: ComfyUI-Impact-Pack ({impact.config.version})")
+
+def do_install():
+    spec = importlib.util.spec_from_file_location('impact_install', os.path.join(os.path.dirname(__file__), 'install.py'))
+    impact_install = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(impact_install)
 
 # ensure dependency
-if impact_config.read_config()[1] < impact_config.dependency_version:
-    import install  # to install dependencies
+if impact.config.read_config()[1] < impact.config.dependency_version:
+    do_install()
+
 # Core
 # recheck dependencies for colab
 try:
@@ -31,10 +40,11 @@ try:
     from skimage.measure import label, regionprops
     from collections import namedtuple
 except:
+    import importlib
     print("### ComfyUI-Impact-Pack: Reinstall dependencies (several dependencies are missing.)")
-    import install
+    do_install()
 
-import impact_server  # to load server api
+import impact.impact_server  # to load server api
 
 def setup_js():
     # remove garbage
@@ -55,10 +65,12 @@ def setup_js():
     
 setup_js()
 
-import legacy_nodes
-from impact_pack import *
-from detectors import *
-from impact_pipe import *
+import impact.legacy_nodes
+from impact.impact_pack import *
+from impact.detectors import *
+from impact.pipe import *
+
+impact.wildcards.read_wildcard_dict(wildcards_path)
 
 NODE_CLASS_MAPPINGS = {
     "SAMLoader": SAMLoader,
@@ -128,13 +140,19 @@ NODE_CLASS_MAPPINGS = {
     "ImageMaskSwitch": ImageMaskSwitch,
     "LatentSwitch": LatentSwitch,
 
-    "MaskPainter": legacy_nodes.MaskPainter,
-    "MMDetLoader": legacy_nodes.MMDetLoader,
-    "SegsMaskCombine": legacy_nodes.SegsMaskCombine,
-    "BboxDetectorForEach": legacy_nodes.BboxDetectorForEach,
-    "SegmDetectorForEach": legacy_nodes.SegmDetectorForEach,
-    "BboxDetectorCombined": legacy_nodes.BboxDetectorCombined,
-    "SegmDetectorCombined": legacy_nodes.SegmDetectorCombined,
+    # "SaveConditioning": SaveConditioning,
+    # "LoadConditioning": LoadConditioning,
+
+    "ImpactWildcardProcessor": ImpactWildcardProcessor,
+    "ImpactLogger": ImpactLogger,
+
+    "MaskPainter": impact.legacy_nodes.MaskPainter,
+    "MMDetLoader": impact.legacy_nodes.MMDetLoader,
+    "SegsMaskCombine": impact.legacy_nodes.SegsMaskCombine,
+    "BboxDetectorForEach": impact.legacy_nodes.BboxDetectorForEach,
+    "SegmDetectorForEach": impact.legacy_nodes.SegmDetectorForEach,
+    "BboxDetectorCombined": impact.legacy_nodes.BboxDetectorCombined,
+    "SegmDetectorCombined": impact.legacy_nodes.SegmDetectorCombined,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

install.py

@@ -4,16 +4,15 @@ import subprocess
 
 
 comfy_path = '../..'
+if sys.argv[0] == 'install.py':
+    sys.path.append('.')   # for portable version
 
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
-sys.path.append('.')   # for portable version
 sys.path.append(comfy_path)
 
-
 import platform
 import folder_paths
 from torchvision.datasets.utils import download_url
-import impact_config
+import impact.config
 
 
 print("### ComfyUI-Impact-Pack: Check dependencies")
@@ -118,7 +117,7 @@ def install():
         print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})")
         os.mkdir(onnx_path)
 
-    impact_config.write_config(comfy_path)
+    impact.config.write_config(comfy_path)
 
 
 install()

js/impact-pack.js

@@ -153,6 +153,42 @@ app.registerExtension({
 				ComfyApp.open_maskeditor();
 			});
 		}
+
+		if(node.comfyClass == "ImpactWildcardProcessor") {
+			let force_serializeValue = async (n,i) =>
+				{
+					if(n.widgets_values[2] == "Fixed") {
+						return node.widgets[1].value;
+					}
+					else {
+						let response = await fetch(`/impact/wildcards`, {
+																method: 'POST',
+																headers: { 'Content-Type': 'application/json' },
+																body: JSON.stringify({text: n.widgets_values[0]})
+															});
+
+						let populated = await response.json();
+
+						n.widgets_values[2] = "Fixed";
+						n.widgets_values[1] = populated.text;
+						node.widgets[1].value = populated.text;
+
+						return populated.text;
+					}
+				};
+
+			// prevent hooking by dynamicPrompt.js
+			Object.defineProperty(node.widgets[0], "serializeValue", {
+				set: () => {},
+				get: (value) => { return (n,i) => { return n.widgets_values[i]; }; }
+			});
+
+			Object.defineProperty(node.widgets[1], "serializeValue", {
+				set: () => {},
+				get: (value) => { return force_serializeValue; }
+			});
+		}
+
 		if (node.comfyClass == "PreviewBridge" || node.comfyClass == "MaskPainter") {
 			node.widgets[0].value = '#placeholder';
 
@@ -169,13 +205,31 @@ app.registerExtension({
 							node.widgets[0].value = {...input_tracking[id][1]};
 							input_dirty[id] = false;
 							need_invalidate = true
+							this._images = app.nodeOutputs[id].images;
 						}
 
-						node.widgets[0].value['image_hash'] = app.nodeOutputs[id]['aux'][0];
-						node.widgets[0].value['forward_filename'] = app.nodeOutputs[id]['aux'][1][0]['filename'];
-						node.widgets[0].value['forward_subfolder'] = app.nodeOutputs[id]['aux'][1][0]['subfolder'];
-						node.widgets[0].value['forward_type'] = app.nodeOutputs[id]['aux'][1][0]['type'];
-						app.nodeOutputs[id].images = [node.widgets[0].value];
+						let filename = app.nodeOutputs[id]['aux'][1][0]['filename'];
+						let subfolder = app.nodeOutputs[id]['aux'][1][0]['subfolder'];
+						let type = app.nodeOutputs[id]['aux'][1][0]['type'];
+
+						let item =
+							{
+								image_hash: app.nodeOutputs[id]['aux'][0],
+								forward_filename: app.nodeOutputs[id]['aux'][1][0]['filename'],
+								forward_subfolder: app.nodeOutputs[id]['aux'][1][0]['subfolder'],
+								forward_type: app.nodeOutputs[id]['aux'][1][0]['type']
+							};
+
+						app.nodeOutputs[id].images = [{
+								...node._images[0],
+								...item
+							}];
+
+						node.widgets[0].value =
+							{
+								...node._images[0],
+								...item
+							};
 
 						if(need_invalidate) {
 							Promise.all(
@@ -184,8 +238,7 @@ app.registerExtension({
 										const img = new Image();
 										img.onload = () => r(img);
 										img.onerror = () => r(null);
-										img.src = "/view?" + new URLSearchParams(src[0]).toString();
-										console.log(`new img => ${img.src}`);
+										img.src = "/view?" + new URLSearchParams(src).toString();
 									});
 								})
 							).then((imgs) => {
@@ -193,6 +246,8 @@ app.registerExtension({
 								this.setSizeForImage?.();
 								app.graph.setDirtyCanvas(true);
 							});
+
+							app.nodeOutputs[id].images[0] = { ...node.widgets[0].value };
 						}
 
 						return app.nodeOutputs[id].images;

js/impact-sam-editor.js

@@ -575,16 +575,26 @@ class ImpactSamEditorDialog extends ComfyDialog {
 			const index = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image');
 
 			if(index >= 0)
-				ComfyApp.clipspace.widgets[index].value = item;
+				ComfyApp.clipspace.widgets[index].value = `${filename} [temp]`;
 		}
 
 		const dataURL = save_canvas.toDataURL();
 		const blob = dataURLToBlob(dataURL);
 
-		const original_blob = loadedImageToBlob(this.image);
+		let original_url = new URL(this.image.src);
+
+		const original_ref = { filename: original_url.searchParams.get('filename') };
+
+		let original_subfolder = original_url.searchParams.get("subfolder");
+		if(original_subfolder)
+			original_ref.subfolder = original_subfolder;
+
+		let original_type = original_url.searchParams.get("type");
+		if(original_type)
+			original_ref.type = original_type;
 
 		formData.append('image', blob, filename);
-		formData.append('original_image', original_blob);
+		formData.append('original_ref', JSON.stringify(original_ref));
 		formData.append('type', "temp");
 
 		await uploadMask(item, formData);

modules/impact/config.py

@@ -1,7 +1,7 @@
 import configparser
 import os
 
-version = "V2.10.2"
+version = "V2.12.3"
 
 dependency_version = 1
 

modules/impact/core.py

@@ -1,20 +1,15 @@
 import os
-import sys
 import mmcv
 from mmdet.apis import (inference_detector, init_detector)
 from mmdet.evaluation import get_classes
 from segment_anything import SamPredictor
 import torch.nn.functional as F
 
-from impact_utils import *
+from impact.utils import *
 from collections import namedtuple
 import numpy as np
 from skimage.measure import label, regionprops
 
-main_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
-sys.path.append(os.path.dirname(__file__))
-sys.path.append(main_dir)
-
 import nodes
 import comfy_extras.nodes_upscale_model as model_upscale
 from server import PromptServer
@@ -290,6 +285,8 @@ def make_sam_mask(sam_model, segs, image, detection_hint, dilation,
     predictor = SamPredictor(sam_model)
     image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
 
+    print(f"image.shape: {image.shape}")
+
     predictor.set_image(image, "RGB")
 
     total_masks = []
@@ -481,7 +478,7 @@ class ONNXDetector(BBoxDetector):
     def detect(self, image, threshold, dilation, crop_factor, drop_size=1):
         drop_size = max(drop_size, 1)
         try:
-            import onnx
+            import impact.onnx as onnx
 
             h = image.shape[1]
             w = image.shape[2]

modules/impact/detectors.py

@@ -1,5 +1,5 @@
-import impact_core as core
-from impact_config import MAX_RESOLUTION
+import impact.core as core
+from impact.config import MAX_RESOLUTION
 
 
 class SAMDetectorCombined:

modules/impact/impact_pack.py

@@ -6,10 +6,15 @@ import comfy.sd
 import warnings
 from segment_anything import sam_model_registry
 
-from impact_utils import *
-import impact_core as core
-from impact_core import SEG, NO_BBOX_DETECTOR, NO_SEGM_DETECTOR
-from impact_config import MAX_RESOLUTION
+from impact.utils import *
+import impact.core as core
+from impact.core import SEG, NO_BBOX_DETECTOR, NO_SEGM_DETECTOR
+from impact.config import MAX_RESOLUTION
+from PIL import Image
+import numpy as np
+import hashlib
+import json
+import safetensors.torch
 
 warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
 
@@ -1203,12 +1208,29 @@ class SubtractMask:
 
 import nodes
 
+def get_image_hash(arr):
+    split_index1 = arr.shape[0] // 2
+    split_index2 = arr.shape[1] // 2
+    part1 = arr[:split_index1, :split_index2]
+    part2 = arr[:split_index1, split_index2:]
+    part3 = arr[split_index1:, :split_index2]
+    part4 = arr[split_index1:, split_index2:]
+
+    # 각 부분을 합산
+    sum1 = np.sum(part1)
+    sum2 = np.sum(part2)
+    sum3 = np.sum(part3)
+    sum4 = np.sum(part4)
+
+    return hash((sum1, sum2, sum3, sum4))
+
+preview_hash_map = {}
 
 class PreviewBridge(nodes.PreviewImage):
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"images": ("IMAGE",), },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
                 "optional": {"image": (["#placeholder"], )},
                 }
 
@@ -1218,8 +1240,23 @@ class PreviewBridge(nodes.PreviewImage):
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, images, image, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        if image == "#placeholder" or image['image_hash'] != id(images) or ('0' in image and 'forward_filename' not in image[0]):
+    def doit(self, images, image, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, unique_id=None):
+        global preview_hash_map
+        if image != "#placeholder" and isinstance(image, str):
+            image_path = folder_paths.get_annotated_filepath(image)
+            img = Image.open(image_path).convert("RGB")
+            data = np.array(img)
+            image_hash = get_image_hash(data)
+        else:
+            data = (255. * images[0].cpu().numpy()).astype(int)
+            image_hash = get_image_hash(data)
+
+        is_changed = False
+        if unique_id not in preview_hash_map or preview_hash_map[unique_id] != image_hash:
+            preview_hash_map[unique_id] = image_hash
+            is_changed = True
+
+        if is_changed or image == "#placeholder":
             # new input image
             res = self.save_images(images, filename_prefix, prompt, extra_pnginfo)
 
@@ -1232,7 +1269,7 @@ class PreviewBridge(nodes.PreviewImage):
 
             image, mask = nodes.LoadImage().load_image(filepath)
 
-            res['ui']['aux'] = [id(images), res['ui']['images']]
+            res['ui']['aux'] = [image_hash, res['ui']['images']]
             res['result'] = (image, mask, )
 
             return res
@@ -1257,7 +1294,7 @@ class PreviewBridge(nodes.PreviewImage):
             if 'type' in image and image['type'] != "":
                 imgpath += f" [{image['type']}]"
 
-            res['ui']['aux'] = [id(images), [forward]]
+            res['ui']['aux'] = [image_hash, [forward]]
             res['result'] = nodes.LoadImage().load_image(imgpath)
 
             return res
@@ -1381,4 +1418,134 @@ class LatentSwitch:
         elif select == 3:
             return (latent3_opt,)
         else:
-            return (latent4_opt,)
+            return (latent4_opt,)
+
+
+class SaveConditioning:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                             "filename_prefix": ("STRING", {"default": "conditioning/ComfyUI"}),
+                             },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "doit"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing"
+
+    def doit(self, conditioning, filename_prefix, prompt=None, extra_pnginfo=None):
+        # support save metadata for latent sharing
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        for tensor_data, meta_data in conditioning:
+            full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+            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}_.conditioning"
+            file = os.path.join(full_output_folder, file)
+
+            print(f"meta_data:{meta_data}")
+            print(f"tensor_data:{tensor_data}")
+
+            output = {"conditioning": tensor_data}
+            metadata['conditioning_aux'] = json.dumps(meta_data)
+
+            safetensors.torch.save_file(output, file, metadata=metadata)
+
+        return {}
+
+
+class LoadConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".conditioning")]
+        return {"required": {"conditioning": [sorted(files), ]}, }
+
+    CATEGORY = "_for_testing"
+
+    RETURN_TYPES = ("CONDITIONING", )
+    FUNCTION = "load"
+
+    def load(self, conditioning):
+        conditioning_path = folder_paths.get_annotated_filepath(conditioning)
+        data = safetensors.torch.load_file(conditioning_path, device="cpu")
+        return ([[data['conditioning'], {}]], )
+
+    @classmethod
+    def IS_CHANGED(s, conditioning):
+        image_path = folder_paths.get_annotated_filepath(conditioning)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, conditioning):
+        if not folder_paths.exists_annotated_filepath(conditioning):
+            return "Invalid conditioning file: {}".format(conditioning)
+        return True
+
+
+class ImpactWildcardProcessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "wildcard_text": ("STRING", {"multiline": True}),
+                        "populated_text": ("STRING", {"multiline": True}),
+                        "mode": (["Populate", "Fixed"], ),
+                    },
+                }
+
+    CATEGORY = "ImpactPack/Prompt"
+
+    RETURN_TYPES = ("STRING", )
+    FUNCTION = "doit"
+
+    def doit(self, wildcard_text, populated_text, mode):
+        return (populated_text, )
+
+
+class ImpactLogger:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "text": ("STRING", {"default": ""}),
+                    },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    CATEGORY = "ImpactPack/Debug"
+
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ()
+    FUNCTION = "doit"
+
+    def doit(self, text, prompt, extra_pnginfo):
+        print(f"[IMPACT LOGGER]: {text}")
+
+        print(f"         PROMPT: {prompt}")
+
+        # for x in prompt:
+        #     if 'inputs' in x and 'populated_text' in x['inputs']:
+        #         print(f"PROMP: {x['10']['inputs']['populated_text']}")
+        #
+        # for x in extra_pnginfo['workflow']['nodes']:
+        #     if x['type'] == 'ImpactWildcardProcessor':
+        #         print(f" WV : {x['widgets_values'][1]}\n")
+
+        return {}

modules/impact/impact_server.py

@@ -5,13 +5,14 @@ from aiohttp import web
 import server
 import folder_paths
 
-import impact_core as core
-import impact_pack
+import impact.core as core
+import impact.impact_pack as impact_pack
 from segment_anything import SamPredictor, sam_model_registry
 import numpy as np
 import nodes
 from PIL import Image
 import io
+import impact.wildcards as wildcards
 
 @server.PromptServer.instance.routes.post("/upload/temp")
 async def upload_image(request):
@@ -93,12 +94,15 @@ async def load_sam_model(request):
 
         sam_predictor.set_image(image, "RGB")
 
+        print(f"ComfyUI-Impact-Pack: SAM model loaded. ")
+
 
 @server.PromptServer.instance.routes.post("/sam/release")
 async def release_sam(request):
     global sam_predictor
 
     with sam_lock:
+        del sam_predictor
         sam_predictor = None
 
     print(f"ComfyUI-Impact-Pack: unloading SAM model")
@@ -146,3 +150,10 @@ async def sam_detect(request):
 
         else:
             return web.Response(status=400)
+
+
+@server.PromptServer.instance.routes.post("/impact/wildcards")
+async def populate_wildcards(request):
+    data = await request.json()
+    populated = wildcards.process(data['text'])
+    return web.json_response({"text": populated})

modules/impact/legacy_nodes.py

@@ -1,9 +1,9 @@
 import folder_paths
-import impact_core as core
-from impact_utils import *
-from impact_core import SEG
+
+import impact.core as core
+from impact.utils import *
+from impact.core import SEG
 import nodes
-import os
 
 class NO_BBOX_MODEL:
     pass

modules/impact/onnx.py

@@ -1,5 +1,5 @@
 import additional_dependencies
-from impact_utils import *
+from impact.utils import *
 
 additional_dependencies.ensure_onnx_package()
 

modules/impact/utils.py

@@ -5,6 +5,7 @@ from PIL import Image, ImageFilter
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
+
 def pil2tensor(image):
     return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 

modules/impact/wildcards.py

@@ -0,0 +1,63 @@
+import re
+import random
+import os
+
+wildcard_dict = {}
+
+
+def read_wildcard_dict(wildcard_path):
+    global wildcard_dict
+    for root, directories, files in os.walk(wildcard_path):
+        for file in files:
+            if file.endswith('.txt'):
+                file_path = os.path.join(root, file)
+                key = os.path.splitext(file)[0]
+
+                with open(file_path, 'r') as f:
+                    lines = f.read().splitlines()
+
+                wildcard_dict[key] = lines
+
+    return wildcard_dict
+
+
+def process(text):
+    def replace_options(string):
+        replacements_found = False
+
+        def replace_option(match):
+            nonlocal replacements_found
+            options = match.group(1).split('|')
+            replacement = random.choice(options)
+            replacements_found = True
+            return replacement
+
+        pattern = r'{([^{}]*?)}'
+        replaced_string = re.sub(pattern, replace_option, string)
+
+        return replaced_string, replacements_found
+
+    def replace_wildcard(string):
+        global wildcard_dict
+        pattern = r"__([\w.-]+)__"
+        matches = re.findall(pattern, string)
+
+        for match in matches:
+            if match in wildcard_dict:
+                replacement = random.choice(wildcard_dict[match])
+                string = string.replace(f"__{match}__", replacement, 1)
+        
+        return string
+
+    # phase1: replace options
+    phase1, is_replaced = replace_options(text)
+
+    while is_replaced:
+        phase1, is_replaced = replace_options(phase1)
+
+    # phase2: replace wildcards
+    phase2 = replace_wildcard(phase1)
+    
+    return phase2
+
+

wildcards/samples/flower.txt

@@ -0,0 +1,9 @@
+rose
+orchid
+iris
+carnation
+lily
+daisy
+chrysanthemum
+daffodil
+dahlia

wildcards/samples/jewel.txt

@@ -0,0 +1,9 @@
+diamond
+emerald
+sapphire
+opal
+ruby
+topaz
+pearl
+rubyamethyst
+aquamarine