End of training

README.md

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+---
+license: creativeml-openrail-m
+library_name: diffusers
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- diffusers-training
+base_model: VAE
+inference: true
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# Text-to-image finetuning - rcannizzaro/image_to_one_hot_causal_factor_vae_dsprites
+
+This Image to One-Hot Causal Factor Encoder/Decoder VAE Network was trained on the **osazuwa/dsprite-counterfactual** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: 
+
+
+
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 7
+* Learning rate: 0.0001
+* Batch size: 100
+* Gradient accumulation steps: 1
+* Image resolution: 64
+* Mixed-precision: fp16
+
+
+More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://microsoft-research.wandb.io/t-ricardoc/image_to_one_hot_causal_factor_vae_dsprites/runs/ckbzzzmd).
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

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checkpoint-1000/vae/config.json

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