Instructions to use tjez/d5flat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tjez/d5flat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tjez/d5flat", 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:
- 99b94d505d389a2ca2373413aae1ac8b71649bbc4a3e8344418db0872263cd17
- Size of remote file:
- 335 MB
- SHA256:
- bcacfc5f986b535ae9d6c0da89a8bafb9a3ef9f08a4048562f7f31bb4e32392c
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