Instructions to use daeunni/CL_rank4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use daeunni/CL_rank4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("daeunni/CL_rank4") prompt = "a photo of sks teddybear" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from daeunni/CL_rank4: direct link, hf CLI and curl.
- Browser
- Download file 641 Bytes
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https://huggingface.co/daeunni/CL_rank4/resolve/main/README.md
- Command line
-
hf download hf://daeunni/CL_rank4/README.md
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curl -L -o README.md https://huggingface.co/daeunni/CL_rank4/resolve/main/README.md
641 Bytes
metadata
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks teddybear
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA DreamBooth - danaleee/CL_rank4
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks teddybear using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.



