各社SDXLプロンプト指示順序指定・全体整理
This commit is contained in:
Algo Kamishiro
+66
-64
@@ -16,76 +16,78 @@ class UniversalLLMNode:
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FUNCTION = "query"
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CATEGORY = "LLM/Universal"
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def query(self, provider, model, prompt, max_tokens):
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try:
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if provider == "openai":
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from openai import OpenAI
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client = OpenAI(api_key=os.getenv("OPENAI_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": 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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def query(self, provider, model, prompt, max_tokens):
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try:
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# SDXLプロンプト生成指示文
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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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elif provider == "anthropic":
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import anthropic
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api_key = os.getenv("ANTHROPIC_API_KEY")
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client = anthropic.Anthropic(api_key=api_key)
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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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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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if provider == "openai":
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from openai import OpenAI
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client = OpenAI(api_key=os.getenv("OPENAI_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 == "google":
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import google.generativeai as genai
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
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model_obj = genai.GenerativeModel(model)
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response = model_obj.generate_content(prompt)
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return (response.text,)
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elif provider == "anthropic":
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import anthropic
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api_key = os.getenv("ANTHROPIC_API_KEY")
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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 == "groq":
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from openai import OpenAI
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client = OpenAI(
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api_key=os.getenv("GROQ_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": 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 == "google":
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import google.generativeai as genai
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genai.configure(api_key=os.getenv("GOOGLE_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 == "mistral":
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from openai import OpenAI
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client = OpenAI(
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api_key=os.getenv("MISTRAL_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": 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 == "groq":
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from openai import OpenAI
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client = OpenAI(
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api_key=os.getenv("GROQ_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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else:
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return ("[ERROR] Unsupported provider.",)
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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=os.getenv("MISTRAL_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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except Exception as e:
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return (f"[LLM Error] {str(e)}",)
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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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