Instructions to use cumprod/control_depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cumprod/control_depth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cumprod/control_depth", 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:
- 4aa5688e2fa676192a67d7208f6bbe506eaa2aa7fe49decdec3d2d17f43421d8
- Size of remote file:
- 3.44 GB
- SHA256:
- 64344ba1666dd5cd1ed412bb36bf94839fa79216f6fad2a4f501b8da198a3613
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