added settings for LLMs
This commit is contained in:
benjamin
+5
-4
@@ -1,14 +1,15 @@
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from .polymath import Polymath, MediaScraper
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from .polymath import PolymathSettings, Polymath, MediaScraper
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from .helper import SaveAbs
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from .concept_eraser import ConceptEraserNode
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from .textmask import TextMaskGeneratorNode
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from .textmask import TextMaskNode
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NODE_CLASS_MAPPINGS = {
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"polymath_settings": PolymathSettings,
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"polymath_chat": Polymath,
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"polymath_scraper": MediaScraper,
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"polymath_helper": SaveAbs,
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"polymath_concept_eraser": ConceptEraserNode,
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"polymath_text_mask": TextMaskGeneratorNode
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"polymath_text_mask": TextMaskNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -16,7 +17,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"polymath_scraper": "LLM Polymath Scraper for various sites",
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"polymath_helper": "Save Image to Absolute Path",
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"polymath_concept_eraser": "Erase Concept from Model",
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"polymath_text_mask": "Generate a mask from text"
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"polymath_text_mask": "Generate mask from text"
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}
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ascii_art = """
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@@ -2,6 +2,8 @@
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{
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"baseurl": "https://api.openai.com/v1",
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"models": {
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"Dall-E-2": "dall-e-2",
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"Dall-E-3": "dall-e-3",
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"GPT 3.5": "gpt-3.5-turbo",
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"GPT 4": "gpt-4-turbo",
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"GPT 4o": "gpt-4o",
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+113
-31
@@ -125,9 +125,12 @@ class OllamaAPI:
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self.base_url = base_url
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self.chat_history = []
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def generate_completion(self, model, prompt, stream=False, keep_alive=5, ollama_chat_mode=False, images=None, seed=42):
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def generate_completion(self, model, prompt, stream=False, ollama_chat_mode=False, images=None, seed=42, options=None):
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url = f"{self.base_url}/api/chat" if ollama_chat_mode else f"{self.base_url}/api/generate"
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payload_options = options if isinstance(options, dict) else {} # Ensure options is a dict
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payload_options['seed'] = seed
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if ollama_chat_mode:
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# Append the new user message to the chat history
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self.chat_history.append({"role": "user", "content": prompt})
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@@ -135,20 +138,27 @@ class OllamaAPI:
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"model": model,
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"messages": self.chat_history, # Include the entire chat history
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"stream": stream,
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"keep_alive": keep_alive,
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"options": {"seed": seed}
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"options": payload_options
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}
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else:
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payload = {
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"model": model,
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"prompt": prompt,
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"stream": stream,
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"keep_alive": keep_alive,
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"options": {"seed": seed}
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"options": payload_options
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}
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if images:
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payload["images"] = images
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if options:
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payload["temperature"] = options.get("temperature", 0.8)
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payload["top_p"] = options.get("top_p", 0.95)
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payload["top_k"] = options.get("top_k", 40)
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payload["num_predict"] = options.get("max_tokens", -1)
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payload["seed"] = options.get("seed", 42)
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payload["repeat_penalty"] = options.get("repeat_penalty", 1.1)
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payload["repeat_last_n"] = options.get("repeat_last_n", 64)
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payload["keep_alive"] = options.get("ollama_keep_alive", 5)
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response = requests.post(url, json=payload)
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response.raise_for_status()
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@@ -160,6 +170,37 @@ class OllamaAPI:
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return response_json
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class PolymathSettings:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"temperature": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.05}),
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"top_p": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.05}),
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"top_k": ("INT", {"default": 40, "min": 0, "max": 100}),
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"max_tokens": ("INT", {"default": 1024, "min": -1, "max": 8192}), # -1 might mean 'unlimited' or 'default'
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"presence_penalty": ("FLOAT", {"default": 0.0, "min": -2.0, "max": 2.0, "step": 0.1}),
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"frequency_penalty": ("FLOAT", {"default": 0.0, "min": -2.0, "max": 2.0, "step": 0.1}),
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"repeat_penalty": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 2.0, "step": 0.05}),
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"repeat_last_n": ("INT", {"default": 64, "min": 0, "max": 2048}),
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"response_format_json": ("BOOLEAN", {"default": False}),
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"ollama_keep_alive": ("INT", {"default": 5, "min": 1, "max": 10}),
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"request_timeout": ("INT", {"default": 120, "min": 0, "max": 600}),
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"dalle_quality": (["standard", "hd"], {"default":"standard"}),
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"dalle_style": (["vivid", "natural"], {"default":"vivid"}),
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"dalle_size": (["1024x1024", "1792x1024", "1024x1792"], {"default":"1024x1024"}),
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"dalle_n": ("INT", {"default": 1, "min": 1, "max": 4})
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}
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}
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# Output a dictionary or a specific tuple/pipe format
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RETURN_TYPES = ("LLM_SETTINGS",) # Or use "BASIC_PIPE" if it fits
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FUNCTION = "get_settings"
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CATEGORY = "Polymath/Settings"
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def get_settings(self, **kwargs):
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# Package settings into a dictionary for easy use
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return (kwargs,) # Return as a tuple containing the dict
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class Polymath:
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chat_history = []
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default_model = None
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@@ -183,9 +224,10 @@ class Polymath:
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"multiline": True
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}
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),
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"seed": ("INT", {"default": 0, "min": 0, "step": 1}),
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"seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffff, "step": 1}),
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},
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"optional": {
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"llm_settings": ("LLM_SETTINGS",),
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"model": (models_list, {"default": default_model}),
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"custom_instruction": (custom_instructions_list, {"default": "None"}),
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"enable_web_search": ("BOOLEAN", {"default": False}),
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@@ -232,10 +274,11 @@ class Polymath:
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return encode_single(image)
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def execute(
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self, prompt, additional_text, seed, model=None, custom_instruction="None",
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enable_web_search=False, list_sources=False, num_search_results=3, keep_context=True, ollama_chat_mode=False, compress=False, compression_level="soft", console_log=True, image=None
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):
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self, prompt, additional_text, seed, llm_settings=None, model=None, custom_instruction="None", enable_web_search=False, list_sources=False, num_search_results=3, keep_context=True, ollama_chat_mode=False, compress=False, compression_level="soft", console_log=True, image=None
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):
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selected_model_value = models_dict.get(model)
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settings = llm_settings if isinstance(llm_settings, dict) else {}
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if not selected_model_value:
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print(f"Error: Model '{model}' not found in config.json. Using default.")
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selected_model_value = list(models_dict.values())[0] if models_dict else None
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@@ -361,12 +404,13 @@ class Polymath:
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ollama_chat_mode,
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custom_instruction,
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seed,
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llm_settings=settings,
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b64=b64
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)
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return response
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def polymath_interaction(self, selected_base_url, api_key_oai, api_key_anthropic, api_key_xai, api_key_deepseek, api_key_gemini,
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model_value, prompt, console_log, keep_context, ollama_chat_mode, custom_instruction, seed=42, b64=None):
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def polymath_interaction(self, selected_base_url, api_key_oai, api_key_anthropic, api_key_xai, api_key_deepseek, api_key_gemini, model_value, prompt, console_log, keep_context, ollama_chat_mode, custom_instruction, seed=42, llm_settings={}, b64=None):
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settings = llm_settings
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# OpenAI-based models (e.g., GPT, o1, o3)
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if model_value.startswith(('gpt', 'o1', 'o3')):
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from openai import OpenAI
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@@ -389,11 +433,22 @@ class Polymath:
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]
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messages.append({"role": "user", "content": user_content})
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messages.append({"role": "user", "content": prompt})
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api_params = {
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"model": model_value,
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"messages": messages,
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"stream": False,
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"temperature": settings.get("temperature", 0.8),
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"top_p": settings.get("top_p", 0.95),
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"max_tokens": settings.get("max_tokens", 1024),
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"seed": settings.get("seed", 42),
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}
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stop_seq = settings.get("stop_sequences", "")
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if stop_seq:
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api_params["stop"] = [s.strip() for s in stop_seq.split(',')]
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request_timeout = settings.get("request_timeout", 120) or None
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completion = client.chat.completions.create(
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model=model_value,
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messages=messages,
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stream=False,
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seed=seed
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**api_params,
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timeout=request_timeout
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)
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output_text = completion.choices[0].message.content
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self.chat_history.extend([{"role": "user", "content": prompt},
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@@ -405,21 +460,33 @@ class Polymath:
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from PIL import Image
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from io import BytesIO
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client = OpenAI(api_key=api_key_oai, base_url=selected_base_url)
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response = client.images.generate(
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model=model_value,
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prompt=prompt,
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n=1,
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size="1024x1024",
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response_format="b64_json"
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)
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dalle_params = {
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"model": model_value,
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"prompt": prompt,
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"n": settings.get("dalle_n", 1),
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"size": settings.get("dalle_size", "1024x1024"),
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"quality": settings.get("dalle_quality", "standard"),
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"style": settings.get("dalle_style", "vivid"),
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"response_format": "b64_json"
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}
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if console_log: print(f"\033[92mDALL-E Params:\033[0m", dalle_params)
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request_timeout = settings.get("request_timeout", 120) or None
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response = client.images.generate(**dalle_params, timeout=request_timeout)
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output_text = response.data[0].revised_prompt
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decoded_data = base64.b64decode(response.data[0].b64_json) # Decode the base64 string
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image_data = Image.open(BytesIO(decoded_data))
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img_array = np.array(image_data).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array)
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if img_tensor.dim() == 3:
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img_tensor = img_tensor.unsqueeze(0)
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return (output_text, img_tensor if img_tensor is not None else "")
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image_tensors = []
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for i, img_data in enumerate(response.data):
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try:
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decoded_data = base64.b64decode(img_data.b64_json)
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image = Image.open(BytesIO(decoded_data)).convert("RGB") # Ensure RGB
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# Convert PIL to Tensor
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img_array = np.array(image).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array).unsqueeze(0) # Add batch dim
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image_tensors.append(img_tensor)
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except Exception as e:
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print(f"Error processing DALL-E image data {i+1}: {e}")
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final_image_output = torch.cat(image_tensors, dim=0) if len(image_tensors) > 1 else (image_tensors[0] if image_tensors else None)
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output_text = output_text if output_text else "(No revised prompt provided)"
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return (output_text, final_image_output)
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# Anthropic (e.g., Claude models) branch
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elif model_value.startswith('claude'):
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@@ -581,14 +648,27 @@ class Polymath:
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# Fallback to Ollama API
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else:
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images = b64 if b64 else None
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ollama_options = {
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"temperature": settings.get("temperature", 0.8),
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"top_p": settings.get("top_p", 0.95),
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"top_k": settings.get("top_k", 40),
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"num_predict": settings.get("max_tokens", -1), # Map max_tokens
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"seed": settings.get("seed", 42),
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"repeat_penalty": settings.get("repeat_penalty", 1.1),
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"repeat_last_n": settings.get("repeat_last_n", 64),
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"keep_alive": settings.get("ollama_keep_alive", 5)
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}
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stop_seq = settings.get("stop_sequences", "")
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if stop_seq:
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ollama_options["stop"] = [s.strip() for s in stop_seq.split(',')]
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response = self.ollama_api.generate_completion(
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ollama_chat_mode=ollama_chat_mode,
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model=model_value,
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prompt=prompt,
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stream=False,
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keep_alive=5,
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images=images,
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seed=seed
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seed=seed,
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options=ollama_options
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)
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if ollama_chat_mode:
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output_text = response.get('message', {}).get('content', '')
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@@ -770,11 +850,13 @@ class MediaScraper:
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NODE_CLASS_MAPPINGS = {
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"polymath_settings": PolymathSettings,
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"polymath_chat": Polymath,
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"polymath_scraper": MediaScraper
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"polymath_settings": "LLM Polymath Chat and API Settings",
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"polymath_chat": "LLM Polymath Chat with Advanced Web and Link Search",
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"polymath_scraper": "LLM Polymath Scraper for various sites"
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}
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