Instructions to use OAOA/DifFace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OAOA/DifFace with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OAOA/DifFace", 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
Zongsheng commited on
Commit ·
d1e53fd
1
Parent(s): 10e7a87
diffsuion steps: 1000->250
Browse files- scheduler_config.json +1 -1
scheduler_config.json
CHANGED
|
@@ -5,7 +5,7 @@
|
|
| 5 |
"beta_schedule": "linear",
|
| 6 |
"beta_start": 0.0001,
|
| 7 |
"clip_sample": "true",
|
| 8 |
-
"num_train_timesteps":
|
| 9 |
"prediction_type": "epsilon",
|
| 10 |
"trained_betas": null,
|
| 11 |
"variance_type": "learned_range"
|
|
|
|
| 5 |
"beta_schedule": "linear",
|
| 6 |
"beta_start": 0.0001,
|
| 7 |
"clip_sample": "true",
|
| 8 |
+
"num_train_timesteps": 1000,
|
| 9 |
"prediction_type": "epsilon",
|
| 10 |
"trained_betas": null,
|
| 11 |
"variance_type": "learned_range"
|