Instructions to use alfredolozano/CODEX_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alfredolozano/CODEX_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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("alfredolozano/CODEX_LoRA") prompt = "<class 'str'>" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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("alfredolozano/CODEX_LoRA")
prompt = "<class 'str'>"
image = pipe(prompt).images[0]LoRA DreamBooth - alfredolozano/CODEX_LoRA
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on <class 'str'> using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
- Downloads last month
- 6
Model tree for alfredolozano/CODEX_LoRA
Base model
runwayml/stable-diffusion-v1-5