Instructions to use MLbackup/9_2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MLbackup/9_2025 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MLbackup/9_2025", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Upload via HuggingFaceUploader Widget 🤗 (file 1/2: VdiffLoraPerfectdfelibertFix.fp16.safetensors)
3b4fbe2 verified - Xet hash:
- fec18e06efb9cbc875997fc0cca400255d7c2be1db3598699aa81ff8f710fb07
- Size of remote file:
- 6.94 GB
- SHA256:
- 6d5526d463353277e61438d4ad9fd76780be7c0ba23af39903c40252dd156b8f
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