...

.gitignore

@@ -4,4 +4,5 @@ API_example/
 clear_vram.py
 web/js/clear_vram.js
 speakers
-*.text
+*.text
+web/js/*.txt

CUSTOM_STRING.py.txt

@@ -1,11 +0,0 @@
-class CustomStringType:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"value": ("STRING", {"multiline": True})}}
-    
-    RETURN_TYPES = ("CUSTOM_STRING",)
-    FUNCTION = "passthrough"
-    CATEGORY = "Bjornulf"
-
-    def passthrough(self, value):
-        return (value,)

clear_vram.py.txt

@@ -1,33 +0,0 @@
-import torch
-import gc
-
-class ClearVRAM:
-    def __init__(self):
-        self.cleared = False
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "clear": ("BOOLEAN", {"default": False, "label": "Clear VRAM"})
-            }
-        }
-    
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "clear_vram"
-    CATEGORY = "memory_management"
-    OUTPUT_NODE = True
-
-    def clear_vram(self, clear):
-        if clear and not self.cleared:
-            torch.cuda.empty_cache()
-            gc.collect()
-            torch.cuda.synchronize()
-            print("VRAM cleared")
-            self.cleared = True
-            return ("VRAM cleared",)
-        elif not clear:
-            self.cleared = False
-            return ("Ready to clear VRAM",)
-        else:
-            return ("VRAM already cleared",)

create_seed_from_text.txt

@@ -1,31 +0,0 @@
-import random
-import hashlib
-
-class TextToStringAndSeed:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "text": ("STRING", {"forceInput": True}),
-            },
-        }
-
-    RETURN_NAMES = ("text","random_seed")
-    RETURN_TYPES = ("STRING", "INT")
-    FUNCTION = "process"
-    CATEGORY = "utils"
-
-    def process(self, text):
-        # Generate a hash from the input text
-        text_hash = hashlib.md5(text.encode()).hexdigest()
-
-        # Use the hash to seed the random number generator
-        random.seed(text_hash)
-
-        # Generate a random seed (integer)
-        random_seed = random.randint(0, 2**32 - 1)
-
-        # Reset the random seed to ensure randomness in subsequent calls
-        random.seed()
-
-        return (text, random_seed)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "bjornulf_custom_nodes"
-description = "Nodes: Ollama, Text to Speech, Combine Texts, Random Texts, Save image for Bjornulf LobeChat, Text with random Seed, Random line from input, Combine images, Image to grayscale (black & white), Remove image Transparency (alpha), Resize Image, ..."
+description = "61 ComfyUI nodes : Display, manipulate, and edit text, images, videos, loras and more. Manage looping operations, generate randomized content, use logical conditions and work with external AI tools, like Ollama or Text To Speech."
-version = "0.53"
+version = "0.54"
 license = {file = "LICENSE"}
 
 [project.urls]

random_checkpoint.py.txt

@@ -1,28 +0,0 @@
-import random
-
-class RandomCheckpoint:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "number_of_inputs": ("INT", {"default": 2, "min": 2, "max": 10, "step": 1}),
-                "model_1": ("MODEL", {"forceInput": True}),
-                "clip_1": ("CLIP", {"forceInput": True}),
-                "vae_1": ("VAE", {"forceInput": True}),
-                "model_2": ("MODEL", {"forceInput": True}),
-                "clip_2": ("CLIP", {"forceInput": True}),
-                "vae_2": ("VAE", {"forceInput": True}),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
-    FUNCTION = "random_select"
-
-    def random_select(self, number_of_inputs, **kwargs):
-        selected_index = random.randint(1, number_of_inputs)
-        
-        selected_model = kwargs[f"model_{selected_index}"]
-        selected_clip = kwargs[f"clip_{selected_index}"]
-        selected_vae = kwargs[f"vae_{selected_index}"]
-        
-        return (selected_model, selected_clip, selected_vae)

save_api_image.py.txt

@@ -1,60 +0,0 @@
-import os
-import numpy as np
-from PIL import Image
-import json
-from PIL.PngImagePlugin import PngInfo
-
-class SaveApiImage:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE", {"forceInput": True}),
-            },
-            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-        }
-
-    FUNCTION = "save_api_image"
-    RETURN_TYPES = ()
-    OUTPUT_NODE = True
-    CATEGORY = "Bjornulf"
-
-    def save_api_image(self, image, prompt=None, extra_pnginfo=None):
-        # Ensure the output directory exists
-        os.makedirs("./output/", exist_ok=True)
-
-        # Convert the image from ComfyUI format to PIL Image
-        i = 255. * image.cpu().numpy()
-        if i.ndim > 3:
-            i = np.squeeze(i)
-        if i.ndim == 2:
-            i = i[:, :, np.newaxis]
-
-        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-
-        # Determine the next available filename
-        counter = 1
-        while True:
-            filename = f"./output/api_{counter:05d}.png"
-            if not os.path.exists(filename):
-                break
-            counter += 1
-
-        # Prepare metadata
-        metadata = PngInfo()
-        if prompt is not None:
-            metadata.add_text("prompt", json.dumps(prompt))
-        if extra_pnginfo is not None:
-            for k, v in extra_pnginfo.items():
-                metadata.add_text(k, json.dumps(v))
-
-        # Save the image with the determined filename and metadata
-        img.save(filename, format="PNG", pnginfo=metadata)
-
-        # Write the number of the last image to a text file with leading zeroes
-        with open("./output/api_next_image.txt", "w") as f:
-            f.write(f"api_{counter+1:05d}.png")
-
-        print(f"Image saved as: {filename}")
-
-        return {"ui": {"images": [{"filename": filename, "type": "output"}]}}