135 lines
4.7 KiB
Python
135 lines
4.7 KiB
Python
OPENAI_MODELS = [
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"gpt-4o", "gpt-4-turbo", "gpt-4", "gpt-3.5-turbo"
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]
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ANTHROPIC_MODELS = [
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"claude-opus-4-20250514",
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"claude-sonnet-4-20250514",
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"claude-3-7-sonnet-20250219",
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"claude-3-5-haiku-20241022",
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"claude-3-5-sonnet-20241022",
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"claude-3-5-sonnet-20240620",
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"claude-3-opus-20240229",
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"claude-3-sonnet-20240229",
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"claude-3-haiku-20240307"
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]
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GOOGLE_MODELS = [
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"gemini-pro", "gemini-pro-vision"
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]
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GROQ_MODELS = [
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"llama2-70b-4096", "mixtral-8x7b-32768"
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]
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MISTRAL_MODELS = [
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"mistral-tiny", "mistral-small", "mistral-medium"
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]
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ALL_MODELS = (
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OPENAI_MODELS +
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ANTHROPIC_MODELS +
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GOOGLE_MODELS +
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GROQ_MODELS +
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MISTRAL_MODELS
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)
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PROVIDER_MODELS = {
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"openai": OPENAI_MODELS,
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"anthropic": ANTHROPIC_MODELS,
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"google": GOOGLE_MODELS,
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"groq": GROQ_MODELS,
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"mistral": MISTRAL_MODELS
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}
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class UniversalLLMNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"provider": (list(PROVIDER_MODELS.keys()),),
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"model": (ALL_MODELS,),
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"api_key": ("STRING", {"multiline": False, "password": True}), # 追加
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"prompt": ("STRING", {"multiline": True}),
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"max_tokens": ("INT", {"default": 300, "min": 50, "max": 4096}),
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}
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "query"
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CATEGORY = "LLM/Universal"
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def query(self, provider, model, api_key, prompt, max_tokens):
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try:
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sdxl_prompt = (
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"You are a professional prompt engineer for Stable Diffusion XL (SDXL).\n"
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"Given a scene description or list of tags, convert them into a clean, high-quality positive prompt in SDXL format.\n"
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"Output the prompt as a single comma-separated line, with no explanations, no preface, and no extra text.\n"
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"Follow this tag order strictly: girl, hairstyle, hair color, bangs, eye color, facial expression, body type, breast size, pose, situation.\n"
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"Example: 1girl, long hair, blonde, straight bangs, blue eyes, smiling, slender, large breasts, sitting, by the lake in early summer\n"
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"Only output the prompt line.\n"
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f"Input: {prompt}"
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)
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if provider == "openai":
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from openai import OpenAI
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client = OpenAI(api_key=api_key)
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completion = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": sdxl_prompt}],
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max_tokens=max_tokens,
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)
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return (completion.choices[0].message.content,)
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elif provider == "anthropic":
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import anthropic
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client = anthropic.Anthropic(api_key=api_key)
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completion = client.messages.create(
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model=model,
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max_tokens=max_tokens,
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messages=[{"role": "user", "content": sdxl_prompt}]
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)
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return (completion.content[0].text,)
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elif provider == "google":
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import google.generativeai as genai
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genai.configure(api_key=api_key)
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model_obj = genai.GenerativeModel(model)
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response = model_obj.generate_content(sdxl_prompt)
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return (response.text,)
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elif provider == "groq":
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from openai import OpenAI
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client = OpenAI(
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api_key=api_key,
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base_url="https://api.groq.com/openai/v1"
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)
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completion = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": sdxl_prompt}],
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max_tokens=max_tokens,
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)
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return (completion.choices[0].message.content,)
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elif provider == "mistral":
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from openai import OpenAI
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client = OpenAI(
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api_key=api_key,
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base_url="https://api.mistral.ai/v1"
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)
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completion = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": sdxl_prompt}],
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max_tokens=max_tokens,
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)
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return (completion.choices[0].message.content,)
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else:
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return ("[ERROR] Unsupported provider.",)
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except Exception as e:
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return (f"[LLM Error] {str(e)}",)
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NODE_CLASS_MAPPINGS = {
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"UniversalLLMNode": UniversalLLMNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"UniversalLLMNode": "Universal LLM Prompt"
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} |