Instructions to use IsaacAkintaro/depth_conditioned_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use IsaacAkintaro/depth_conditioned_generation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("IsaacAkintaro/depth_conditioned_generation", 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
Download training_metrics.csv from IsaacAkintaro/depth_conditioned_generation: direct link, hf CLI and curl.
- Browser
- Download file 256 Bytes
-
https://huggingface.co/IsaacAkintaro/depth_conditioned_generation/resolve/main/training_metrics.csv
- Command line
-
hf download hf://IsaacAkintaro/depth_conditioned_generation/training_metrics.csv
-
curl -L -o training_metrics.csv https://huggingface.co/IsaacAkintaro/depth_conditioned_generation/resolve/main/training_metrics.csv
256 Bytes
| epoch,train_loss,val_loss,learning_rate,psnr,ssim,lpips | |
| 1,0.7744362354278564,0.7703925783817585,1.02e-05,14.119310032555102,0.0865245,1.1070743249012873 | |
| 2,0.17456243932247162,0.18153595007382906,2.04e-05,16.366520573188453,0.28110865,0.7520961440526522 | |