Instructions to use pietrobonazzi/stable-diffusion-2-1-denoising with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pietrobonazzi/stable-diffusion-2-1-denoising with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pietrobonazzi/stable-diffusion-2-1-denoising", 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
Model Card for Stable Denoising of Point Clouds
The model takes images of noisy meshes and returns images of the same meshes without noise.
Model Details
Model Description
We trained the token "@clean mesh, white background" to finetune stable diffusion for this procedure.
- Developed by: Pietro Bonazzi
- Shared by [optional]: Pietro Bonazzi
- License: None
- Finetuned from model [optional]: stability-ai/stable-diffusion-2-1
Model Sources [optional]
- Repository: None
- Paper [optional]: None
- Demo [optional]: None
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support