Instructions to use Jingya/tiny-stable-diffusion-torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jingya/tiny-stable-diffusion-torch with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jingya/tiny-stable-diffusion-torch", 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,11 @@
|
|
| 1 |
---
|
| 2 |
-
license:
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
duplicated_from: hf-internal-testing/tiny-stable-diffusion-torch
|
| 4 |
---
|
| 5 |
+
|
| 6 |
+
```python
|
| 7 |
+
from diffusers import StableDiffusionPipeline
|
| 8 |
+
|
| 9 |
+
pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
|
| 10 |
+
```
|
| 11 |
+
|