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--- |
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base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 |
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library_name: diffusers |
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license: creativeml-openrail-m |
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inference: true |
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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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- textual_inversion |
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- diffusers-training |
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language: |
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- en |
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datasets: |
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- diffusers/cat_toy_example |
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--- |
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<!-- This model card has been generated automatically according to the information the training script had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Textual inversion text2image fine-tuning - intipendrdra/textual_inversion_cat |
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These are textual inversion adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. You can find some example images in the following. |
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## Intended uses & limitations |
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#### How to use |
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```python |
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from diffusers import DiffusionPipeline |
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pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") |
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pipe.load_textual_inversion("intipendrdra/textual_inversion_cat") |
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prompt = "a <cat> sitting on a sofa, highly detailed, 4k" |
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image = pipe(prompt).images[0] |
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image.show() |
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image.save("cat.png") |
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``` |
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#### Limitations and bias |
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[TODO: provide examples of latent issues and potential remediations] |
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## Training details |
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[TODO: describe the data used to train the model] |
