Instructions to use badul13/0211 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use badul13/0211 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("badul13/0211") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Model card auto-generated by SimpleTuner
Browse files
README.md
CHANGED
|
@@ -59,7 +59,7 @@ You may reuse the base model text encoder for inference.
|
|
| 59 |
## Training settings
|
| 60 |
|
| 61 |
- Training epochs: 8
|
| 62 |
-
- Training steps:
|
| 63 |
- Learning rate: 8e-05
|
| 64 |
- Learning rate schedule: polynomial
|
| 65 |
- Warmup steps: 100
|
|
|
|
| 59 |
## Training settings
|
| 60 |
|
| 61 |
- Training epochs: 8
|
| 62 |
+
- Training steps: 10000
|
| 63 |
- Learning rate: 8e-05
|
| 64 |
- Learning rate schedule: polynomial
|
| 65 |
- Warmup steps: 100
|