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--- |
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license: creativeml-openrail-m |
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base_model: CompVis/stable-diffusion-v1-4 |
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datasets: |
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- None |
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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 - Aminrabi/diff1000 |
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This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['[necklace in flowers shape]']: |
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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("Aminrabi/diff1000", torch_dtype=torch.float16) |
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prompt = "[necklace in flowers shape]" |
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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: 77 |
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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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