Instructions to use day-dream/MechAnything-Kontext-Dev-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use day-dream/MechAnything-Kontext-Dev-Lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("day-dream/MechAnything-Kontext-Dev-Lora") prompt = "convert the subject to a robot with with white translucent panels and exposed red and black wiring and golden accented metal bits" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- d9946fb550720302fdb14eab0e564453d5e712799d4d0105cfec611af543ca8d
- Size of remote file:
- 1.01 MB
- SHA256:
- 3bf1ebd33728a743ef83d0010a1c71f0168730967c8ea01307e0416c1056fbba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.