Instructions to use rag000/rag000m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rag000/rag000m2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rag000/rag000m2", dtype=torch.bfloat16, device_map="cuda") 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:
- 629e52bf9746571bc81133606c103dffb67346dc76b9b84441eaa4855a70ac51
- Size of remote file:
- 246 MB
- SHA256:
- a9f284da4ccc8d475c0d0afee5456b23a96f21bcfe47e5a46898e25d567f7a9e
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