pref: receive clip embedding as a parameter #1
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instead of generating it itself. Also some optimizations for generating embeddings.
This was referenced Aug 3, 2022
JoanFM
reviewed
Aug 3, 2022
numb3r3
suggested changes
Aug 4, 2022
dalle_flow_glid3/sample.py
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| @@ -95,29 +98,41 @@ def set_requires_grad(model, value): | |||
| bert.half().eval() | |||
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half-precision mode should only works on CUDA device
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if device == 'cuda:0':
bert.half().eval()
else:
bert.eval()is it good?
I am not sure because original code has this https://github.com/Jack000/glid-3-xl/blob/master/sample.py#L294
numb3r3
suggested changes
Aug 4, 2022
dalle_flow_glid3/sample.py
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| res = requests.post('https://demo-cas.jina.ai:8443/post', | ||
| data=json.dumps({'execEndpoint':'/', 'data': [{'text': runtime_args.negative}]}), | ||
| headers={'content-type': 'application/json'} | ||
| ).json() | ||
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| blank_clip_embedding = np.array(res['data'][0]['embedding']).astype( | ||
| model_config['use_fp16'] and np.float16 or np.float32 | ||
| ).reshape(1, -1) |
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why not use grpc via cas client? What's more, the endpoints of cas-server would have some broken changes (to support multi-models by a single flow). So I recommend to use the cas-client instead.
JoanFM
requested changes
Aug 5, 2022
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instead of generating it itself.
Also some optimisations for generating embeddings.