Instructions to use amd/Nitro-T-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amd/Nitro-T-0.6B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amd/Nitro-T-0.6B", 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 pipeline.py
Browse files- pipeline.py +1 -1
pipeline.py
CHANGED
|
@@ -44,7 +44,7 @@ if is_ftfy_available():
|
|
| 44 |
|
| 45 |
|
| 46 |
# Modified from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py
|
| 47 |
-
class
|
| 48 |
|
| 49 |
bad_punct_regex = re.compile(
|
| 50 |
r"["
|
|
|
|
| 44 |
|
| 45 |
|
| 46 |
# Modified from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py
|
| 47 |
+
class NitroDiTPipeline(DiffusionPipeline):
|
| 48 |
|
| 49 |
bad_punct_regex = re.compile(
|
| 50 |
r"["
|