Instructions to use neil-code/majiancang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neil-code/majiancang with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("neil-code/majiancang") prompt = "parody, teeth, standing, tears, sad, no humans, full body, crying, clenched teeth, simple background, black background, red hair, flat color, solo, blue eyes" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- fa4fdb65559789b128ead465cc9c9d5449f585fbc9feab384169021fec80d6b0
- Size of remote file:
- 119 MB
- SHA256:
- 932f52e12ce00b3e1b4eb54e273ee568b550ade58931d7f8584292bddcedfbe0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.