Instructions to use abdabd22001/micheal_scott_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abdabd22001/micheal_scott_LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("abdabd22001/micheal_scott_LoRA") prompt = "a photo of Micheal Scott from The Office" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - abdabd22001/micheal_scott_LoRA
Model description
These are abdabd22001/micheal_scott_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Trigger words
You should use "a photo of Micheal Scott from The Office" to trigger the image generation. It is preferable to specify the type of shot, such as “A close-up portrait of Michael Scott…” or “A full-body shot of Michael Scott…”.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for abdabd22001/micheal_scott_LoRA
Base model
stabilityai/stable-diffusion-xl-base-1.0