Instructions to use litfeng/outzh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use litfeng/outzh with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("litfeng/outzh") prompt = "a photo of zh" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from litfeng/outzh: direct link, hf CLI and curl.
- Browser
- Download file 617 Bytes
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https://huggingface.co/litfeng/outzh/resolve/main/README.md
- Command line
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hf download hf://litfeng/outzh/README.md
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curl -L -o README.md https://huggingface.co/litfeng/outzh/resolve/main/README.md
617 Bytes
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of zh
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA DreamBooth - litfeng/outzh
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of zh using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.



