Instructions to use videosoftware/snehamalapaka-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use videosoftware/snehamalapaka-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("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("videosoftware/snehamalapaka-lora") prompt = "snehamalapaka" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
snehamalapaka lora
Model description
Custom LoRA trained on 4 personal videos of Sneha Malapaka for Wan 2.2 video generation.
Trigger words
You should use snehamalapaka to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/wan-trainer.
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