<p>Here is how you can save a model in Keras.</p>
<p><code>model = ...  # Get model (Sequential,Functional Model, or Model subclass)</code>
<code>model.save('path/to/location.keras') #The file needs to end with the .keras extension.</code></p>
<p>Loading the model back:<br />
<code>model = keras.models.load_model('path/to/location.keras') </code></p>
<p>reference:<br />
<a href="https://keras.io/guides/serialization_and_saving/" rel="nofollow noreferrer">https://keras.io/guides/serialization_and_saving/</a></p>

CC BY-SA 4.0
2024-07-09T16:15:11.527Z
Please post the relevant details here.  Answers consisting only of links are liable to be deleted.
Chenmunka
CC BY-SA 4.0
2024-07-09T13:18:00.527Z
2024-07-09T18:52:52.667Z
2024-07-09T18:52:52.667Z
saleh
<p>I am new to ML and plan to use KerasCV stabledifussion model to generate images from text. The example on the KerasCV website is straightforward but I could not find a way to save the model locally for later use. I also noticed that the library connects to hugging face to download encoder and diffusion model.</p>
<p>Could you please point me to the right direction to do this locally? I would like all the model and its parameters to be local and I will be using it in a server.</p>
<p>Also, if you have experience running such a model/server on the could, I would appreciate your guidance on the best approach wrt costs. Should I upload everything and store the whole data on the cloud or load it from hugging face? Which one would make more sense for cloud applications?</p>
<p>Thanks,</p>
<p>Doug</p>

<tensorflow><keras><diffusion-models>
Saving a stable diffusion model for later use
Doug
# Saving a stable diffusion model for later use

(Asked by: username_0 on 2024-07-06)

I am new to ML and plan to use KerasCV stabledifussion model to generate images from text. The example on the KerasCV website is straightforward but I could not find a way to save the model locally for later use. I also noticed that the library connects to hugging face to download encoder and diffusion model.

Could you please point me to the right direction to do this locally? I would like all the model and its parameters to be local and I will be using it in a server.

Also, if you have experience running such a model/server on the could, I would appreciate your guidance on the best approach wrt costs. Should I upload everything and store the whole data on the cloud or load it from hugging face? Which one would make more sense for cloud applications?

Thanks,

username_0

**Tags:** tensorflow, keras, diffusion-models


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## 1 answers

### Answer by username_1 on 2024-07-09. Score: 0

Here is how you can save a model in Keras.

`model = ... # Get model (Sequential,Functional Model, or Model subclass)`
`model.save('path/to/location.keras') #The file needs to end with the .keras extension.`

Loading the model back:  
`model = keras.models.load_model('path/to/location.keras')`

reference:  
<https://keras.io/guides/serialization_and_saving/>

> **Comments:**
> **username_2**: Please post the relevant details here.  Answers consisting only of links are liable to be deleted.  

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