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| import requests | |
| import os | |
| from typing import List, Dict, Optional | |
| def download_model_from_civitai(model_id: int, api_key: str, download_path: str) -> bool: | |
| """ | |
| Download a model from CivitAI (placeholder function) | |
| In a real implementation, this would handle the actual download | |
| """ | |
| try: | |
| headers = {"Authorization": f"Bearer {api_key}"} | |
| # Get model details | |
| model_response = requests.get( | |
| f"https://civitai.com/api/v1/models/{model_id}", | |
| headers=headers | |
| ) | |
| if model_response.status_code != 200: | |
| return False | |
| model_data = model_response.json() | |
| # Get download URL (this is simplified - in reality you'd need to get the actual file URL) | |
| # This would typically be from model_data['modelVersions'][0]['files'][0]['downloadUrl'] | |
| # Download the model file | |
| # response = requests.get(download_url, headers=headers) | |
| # with open(download_path, 'wb') as f: | |
| # f.write(response.content) | |
| return True | |
| except Exception as e: | |
| print(f"Error downloading model: {e}") | |
| return False | |
| def apply_lora_to_model(pipe, lora_id: int, api_key: str) -> bool: | |
| """ | |
| Apply a LoRA to a model (placeholder function) | |
| In a real implementation, this would handle LoRA integration | |
| """ | |
| try: | |
| # Download and apply LoRA | |
| # This is highly dependent on the specific model and LoRA format | |
| return True | |
| except Exception as e: | |
| print(f"Error applying LoRA: {e}") | |
| return False | |
| def validate_api_key(api_key: str) -> bool: | |
| """Validate the CivitAI API key""" | |
| try: | |
| headers = {"Authorization": f"Bearer {api_key}"} | |
| response = requests.get( | |
| "https://civitai.com/api/v1/models?limit=1", | |
| headers=headers | |
| ) | |
| return response.status_code == 200 | |
| except Exception: | |
| return False |