Instructions to use liujx233/OneModelForAll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use liujx233/OneModelForAll with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("liujx233/OneModelForAll", 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
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -26,7 +26,7 @@ Our project supports virtual try-on, virtual try-off, and arbitrary-pose try-on
|
|
| 26 |
|
| 27 |
We release two sets of weights for inference on the VITON-HD and DeepFashion-MultiModal datasets, respectively.
|
| 28 |
|
| 29 |
-
Both support high-resolution image generation at
|
| 30 |
|
| 31 |
|
| 32 |
|
|
|
|
| 26 |
|
| 27 |
We release two sets of weights for inference on the VITON-HD and DeepFashion-MultiModal datasets, respectively.
|
| 28 |
|
| 29 |
+
Both support high-resolution image generation at [768, 1024].
|
| 30 |
|
| 31 |
|
| 32 |
|