change all the things

py/constrain_image.py

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+import torch
+import numpy as np
+from PIL import Image
+
+class ConstrainImage:
+    """
+    A node that constrains an image to a maximum and minimum size while maintaining aspect ratio.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "max_width": ("INT", {"default": 1024, "min": 0}),
+                "max_height": ("INT", {"default": 1024, "min": 0}),
+                "min_width": ("INT", {"default": 0, "min": 0}),
+                "min_height": ("INT", {"default": 0, "min": 0}),
+                "crop_if_required": (["yes", "no"], {"default": "no"}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "constrain_image"
+    CATEGORY = "image"
+    OUTPUT_IS_LIST = (True,)
+
+    def constrain_image(self, images, max_width, max_height, min_width, min_height, crop_if_required):
+        crop_if_required = crop_if_required == "yes"
+        results = []
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB")
+
+            current_width, current_height = img.size
+            aspect_ratio = current_width / current_height
+
+            constrained_width = max(min(current_width, min_width), max_width)
+            constrained_height = max(min(current_height, min_height), max_height)
+
+            if constrained_width / constrained_height > aspect_ratio:
+                constrained_width = max(int(constrained_height * aspect_ratio), min_width)
+                if crop_if_required:
+                    constrained_height = int(current_height / (current_width / constrained_width))
+            else:
+                constrained_height = max(int(constrained_width / aspect_ratio), min_height)
+                if crop_if_required:
+                    constrained_width = int(current_width / (current_height / constrained_height))
+
+            resized_image = img.resize((constrained_width, constrained_height), Image.ANTIALIAS)
+
+            if crop_if_required and (constrained_width > max_width or constrained_height > max_height):
+                left = max((constrained_width - max_width) // 2, 0)
+                top = max((constrained_height - max_height) // 2, 0)
+                right = min(constrained_width, max_width) + left
+                bottom = min(constrained_height, max_height) + top
+                resized_image = resized_image.crop((left, top, right, bottom))
+
+            resized_image = np.array(resized_image).astype(np.float32) / 255.0
+            resized_image = torch.from_numpy(resized_image)[None,]
+            results.append(resized_image)
+                
+        return (results,)
+
+NODE_CLASS_MAPPINGS = {
+    "ConstrainImage|pysssss": ConstrainImage,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ConstrainImage|pysssss": "Constrain Image 🐍",
+}

py/show_text.py

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+class ShowText:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "text": ("STRING", {"forceInput": True}),
+        }}
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "notify"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "utils"
+
+    def notify(self, text):
+        return {"ui": {"text": text}, "result": (text,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "ShowText|pysssss": ShowText,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ShowText|pysssss": "Show Text 🐍",
+}

py/string_function.py

@@ -0,0 +1,45 @@
+import re
+
+class StringFunction:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "action": (["append", "replace"], {}),
+                "tidy_tags": (["yes", "no"], {}),
+                "text_a": ("STRING", {"multiline": True}),
+                "text_b": ("STRING", {"multiline": True}),
+            },
+            "optional": {
+                "text_c": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "exec"
+    CATEGORY = "utils"
+
+    def exec(self, action, tidy_tags, text_a, text_b, text_c=""):
+        tidy_tags = tidy_tags == "yes"
+        out = ""
+        if action == "append":
+            out = (", " if tidy_tags else "").join(filter(None, [text_a, text_b, text_c]))
+        else:
+           if text_c is None:
+               text_c = ""
+           if text_b.startswith("/") and text_b.endswith("/"):
+               regex = text_b[1:-1]
+               out = re.sub(regex, text_c, text_a)
+           else:
+               out = text_a.replace(text_b, text_c)
+        if tidy_tags:
+            out = out.replace("  ", " ").replace(" ,", ",").replace(",,", ",").replace(",,", ",")
+        return (out, )
+            
+NODE_CLASS_MAPPINGS = {
+    "StringFunction|pysssss": StringFunction,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "StringFunction|pysssss": "String Function 🐍",
+}

py/workflows.py

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+from server import PromptServer
+from aiohttp import web
+import os
+import inspect
+import json
+
+root_directory = os.path.dirname(inspect.getfile(PromptServer))
+workflows_directory = os.path.join(root_directory, "pysssss-workflows")
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+
+@PromptServer.instance.routes.get("/pysssss/workflows")
+async def get_workflows(request):
+    files = []
+    for dirpath, directories, file in os.walk(workflows_directory):
+        for file in file:
+            if (file.endswith(".json")):
+                files.append(os.path.relpath(os.path.join(
+                    dirpath, file), workflows_directory))
+    return web.json_response(list(map(lambda f: os.path.splitext(f)[0].replace("\\", "/"), files)))
+
+
+@PromptServer.instance.routes.get("/pysssss/workflows/{name:.+}")
+async def get_workflow(request):
+    file = os.path.abspath(os.path.join(
+        workflows_directory, request.match_info["name"] + ".json"))
+    if os.path.commonpath([file, workflows_directory]) != workflows_directory:
+        return web.Response(status=403)
+
+    return web.FileResponse(file)
+
+
+@PromptServer.instance.routes.post("/pysssss/workflows")
+async def save_workflow(request):
+    json_data = await request.json()
+    file = os.path.abspath(os.path.join(
+        workflows_directory, json_data["name"] + ".json"))
+    if os.path.commonpath([file, workflows_directory]) != workflows_directory:
+        return web.Response(status=403)
+
+    if os.path.exists(file) and ("overwrite" not in json_data or json_data["overwrite"] == False):
+        return web.Response(status=409)
+
+    sub_path = os.path.dirname(file)
+    if not os.path.exists(sub_path):
+        os.makedirs(sub_path)
+
+    with open(file, "w") as f:
+        f.write(json.dumps(json_data["workflow"]))
+
+    return web.Response(status=201)