Instructions to use klckmd/bdsht with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use klckmd/bdsht with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.2-T2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("klckmd/bdsht") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from klckmd/bdsht: direct link, hf CLI and curl.
- Browser
- Download file 333 Bytes
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https://huggingface.co/klckmd/bdsht/resolve/main/README.md
- Command line
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hf download hf://klckmd/bdsht/README.md
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curl -L -o README.md https://huggingface.co/klckmd/bdsht/resolve/main/README.md
333 Bytes
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/SCR-20250821-bagw.png | |
| text: '-' | |
| base_model: Wan-AI/Wan2.2-T2V-A14B | |
| instance_prompt: null | |
| # wan22 bdcmst v2 | |
| <Gallery /> | |
| ## Download model | |
| [Download](/klckmd/bdsht/tree/main) them in the Files & versions tab. | |