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:
- 9f4560efb434cfccb92ae682afd29381f6763b8903788b367da2ce4e3dd5b68d
- Size of remote file:
- 1.31 MB
- SHA256:
- 7e6bd501bb8a0c45387d831b841f0e04ca6db1455a452524a8347a5009dce540
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.