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--- |
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license: creativeml-openrail-m |
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tags: |
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- pytorch |
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- diffusers |
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- stable-diffusion |
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- text-to-image |
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- diffusion-models-class |
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- dreambooth-hackathon |
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- science |
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widget: |
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- text: a photo of kldscp pattern as a fruit salad |
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datasets: |
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- rmahfuz/kaleidoscope |
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--- |
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# DreamBooth model for the kldscp concept trained by rmahfuz on the rmahfuz/kaleidoscope dataset. |
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This is a Stable Diffusion model fine-tuned on the kldscp concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of kldscp pattern** |
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Here are examples of some images generated with this fine-tuned model, along with the text prompts: |
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<img src="https://huggingface.co/rmahfuz/kldscp-pattern/resolve/main/fruit_salad.png"> |
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<p>kldscp pattern as a fruit salad, guidance: 11</p> |
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<img src="https://huggingface.co/rmahfuz/kldscp-pattern/resolve/main/fruit_salad_30.png"> |
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<p>kldscp pattern as a fruit salad, guidance: 30</p> |
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<img src="https://huggingface.co/rmahfuz/kldscp-pattern/resolve/main/vegetable_salad_30.png"> |
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<p>kldscp pattern as a vegetable salad, guidance: 30</p> |
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<img src="https://huggingface.co/rmahfuz/kldscp-pattern/resolve/main/bouquet_20.png"> |
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<p>kldscp pattern as a bouquet, guidance: 20</p> |
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<img src="https://huggingface.co/rmahfuz/kldscp-pattern/resolve/main/starfish_shape_30.png"> |
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<p>kldscp pattern in starfish shape, guidance: 30</p> |
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<img src="https://huggingface.co/rmahfuz/kldscp-pattern/resolve/main/black_night_sky_w_sparse_sparkling_white_stars_40.png"> |
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<p>kldscp pattern as black night sky with sparse sparkling white stars, guidance: 40</p> |
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This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! |
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## Description |
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This is a Stable Diffusion model fine-tuned on kaleidoscope `pattern` images for the science theme. |
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## Usage |
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```python |
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from diffusers import StableDiffusionPipeline |
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pipeline = StableDiffusionPipeline.from_pretrained('rmahfuz/kldscp-pattern') |
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image = pipeline().images[0] |
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image |
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``` |
