End of training

README.md

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+---
+base_model: michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors
+library_name: diffusers
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- diffusers-training
+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 - michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0
+
+This pipeline was finetuned from **michaelyli/sd-dsprites-incorrect_counterfactual_coupled_factors** on the **michaelyli/dsprites-counterfactual-exp-decouple_factors-filtered-iter-0** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A square.', 'A ellipse.', 'A heart.']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained("michaelyli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0", torch_dtype=torch.float16)
+prompt = "A square."
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 400
+* Learning rate: 1e-05
+* 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-michaelli/sd-dsprites-counterfactual-exp-decouple_factors-iter-0/runs/wkl7e3m7).
+
+
+## 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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