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--- |
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license: creativeml-openrail-m |
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library_name: diffusers |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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- diffusers-training |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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- diffusers-training |
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inference: true |
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--- |
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<!-- This model card has been generated automatically according to the information the training script had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Text-to-image finetuning - rcannizzaro/vae-dsprites-counterfactual |
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This pipeline was finetuned from **None** on the **osazuwa/dsprite-counterfactual** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: |
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## Training info |
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These are the key hyperparameters used during training: |
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* Epochs: 1 |
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* Learning rate: 1e-05 |
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* Batch size: 250 |
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* Gradient accumulation steps: 1 |
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* Image resolution: 64 |
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* Mixed-precision: fp16 |
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More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://microsoft-research.wandb.io/t-ricardoc/vae-dsprites-counterfactual/runs/ju56tobe). |
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## Intended uses & limitations |
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#### How to use |
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```python |
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# TODO: add an example code snippet for running this diffusion pipeline |
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``` |
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#### Limitations and bias |
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[TODO: provide examples of latent issues and potential remediations] |
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## Training details |
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[TODO: describe the data used to train the model] |
