Instructions to use aphexblake/aphexblake-600-msf-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aphexblake/aphexblake-600-msf-v4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aphexblake/200-msf-v4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("aphexblake/aphexblake-600-msf-v4") prompt = "Doggastyle" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRA DreamBooth - aphexblake-600-msf-v4
These are LoRA adaption weights for aphexblake/200-msf-v4. The weights were trained on the instance prompt "Doggastyle" using DreamBooth. You can find some example images in the following.
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Model tree for aphexblake/aphexblake-600-msf-v4
Base model
aphexblake/200-msf-v4