Instructions to use alexsuakim/modeloutput with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alexsuakim/modeloutput 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("alexsuakim/modeloutput") prompt = "a photo of sks" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
|
Download README.md from alexsuakim/modeloutput: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/alexsuakim/modeloutput/resolve/main/README.md
- Command line
-
hf download hf://alexsuakim/modeloutput/README.md
-
curl -L -o README.md https://huggingface.co/alexsuakim/modeloutput/resolve/main/README.md
1.22 kB
metadata
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- diffusers
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- stable-diffusion
- stable-diffusion-diffusers
inference: true
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks
LoRA DreamBooth - alexsuakim/modeloutput
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks 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]