Instructions to use shuttleai/shuttle-3.1-aesthetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shuttleai/shuttle-3.1-aesthetic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shuttleai/shuttle-3.1-aesthetic", 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
add fp8
Browse files
README.md
CHANGED
|
@@ -18,9 +18,9 @@ Join our [Discord](https://discord.gg/shuttleai) to get the latest updates, news
|
|
| 18 |
|
| 19 |
## Model Variants
|
| 20 |
These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases
|
| 21 |
-
- [bfloat16](https://huggingface.co/shuttleai/shuttle-3.1-aesthetic)
|
| 22 |
-
- [
|
| 23 |
-
-
|
| 24 |
|
| 25 |
Shuttle 3.1 Aesthetic is a text-to-image AI model designed to create detailed and aesthetic images from textual prompts in just 4 to 6 steps. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency.
|
| 26 |
|
|
|
|
| 18 |
|
| 19 |
## Model Variants
|
| 20 |
These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases
|
| 21 |
+
- [bfloat16](https://huggingface.co/shuttleai/shuttle-3.1-aesthetic/resolve/main/shuttle-3.1-aesthetic.safetensors)
|
| 22 |
+
- [fp8](https://huggingface.co/shuttleai/shuttle-3.1-aesthetic/resolve/main/fp8/shuttle-3.1-aesthetic-fp8.safetensors)
|
| 23 |
+
- GGUF (soon)
|
| 24 |
|
| 25 |
Shuttle 3.1 Aesthetic is a text-to-image AI model designed to create detailed and aesthetic images from textual prompts in just 4 to 6 steps. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency.
|
| 26 |
|