Answers.Body: <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>
  
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Body: <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>
  
Tags: <tensorflow><keras><diffusion-models>
Title: Saving a stable diffusion model for later use
UserDisplayName: Doug
ThreadText: # 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,
  
  Doug
  
  **Tags:** tensorflow, keras, diffusion-models
  
  ---
  
  ## 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.  
  
  ---
  
  