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
- Draw Things
- DiffusionBee
- Xet hash:
- 993a3bddd1ad9a4204b1c472345df9fac430b4bad0366f99d08922f932d7b159
- Size of remote file:
- 3.44 GB
- SHA256:
- 90ad37c327cf7383edcdc4a09a6e7a86b9d9deed4422a4d0dd9eb7cd856d76c4
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