Instructions to use zekaili/backpack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zekaili/backpack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("zekaili/backpack") prompt = "a photo of sks backpack" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from zekaili/backpack: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
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https://huggingface.co/zekaili/backpack/resolve/main/README.md
- Command line
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hf download hf://zekaili/backpack/README.md
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curl -L -o README.md https://huggingface.co/zekaili/backpack/resolve/main/README.md
1.21 kB
metadata
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks backpack
tags:
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
LoRA DreamBooth - logits/backpack
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks backpack using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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]



