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--- |
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base_model: stabilityai/stable-diffusion-3-medium-diffusers |
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library_name: diffusers |
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license: openrail++ |
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tags: |
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- text-to-image |
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- diffusers-training |
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- diffusers |
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- sd3 |
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- sd3-diffusers |
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- template:sd-lora |
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instance_prompt: niul, colorful object, engraved with NiUl |
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widget: |
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- text: niul, colorful object, engraved with NiUl |
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output: |
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url: image_0.png |
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- text: niul, colorful object, engraved with NiUl |
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output: |
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url: image_1.png |
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- text: niul, colorful object, engraved with NiUl |
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output: |
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url: image_2.png |
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- text: niul, colorful object, engraved with NiUl |
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output: |
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url: image_3.png |
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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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# SD3 DreamBooth - hjvision/models |
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<Gallery /> |
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## Model description |
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These are hjvision/models DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers. |
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md). |
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Was the text encoder fine-tuned? False. |
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## Trigger words |
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You should use `niul, colorful object, engraved with NiUl` to trigger the image generation. |
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) |
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```py |
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from diffusers import AutoPipelineForText2Image |
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import torch |
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pipeline = AutoPipelineForText2Image.from_pretrained('hjvision/models', torch_dtype=torch.float16).to('cuda') |
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image = pipeline('niul, colorful object, engraved with NiUl').images[0] |
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``` |
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## License |
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Please adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`. |
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## Intended uses & limitations |
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#### How to use |
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```python |
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# TODO: add an example code snippet for running this diffusion pipeline |
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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] |
