Instructions to use dataautogpt3/Proteus-RunDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/Proteus-RunDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataautogpt3/Proteus-RunDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "score_9, Side View of a Roman Warrior pierced By a spear, cinimatic " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Update README.md
Browse files
README.md
CHANGED
|
@@ -48,7 +48,7 @@ Tagging Versatility:
|
|
| 48 |
The model supports the Pony tagging format and encourages experimentation. Users can explore tags like score_9, score_8_up, etc., or freely describe their creative vision.
|
| 49 |
|
| 50 |
Backward Compatibility:
|
| 51 |
-
|
| 52 |
|
| 53 |
## Artistic Domains Showcase:
|
| 54 |
|
|
|
|
| 48 |
The model supports the Pony tagging format and encourages experimentation. Users can explore tags like score_9, score_8_up, etc., or freely describe their creative vision.
|
| 49 |
|
| 50 |
Backward Compatibility:
|
| 51 |
+
Proteus-RunDiffusion’s adaptation of the Pony CLIP architecture guarantees backward compatibility with all SDXL models, offering a seamless and flexible creative experience.
|
| 52 |
|
| 53 |
## Artistic Domains Showcase:
|
| 54 |
|