Instructions to use PSJJ/project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PSJJ/project with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("PSJJ/project") prompt = "Please generate a realistic human image with clear facial features, natural body proportions, and diverse poses. The generated person should resemble a genuine photograph with realistic lighting, clothing, and background. Pay attention to details like facial expressions, hair, and skin texture. Thank you!" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 07f09dd38365f052f7eaabe8668faf00ac6ebff5da3e73a0bfc24c25639264ee
- Size of remote file:
- 6.64 MB
- SHA256:
- 8d5645ae3f3684b9f7640d3c7c62aa60d3444208694201bb7a6c0dbe635dec4f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.