Updated groq language model with vision and audio support. Renamed so…

…me scripts to match the node names. The old Groq API is removed, sorry.

__init__.py

@@ -1,13 +1,22 @@
-from .nodes.fetch_and_save_image import FetchAndSaveImage
+from .nodes.download_image_from_url import DownloadImageFromURL
 from .nodes.generate_negative_prompt import GenerateNegativePrompt
-from .nodes.groq_api_completion import GroqAPICompletion
 from .nodes.save_text_file import SaveTextFile
+from .nodes.get_file_path import GetFilePath
+from .nodes.groq_api_llm import GroqAPILLM
+from .nodes.groq_api_vlm import GroqAPIVLM
+from .nodes.groq_api_alm_transcribe import GroqAPIALMTranscribe
+#from .nodes.groq_api_alm_translate import GroqAPIALMTranslate
+
 
 NODE_CLASS_MAPPINGS = { 
+    "📁 Get File Path": GetFilePath,
     "💾 Save Text File With Path": SaveTextFile,
-    "🖼️ Download Image from URL": FetchAndSaveImage,
-    "✨ Groq LLM API": GroqAPICompletion,
+    "🖼️ Download Image from URL": DownloadImageFromURL,
+    "✨💬 Groq LLM API": GroqAPILLM,
+    "✨📷 Groq VLM API": GroqAPIVLM,
+    "✨📝 Groq ALM API - Transcribe": GroqAPIALMTranscribe,
+    #"✨🌐 Groq ALM API - Translate [EN only]": GroqAPIALMTranslate,
     "⛔ Generate Negative Prompt": GenerateNegativePrompt,
 }
 
-print("\033[34mMNeMiC Nodes: \033[92mLoaded\033[0m")
+print("\033[34mMNeMiC Nodes: \033[92mLoaded\033[0m")

nodes/__init__.py

@@ -1,11 +1,19 @@
-from .fetch_and_save_image import FetchAndSaveImage
+from .download_image_from_url import DownloadImageFromURL
 from .generate_negative_prompt import GenerateNegativePrompt
-from .groq_api_completion import GroqAPICompletion
 from .save_text_file import SaveTextFile
+from .get_file_path import GetFilePath
+from .groq_api_llm import GroqAPILLM
+from .groq_api_vlm import GroqAPIVLM
+from .groq_api_alm_transcribe import GroqAPIALMTranscribe
+#from .groq_api_alm_translate import GroqAPIALMTranslate
 
 __all__ = [
-    "FetchAndSaveImage",
-    "GenerateNegativePrompt",
-    "GroqAPICompletion",
+    "DownloadImageFromURL",
     "SaveTextFile",
+    "GetFilePath",
+    "GroqAPILLM",
+    "GroqAPIVLM",
+    "GroqAPIALMTranscribe",
+    #"GroqAPIALMTranslate",
+    "GenerateNegativePrompt",
 ]

nodes/fetch_and_save_image.py → nodes/download_image_from_url.py

@@ -8,26 +8,28 @@
 def pil2tensor(image):
     return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 
-class FetchAndSaveImage:
+class DownloadImageFromURL:
     OUTPUT_NODE = True
     RETURN_TYPES = ("IMAGE", "INT", "INT")  # Image, Width, Height
     RETURN_NAMES = ("image", "width", "height")
-    FUNCTION = "FetchAndSaveImage"
+    OUTPUT_TOOLTIPS = ("The downloaded image", "The width of the image", "The height of the image")
+    FUNCTION = "DownloadImageFromURL"
     CATEGORY = "⚡ MNeMiC Nodes"
+    DESCRIPTION = "Downloads an image from a URL."
 
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "image_url": ("STRING", {"multiline": False, "default": ""}),
+                "image_url": ("STRING", {"multiline": False, "default": "", "tooltip": "URL of the image to download."}),
             },
             "optional": {
-                "save_file_name_override": ("STRING", {"default": "", "multiline": False}),
-                "save_path": ("STRING", {"default": "", "multiline": False})
+                "save_file_name_override": ("STRING", {"default": "", "multiline": False, "tooltip": "Optional override for the name of the saved image file."}),
+                "save_path": ("STRING", {"default": "", "multiline": False, "tooltip": "Optional path to save the image. Defaults to the current directory."})
             }
         }
 
-    def FetchAndSaveImage(self, image_url, save_path='', save_file_name_override=''):
+    def DownloadImageFromURL(self, image_url, save_path='', save_file_name_override=''):
         if not image_url:
             print("Error: No image URL provided.")
             return None, None, None

nodes/generate_negative_prompt.py

@@ -11,21 +11,23 @@ def __init__(self):
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "input_prompt": ("STRING", {"forceInput": True}),
-                "max_length": ("INT", {"default": 100, "min": 1, "max": 1024, "step": 1}),
-                "num_beams": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
-                "temperature": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 2.0, "step": 0.1}),
-                "top_k": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}),
-                "top_p": ("FLOAT", {"default": 0.92, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "blocked_words": ("STRING", {"default": "Blocked words, one per line, remove unwanted embeddings or words", "multiline": True}),
+                "input_prompt": ("STRING", {"forceInput": True, "tooltip": "The positive prompt you want to generate a negative prompt for."}),
+                "max_length": ("INT", {"default": 100, "min": 1, "max": 1024, "step": 1, "tooltip": "Maximum token length of the generated output."}),
+                "num_beams": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1, "tooltip": "Number of beams for beam search. Higher values improve accuracy."}),
+                "temperature": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 2.0, "step": 0.1, "tooltip": "Sampling temperature. Lower values make the output more deterministic."}),
+                "top_k": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1, "tooltip": "Limits how many of the most likely words are considered for each choice.\n\nFor example, top_k=50 means the model picks from the top 50 most likely words.\n\nA lower value narrows the choices, making the output more predictable, while a higher value adds diversity."}),
+                "top_p": ("FLOAT", {"default": 0.92, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Limits the pool of words the model can choose from based on their combined probability.\n\nSet it closer to 1 to allow more variety in output. Lowering this (e.g., 0.9) will restrict the output to the most likely words, making responses more focused."}),
+                "blocked_words": ("STRING", {"default": "Blocked words, one per line, remove unwanted embeddings or words", "multiline": True, "tooltip": "Words to exclude from the output."}),
             }
         }
 
     OUTPUT_NODE = True
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("negative_prompt",)
+    OUTPUT_TOOLTIPS = ("The generated negative prompt",)
     FUNCTION = "generate_negative_prompt"
     CATEGORY = "⚡ MNeMiC Nodes"
+    DESCRIPTION = "EXPERIMENTAL: Generates a negative prompt matching the input.\n\nThe model is quite weak and random though, so it doesn't work well. It mostly just generates random negative prompts trained on CivitAI negative prompts.\n\nNSFW words may appear."
 
     def generate_negative_prompt(self, input_prompt, max_length, num_beams, temperature, top_k, top_p, blocked_words):
         current_directory = os.path.dirname(os.path.realpath(__file__))

nodes/get_file_path.py

@@ -0,0 +1,78 @@
+import os
+from pathlib import Path
+from aiohttp import web
+import folder_paths
+
+class GetFilePath:
+    OUTPUT_NODE = True
+    RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("full_file_path", "file_path_only", "file_name_only", "file_extension_only")
+    OUTPUT_TOOLTIPS = ("The full path to the file", "The path to the file", "The name of the file", "The extension of the file")
+    FUNCTION = "get_file_path"
+    CATEGORY = "⚡ MNeMiC Nodes"
+    DESCRIPTION = "Gets a file path and returns components of the file path."
+    DOCUMENTATION = "This is documentation"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        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))]
+        return {
+            "required": {
+                "file": (sorted(files), {"file_upload": True, "tooltip": "Place your files in the 'input'-folder inside ComfyUI.\n\nBrowsing functionality is not yet supported. Please send help!"}),
+            }
+        }
+
+    def get_file_path(self, file):
+        try:
+            # Handle file upload within the node logic
+            uploaded_file_path = self.upload_file(file)
+
+            # Resolve the full file path using folder_paths
+            full_file_path = Path(uploaded_file_path)
+
+            # Check if the file exists
+            if not full_file_path.exists():
+                print(f"Error: File does not exist: {full_file_path}")
+                return None, None, None, None
+
+            # Extract file components
+            file_path_only = str(full_file_path.parent)
+            file_name_only = full_file_path.stem  # File name without the extension
+            file_extension_only = full_file_path.suffix  # File extension
+
+            # Return all as strings
+            return (
+                str(full_file_path),  # Full file path
+                file_path_only,  # Path only
+                file_name_only,  # File name without extension
+                file_extension_only,  # File extension
+            )
+
+        except Exception as e:
+            # Handle any unexpected errors
+            print(f"Error: Failed to process file path. Details: {str(e)}")
+            return None, None, None, None
+
+    def upload_file(self, file):
+        try:
+            # Define where to save uploaded files (e.g., input directory)
+            input_dir = folder_paths.get_input_directory()
+            file_path = os.path.join(input_dir, file)
+
+            # Check if file already exists in the directory
+            if os.path.exists(file_path):
+                print(f"File {file} already exists in {input_dir}. Skipping upload.")
+                return file_path
+
+            # Mimic the upload logic
+            with open(file_path, "wb") as f:
+                # Here, you would write the file content to disk
+                f.write(file)  # Assuming `file` contains the file data
+
+            print(f"File uploaded successfully: {file_path}")
+            return file_path
+
+        except Exception as e:
+            print(f"Error uploading file: {str(e)}")
+            return None

nodes/groq/DefaultPrompts_ALM_Transcribe.json

@@ -0,0 +1,10 @@
+[
+    {
+        "name": "Transcribe the song lyrics",
+        "content": ""
+    },
+    {
+        "name": "Transcribe meeting notes accurately",
+        "content": "Write [INAUDIBLE] when unclear."
+    }    
+]

nodes/groq/DefaultPrompts_ALM_Translate.json

@@ -0,0 +1,6 @@
+[
+    {
+        "name": "Translate the audio file using the style and guidance of [user_input]",
+        "content": ""
+     }
+]

nodes/groq/DefaultPrompts_VLM.json

@@ -0,0 +1,6 @@
+[
+    {
+        "name": "Describe the attached image following the [user_input] instruction",
+        "content": "You are a vision-language model. Analyze the attached image and respond to the user request based on their query. If the query is empty, describe the image in a clear and descriptive manner."
+     }
+]

nodes/groq/UserPrompts_ALM_Transcribe.json

@@ -0,0 +1,6 @@
+[
+    {
+        "name": "Add your own presets in UserPrompts.json",
+        "content": ""
+    }
+]

nodes/groq/UserPrompts_ALM_Translate.json

@@ -0,0 +1,6 @@
+[
+    {
+        "name": "Add your own presets in UserPrompts.json",
+        "content": ""
+    }
+]

nodes/groq/UserPrompts_VLM.json

@@ -0,0 +1,6 @@
+[
+    {
+        "name": "Add your own presets in UserPrompts.json",
+        "content": ""
+    }
+]