Instructions to use rrw23/train8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rrw23/train8 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("rrw23/train8") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- a7c4e637b96e8c048ce9dbf5d19b6ab156cfe4d64ccade373af298dc2f9b671b
- Size of remote file:
- 25.7 MB
- SHA256:
- c3bd7da85520ccf18ef1d266399801fdf0c8abc79e5a58fbdc942cc3cb29ed41
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