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|
| --- |
| license: creativeml-openrail-m |
| base_model: runwayml/stable-diffusion-v1-5 |
| datasets: |
| - None |
| tags: |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| inference: true |
| --- |
| |
| # Text-to-image finetuning - gremlin97/RemoteDiff |
| |
| This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A satellite image of a crop field']: |
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| ## Pipeline usage |
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| You can use the pipeline like so: |
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| ```python |
| from diffusers import DiffusionPipeline |
| import torch |
| |
| pipeline = DiffusionPipeline.from_pretrained("gremlin97/RemoteDiff", torch_dtype=torch.float16) |
| prompt = "A satellite image of a crop field" |
| image = pipeline(prompt).images[0] |
| image.save("my_image.png") |
| ``` |
|
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| ## Training info |
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| These are the key hyperparameters used during training: |
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| * Epochs: 5 |
| * Learning rate: 1e-06 |
| * Batch size: 4 |
| * Gradient accumulation steps: 4 |
| * Image resolution: 224 |
| * 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://wandb.ai/gremlin/text2image-fine-tune/runs/tegl1gtv). |
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