Instructions to use p1atdev/pvc-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use p1atdev/pvc-v3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("p1atdev/pvc-v3", dtype=torch.bfloat16, device_map="cuda") prompt = "pvc, anime, masterpiece, best quality, exceptional, 1girl, bangs, bare shoulders, beret, black hair, black shorts, blue hair, bracelet, breasts, buttons, colored inner hair, double-breasted, eyewear removed, green headwear, green jacket, grey eyes, grey sky, hat, jacket, jewelry, long hair, looking at viewer, multicolored hair, neck ring, o-ring, off shoulder, rain, round eyewear, shorts, sidelocks, small breasts, solo, sunglasses, wavy hair, wet, zipper" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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license: other
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---
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license: other
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datasets:
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- p1atdev/pvc
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language:
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- en
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library_name: diffusers
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tags:
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- text-to-image
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- stable-diffusion
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- safetensors
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---
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# PVC v3
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This model is a latent diffusion model finetuned on Waifu Diffusion v1.5 beta 2 with PVC figure images.
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You can use Danbooru tags to generate images.
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## Downloads
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## Prompt guide
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### Trigger words
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- `pvc` means the pvc material style but not needed always.
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- `figma` is the figure style that has joints, and more tend to be product thumbnail images.
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- `nendoroid` means the style of chibi figures. Use with `chibi` to get better results.
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### Tips
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The PVC figure style is closer to the anime style than to the realistic style.
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So, it is recommended to put `anime` to **positive** prompt and `realistic` to **negative** prompt to get better results sometimes.
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If you want to avoid too realistic faces, try this! Setting `realistic` to negative does not affect the pvc style, only the faces of the characters.
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## Examples
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## Training information
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<details>
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<table>
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<thead>
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<tr><th>Parameter</td><td>Value</th></tr>
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</thead>
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<tbody>
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<tr><td>Service</td><td>Runpod</td></tr>
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<tr><td>GPU</td><td>A5000</td></tr>
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<tr><td>Notebook</td><td><a href="https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-trainer.ipynb" target="_blank">Linaqruf/kohya-trainer</a></td></tr>
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<tr><td>Cost</td><td>about $2</td></tr>
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<tr><td>Hours</td><td>about 6 hours</td></tr>
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<tr><td>Dataset</td><td>7467 images from p1atdev/pvc</td></tr>
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<tr><td>Resolution</td><td>896</td></tr>
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<tr><td>Epochs</td><td>5</td></tr>
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<tr><td>Optimizer</td><td>Lion</td></tr>
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<tr><td>LR</td><td>4e-7</td></tr>
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<tr><td>Scheduler</td><td>cosine_with_restarts</td></tr>
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<tr><td>Train Batch Size</td><td>1</td></tr>
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</tbody>
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</table>
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</details>
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## License
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PVC v3 is released under the Fair AI Public License 1.0-SD (https://freedevproject.org/faipl-1.0-sd/). If any derivative of this model is made, please share your changes accordingly. Special thanks to ronsor/undeleted (https://undeleted.ronsor.com/) for help with the license.
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