Instructions to use cuongdev/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuongdev/lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuongdev/test_hntAnh", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("cuongdev/lora") 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
File size: 1,048 Bytes
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base_model: cuongdev/test_hntAnh
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
inference: true
---
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# LoRA text2image fine-tuning - cuongdev/lora
These are LoRA adaption weights for cuongdev/test_hntAnh. The weights were fine-tuned on the cuongdev/data-set-hntanh dataset. You can find some example images in the following.




## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |