Instructions to use limingcv/InstructDiffusion_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use limingcv/InstructDiffusion_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("limingcv/InstructDiffusion_diffusers", 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:
- 571e63a8c4b387c6d9df3d940e607a48a0871bbbef6c0c03bd93d9ebb00e7803
- Size of remote file:
- 335 MB
- SHA256:
- 913dd4903880b2700a8f735b9a9abf52ab74c875e29b10fcdca13e404a840ab1
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