Instructions to use codemanCheng/lora-trained-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codemanCheng/lora-trained-xl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("codemanCheng/lora-trained-xl") prompt = "a photo of sks dog" 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("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("codemanCheng/lora-trained-xl")
prompt = "a photo of sks dog"
image = pipe(prompt).images[0]YAML Metadata Error:"base_model" with value "/home/cas/stable-diffusion-xl-base-0.9" is not valid. Use a model id from https://hf.co/models.
LoRA DreamBooth - codemanCheng/lora-trained-xl
These are LoRA adaption weights for /home/cas/stable-diffusion-xl-base-0.9. The weights were trained on a photo of sks dog using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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