Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
lora
Instructions to use giangvlcs/LongGiang_dreambooth_face_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use giangvlcs/LongGiang_dreambooth_face_v2 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("giangvlcs/LongGiang_dreambooth_face_v2") prompt = "sks man portrait" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - giangvlcs/LongGiang_dreambooth_face_v2
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on sks man portrait using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
- Downloads last month
- 8
Model tree for giangvlcs/LongGiang_dreambooth_face_v2
Base model
runwayml/stable-diffusion-v1-5