Instructions to use limingcv/SuperEdit_InstructP2P_SD15_BaseInstructDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use limingcv/SuperEdit_InstructP2P_SD15_BaseInstructDiffusion 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/SuperEdit_InstructP2P_SD15_BaseInstructDiffusion", 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
- Xet hash:
- 24ee3707f2b7ecf309eedcacc532234025720ed74d1f8ee6599138a038af9076
- Size of remote file:
- 3.44 GB
- SHA256:
- e4f36f084239f1f1415d4ea70b0371b21b935a0af62081a4d7169919e08d44a6
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