Instructions to use vcolamatteo/can_bs16_256_ppl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vcolamatteo/can_bs16_256_ppl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("vcolamatteo/can_bs16_256_ppl") prompt = "a photo of resolution sks" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - vcolamatteo/can_bs16_256_ppl
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of resolution sks using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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Model tree for vcolamatteo/can_bs16_256_ppl
Base model
runwayml/stable-diffusion-v1-5








