Instructions to use radoslavvv/coca-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use radoslavvv/coca-cola 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("radoslavvv/coca-cola") prompt = "A photo of coca cola bottle" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA DreamBooth - radoslavvv/coca-cola
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on A photo of coca cola bottle using DreamBooth. You can find some example images in the following.
- Downloads last month
- 5
Model tree for radoslavvv/coca-cola
Base model
CompVis/stable-diffusion-v1-4


