Instructions to use bartar/prci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bartar/prci with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("bartar/prci") prompt = "Photorealistic photo of prci, gray nissan qashqai, standing on a grey brick pavement, trees and blue sky in the background. Taken by phone" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Bartosz commited on
Update README.md
Browse files
README.md
CHANGED
|
@@ -17,7 +17,8 @@ license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICE
|
|
| 17 |
|
| 18 |
# Photorealistic Car Images
|
| 19 |
|
| 20 |
-
A Flux LoRA trained on
|
|
|
|
| 21 |
|
| 22 |
<Gallery />
|
| 23 |
|
|
|
|
| 17 |
|
| 18 |
# Photorealistic Car Images
|
| 19 |
|
| 20 |
+
A Flux LoRA trained locally on dataset of various low/midrange cars close-up photos with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
|
| 21 |
+
You can use it to get better quality of car photos with realistic reflections, and more realistic backgrounds.
|
| 22 |
|
| 23 |
<Gallery />
|
| 24 |
|