| # ================================================================================================== | |
| # ZERO-SHOT-VIDEO-GENERATION - hf_utils.py (Hugging Face Integrations) | |
| # ================================================================================================== | |
| # | |
| # 📝 DESCRIPTION | |
| # This module encapsulates the fetching and parsing mechanisms for resolving dynamic model check- | |
| # points directly from the Hugging Face Hub ecosystem. By dynamically sourcing the latest public | |
| # model definitions (specifically Dreambooth derivatives), the interface guarantees up-to-date | |
| # experimentation bounds without relying strictly on static programmatic hardcoding. | |
| # | |
| # 👤 AUTHORS | |
| # - Amey Thakur (https://github.com/Amey-Thakur) | |
| # | |
| # 🤝🏻 CREDITS | |
| # Based directly on the foundational logic of Text2Video-Zero. | |
| # Source Authors: Picsart AI Research (PAIR), UT Austin, U of Oregon, UIUC | |
| # Reference: https://arxiv.org/abs/2303.13439 | |
| # | |
| # 🔗 PROJECT LINKS | |
| # Repository: https://github.com/Amey-Thakur/ZERO-SHOT-VIDEO-GENERATION | |
| # Live Demo: https://huggingface.co/spaces/ameythakur/Zero-Shot-Video-Generation | |
| # Video Demo: https://youtu.be/za9hId6UPoY | |
| # | |
| # 📅 RELEASE DATE | |
| # November 22, 2023 | |
| # | |
| # 📜 LICENSE | |
| # Released under the MIT License | |
| # ================================================================================================== | |
| from bs4 import BeautifulSoup | |
| import requests | |
| def model_url_list(): | |
| """ | |
| Constructs the target API evaluation sequences to query the Hugging Face Hub for optimal | |
| structural matches (specifically targeting 'dreambooth' models sorted by highest download count). | |
| """ | |
| url_list = [] | |
| # Queries the first pagination structure to avoid bandwidth over-saturation while | |
| # capturing statistically relevant diffusion models. | |
| for i in range(0, 1): | |
| url_list.append( | |
| f"https://huggingface.co/models?p={i}&sort=downloads&search=dreambooth") | |
| return url_list | |
| def data_scraping(url_list): | |
| """ | |
| Executes a web-scraping protocol against the constructed Hub endpoints. It parses the | |
| HTML Document Object Model (DOM) to strictly extract the unique stylistic model identifiers. | |
| """ | |
| model_list = [] | |
| for url in url_list: | |
| try: | |
| response = requests.get(url, timeout=5) | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| # Target the specific tailwind-defined grid structure hosting the model cards. | |
| div_class = 'grid grid-cols-1 gap-5 2xl:grid-cols-2' | |
| div = soup.find('div', {'class': div_class}) | |
| # Extract associative link coordinates (hrefs) representing the model paths. | |
| if div: | |
| for a in div.find_all('a', href=True): | |
| model_list.append(a['href']) | |
| except Exception as e: | |
| print(f"Request to {url} failed: {e}") | |
| return model_list | |
| def get_model_list(): | |
| """ | |
| Assembles the definitive list of diffusion model pipelines available for inference. | |
| Fuses statically defined 'guaranteed' baseline models (e.g., Dreamlike Photoreal) with | |
| the dynamically acquired structural checkpoints scraped from the Hub. Incorporates robust | |
| offline fallback protocols ensuring interface continuity when API connectivity fails. | |
| """ | |
| # Baseline stable diffusion model path | |
| best_model_list = [ | |
| "dreamlike-art/dreamlike-photoreal-2.0", | |
| ] | |
| try: | |
| model_list = data_scraping(model_url_list()) | |
| # Trim leading slashes establishing standardized Hugging Face API notation (User/Model). | |
| for i in range(len(model_list)): | |
| model_list[i] = model_list[i][1:] | |
| model_list = best_model_list + model_list | |
| except Exception as e: | |
| # Graceful degradation into offline operation modality. | |
| print(f"Warning: Could not fetch model list from HuggingFace (offline mode?). Using default model only. Error: {e}") | |
| model_list = best_model_list | |
| return model_list | |