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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Translate and optimize code to comply with coding styles
This commit is contained in:
ChristopheZhao
parent
4ded88c46e
commit
654ee7ec00
@ -1,17 +1,25 @@
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import modules.scripts as scripts
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"""
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Txt2Img Prompt Optimizer (Multilingual)
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This script optimizes text prompts for Stable Diffusion image generation.
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It can detect non-English prompts, translate them to English, and then optimize them
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for better image generation results.
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The script uses a LangGraph workflow to manage the optimization process, with nodes for
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language detection, translation, and optimization. If LangGraph is not available,
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it falls back to a simplified workflow.
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"""
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from modules import scripts
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from modules.processing import StableDiffusionProcessingTxt2Img
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import os
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import re
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import json
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from dotenv import load_dotenv
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import requests
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from typing import Dict, List, Literal, TypedDict, Union, Optional, Any, Callable, Annotated
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import functools
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from typing import Dict, Literal, TypedDict, Optional, Any
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# Try to import LangGraph related libraries
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try:
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from langgraph.graph import StateGraph, END, START
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.graph import StateGraph, END
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LANGGRAPH_AVAILABLE = True
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except ImportError:
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LANGGRAPH_AVAILABLE = False
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@ -36,14 +44,13 @@ DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
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# Define state type
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class PromptState(TypedDict):
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original_prompt: str
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language: str # Changed: from is_chinese to language, can be "english", "chinese", "other", etc.
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language: str
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translated_prompt: Optional[str]
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optimized_prompt: Optional[str]
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error: Optional[str]
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class PromptTemplate(BaseModel):
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"""Prompt template Pydantic model"""
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"""Prompt template for specific tasks"""
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name: str = Field(..., description="Template name")
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content: str = Field(..., description="Template content")
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@ -55,7 +62,7 @@ class PromptTemplates(BaseModel):
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txt2img_optimizer: PromptTemplate = Field(
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default=PromptTemplate(
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name="Stable Diffusion Prompt Optimizer",
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content="""
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content="""\
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You are an expert prompt engineer for Stable Diffusion image generation with deep knowledge of how SD models interpret text.
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Your task is to transform standard prompts into highly optimized versions that produce exceptional quality images. Follow these guidelines:
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@ -85,7 +92,7 @@ class PromptTemplates(BaseModel):
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language_detector: PromptTemplate = Field(
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default=PromptTemplate(
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name="Language Detector",
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content="""
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content="""\
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You are a language detection expert. Your task is to identify if the given text is in English or not.
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Analyze the provided text and determine if it's in English. Return ONLY 'yes' if the text is primarily in English, or 'no' if it's primarily in another language.
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@ -95,14 +102,14 @@ class PromptTemplates(BaseModel):
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Return ONLY 'yes' or 'no' without any explanations or additional text.
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"""
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),
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),
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description="Language detection template"
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)
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universal_translator: PromptTemplate = Field(
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default=PromptTemplate(
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name="Universal Translator",
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content="""
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content="""\
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You are a professional translator specializing in translating text to English for image generation.
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Your task is to accurately translate prompts from any language to English while preserving the original meaning and intent. Follow these guidelines:
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@ -129,6 +136,21 @@ class PromptTemplates(BaseModel):
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TEMPLATES = PromptTemplates()
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# Helper function for simple language detection
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def simple_language_detection(prompt: str) -> str:
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"""Simple language detection based on ASCII character ratio"""
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if not prompt:
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return "unknown"
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non_ascii_chars = 0
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for char in prompt:
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if ord(char) > 127:
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non_ascii_chars += 1
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language = "english" if (non_ascii_chars / len(prompt) < 0.3) else "other"
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print(f"Simple language detection: Prompt '{prompt}' detected as '{'English' if language == 'english' else 'Non-English'}'")
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return language
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# Agent functions
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def router_agent(state: PromptState) -> Dict[str, Any]:
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"""Determine the language of the prompt"""
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@ -171,38 +193,25 @@ def router_agent(state: PromptState) -> Dict[str, Any]:
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else:
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print(f"RouterAgent: Language detection failed - {response.status_code} - {response.text}")
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# Fallback to simple detection
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non_ascii_chars = 0
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for char in prompt:
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if ord(char) > 127:
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non_ascii_chars += 1
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language = "english" if (non_ascii_chars / len(prompt) < 0.3) else "other"
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print(f"RouterAgent: Prompt '{prompt}' detected as '{'English' if language == 'english' else 'Non-English'}' (simple detection)")
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language = simple_language_detection(prompt)
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return {"language": language}
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except Exception as e:
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print(f"RouterAgent: Language detection failed - {str(e)}")
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# Fallback to simple detection
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non_ascii_chars = 0
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for char in prompt:
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if ord(char) > 127:
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non_ascii_chars += 1
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language = "english" if (non_ascii_chars / len(prompt) < 0.3) else "other"
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print(f"RouterAgent: Prompt '{prompt}' detected as '{'English' if language == 'english' else 'Non-English'}' (simple detection)")
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language = simple_language_detection(prompt)
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return {"language": language}
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def translator_agent(state: PromptState) -> Dict[str, Any]:
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"""Translate non-English prompts to English"""
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prompt = state["original_prompt"]
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language = state["language"]
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if language == "english":
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print(f"TranslatorAgent: Prompt is already in English, no translation needed")
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print("TranslatorAgent: Prompt is already in English, no translation needed")
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return {"translated_prompt": prompt}
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if not DEEPSEEK_API_KEY:
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print(f"TranslatorAgent: Warning - DEEPSEEK_API_KEY not set, using simplified translation")
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print("TranslatorAgent: Warning - DEEPSEEK_API_KEY not set, using simplified translation")
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return {"error": "DEEPSEEK_API_KEY not set", "translated_prompt": prompt}
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try:
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@ -332,19 +341,19 @@ def create_prompt_optimization_graph():
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return None
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# Create state graph
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workflow = StateGraph(PromptState)
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graph = StateGraph(PromptState)
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# Add nodes
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workflow.add_node("router", router_agent)
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workflow.add_node("translator", translator_agent)
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workflow.add_node("optimizer", optimizer_agent)
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graph.add_node("router", router_agent)
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graph.add_node("translator", translator_agent)
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graph.add_node("optimizer", optimizer_agent)
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# Add edges
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# From start to router
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workflow.set_entry_point("router")
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graph.set_entry_point("router")
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# From router to translator or optimizer (based on language)
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workflow.add_conditional_edges(
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graph.add_conditional_edges(
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"router",
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should_translate,
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{
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@ -354,18 +363,18 @@ def create_prompt_optimization_graph():
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)
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# From translator to optimizer
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workflow.add_edge("translator", "optimizer")
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graph.add_edge("translator", "optimizer")
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# From optimizer to end
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workflow.add_edge("optimizer", END)
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graph.add_edge("optimizer", END)
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# Compile workflow
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return workflow.compile()
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return graph.compile()
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# Simplified workflow (used when LangGraph is not available)
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def simple_prompt_optimization_workflow(prompt: str) -> str:
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"""Simplified prompt optimization workflow"""
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print(f"\n--- Simplified workflow started ---")
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print("\n--- Simplified workflow started ---")
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print(f"Original prompt: '{prompt}'")
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# Initialize state
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@ -395,7 +404,7 @@ def simple_prompt_optimization_workflow(prompt: str) -> str:
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state["error"] = optimizer_result["error"]
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print(f"Final optimized prompt: '{state['optimized_prompt']}'")
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print(f"--- Simplified workflow finished ---\n")
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print("--- Simplified workflow finished ---\n")
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return state["optimized_prompt"] or prompt
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@ -479,11 +488,16 @@ class PromptOptimizer(scripts.Script):
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def postprocess(self, p, processed):
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"""Post-process after image generation"""
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# Add original prompt to extra generation params
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if hasattr(self, 'extra_generation_params') and hasattr(self, 'main_prompt'):
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processed.infotexts[0] = processed.infotexts[0].replace(
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"Prompt: ", f"Prompt: {self.extra_generation_params.get('Original prompt', '')}\nOptimized: "
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)
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# Nothing to do here
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return processed
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def optimize_prompt(self, prompt):
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"""Optimize prompt to improve generation quality"""
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def optimize_prompt(self, prompt: str) -> str:
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"""Optimize a prompt using the workflow"""
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if not prompt:
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return prompt
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@ -491,7 +505,7 @@ class PromptOptimizer(scripts.Script):
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if self.graph is not None:
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# Use LangGraph workflow
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try:
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print(f"\n--- LangGraph started ---")
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print("\n--- LangGraph started ---")
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print(f"Original prompt: '{prompt}'")
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# Create initial state
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@ -508,43 +522,21 @@ class PromptOptimizer(scripts.Script):
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optimized = final_state.get("optimized_prompt") or prompt
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print(f"Final optimized prompt: '{optimized}'")
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print(f"--- LangGraph finished ---\n")
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print("--- LangGraph finished ---\n")
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return optimized
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except Exception as e:
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print(f"LangGraph failed: {str(e)}")
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# Fallback to simplified workflow
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print(f"LangGraph workflow error: {str(e)}")
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print("Falling back to simplified workflow")
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return simple_prompt_optimization_workflow(prompt)
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else:
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# Use simplified workflow
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return simple_prompt_optimization_workflow(prompt)
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# Check if we're in a Stable Diffusion Webui environment
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try:
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from modules.processing import StableDiffusionProcessingTxt2Img
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except ImportError:
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# Create a mock class for testing outside of webui
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class StableDiffusionProcessingTxt2Img:
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"""Mock class for testing outside of webui"""
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def __init__(self):
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self.prompt = ""
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self.all_prompts = []
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self.main_prompt = ""
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self.extra_generation_params = {}
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def setup_prompts(self):
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"""Mock setup_prompts method"""
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pass
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# For standalone testing
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if __name__ == "__main__":
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# Test the prompt optimization workflow
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test_prompts = [
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"a cat",
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"beautiful landscape",
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"portrait of a woman",
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"科幻城市", # Chinese: "sci-fi city"
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"a beautiful landscape with mountains", # English
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"美丽的山水画", # Chinese: "beautiful landscape painting"
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]
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