Instructions to use waadarsh/magnite-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use waadarsh/magnite-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("playgroundai/playground-v2.5-1024px-aesthetic", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("waadarsh/magnite-lora") prompt = "a black and white photo of sks nissan magnite" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Playground LoRA DreamBooth - waadarsh/magnite-lora

- Prompt
- a black and white photo of sks nissan magnite

- Prompt
- a black and white photo of sks nissan magnite

- Prompt
- a black and white photo of sks nissan magnite

- Prompt
- a black and white photo of sks nissan magnite
Model description
These are waadarsh/magnite-lora LoRA adaption weights for playgroundai/playground-v2.5-1024px-aesthetic.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
Trigger words
You should use a photo of sks nissan magnite to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
License
Please adhere to the licensing terms as described here.
Intended uses & limitations
How to use
# 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]
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