Instructions to use cxj009/model_lora_optimus_truck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cxj009/model_lora_optimus_truck with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stablediffusionapi/rev-anim", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("cxj009/model_lora_optimus_truck") prompt = "optimus_prime_truck" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - cxj009/model_lora_optimus_truck
These are LoRA adaption weights for stablediffusionapi/rev-anim. The weights were trained on optimus_prime_truck using DreamBooth. You can find some example images in the following.
- Downloads last month
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Model tree for cxj009/model_lora_optimus_truck
Base model
stablediffusionapi/rev-anim


