|
|
| --- |
| license: creativeml-openrail-m |
| base_model: stabilityai/stable-diffusion-2 |
| datasets: |
| - rgres/AerialDreams |
| tags: |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| inference: true |
| --- |
| |
| # Text-to-image finetuning - rgres/Seg2Map-finetuned |
| |
| This pipeline was finetuned from **stabilityai/stable-diffusion-2** on the **rgres/AerialDreams** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Chemin de Saint-Antoine, Saint-Cyr-sur-Mer, Toulon, Var, Provence-Alpes-Cote d'Azur, Frane", 'Aerial view of Rond-Point de la 1e Armee Francaise - Lieutenant Paul Meyer, Mulhouse, Haut-Rhin, Grand Est, France metropolitaine, 68100, France', '31, Rue Molière, SS ace Coeur, Pyramides, La Roche-sur-Yon, Vendee, Pays de la Loire, France metropolitaine, 85000, France', 'Aerial view of Mourenx, Pau, Pyrenees-Atlantiques, Nouvelle-Aquitaine, France metropolitaine, 64150, France', '17 rue du moutier, Angousrine-Vileneuve-Les-Escaldes, Pyrenees Orientales, Occitanie, France metropolitaine, 66760, France']: |
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|  |
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| ## Pipeline usage |
|
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| You can use the pipeline like so: |
|
|
| ```python |
| from diffusers import DiffusionPipeline |
| import torch |
| |
| pipeline = DiffusionPipeline.from_pretrained("rgres/Seg2Map-finetuned", torch_dtype=torch.float16) |
| prompt = "Chemin de Saint-Antoine, Saint-Cyr-sur-Mer, Toulon, Var, Provence-Alpes-Cote d'Azur, Frane" |
| image = pipeline(prompt).images[0] |
| image.save("my_image.png") |
| ``` |
|
|
| ## Training info |
|
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| These are the key hyperparameters used during training: |
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| * Epochs: 1 |
| * Learning rate: 1e-05 |
| * Batch size: 1 |
| * Gradient accumulation steps: 4 |
| * Image resolution: 512 |
| * 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/rubengres/text2image-fine-tune/runs/u9u76o1e). |
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