Instructions to use tensorkelechi/sky_diffuse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tensorkelechi/sky_diffuse with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tensorkelechi/sky_diffuse", 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
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Download README.md from tensorkelechi/sky_diffuse: direct link, hf CLI and curl.
- Browser
- Download file 389 Bytes
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https://huggingface.co/tensorkelechi/sky_diffuse/resolve/main/README.md
- Command line
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hf download hf://tensorkelechi/sky_diffuse/README.md
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curl -L -o README.md https://huggingface.co/tensorkelechi/sky_diffuse/resolve/main/README.md
389 Bytes
| library_name: diffusers | |
| license: apache-2.0 | |
| tags: | |
| - pytorch | |
| - diffusers | |
| - unconditional-image-generation | |
| - diffusion-models-class | |
| This model is a diffusion model for unconditional image generation of clouds, skies, etc | |
| ## Usage | |
| ```python | |
| from diffusers import DDPMPipeline | |
| pipeline = DDPMPipeline.from_pretrained('tensorkelechi/sky_diffuse') | |
| image = pipeline().images[0] | |
| image | |