Instructions to use mcvertix/dreembooth_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mcvertix/dreembooth_output 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("mcvertix/dreembooth_output") prompt = "penvink laying and standing on the stony ground, with arctic landscape in the background" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - mcvertix/dreembooth_output
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on penvink laying and standing on the stony ground, with arctic landscape in the background using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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Model tree for mcvertix/dreembooth_output
Base model
CompVis/stable-diffusion-v1-4
