Instructions to use ldhnam/cat-dog-controlnet-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ldhnam/cat-dog-controlnet-v0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ldhnam/cat-dog-controlnet-v0", 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:
- 03e1349198d7f2924835bd1bfa0fb201efc136a1508ff1aab0d4ee9cc09b24a2
- Size of remote file:
- 1.46 GB
- SHA256:
- b271e9d4f838fe1b7cd2611db2acfaca69e8e9b79138ed874e0a67d0f93ebc18
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