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--- |
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license: creativeml-openrail-m |
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base_model: runwayml/stable-diffusion-v1-5 |
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datasets: |
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- iamkaikai/amazing_logos_v2 |
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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 - iamkaikai/amazing-logos |
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This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **iamkaikai/amazing_logos_v2** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Simple elegant logo for Digital Art, D A Square Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white']: |
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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("iamkaikai/amazing-logos", torch_dtype=torch.float16) |
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prompt = "Simple elegant logo for Digital Art, D A Square Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white" |
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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: 16 |
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* Learning rate: 1e-07 |
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* Batch size: 1 |
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* Gradient accumulation steps: 1 |
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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/iam-kai-kai/text2image-fine-tune/runs/0av1w9qj). |
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