""" title: Venice.ai Image Generator author: Claude Code version: 1.0.0 license: MIT description: Generate images using Venice.ai API with customizable parameters requirements: pydantic, venice-ai """ import os import base64 from typing import Optional, Literal from pydantic import BaseModel, Field from venice_ai import VeniceClient class Tools: class Valves(BaseModel): VENICE_API_KEY: str = Field( default="", description="Venice.ai API key - set this in the tool settings" ) DEFAULT_MODEL: str = Field( default="venice-sd35", description="Default model for image generation" ) DEFAULT_STEPS: int = Field( default=25, description="Default number of generation steps" ) def __init__(self): self.valves = self.Valves() def generate_image( self, prompt: str, model: str = None, size: Literal["1024x1024", "512x512", "256x256"] = "1024x1024", steps: int = None, __user__: dict = None, ) -> str: """ Generate an image using Venice.ai API. :param prompt: The text description of the image to generate :param model: The model to use (defaults to venice-sd35) :param size: The size of the generated image :param steps: Number of generation steps (defaults to 25) :return: Base64 encoded image or error message """ # Use default values if not provided if not model: model = self.valves.DEFAULT_MODEL if not steps: steps = self.valves.DEFAULT_STEPS # Check if API key is set if not self.valves.VENICE_API_KEY: return "❌ Error: Venice.ai API key not set. Please configure it in the function settings." # Set the API key for the Venice client os.environ["VENICE_API_KEY"] = self.valves.VENICE_API_KEY try: width, height = map(int, size.split("x")) with VeniceClient() as client: response = client.image.generate( model=model, prompt=prompt, width=width, height=height, steps=steps ) if response.images: # Return the base64 image data image_b64 = response.images[0] return f"✅ Image generated successfully!\n\n\n\n**Prompt:** {prompt}\n**Model:** {model}\n**Size:** {size}\n**Steps:** {steps}" else: return "❌ Error: No image was generated by Venice.ai" except Exception as e: return f"❌ Error generating image: {str(e)}" def simple_generate_image( self, prompt: str, model: str = None, size: Literal["1024x1024", "512x512", "256x256"] = "1024x1024", __user__: dict = None, ) -> str: """ Generate an image using Venice.ai simple API (OpenAI-compatible). :param prompt: The text description of the image to generate :param model: The model to use (defaults to venice-sd35) :param size: The size of the generated image :return: Base64 encoded image or error message """ # Use default model if not provided if not model: model = self.valves.DEFAULT_MODEL # Check if API key is set if not self.valves.VENICE_API_KEY: return "❌ Error: Venice.ai API key not set. Please configure it in the function settings." # Set the API key for the Venice client os.environ["VENICE_API_KEY"] = self.valves.VENICE_API_KEY try: with VeniceClient() as client: response = client.image.simple_generate( model=model, prompt=prompt, size=size ) if response.images: # Return the base64 image data image_b64 = response.images[0] return f"✅ Image generated successfully!\n\n\n\n**Prompt:** {prompt}\n**Model:** {model}\n**Size:** {size}" else: return "❌ Error: No image was generated by Venice.ai" except Exception as e: return f"❌ Error generating image: {str(e)}" def list_available_models(self, __user__: dict = None) -> str: """ List available Venice.ai models for image generation. :return: List of available models """ # Check if API key is set if not self.valves.VENICE_API_KEY: return "❌ Error: Venice.ai API key not set. Please configure it in the function settings." # Set the API key for the Venice client os.environ["VENICE_API_KEY"] = self.valves.VENICE_API_KEY try: with VeniceClient() as client: # Venice.ai commonly supports these models models = [ "venice-sd35", "flux-dev", "flux-schnell", "sd-xl-base-1.0", "sd-xl-turbo" ] models_list = "\n".join([f"• {model}" for model in models]) return f"🎨 **Available Venice.ai Models:**\n\n{models_list}\n\n*Note: Model availability may vary. Check Venice.ai documentation for the latest models.*" except Exception as e: return f"❌ Error listing models: {str(e)}"