Getting Embedding from CLIP model #1940
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lelefontaa
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Q&A
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Yup — definitely doable. You can cache the CLIP embeddings easily after generation — just serialize the tensor (or Here's a very quick sketch of the idea: import os, hashlib, torch
def get_cache_path(image_path):
h = hashlib.md5(open(image_path, 'rb').read()).hexdigest()
return f"./clip_cache/{h}.pt"
def get_clip_embedding(image, model):
cache_path = get_cache_path(image)
if os.path.exists(cache_path):
return torch.load(cache_path)
else:
embedding = model.encode_image(image)
torch.save(embedding, cache_path)
return embeddingThat’ll save you time and GPU sanity. PS: |
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Is there a way to save the embeddings of images generated by the CLIP model (in chat_handler) so that they can be reused without recalculating them?
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