Instructions to use furusu/LCM-Acertainty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use furusu/LCM-Acertainty with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("furusu/LCM-Acertainty", 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
- Draw Things
- DiffusionBee
Update README.md
Browse files
README.md
CHANGED
|
@@ -21,7 +21,7 @@ prompt = "anime, masterpiece, best quality, 1girl, solo, blush, sitting, twintai
|
|
| 21 |
num_inference_steps =4
|
| 22 |
images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=5.0, lcm_origin_steps=50, height=768, width=768, output_type="pil").images
|
| 23 |
|
| 24 |
-
|
| 25 |
```
|
| 26 |
|
| 27 |
|
|
|
|
| 21 |
num_inference_steps =4
|
| 22 |
images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=5.0, lcm_origin_steps=50, height=768, width=768, output_type="pil").images
|
| 23 |
|
| 24 |
+
images[0].save("./aaaaa.png")
|
| 25 |
```
|
| 26 |
|
| 27 |
|