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--- |
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license: openrail++ |
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library_name: diffusers |
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tags: |
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- text-to-image |
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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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- template:sd-lora |
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- stable-diffusion-xl |
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- stable-diffusion-xl-diffusers |
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- text-to-image |
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- text-to-image |
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- diffusers-training |
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- diffusers |
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- dora |
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- template:sd-lora |
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- stable-diffusion-xl |
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- stable-diffusion-xl-diffusers |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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instance_prompt: a photo of TOK cat |
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widget: [] |
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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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# SDXL LoRA DreamBooth - basakozsoy/maya_LoRA |
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<Gallery /> |
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## Model description |
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These are basakozsoy/maya_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. |
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The weights were trained using [DreamBooth](https://dreambooth.github.io/). |
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LoRA for the text encoder was enabled: True. |
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. |
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## Trigger words |
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You should use a photo of TOK cat to trigger the image generation. |
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## Download model |
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Weights for this model are available in Safetensors format. |
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[Download](basakozsoy/maya_LoRA/tree/main) them in the Files & versions tab. |
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## Intended uses & limitations |
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#### How to use |
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```python |
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import torch |
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from diffusers import DiffusionPipeline, AutoencoderKL |
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repo_id = 'basakozsoy/maya_LoRA' |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = DiffusionPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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vae=vae, |
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torch_dtype=torch.float16, |
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variant="fp16", |
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use_safetensors=True |
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) |
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pipe.load_lora_weights(repo_id) |
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_ = pipe.to("cuda") |
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pipe.load_lora_weights(repo_id) |
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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] |
