Instructions to use fernandabufon/ft_stable_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernandabufon/ft_stable_diffusion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fernandabufon/ft_stable_diffusion") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("fernandabufon/ft_stable_diffusion") model = AutoModelForImageClassification.from_pretrained("fernandabufon/ft_stable_diffusion") - Notebooks
- Google Colab
- Kaggle
fernandabufon/ft_stable_diffusion_trainer
Browse files
README.md
CHANGED
|
@@ -3,6 +3,7 @@ library_name: transformers
|
|
| 3 |
license: apache-2.0
|
| 4 |
base_model: google/vit-base-patch16-224
|
| 5 |
tags:
|
|
|
|
| 6 |
- generated_from_trainer
|
| 7 |
metrics:
|
| 8 |
- accuracy
|
|
@@ -16,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 16 |
|
| 17 |
# ft_stable_diffusion
|
| 18 |
|
| 19 |
-
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on
|
| 20 |
It achieves the following results on the evaluation set:
|
| 21 |
- Loss: 0.3650
|
| 22 |
- Accuracy: 0.9194
|
|
|
|
| 3 |
license: apache-2.0
|
| 4 |
base_model: google/vit-base-patch16-224
|
| 5 |
tags:
|
| 6 |
+
- image-classification
|
| 7 |
- generated_from_trainer
|
| 8 |
metrics:
|
| 9 |
- accuracy
|
|
|
|
| 17 |
|
| 18 |
# ft_stable_diffusion
|
| 19 |
|
| 20 |
+
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the generated by stable diffusion dataset.
|
| 21 |
It achieves the following results on the evaluation set:
|
| 22 |
- Loss: 0.3650
|
| 23 |
- Accuracy: 0.9194
|