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--- |
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base_model: Alpha-VLLM/Lumina-Image-2.0 |
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library_name: diffusers |
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license: apache-2.0 |
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instance_prompt: a photo of sks dog |
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widget: |
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- text: A photo of sks dog in a bucket |
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output: |
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url: image_0.png |
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- text: A photo of sks dog in a bucket |
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output: |
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url: image_1.png |
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- text: A photo of sks dog in a bucket |
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output: |
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url: image_2.png |
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- text: A photo of sks dog in a bucket |
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output: |
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url: image_3.png |
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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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- lora |
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- lumina2 |
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- lumina2-diffusers |
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- template:sd-lora |
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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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# Lumina2 DreamBooth LoRA - Dino-LeeTaeHun/trained-lumina2-lora3 |
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<Gallery /> |
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## Model description |
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These are Dino-LeeTaeHun/trained-lumina2-lora3 DreamBooth LoRA weights for Alpha-VLLM/Lumina-Image-2.0. |
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Lumina2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_lumina2.md). |
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## Trigger words |
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You should use `a photo of sks dog` to trigger the image generation. |
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The following `system_prompt` was also used used during training (ignore if `None`): None. |
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## Download model |
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[Download the *.safetensors LoRA](Dino-LeeTaeHun/trained-lumina2-lora3/tree/main) in the Files & versions tab. |
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) |
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```py |
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TODO |
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``` |
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) |
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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] |
