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--- |
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license: creativeml-openrail-m |
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base_model: stabilityai/stable-diffusion-2 |
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datasets: |
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- rgres/AerialDreams |
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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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inference: true |
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--- |
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# Text-to-image finetuning - rgres/Seg2Map-finetuned |
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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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## Pipeline usage |
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You can use the pipeline like so: |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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pipeline = DiffusionPipeline.from_pretrained("rgres/Seg2Map-finetuned", torch_dtype=torch.float16) |
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prompt = "Chemin de Saint-Antoine, Saint-Cyr-sur-Mer, Toulon, Var, Provence-Alpes-Cote d'Azur, Frane" |
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image = pipeline(prompt).images[0] |
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image.save("my_image.png") |
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``` |
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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: 1 |
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* Gradient accumulation steps: 4 |
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* Image resolution: 512 |
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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://wandb.ai/rubengres/text2image-fine-tune/runs/u9u76o1e). |
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