684a4805d7
• Implemented global offline mode for all HuggingFace-dependent modules to eliminate unauthenticated request warnings and prevent external connection attempts during startup and runtime. Changes • Global Offline Configuration: Added HF_HUB_OFFLINE=1 and TRANSFORMERS_OFFLINE=1 to the environment variables at the top of edit_api.py, embeddings.py, watcher.py, and pose_llm/pose_llm_api.py. • Explicit Local Files Only: Updated all huggingface_hub.hf_hub_download calls in edit_api.py to include local_files_only=True, ensuring they never attempt to reach the Hub if models are already cached. • LLM Service Optimization: Updated AutoTokenizer and AutoModelForCausalLM calls in pose_llm/pose_llm_api.py to use local_files_only=True. • Consistency: Ensured that shared modules like embeddings.py which uses open_clip are also locked to offline mode to prevent background downloads.
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import torch
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import os
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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import open_clip
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from PIL import Image
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import threading
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_model = None
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_preprocess = None
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_device = None
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_lock = threading.Lock()
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def get_model():
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global _model, _preprocess, _device
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if _model is None:
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with _lock:
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if _model is None:
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dev = "cuda" if torch.cuda.is_available() else "cpu"
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# ViT-H-14 is 1024-dim
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model, _, preprocess = open_clip.create_model_and_transforms('ViT-H-14', pretrained='laion2b_s32b_b79k')
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model = model.to(dev)
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model.eval()
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# Set globals only when fully ready to avoid race conditions
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_preprocess = preprocess
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_device = dev
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_model = model
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return _model, _preprocess, _device
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def generate_embedding(image_path):
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model, preprocess, device = get_model()
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try:
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with Image.open(image_path) as img:
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image = preprocess(img.convert("RGB")).unsqueeze(0).to(device)
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with torch.no_grad():
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image_features = model.encode_image(image)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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return image_features.cpu().numpy()[0].tolist()
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except Exception as e:
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print(f"Error generating embedding for {image_path}: {e}")
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return None
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